diff --git "a/buster/data/document_embeddings.csv" "b/buster/data/document_embeddings.csv"
new file mode 100644--- /dev/null
+++ "b/buster/data/document_embeddings.csv"
@@ -0,0 +1,7553 @@
+,documents,n_tokens,embedding
+0,"Login nodes. To connect to these, you would typically
+use a remote shell connection. The most usual tool to do so is SSH. You’ll hear
+and read a lot about this tool. Imagine it as a very long (and somewhat
+magical) extension cord which connects the computer you are using now, such as
+your laptop, to a remote computer’s terminal shell. You might already know what
+a terminal shell is if you ever used the command line.",96,"[-0.004753311164677143, -0.005505518522113562, 0.023699335753917694, -0.01755364052951336, -0.024416334927082062, -0.005828807596117258, -0.01752803474664688, -0.01659337617456913, 0.004583664238452911, -0.015133773908019066, 0.016644591465592384, 0.01134392898529768, -0.03477438911795616, 0.008943266235291958, -0.010601324029266834, 0.006824281997978687, 0.025709491223096848, -0.015018542297184467, 0.026144810020923615, -0.020472846925258636, -0.019435759633779526, -0.009871522895991802, 0.0011699226452037692, -0.01728476770222187, 0.009128917939960957, 0.00809183157980442, 0.011696025729179382, -0.018501102924346924, 0.008495142683386803, -0.014135098084807396, 0.012080132029950619, -0.004452427849173546, -0.009769094176590443, -0.015953199937939644, -0.005732781253755093, 0.038922734558582306, 0.012349005788564682, -0.02998586930334568, -0.042610153555870056, -0.017924943938851357, 0.023763353005051613, 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+1,"The compute nodes
+In the field of artificial intelligence, you will usually be on the hunt for
+GPUs. In most clusters, the compute nodes are the ones with GPU capacity.
+While there is a general paradigm to tend towards a homogeneous configuration
+for nodes, this is not always possible in the field of artificial intelligence
+as the hardware evolve rapidly as is being complemented by new hardware and so
+on. Hence, you will often read about computational node classes. Some of which
+might have different GPU models or even no GPU at all. For the Mila cluster you
+will find this information in the Node profile description section. For
+now, you should note that is important to keep in mind that you should be aware
+of which nodes your code is running on.  More on that later.",165,"[0.0021230564452707767, -0.008092104457318783, -0.0010986083652824163, -0.04652442783117294, -0.008209381252527237, 0.02624242566525936, -0.031375013291835785, 0.005184327717870474, -0.00741603784263134, -0.03333422914147377, 0.01724660024046898, 0.012500335462391376, -0.0043288967572152615, -0.011375857517123222, -0.008802664466202259, 0.00437373761087656, 0.014983845874667168, 0.0020178521517664194, 0.014638913795351982, -0.016377370804548264, -0.02333120070397854, 0.0018816040828824043, -0.000400336692109704, -0.02219982258975506, -0.02362094260752201, 0.010575614869594574, 0.029029477387666702, -0.01619800738990307, -0.000224421382881701, -0.0017082757549360394, 0.01216920092701912, -0.016901668161153793, -0.003552799578756094, -0.02219982258975506, -0.010734283365309238, 0.01741216704249382, 0.03115425817668438, -0.02511104941368103, -0.007588503882288933, -0.008195584639906883, 0.03449320048093796, 0.004866990260779858, 0.0011063694255426526, 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+2,"The storage nodes
+Some computers on a cluster function to only store and serve files.  While the
+name of these computers might matter to some, as a user, you’ll only be
+concerned about the path to the data. More on that in the Processing data section.",60,"[-0.010172166861593723, -0.0063959332183003426, 0.02501046471297741, -0.03330284729599953, -0.009625562466681004, 0.028396742418408394, -0.01505160704255104, -0.013618438504636288, 0.013298475183546543, -0.02485048398375511, 0.00816573016345501, 0.012411910109221935, -0.009952192194759846, 0.011172052472829819, -0.008592347614467144, 0.011105393059551716, 0.010858754627406597, -0.020370997488498688, 0.007185842376202345, -0.006645904388278723, -0.00845236424356699, -0.0021914152894169092, -0.012178603559732437, -0.004746122285723686, -0.012805198319256306, -0.007479142397642136, 0.04682129621505737, -0.031089767813682556, 0.02118423767387867, -0.01229192316532135, 0.0020331002306193113, -0.04300840198993683, 0.001486496184952557, -0.024103902280330658, -0.017717968672513962, -0.009818874299526215, 0.012438573874533176, -0.031729694455862045, 0.0036495814565569162, -0.020864274352788925, 0.01657143235206604, -0.024170560762286186, 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+3,"Different nodes for different uses
+It is important to note here the difference in intended uses between the
+compute nodes and the login nodes. While the compute nodes are meant for heavy
+computation, the login nodes are not.
+The login nodes however are used by everyone who uses the cluster and care must
+be taken not to overburden these nodes. Consequently, only very short and light
+processes should be run on these otherwise the cluster may become inaccessible.
+In other words, please refrain from executing long or compute intensive
+processes on login nodes because it affects all other users. In some cases, you
+will also find that doing so might get you into trouble.",138,"[-4.763212564284913e-05, -0.01553431712090969, 0.015028802677989006, -0.03858303651213646, -0.015875881537795067, 0.010636293329298496, -0.02806287445127964, 0.00893530622124672, -0.0031594643369317055, -0.0371348038315773, 0.00720016285777092, 0.005769011098891497, -0.010062466375529766, 0.0037298756651580334, -0.010984688997268677, 0.0056426324881613255, 0.021231599152088165, -0.025986166670918465, 0.010465512052178383, -0.01971505768597126, -0.020534809678792953, 0.010793413035571575, 0.0001030561761581339, 0.007944772019982338, -0.011633659712970257, 0.007425595074892044, 0.02504345029592514, -0.020042957738041878, 0.0022287031169980764, -0.014837527647614479, 0.0003035647387150675, -0.00758954556658864, -0.0018324892735108733, -0.0033917275723069906, -0.014714564196765423, 0.023991433903574944, 0.01613546907901764, -0.01855373941361904, -0.010697775520384312, -0.025740239769220352, 0.014086087234318256, 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+4,"Data parallelism
+The first technique is called data parallelism (aka task
+parallelism in formal computer science). You simply run lots of
+processes each handling a portion of the data you want to
+process. This is by far the easiest technique to use and should be
+favored whenever possible. A common example of this is
+hyperparameter optimisation.
+For really small computations the time to setup multiple processes
+might be longer than the processing time and lead to waste. This can
+be addressed by bunching up some of the processes together by doing
+sequential processing of sub-partitions of the data.
+For the cluster systems it is also inadvisable to launch thousands of
+jobs and even if each job would run for a reasonable amount of time
+(several minutes at minimum), it would be best to make larger groups
+until the amount of jobs is in the low hundreds at most.
+Finally another thing to keep in mind is that the transfer bandwidth
+is limited between the filesystems (see Filesystem concerns)
+and the compute nodes and if you run too many jobs using too much data
+at once they may end up not being any faster because they will spend
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+5,"Model parallelism
+The second technique is called model parallelism (which doesn’t
+have a single equivalent in formal computer science). It is used
+mostly when a single instance of a model will not fit in a computing
+resource (such as the GPU memory being too small for all the
+parameters).
+In this case, the model is split into its constituent parts, each
+processed independently and their intermediate results communicated
+with each other to arrive at a final result.
+This is generally harder but necessary to work with larger, more
+powerful models like GPT.",116,"[-0.017057692632079124, 0.006039037834852934, 0.017525576055049896, -0.01827418990433216, 0.0004373874398879707, 0.029035506770014763, -0.0017328396206721663, -0.015774354338645935, -0.021509269252419472, -0.05694809556007385, 0.02685650810599327, 0.0364949069917202, -0.009330932050943375, 0.00040501158218830824, -0.0036929368507117033, -0.004224318545311689, 0.019811520352959633, 0.008756103925406933, 0.023140177130699158, 0.014758379198610783, 0.0014161828439682722, 0.006069116294384003, -0.0026518963277339935, -0.024303201586008072, -0.00859568640589714, 0.02573358826339245, 0.04609319940209389, -0.013461673632264137, 0.0005234445561654866, 0.0009449573699384928, -0.006396634504199028, -0.023888789117336273, -0.010346907190978527, -0.018955962732434273, -0.008214695379137993, -0.0074593983590602875, 0.027217445895075798, 0.00179968005977571, 4.6396697143791243e-05, 0.0003126881492789835, 0.011142308823764324, 0.008756103925406933, 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+6,"Communication concerns
+The main difference of these two approaches is the need for
+communication between the multiple processes. Some common training
+methods, like stochastic gradient descent sit somewhere between the
+two, because they require some communication, but not a lot. Most
+people classify it as data parallelism since it sits closer to that
+end.
+In general for data parallelism tasks or tasks that communicate
+infrequently it doesn’t make a lot of difference where the processes
+sit because the communication bandwidth and latency will not have a
+lot of impact on the time it takes to complete the job.  The
+individual tasks can generally be scheduled independently.
+On the contrary for model parallelism you need to pay more attention
+to where your tasks are.  In this case it is usually required to use
+the facilities of the workload manager to group the tasks so that they
+are on the same machine or machines that are closely linked to ensure
+optimal communication.  What is the best allocation depends on the
+specific cluster architecture available and the technologies it
+support (such as InfiniBand,
+RDMA,
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+7,"Filesystem concerns
+When working on a cluster, you will generally encounter several
+different filesystems.  Usually there will be names such as ‘home’,
+‘scratch’, ‘datasets’, ‘projects’, ‘tmp’.
+The reason for having different filesystems available instead of a
+single giant one is to provide for different use cases. For example,
+the ‘datasets’ filesystem would be optimized for fast reads but have
+slow write performance. This is because datasets are usually written
+once and then read very often for training.
+Different filesystems have different performance levels. For instance, backed
+up filesystems (such as $PROJECT in Digital Research Alliance of Canada
+clusters) provide more space and can handle large files but cannot sustain
+highly parallel accesses typically required for high speed model training.
+The set of filesystems provided by the cluster you are using should be
+detailed in the documentation for that cluster and the names can
+differ from those above. You should pay attention to their recommended
+use case in the documentation and use the appropriate filesystem for
+the appropriate job. There are cases where a job ran hundreds of times
+slower because it tried to use a filesystem that wasn’t a good fit for
+the job.
+One last thing to pay attention to is the data retention policy for
+the filesystems. This has two subpoints: how long is the data kept
+for, and are there backups.
+Some filesystems will have a limit on how long they keep their
+files. Typically the limit is some number of days (like 90 days) but
+can also be ‘as long as the job runs’ for some.
+As for backups, some filesystems will not have a limit for data, but
+will also not have backups. For those it is important to maintain a
+copy of any crucial data somewhere else. The data will not be
+purposefully deleted, but the filesystem may fail and lose all or part
+of its data. If you have any data that is crucial for a paper or your
+thesis keep an additional copy of it somewhere else.",424,"[0.004579077009111643, -0.00905810110270977, 0.010225382633507252, -0.049812886863946915, -0.023479025810956955, 0.01619519107043743, -0.007283833809196949, -0.004082148429006338, 0.012373179197311401, -0.029455503448843956, 0.012333158403635025, 0.0005452870391309261, 0.008431104011833668, 0.00556959817185998, -0.0063233282417058945, 0.0029432158917188644, 0.03263050690293312, 0.00912480242550373, -0.010465508326888084, -0.03668597713112831, -0.002759785857051611, 0.0021294541656970978, -0.026240479201078415, -0.005576268304139376, -0.007650693412870169, 0.004212216939777136, 0.0165020190179348, -0.004202211741358042, -0.0003512265393510461, 0.00572968227788806, 0.007403897121548653, -0.015648236498236656, -0.00832438189536333, -0.012346498668193817, -0.033751096576452255, -0.006136563140898943, 0.021357908844947815, -0.017756013199687004, 0.0036419169045984745, -0.003296735230833292, 0.015341408550739288, 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+8,"Cluster software modules
+Modules are small files which modify your environment variables to point to
+specific versions of various software and libraries. For instance, a module
+might provide the python command to point to Python 3.7, another might
+activate CUDA version 11.0, another might provide the torch package, and so
+on.
+For more information, see The module command.",79,"[-0.0077234129421412945, -0.0229937806725502, 0.0047304206527769566, -0.037001799792051315, -0.026998011395335197, 0.021921461448073387, -0.0010375366546213627, 0.010085229761898518, -0.007350137457251549, -0.014713848009705544, 0.029373401775956154, -0.010689257644116879, -0.010017361491918564, 0.005734871607273817, -0.026400769129395485, -0.005368382669985294, 0.010200605727732182, -0.011286498978734016, 0.00342904613353312, -0.004879731219261885, -0.015066763386130333, 0.004435193724930286, -0.012467407621443272, -0.012094131670892239, -0.010668897069990635, 0.015772594138979912, 0.013478645123541355, -0.01370261050760746, 0.02025190182030201, -0.0012190843699499965, 0.016139082610607147, -0.008863599970936775, -0.007384071592241526, -0.01330218743532896, -0.023360269144177437, -0.00025896000443026423, 0.010030935518443584, -0.027188042178750038, -0.02152782492339611, 0.01865020953118801, 0.01756431721150875, 0.0217178575694561, 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+9,"Containers
+Containers are a special form of isolation of software and its dependencies. A
+container is essentially a lightweight virtual machine: it encapsulates a
+virtual file system for a full OS installation, as well as a separate network
+and execution environment.
+For example, you can create an Ubuntu container in which you install various
+packages using apt, modify settings as you would as a root user, and so on,
+but without interfering with your main installation. Once built, a container can
+be run on any compatible system.
+For more information, see Using containers on clusters.",117,"[0.003933650907129049, -0.014275675639510155, 0.020411351695656776, -0.020452257245779037, -0.01805252581834793, 0.003718902124091983, -0.008487686514854431, 0.0059311543591320515, -0.0015859020641073585, -0.009776178747415543, 0.019334200769662857, 0.03834116458892822, -0.012380433268845081, -0.0067662885412573814, 0.0009109776001423597, 0.009823900647461414, 0.03193278983235359, -0.008617217652499676, -0.008671756833791733, -0.013505307026207447, -0.02131125144660473, 0.0007047507097013295, -0.003340535331517458, -0.009510299190878868, -0.02297470159828663, 0.011323733255267143, 0.017411688342690468, -0.02991483360528946, 0.01439838856458664, -0.02773326076567173, 0.020424988120794296, -0.004243843257427216, -0.029805755242705345, -0.012639494612812996, -0.015080130659043789, 0.0005799066857434809, 0.0036336842458695173, -0.03348716348409653, -0.010676078498363495, -0.003923424519598484, 0.02382006123661995, 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+10,"Python Virtual environments
+A virtual environment in Python is a local, isolated environment in which you
+can install or uninstall Python packages without interfering with the global
+environment (or other virtual environments). In order to use a virtual
+environment, you first have to activate it.
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+12,"The IDT team
+See https://mila.quebec/en/mila/team/?cat_id=143",24,"[-0.0011296229204162955, -0.012272964231669903, 0.005210418254137039, -0.009561169892549515, -0.010672446340322495, 0.030137255787849426, -0.022029830142855644, -0.018716968595981598, -0.0018433908699080348, -0.00495880888774991, 0.026880307123064995, -0.00414806604385376, -0.007076524198055267, -0.028124378994107246, -0.013370262458920479, -0.006402070168405771, 0.004846982192248106, -0.011042872443795204, -0.0014502506237477064, -0.0066956146620213985, -0.008960102684795856, 0.013663806952536106, 0.026265261694788933, -0.0020530656911432743, -0.017598703503608704, -0.014942822977900505, -0.0046792421489953995, -0.02324594557285309, 0.012119202874600887, 0.005469017196446657, 0.00818430632352829, -0.0162567850202322, -0.009679986163973808, -0.009379452094435692, -0.01899653486907482, -0.019276103004813194, 0.009064939804375172, -0.017053550109267235, 0.0038545215502381325, -0.020436301827430725, 0.026167413219809532, 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+13,"Intended audience
+This documentation is mainly intended for Mila researchers having access to the
+Mila cluster. This access is determined by your researcher status. See
+Roles and authorizations for more information. The core of the
+information with this purpose can be found in the following section:
+Computing infrastructure and policies.
+However, we also aim to provide more general information which can be useful
+outside the scope of using the Mila cluster. For instance, more general theory
+on computational considerations and such. In this perspective, we hope the
+documentation can be of use for all of Mila members.",124,"[-0.008576859720051289, -0.00037237806827761233, 0.007361349184066057, -0.05626856908202171, -0.004360130988061428, 0.01847848854959011, -0.031739842146635056, -0.0055517409928143024, -0.023777566850185394, -0.02558034658432007, 0.0228078905493021, -0.017222005873918533, 0.008105678483843803, -0.0007682298310101032, -0.02969123050570488, 0.0014075677609071136, 0.03135743364691734, -0.027314839884638786, -0.002818549983203411, -0.0017737573944032192, -0.010816676542162895, 0.003470691852271557, -0.011881954967975616, 0.01033866684883833, -0.016320616006851196, 0.006623848341405392, 0.017331264913082123, -0.033242158591747284, -0.0016423047054558992, -0.011875126510858536, 0.01976228691637516, -0.004049425013363361, 0.0021049499046057463, -0.01017477735877037, -0.012954062782227993, -0.001918867463245988, 0.02969123050570488, -0.02115534245967865, -0.015350939705967903, 0.0038718783762305975, 0.02198844589293003, 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+14,"Connecting to the cluster
+VSCode cannot be used to edit code on the login nodes, because it is a heavy
+enough process (a node process, plus the language server, linter, and
+possibly other plugins depending on your configured environment) that there is a
+risk of overloading the login nodes if too many researchers did it at the same
+time.
+Therefore, to use VSCode on the cluster, you first need to allocate a compute
+node, then connect to that node.
+The milatools package provides a command to make the operation easier. More
+info can be found here.",125,"[0.0038284112233668566, 0.006614935118705034, 0.0012140782782807946, -0.04394106939435005, -0.04108811542391777, 0.03180203586816788, -0.037396058440208435, -0.0008408519206568599, 0.015956956893205643, -0.03952179104089737, 0.020683908835053444, 0.002263832138851285, -0.010293006896972656, -0.00760787446051836, -0.029676305130124092, -0.013978070579469204, 0.015803121030330658, -0.017649149522185326, 0.02948051318526268, -0.014726271852850914, -0.028893141075968742, 0.01167752780020237, 0.013313780538737774, -0.019257431849837303, -0.01184534840285778, -0.0022078920155763626, 0.01383822038769722, -0.01481018215417862, 0.001377528766170144, 0.005303835496306419, 0.015705225989222527, -0.03306068852543831, -0.001258655684068799, -0.024180175736546516, -0.010516767390072346, 0.008335097692906857, 0.022390086203813553, -0.023005429655313492, -0.017005836591124535, -0.02689327672123909, 0.024152204394340515, -0.013229870237410069, -0.01024405937641859, 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+15,"Activating an environment
+Reference
+To activate a conda or pip environment, you can open the command palette with
+Ctrl+Shift+P and type “Python: Select interpreter”. This will prompt you for the
+path to the Python executable for your environment.
+
+Tip
+If you already have the environment activated in a terminal session, you can
+run the command which python to get the path for this environment. This
+path can be pasted into the interpreter selection prompt in VSCode to use
+that same environment.",107,"[0.014973689801990986, -0.02109268121421337, 0.023762306198477745, -0.027330618351697922, -0.022638950496912003, 0.012006704695522785, -0.015951670706272125, -0.02693413943052292, 0.0054317619651556015, 0.0002589503419585526, 0.03782409429550171, 0.012119040824472904, -0.015766646713018417, -0.003176787868142128, -0.02131735347211361, -0.015925239771604538, 0.009627831168472767, -0.0001478536141803488, 0.005573833826929331, -0.022295335307717323, -0.037189729511737823, -0.003865669947117567, 0.0298945140093565, -0.01777547411620617, -0.037189729511737823, 0.00032668214407749474, -0.0006149554392322898, -0.02055082656443119, 0.010050741955637932, -0.027198458090424538, 0.006925166118890047, -0.03134826943278313, -0.012839310802519321, -0.02894296497106552, -0.0005910014733672142, 0.004592548124492168, 0.005124490708112717, -0.04197390750050545, 0.022480357438325882, 0.0194274690002203, 0.009356903843581676, 0.0010110214352607727, -0.0008280628826469183, 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+16,"Troubleshooting
+
+“Cannot reconnect”
+When connecting to multiple compute nodes (and/or from multiple computers), some
+instances may crash with that message because of conflicts in the lock files
+VSCode installs in ~/.vscode-server (which is shared on all compute nodes).
+To fix this issue, you can change this setting in your settings.json file:
+{ ""remote.SSH.lockfilesInTmp"": true }
+
+
+This will store the necessary lockfiles in /tmp on the compute nodes (which
+are local to the node).
+
+
+Debugger timeouts
+Sometimes, slowness on the compute node or the networked filesystem might cause
+the VSCode debugger to timeout when starting a remote debug process. As a quick
+fix, you can add this to your ~/.bashrc or ~/.profile or equivalent
+resource file for your preferred shell, to increase the timeout delay to 500
+seconds:
+export DEBUGPY_PROCESS_SPAWN_TIMEOUT=500",194,"[-0.01063205860555172, -0.005515724886208773, 0.022613782435655594, -0.04313420504331589, -0.03897503390908241, 0.009227304719388485, -0.04043487459421158, -0.009330595843493938, -0.01398556400090456, -0.012105672620236874, 0.02213175967335701, 0.0013488046824932098, 0.010032972320914268, -0.0020296627189964056, -0.018440837040543556, -0.0020968017634004354, 0.020961130037903786, -0.029582463204860687, 0.03222670778632164, -0.03644096851348877, -0.017063627019524574, -0.018509697169065475, -0.011754484847187996, -0.022613782435655594, -0.007085743825882673, -0.0002541382273193449, 0.013221212662756443, -0.027227435261011124, 0.019528832286596298, -0.011747598648071289, 0.006624378263950348, -0.014915180392563343, -0.020699461922049522, -0.006255974993109703, -0.0016681451816111803, 0.007092629559338093, 0.018950404599308968, -0.031593188643455505, -0.007292325142771006, -0.01214698888361454, 0.01596185937523842, -0.02126411721110344, 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+17,"“Cannot reconnect”
+When connecting to multiple compute nodes (and/or from multiple computers), some
+instances may crash with that message because of conflicts in the lock files
+VSCode installs in ~/.vscode-server (which is shared on all compute nodes).
+To fix this issue, you can change this setting in your settings.json file:
+{ ""remote.SSH.lockfilesInTmp"": true }
+
+
+This will store the necessary lockfiles in /tmp on the compute nodes (which
+are local to the node).",104,"[-0.013952026143670082, -0.00891229510307312, 0.03600000590085983, -0.03805097937583923, -0.0297391340136528, 0.013027737848460674, -0.054431792348623276, -0.0020847078412771225, 0.0006674944888800383, -0.011010495945811272, 0.02795802429318428, -0.0016225639265030622, 0.014316343702375889, -0.011563719250261784, -0.022250378504395485, -0.012245128862559795, 0.01976761966943741, -0.019673166796565056, 0.01964617893099785, -0.04037182033061981, -0.01847226545214653, -0.009080960415303707, -0.0049992511048913, -0.02436881884932518, -0.005697526969015598, -0.007988006807863712, 0.01756821759045124, -0.021724140271544456, 0.016124440357089043, -0.01679910346865654, 0.005559220910072327, -0.010167167522013187, -0.01963268592953682, -0.01372938696295023, -0.01121964119374752, -0.010403299704194069, 0.02117091789841652, -0.03732234239578247, -0.02156222239136696, -0.013607948087155819, 0.0014699214370921254, -0.02681109867990017, -0.00891229510307312, 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+18,"Debugger timeouts
+Sometimes, slowness on the compute node or the networked filesystem might cause
+the VSCode debugger to timeout when starting a remote debug process. As a quick
+fix, you can add this to your ~/.bashrc or ~/.profile or equivalent
+resource file for your preferred shell, to increase the timeout delay to 500
+seconds:
+export DEBUGPY_PROCESS_SPAWN_TIMEOUT=500",84,"[-0.011595424264669418, -0.011575057171285152, 0.011208457872271538, -0.03345565125346184, -0.04515969753265381, 0.006181284319609404, -0.017243780195713043, -0.01921255886554718, -0.011486802250146866, -0.023625336587429047, 0.025594115257263184, 0.011778724379837513, 0.006398528814315796, -0.010142602026462555, -0.017528913915157318, -0.0017498363740742207, 0.017488181591033936, -0.020774003118276596, 0.0385608971118927, -0.030767248943448067, -0.0036999450530856848, -0.023380937054753304, -0.018723757937550545, -0.010936901904642582, -0.013129713013768196, 0.004188745282590389, 0.004721673205494881, -0.028187470510601997, 0.014487490989267826, -0.013523468747735023, 0.004850661847740412, -0.0312560498714447, -0.003253575647249818, -0.0043448894284665585, -0.001015787711367011, 0.01602178066968918, -0.0033163728658109903, -0.024059826508164406, 0.009409401565790176, -0.012369357980787754, 0.019158247858285904, 0.0042328727431595325, 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+19,"It is opt-in and must be requested via
+CCDB manually. Compute resources in Niagara are not assigned to jobs on a per-CPU,
+but on a per-node basis.
+
+
+
+Beluga
+Beluga is a cluster located at ÉTS in Montreal. It
+uses SLURM to schedule jobs. Its full documentation can be found here, and its current status
+here.
+You can access Beluga via ssh:
+ssh <user>@beluga.computecanada.ca
+Where <user> is the username you created previously (see Account Creation).
+
+Launching Jobs
+Users must specify the resource allocation Group Name using the flag
+--account=rrg-bengioy-ad.  To launch a CPU-only job:
+sbatch --time=1:0:0 --account=rrg-bengioy-ad job.sh
+
+Note
+The account name will differ based on your affiliation.
+
+To launch a GPU job:
+sbatch --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1 job.sh
+And to get an interactive session, use the salloc command:
+salloc --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1
+The full documentation for jobs launching on Beluga can be found here.
+
+
+Beluga nodes description
+Each GPU node consists of:
+
+40 CPU cores
+186 GB RAM
+4 GPU NVIDIA V100 (16GB)
+
+
+Tip
+You should ask for max 10 CPU cores and 32 GB of RAM per GPU you are
+requesting (as explained here),
+otherwise, your job will count for more than 1 allocation, and will take
+more time to get scheduled.
+
+
+
+Beluga Storage
+
+
+
+
+
+
+
+Storage
+Path
+Usage
+
+
+
+$HOME
+/home/<user>/
+
+Code
+Specific libraries
+
+
+
+$HOME/projects
+/project/rpp-bengioy
+
+Compressed raw datasets
+
+
+
+$SCRATCH
+/scratch/<user>
+
+Processed datasets
+Experimental results
+Logs of experiments
+
+
+
+$SLURM_TMPDIR
+
+
+Temporary job results
+
+
+
+
+
+They are roughly listed in order of increasing performance and optimized for
+different uses:
+
+The $HOME folder on NFS is appropriate for codes and libraries which are
+small and read once. Do not write experiemental results here!
+The $HOME/projects folder should only contain compressed raw datasets
+(processed datasets should go in $SCRATCH). We have a limit on the
+size and number of file in $HOME/projects, so do not put anything else
+there.  If you add a new dataset there (make sure it is readable by every
+member of the group using chgrp -R rpp-bengioy <dataset>).
+The $SCRATCH space can be used for short term storage. It has good
+performance and large quotas, but is purged regularly (every file that has
+not been used in the last 3 months gets deleted, but you receive an email
+before this happens).
+$SLURM_TMPDIR points to the local disk of the node on which a job is
+running. It should be used to copy the data on the node at the beginning of
+the job and write intermediate checkpoints. This folder is cleared after each
+job.
+
+When an experiment is finished, results should be transferred back to Mila
+servers.
+More details on storage can be found here.
+
+
+Modules
+Many software, such as Python or MATLAB are already compiled and available on
+Beluga through the module command and its subcommands. Its full
+documentation can be found here.
+
+
+
+
+
+
+module avail
+Displays all the available modules
+
+module load <module>
+Loads <module>
+
+module spider <module>
+Shows specific details about <module>
+
+
+
+In particular, if you with to use Python 3.6 you can simply do:
+module load python/3.6
+
+Tip
+If you wish to use Python on the cluster, we strongly encourage you to
+read Alliance Python Documentation, and in particular the Pytorch and/or Tensorflow pages.
+
+The cluster has many Python packages (or wheels), such already compiled for
+the cluster. See here for the
+details. In particular, you can browse the packages by doing:
+avail_wheels <wheel>
+Such wheels can be installed using pip. Moreover, the most efficient way to use
+modules on the cluster is to build your environnement inside your job.
+See the script example below.
+
+
+Script Example
+Here is a sbatch script that follows good practices on Beluga:
+ 1#!/bin/bash
+ 2#SBATCH --account=rrg-bengioy-ad         # Yoshua pays for your job
+ 3#SBATCH --cpus-per-task=6                # Ask for 6 CPUs
+ 4#SBATCH --gres=gpu:1                     # Ask for 1 GPU
+ 5#SBATCH --mem=32G                        # Ask for 32 GB of RAM
+ 6#SBATCH --time=3:00:00                   # The job will run for 3 hours
+ 7#SBATCH -o /scratch/<user>/slurm-%j.out  # Write the log in $SCRATCH
+ 8
+ 9# 1. Create your environement locally
+10module load python/3.6
+11virtualenv --no-download $SLURM_TMPDIR/env
+12source $SLURM_TMPDIR/env/bin/activate
+13pip install --no-index torch torchvision
+14
+15# 2. Copy your dataset on the compute node
+16# IMPORTANT: Your dataset must be compressed in one single file (zip, hdf5, ...)!!!
+17cp $SCRATCH/<dataset.zip> $SLURM_TMPDIR
+18
+19# 3. Eventually unzip your dataset
+20unzip $SLURM_TMPDIR/<dataset.zip> -d $SLURM_TMPDIR
+21
+22# 4. Launch your job, tell it to save the model in $SLURM_TMPDIR
+23#    and look for the dataset into $SLURM_TMPDIR
+24python main.py --path $SLURM_TMPDIR --data_path $SLURM_TMPDIR
+25
+26# 5. Copy whatever you want to save on $SCRATCH
+27cp $SLURM_TMPDIR/<to_save> $SCRATCH
+
+
+
+
+Using CometML and Wandb
+The compute nodes for Beluga don’t have access to the internet,
+but there is a special module that can be loaded in order to allow
+training scripts to access some specific servers, which includes
+the necessary servers for using CometML and Wandb (“Weights and Biases”).
+module load httpproxy
+More documentation about this can be found here.
+
+
+
+Graham
+Graham is a cluster located at University of Waterloo. It uses SLURM to schedule
+jobs. Its full documentation can be found here, and its current status here.
+You can access Graham via ssh:
+ssh <user>@graham.computecanada.ca
+Where <user> is the username you created previously (see Account Creation).
+Since its structure is similar to Beluga, please look at the Beluga
+documentation, as well as relevant parts of the Digital Research Alliance of
+Canada Documentation.
+
+Note
+For GPU jobs the ressource allocation Group Name is the same as Beluga, so you should use the flag --account=rrg-bengioy-ad for GPU jobs.
+
+
+
+Cedar
+Cedar is a cluster located at Simon Fraser University. It uses SLURM to schedule
+jobs. Its full documentation can be found here, and its current status here.
+You can access Cedar via ssh:
+ssh <user>@cedar.computecanada.ca
+Where <user> is the username you created previously (see Account Creation).
+Since its structure is similar to Beluga, please look at the Beluga
+documentation, as well as relevant parts of the Digital Research Alliance of
+Canada Documentation.
+
+Note
+However, we don’t have any CPU priority on Cedar, in this case you can
+use --account=def-bengioy for CPU. Thus, it might take some time before
+they start.
+
+
+
+Niagara
+Niagara is a cluster located at University of Toronto. It uses SLURM to schedule
+jobs. Its full documentation can be found here, and its current status here.
+You can access Niagara via ssh:
+ssh <user>@niagara.computecanada.ca
+Where <user> is the username you created previously (see Account Creation).
+Since its structure is similar to Beluga, please look at the Beluga
+documentation, as well as relevant parts of the Digital Research Alliance of
+Canada Documentation.
+
+
+
+FAQ
+
+What to do with  ImportError: /lib64/libm.so.6: version GLIBC_2.23 not found?
+The structure of the file system is different than a classical Linux, so your
+code has trouble finding libraries. See how to install binary packages.
+
+
+Disk quota exceeded error on /project file systems
+You have files in /project with the wrong permissions. See how to change
+permissions.",1873,"[-0.007660632021725178, -0.026531251147389412, -0.010391999036073685, -0.04660395532846451, -0.0066790468990802765, 0.009837190620601177, -0.03675965219736099, 0.0030959052965044975, -0.01796727627515793, -0.034369707107543945, 0.017455145716667175, 0.001941831549629569, -0.009837190620601177, -0.0010776098351925611, -0.005516082048416138, -0.0018458068370819092, 0.009758948348462582, -0.016843432560563087, 0.005889511201530695, -0.0006388305337168276, -0.011437601409852505, -0.0040010265074670315, 0.022932104766368866, -0.019802413880825043, -0.017412466928362846, 0.0021445502061396837, 0.02190784364938736, -0.004353116732090712, 0.0025322053115814924, 0.013073576614260674, 0.0022565789986401796, -0.005665453616529703, 9.652476728660986e-05, -0.010790323838591576, -0.03266260027885437, -0.011715005151927471, 0.01443925965577364, -0.004100607708096504, 0.014425033703446388, 0.012355170212686062, 0.02384256199002266, 0.012191572226583958, -0.0074401311576366425, 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+20,"Current allocation description
+Clusters of the Alliance are shared with researchers across the country.
+Allocations are given by the Alliance to selected research groups to ensure to
+a minimal amount of computational resources throughout the year.
+Depending on your affiliation, you will have access to different allocations. If
+you are a student at University of Montreal, you can have access to the
+rrg-bengioy-ad allocation described below. For students from other
+universities, you should ask your advisor to know which allocations you could
+have access to.
+From the Alliance’s documentation: An allocation is an amount of resources
+that a research group can target for use for a period of time, usually a year.
+To be clear, it is not a maximal amount of resources that can be used
+simultaneously, it is a weighting factor of the workload manager to balance
+jobs. For instance, even though we are allocated 400 GPU-years across all
+clusters, we can use more or less than 400 GPUs simultaneously depending on the
+history of usage from our group and other groups using the cluster at a given
+period of time. Please see the Alliance’s documentation for
+more information on how allocations and resource scheduling are configured for
+these installations.
+The table below provides information on the allocation for
+rrg-bengioy-ad for the period which spans from April 2022 to
+April 2023. Note that there are no special allocations for GPUs on
+Graham and therefore jobs with GPUs should be submitted with the
+account def-bengioy.
+
+
+
+
+
+
+
+
+
+
+
+Cluster
+CPUs
+GPUs
+
+#
+account
+Model
+#
+SLURM type specifier
+account
+
+Beluga
+238
+rrg-bengioy-ad
+V100-16G
+77
+v100
+rrg-bengioy-ad
+
+Cedar
+34
+rrg-bengioy-ad
+V100-32G
+138
+v100l
+rrg-bengioy-ad
+
+Graham
+34
+rrg-bengioy-ad
+various
+–
+–
+def-bengioy
+
+Narval
+34
+rrg-bengioy-ad
+A100-40G
+185
+a100
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+21,"Account Creation
+To access the Alliance clusters you have to first create an account at
+https://ccdb.computecanada.ca. Use a password with at least 8 characters, mixed
+case letters, digits and special characters. Later you will be asked to create
+another password with those rules, and it’s really convenient that the two
+password are the same.
+Then, you have to apply for a role at
+https://ccdb.computecanada.ca/me/add_role, which basically means telling the
+Alliance that you are part of the lab so they know which cluster you can have
+access to, and track your usage.
+You will be asked for the CCRI (See screenshot below). Please reach out to your
+sponsor to get the CCRI.
+
+You will need to wait for your sponsor to accept before being able to login
+to the Alliance clusters.",182,"[0.0029240769799798727, -0.02025112882256508, -0.012006412260234356, -0.06536449491977692, -0.02239488996565342, 0.03454287350177765, -0.0388573594391346, -0.013752431608736515, -0.010698582977056503, -0.019239921122789383, 0.006100955884903669, -0.010435668751597404, 0.00804921705275774, -0.009498615749180317, -0.003331931075081229, 0.015950124710798264, 0.025576826184988022, -0.007793044671416283, 0.0023342056665569544, 0.0034650734160095453, -0.035351838916540146, -0.01171653252094984, -0.003131374716758728, -0.0061885942704975605, -0.0023527443408966064, -0.021680302917957306, -0.015747882425785065, -0.0094716502353549, 0.024646515026688576, 0.0007095310720615089, 0.00742226792499423, 0.019415197893977165, 0.004247074481099844, -0.022408373653888702, -0.012431119568645954, 0.018727576360106468, 0.02112750895321369, -0.008163820952177048, 0.0021538734436035156, -0.012855826877057552, 0.03233169764280319, 0.012936723418533802, -0.012303032912313938, 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+22,"It is opt-in and must be requested via
+CCDB manually. Compute resources in Niagara are not assigned to jobs on a per-CPU,
+but on a per-node basis.
+
+
+
+Beluga
+Beluga is a cluster located at ÉTS in Montreal. It
+uses SLURM to schedule jobs. Its full documentation can be found here, and its current status
+here.
+You can access Beluga via ssh:
+ssh <user>@beluga.computecanada.ca
+Where <user> is the username you created previously (see Account Creation).
+
+Launching Jobs
+Users must specify the resource allocation Group Name using the flag
+--account=rrg-bengioy-ad.  To launch a CPU-only job:
+sbatch --time=1:0:0 --account=rrg-bengioy-ad job.sh
+
+Note
+The account name will differ based on your affiliation.
+
+To launch a GPU job:
+sbatch --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1 job.sh
+And to get an interactive session, use the salloc command:
+salloc --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1
+The full documentation for jobs launching on Beluga can be found here.
+
+
+Beluga nodes description
+Each GPU node consists of:
+
+40 CPU cores
+186 GB RAM
+4 GPU NVIDIA V100 (16GB)
+
+
+Tip
+You should ask for max 10 CPU cores and 32 GB of RAM per GPU you are
+requesting (as explained here),
+otherwise, your job will count for more than 1 allocation, and will take
+more time to get scheduled.
+
+
+
+Beluga Storage
+
+
+
+
+
+
+
+Storage
+Path
+Usage
+
+
+
+$HOME
+/home/<user>/
+
+Code
+Specific libraries
+
+
+
+$HOME/projects
+/project/rpp-bengioy
+
+Compressed raw datasets
+
+
+
+$SCRATCH
+/scratch/<user>
+
+Processed datasets
+Experimental results
+Logs of experiments
+
+
+
+$SLURM_TMPDIR
+
+
+Temporary job results
+
+
+
+
+
+They are roughly listed in order of increasing performance and optimized for
+different uses:
+
+The $HOME folder on NFS is appropriate for codes and libraries which are
+small and read once. Do not write experiemental results here!
+The $HOME/projects folder should only contain compressed raw datasets
+(processed datasets should go in $SCRATCH). We have a limit on the
+size and number of file in $HOME/projects, so do not put anything else
+there.  If you add a new dataset there (make sure it is readable by every
+member of the group using chgrp -R rpp-bengioy <dataset>).
+The $SCRATCH space can be used for short term storage. It has good
+performance and large quotas, but is purged regularly (every file that has
+not been used in the last 3 months gets deleted, but you receive an email
+before this happens).
+$SLURM_TMPDIR points to the local disk of the node on which a job is
+running. It should be used to copy the data on the node at the beginning of
+the job and write intermediate checkpoints. This folder is cleared after each
+job.
+
+When an experiment is finished, results should be transferred back to Mila
+servers.
+More details on storage can be found here.
+
+
+Modules
+Many software, such as Python or MATLAB are already compiled and available on
+Beluga through the module command and its subcommands. Its full
+documentation can be found here.
+
+
+
+
+
+
+module avail
+Displays all the available modules
+
+module load <module>
+Loads <module>
+
+module spider <module>
+Shows specific details about <module>
+
+
+
+In particular, if you with to use Python 3.6 you can simply do:
+module load python/3.6
+
+Tip
+If you wish to use Python on the cluster, we strongly encourage you to
+read Alliance Python Documentation, and in particular the Pytorch and/or Tensorflow pages.
+
+The cluster has many Python packages (or wheels), such already compiled for
+the cluster. See here for the
+details. In particular, you can browse the packages by doing:
+avail_wheels <wheel>
+Such wheels can be installed using pip. Moreover, the most efficient way to use
+modules on the cluster is to build your environnement inside your job.
+See the script example below.
+
+
+Script Example
+Here is a sbatch script that follows good practices on Beluga:
+ 1#!/bin/bash
+ 2#SBATCH --account=rrg-bengioy-ad         # Yoshua pays for your job
+ 3#SBATCH --cpus-per-task=6                # Ask for 6 CPUs
+ 4#SBATCH --gres=gpu:1                     # Ask for 1 GPU
+ 5#SBATCH --mem=32G                        # Ask for 32 GB of RAM
+ 6#SBATCH --time=3:00:00                   # The job will run for 3 hours
+ 7#SBATCH -o /scratch/<user>/slurm-%j.out  # Write the log in $SCRATCH
+ 8
+ 9# 1. Create your environement locally
+10module load python/3.6
+11virtualenv --no-download $SLURM_TMPDIR/env
+12source $SLURM_TMPDIR/env/bin/activate
+13pip install --no-index torch torchvision
+14
+15# 2. Copy your dataset on the compute node
+16# IMPORTANT: Your dataset must be compressed in one single file (zip, hdf5, ...)!!!
+17cp $SCRATCH/<dataset.zip> $SLURM_TMPDIR
+18
+19# 3. Eventually unzip your dataset
+20unzip $SLURM_TMPDIR/<dataset.zip> -d $SLURM_TMPDIR
+21
+22# 4. Launch your job, tell it to save the model in $SLURM_TMPDIR
+23#    and look for the dataset into $SLURM_TMPDIR
+24python main.py --path $SLURM_TMPDIR --data_path $SLURM_TMPDIR
+25
+26# 5. Copy whatever you want to save on $SCRATCH
+27cp $SLURM_TMPDIR/<to_save> $SCRATCH
+
+
+
+
+Using CometML and Wandb
+The compute nodes for Beluga don’t have access to the internet,
+but there is a special module that can be loaded in order to allow
+training scripts to access some specific servers, which includes
+the necessary servers for using CometML and Wandb (“Weights and Biases”).
+module load httpproxy
+More documentation about this can be found here.
+
+
+
+Graham
+Graham is a cluster located at University of Waterloo. It uses SLURM to schedule
+jobs. Its full documentation can be found here, and its current status here.
+You can access Graham via ssh:
+ssh <user>@graham.computecanada.ca
+Where <user> is the username you created previously (see Account Creation).
+Since its structure is similar to Beluga, please look at the Beluga
+documentation, as well as relevant parts of the Digital Research Alliance of
+Canada Documentation.
+
+Note
+For GPU jobs the ressource allocation Group Name is the same as Beluga, so you should use the flag --account=rrg-bengioy-ad for GPU jobs.
+
+
+
+Cedar
+Cedar is a cluster located at Simon Fraser University. It uses SLURM to schedule
+jobs. Its full documentation can be found here, and its current status here.
+You can access Cedar via ssh:
+ssh <user>@cedar.computecanada.ca
+Where <user> is the username you created previously (see Account Creation).
+Since its structure is similar to Beluga, please look at the Beluga
+documentation, as well as relevant parts of the Digital Research Alliance of
+Canada Documentation.
+
+Note
+However, we don’t have any CPU priority on Cedar, in this case you can
+use --account=def-bengioy for CPU. Thus, it might take some time before
+they start.
+
+
+
+Niagara
+Niagara is a cluster located at University of Toronto. It uses SLURM to schedule
+jobs. Its full documentation can be found here, and its current status here.
+You can access Niagara via ssh:
+ssh <user>@niagara.computecanada.ca
+Where <user> is the username you created previously (see Account Creation).
+Since its structure is similar to Beluga, please look at the Beluga
+documentation, as well as relevant parts of the Digital Research Alliance of
+Canada Documentation.",1781,"[-0.009789498522877693, -0.029214439913630486, -0.008206932805478573, -0.040698543190956116, -0.0009470884106121957, 0.01232440397143364, -0.04072655364871025, 0.0030583429615944624, -0.018332550302147865, -0.033219870179891586, 0.019719045609235764, 0.005637014284729958, -0.008332978002727032, 0.000261280860286206, -0.004506110213696957, -0.004824724048376083, 0.008473027497529984, -0.020951487123966217, 0.003573026740923524, -0.002956806682050228, -0.01632983610033989, -0.0023440876975655556, 0.017604291439056396, -0.015475530177354813, -0.023584427312016487, 0.005630011670291424, 0.02267410233616829, -0.0027974997647106647, 0.0026154345832765102, 0.010328690521419048, 0.005482959095388651, -0.0011974278604611754, 0.0017322440398856997, -0.010881888680160046, -0.030166780576109886, -0.004240015055984259, 0.014110041782259941, -0.006956986151635647, 0.013402789831161499, 0.013206719420850277, 0.022940197959542274, 0.012338409200310707, 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+23,"Beluga
+Beluga is a cluster located at ÉTS in Montreal. It
+uses SLURM to schedule jobs. Its full documentation can be found here, and its current status
+here.
+You can access Beluga via ssh:
+ssh <user>@beluga.computecanada.ca
+Where <user> is the username you created previously (see Account Creation).
+
+Launching Jobs
+Users must specify the resource allocation Group Name using the flag
+--account=rrg-bengioy-ad.  To launch a CPU-only job:
+sbatch --time=1:0:0 --account=rrg-bengioy-ad job.sh
+
+Note
+The account name will differ based on your affiliation.
+
+To launch a GPU job:
+sbatch --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1 job.sh
+And to get an interactive session, use the salloc command:
+salloc --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1
+The full documentation for jobs launching on Beluga can be found here.
+
+
+Beluga nodes description
+Each GPU node consists of:
+
+40 CPU cores
+186 GB RAM
+4 GPU NVIDIA V100 (16GB)
+
+
+Tip
+You should ask for max 10 CPU cores and 32 GB of RAM per GPU you are
+requesting (as explained here),
+otherwise, your job will count for more than 1 allocation, and will take
+more time to get scheduled.
+
+
+
+Beluga Storage
+
+
+
+
+
+
+
+Storage
+Path
+Usage
+
+
+
+$HOME
+/home/<user>/
+
+Code
+Specific libraries
+
+
+
+$HOME/projects
+/project/rpp-bengioy
+
+Compressed raw datasets
+
+
+
+$SCRATCH
+/scratch/<user>
+
+Processed datasets
+Experimental results
+Logs of experiments
+
+
+
+$SLURM_TMPDIR
+
+
+Temporary job results
+
+
+
+
+
+They are roughly listed in order of increasing performance and optimized for
+different uses:
+
+The $HOME folder on NFS is appropriate for codes and libraries which are
+small and read once. Do not write experiemental results here!
+The $HOME/projects folder should only contain compressed raw datasets
+(processed datasets should go in $SCRATCH). We have a limit on the
+size and number of file in $HOME/projects, so do not put anything else
+there.  If you add a new dataset there (make sure it is readable by every
+member of the group using chgrp -R rpp-bengioy <dataset>).
+The $SCRATCH space can be used for short term storage. It has good
+performance and large quotas, but is purged regularly (every file that has
+not been used in the last 3 months gets deleted, but you receive an email
+before this happens).
+$SLURM_TMPDIR points to the local disk of the node on which a job is
+running. It should be used to copy the data on the node at the beginning of
+the job and write intermediate checkpoints. This folder is cleared after each
+job.
+
+When an experiment is finished, results should be transferred back to Mila
+servers.
+More details on storage can be found here.
+
+
+Modules
+Many software, such as Python or MATLAB are already compiled and available on
+Beluga through the module command and its subcommands. Its full
+documentation can be found here.
+
+
+
+
+
+
+module avail
+Displays all the available modules
+
+module load <module>
+Loads <module>
+
+module spider <module>
+Shows specific details about <module>
+
+
+
+In particular, if you with to use Python 3.6 you can simply do:
+module load python/3.6
+
+Tip
+If you wish to use Python on the cluster, we strongly encourage you to
+read Alliance Python Documentation, and in particular the Pytorch and/or Tensorflow pages.
+
+The cluster has many Python packages (or wheels), such already compiled for
+the cluster. See here for the
+details. In particular, you can browse the packages by doing:
+avail_wheels <wheel>
+Such wheels can be installed using pip. Moreover, the most efficient way to use
+modules on the cluster is to build your environnement inside your job.
+See the script example below.
+
+
+Script Example
+Here is a sbatch script that follows good practices on Beluga:
+ 1#!/bin/bash
+ 2#SBATCH --account=rrg-bengioy-ad         # Yoshua pays for your job
+ 3#SBATCH --cpus-per-task=6                # Ask for 6 CPUs
+ 4#SBATCH --gres=gpu:1                     # Ask for 1 GPU
+ 5#SBATCH --mem=32G                        # Ask for 32 GB of RAM
+ 6#SBATCH --time=3:00:00                   # The job will run for 3 hours
+ 7#SBATCH -o /scratch/<user>/slurm-%j.out  # Write the log in $SCRATCH
+ 8
+ 9# 1. Create your environement locally
+10module load python/3.6
+11virtualenv --no-download $SLURM_TMPDIR/env
+12source $SLURM_TMPDIR/env/bin/activate
+13pip install --no-index torch torchvision
+14
+15# 2. Copy your dataset on the compute node
+16# IMPORTANT: Your dataset must be compressed in one single file (zip, hdf5, ...)!!!
+17cp $SCRATCH/<dataset.zip> $SLURM_TMPDIR
+18
+19# 3. Eventually unzip your dataset
+20unzip $SLURM_TMPDIR/<dataset.zip> -d $SLURM_TMPDIR
+21
+22# 4. Launch your job, tell it to save the model in $SLURM_TMPDIR
+23#    and look for the dataset into $SLURM_TMPDIR
+24python main.py --path $SLURM_TMPDIR --data_path $SLURM_TMPDIR
+25
+26# 5. Copy whatever you want to save on $SCRATCH
+27cp $SLURM_TMPDIR/<to_save> $SCRATCH
+
+
+
+
+Using CometML and Wandb
+The compute nodes for Beluga don’t have access to the internet,
+but there is a special module that can be loaded in order to allow
+training scripts to access some specific servers, which includes
+the necessary servers for using CometML and Wandb (“Weights and Biases”).
+module load httpproxy
+More documentation about this can be found here.",1338,"[-0.008518395014107227, -0.013767942786216736, -0.0093494588509202, -0.0371762178838253, -0.018144875764846802, 0.0013080585049465299, -0.03703770786523819, 0.00247760652564466, 0.00042310627759434283, -0.02718961052596569, 0.012590603902935982, -0.006146403029561043, -0.02322820946574211, -0.004238421097397804, -0.008061311207711697, -0.008227523416280746, 0.009224798530340195, -0.023089699447155, -0.0011530998162925243, -0.005450387950986624, -0.006440738216042519, 0.0035597197711467743, 0.015346962958574295, -0.029308820143342018, -0.018934383988380432, -0.0007241501589305699, 0.021940061822533607, -0.008255225606262684, -0.0047855377197265625, 0.006911673583090305, 0.00018385103612672538, -0.018144875764846802, -0.01699523814022541, -0.011094690300524235, -0.024059273302555084, -0.0030229915864765644, 0.026677120476961136, -0.005218382924795151, 0.012285880744457245, 0.0049517503939569, 0.030306097120046616, 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+24,"Launching Jobs
+Users must specify the resource allocation Group Name using the flag
+--account=rrg-bengioy-ad.  To launch a CPU-only job:
+sbatch --time=1:0:0 --account=rrg-bengioy-ad job.sh
+
+Note
+The account name will differ based on your affiliation.
+
+To launch a GPU job:
+sbatch --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1 job.sh
+And to get an interactive session, use the salloc command:
+salloc --time=1:0:0 --account=rrg-bengioy-ad --gres=gpu:1
+The full documentation for jobs launching on Beluga can be found here.",160,"[-0.0019417053554207087, -0.01596130058169365, -0.011181187815964222, -0.03498518466949463, -0.013209113851189613, -0.01127085741609335, -0.02897038124501705, 0.0038834107108414173, -0.02365914359688759, -0.030294740572571754, 0.023755712434649467, 0.012340002693235874, -0.004842192400246859, -0.0014554167864844203, 0.002252102131024003, -0.01202960591763258, 0.0024210962001234293, -0.009505043737590313, 0.012264127843081951, -0.005218117497861385, -0.021603627130389214, 0.010919074527919292, 0.011181187815964222, 0.005176731385290623, -0.004738726653158665, -0.008622136898338795, 0.016306186094880104, 0.0021038015838712454, 0.0130021832883358, 0.006604556925594807, -0.011953731067478657, 0.006170001346617937, 0.005935479421168566, 0.006121717393398285, -0.02797711081802845, 0.010725938715040684, -0.0036454396322369576, 0.008642829954624176, 0.018554838374257088, -0.0014442079700529575, 0.030294740572571754, 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+25,"Beluga nodes description
+Each GPU node consists of:
+
+40 CPU cores
+186 GB RAM
+4 GPU NVIDIA V100 (16GB)
+
+
+Tip
+You should ask for max 10 CPU cores and 32 GB of RAM per GPU you are
+requesting (as explained here),
+otherwise, your job will count for more than 1 allocation, and will take
+more time to get scheduled.",82,"[-0.0035232477821409702, -0.008121384307742119, -0.002176318783313036, -0.03436991199851036, -0.015486365184187889, -0.003317559137940407, -0.05499185994267464, 0.012533738277852535, -0.02301058918237686, -0.030786948278546333, -0.011571645736694336, 0.038669466972351074, -0.036227744072675705, -0.02222764492034912, -0.007013319991528988, 0.013170708902180195, 0.0003713592595886439, -0.015207690186798573, 0.017981169745326042, -0.003679173067212105, -0.006946968380361795, -0.001693613943643868, -0.008924233727157116, -0.010881593450903893, -0.009076841175556183, 0.010483486577868462, 0.031715866178274155, -0.004389130510389805, 0.005400985945016146, -0.015818120911717415, 0.014836124144494534, 0.007199103012681007, -0.004681075923144817, -0.0018462216248735785, -0.008287262171506882, 0.009421867318451405, 0.030786948278546333, -0.007132751867175102, 0.020369812846183777, 0.007564034778624773, 0.010762161575257778, 0.0007588916341774166, 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+26,"Beluga Storage
+
+
+
+
+
+
+
+Storage
+Path
+Usage
+
+
+
+$HOME
+/home/<user>/
+
+Code
+Specific libraries
+
+
+
+$HOME/projects
+/project/rpp-bengioy
+
+Compressed raw datasets
+
+
+
+$SCRATCH
+/scratch/<user>
+
+Processed datasets
+Experimental results
+Logs of experiments
+
+
+
+$SLURM_TMPDIR
+
+
+Temporary job results
+
+
+
+
+
+They are roughly listed in order of increasing performance and optimized for
+different uses:
+
+The $HOME folder on NFS is appropriate for codes and libraries which are
+small and read once. Do not write experiemental results here!
+The $HOME/projects folder should only contain compressed raw datasets
+(processed datasets should go in $SCRATCH). We have a limit on the
+size and number of file in $HOME/projects, so do not put anything else
+there.  If you add a new dataset there (make sure it is readable by every
+member of the group using chgrp -R rpp-bengioy <dataset>).
+The $SCRATCH space can be used for short term storage. It has good
+performance and large quotas, but is purged regularly (every file that has
+not been used in the last 3 months gets deleted, but you receive an email
+before this happens).
+$SLURM_TMPDIR points to the local disk of the node on which a job is
+running. It should be used to copy the data on the node at the beginning of
+the job and write intermediate checkpoints. This folder is cleared after each
+job.
+
+When an experiment is finished, results should be transferred back to Mila
+servers.
+More details on storage can be found here.",336,"[-0.006038684397935867, 0.008936967700719833, 0.010097705759108067, -0.044207729399204254, -0.040504761040210724, 0.00046287087025120854, -0.01986784115433693, -0.02140599675476551, 0.0057894461788237095, -0.03392487391829491, 0.007797593716531992, -0.014683688059449196, -0.006928820628672838, 0.003241876373067498, -0.0011313632130622864, 0.006900336127728224, 0.005938989110291004, -0.019497545436024666, 0.006629734765738249, -0.01875695213675499, -0.014057031832635403, 0.02418322116136551, -0.01313128974288702, -0.03742844983935356, -0.003941523376852274, -0.015239132568240166, 0.016777288168668747, -0.014669445343315601, 0.009827104397118092, -0.004457802511751652, 0.010147553868591785, -0.025436533614993095, 0.004867265000939369, -0.011329654604196548, -0.025279870256781578, 0.00023477344075217843, 0.011023447848856449, -0.008367280475795269, 0.017774241045117378, 0.007712140679359436, 0.0032721408642828465, 0.005009687040001154, 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+27,"Modules
+Many software, such as Python or MATLAB are already compiled and available on
+Beluga through the module command and its subcommands. Its full
+documentation can be found here.
+
+
+
+
+
+
+module avail
+Displays all the available modules
+
+module load <module>
+Loads <module>
+
+module spider <module>
+Shows specific details about <module>
+
+
+
+In particular, if you with to use Python 3.6 you can simply do:
+module load python/3.6
+
+Tip
+If you wish to use Python on the cluster, we strongly encourage you to
+read Alliance Python Documentation, and in particular the Pytorch and/or Tensorflow pages.
+
+The cluster has many Python packages (or wheels), such already compiled for
+the cluster. See here for the
+details. In particular, you can browse the packages by doing:
+avail_wheels <wheel>
+Such wheels can be installed using pip. Moreover, the most efficient way to use
+modules on the cluster is to build your environnement inside your job.
+See the script example below.",210,"[0.0008739479817450047, -0.007414562162011862, 0.005203700624406338, -0.01633620820939541, -0.01620824821293354, 0.012582718394696712, -0.017957031726837158, 0.002338821766898036, 0.006401546765118837, -0.019904864951968193, 0.027454497292637825, -0.004208457190543413, -0.013819663785398006, 0.00542052136734128, -0.01858261413872242, -0.010101718828082085, 0.008914535865187645, 0.006266477983444929, 0.004275991581380367, -0.004212011583149433, -0.0007046677637845278, 0.016393078491091728, 0.017544716596603394, -0.042283620685338974, -0.026644084602594376, 0.021881133317947388, 0.0034300347324460745, -0.018710574135184288, -0.0017594479722902179, 0.008658616803586483, 0.022065965458750725, -0.004315090365707874, 0.0015950551023706794, -0.02867722325026989, -0.019023364409804344, -0.003952537663280964, 0.03184778615832329, -0.013976058922708035, 0.0030248286202549934, 0.01844043657183647, 0.03460603207349777, 0.03153499215841293, 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+28,"Script Example
+Here is a sbatch script that follows good practices on Beluga:
+ 1#!/bin/bash
+ 2#SBATCH --account=rrg-bengioy-ad         # Yoshua pays for your job
+ 3#SBATCH --cpus-per-task=6                # Ask for 6 CPUs
+ 4#SBATCH --gres=gpu:1                     # Ask for 1 GPU
+ 5#SBATCH --mem=32G                        # Ask for 32 GB of RAM
+ 6#SBATCH --time=3:00:00                   # The job will run for 3 hours
+ 7#SBATCH -o /scratch/<user>/slurm-%j.out  # Write the log in $SCRATCH
+ 8
+ 9# 1. Create your environement locally
+10module load python/3.6
+11virtualenv --no-download $SLURM_TMPDIR/env
+12source $SLURM_TMPDIR/env/bin/activate
+13pip install --no-index torch torchvision
+14
+15# 2. Copy your dataset on the compute node
+16# IMPORTANT: Your dataset must be compressed in one single file (zip, hdf5, ...)!!!
+17cp $SCRATCH/<dataset.zip> $SLURM_TMPDIR
+18
+19# 3. Eventually unzip your dataset
+20unzip $SLURM_TMPDIR/<dataset.zip> -d $SLURM_TMPDIR
+21
+22# 4. Launch your job, tell it to save the model in $SLURM_TMPDIR
+23#    and look for the dataset into $SLURM_TMPDIR
+24python main.py --path $SLURM_TMPDIR --data_path $SLURM_TMPDIR
+25
+26# 5. Copy whatever you want to save on $SCRATCH
+27cp $SLURM_TMPDIR/<to_save> $SCRATCH",389,"[-0.018072377890348434, 0.0018311974126845598, 0.01637466996908188, -0.013123011216521263, -0.003434778656810522, 0.008851357735693455, -0.009919270873069763, -0.013396834954619408, -0.008474850095808506, -0.017538420855998993, 0.02350778318941593, 0.012486370280385017, -0.014006093144416809, 0.0018979419255629182, 0.0029555868823081255, 0.006010435055941343, 0.016785405576229095, -0.012185163795948029, 0.002164920326322317, -0.014293608255684376, -0.012814958579838276, 0.007591767702251673, 0.009953498840332031, -0.015690110623836517, -0.009036188945174217, -0.007701297290623188, 0.0061233872547745705, -0.01571749337017536, 0.004322994966059923, 0.009015652351081371, 0.0032379678450524807, -0.017415201291441917, 0.006181574892252684, -0.006783987395465374, -0.023877445608377457, 0.00041287511703558266, -0.0019732436630874872, -0.009590682573616505, 0.015046623535454273, -0.0053566801361739635, 0.0226726196706295, 0.02306966483592987, 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+29,"Using CometML and Wandb
+The compute nodes for Beluga don’t have access to the internet,
+but there is a special module that can be loaded in order to allow
+training scripts to access some specific servers, which includes
+the necessary servers for using CometML and Wandb (“Weights and Biases”).
+module load httpproxy
+More documentation about this can be found here.",82,"[-0.022885732352733612, -0.018110807985067368, 0.022306526079773903, -0.01950938068330288, -0.0031538517214357853, 0.009189610369503498, -0.004605400376021862, -0.0180966816842556, 0.015666836872696877, -0.03144669160246849, 0.027265101671218872, 0.023168271407485008, -0.009005959145724773, -0.0055413139052689075, -0.008885880000889301, 0.006848060991615057, 0.007522624917328358, -0.004421749617904425, -0.008151276037096977, -0.015666836872696877, -0.005421234294772148, -0.010206754319369793, 0.0063606793992221355, -0.03681495040655136, -0.03136193007230759, -0.003494665492326021, 0.036588918417692184, -0.0290451031178236, -0.009733499959111214, -0.002535795560106635, 0.016457948833703995, -0.012120962142944336, -0.007854609750211239, -0.027604149654507637, 0.0011204472975805402, -0.010235007852315903, 0.019269222393631935, -0.017616363242268562, 0.013583105988800526, -0.0040579792112112045, 0.01720668002963066, 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+30,"Graham
+Graham is a cluster located at University of Waterloo. It uses SLURM to schedule
+jobs. Its full documentation can be found here, and its current status here.
+You can access Graham via ssh:
+ssh <user>@graham.computecanada.ca
+Where <user> is the username you created previously (see Account Creation).
+Since its structure is similar to Beluga, please look at the Beluga
+documentation, as well as relevant parts of the Digital Research Alliance of
+Canada Documentation.
+
+Note
+For GPU jobs the ressource allocation Group Name is the same as Beluga, so you should use the flag --account=rrg-bengioy-ad for GPU jobs.",147,"[-0.013273514807224274, -0.020435990765690804, -0.011241206899285316, -0.04425916448235512, -0.0037082573398947716, 0.0074941376224160194, -0.04575517028570175, 0.006851984653621912, -0.014578991569578648, -0.01656896062195301, 0.00021280140208546072, -0.016244355589151382, 0.003036113688722253, 0.0062380582094192505, -0.004233976360410452, -0.0064003607258200645, 0.001181985018774867, -0.008637310937047005, -0.025206271559000015, -0.011953925713896751, -0.017429867759346962, -0.013506383635103703, 0.019956141710281372, -0.008369159884750843, -0.00932886078953743, 0.00422691972926259, 0.014607218094170094, -0.0011572868097573519, 0.006513266824185848, 0.0003223996318411082, 0.002027016133069992, -0.008623197674751282, -0.014310839585959911, -0.0026303576305508614, -0.030061230063438416, 0.005729981232434511, 0.012716041877865791, 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+31,"Cedar
+Cedar is a cluster located at Simon Fraser University. It uses SLURM to schedule
+jobs. Its full documentation can be found here, and its current status here.
+You can access Cedar via ssh:
+ssh <user>@cedar.computecanada.ca
+Where <user> is the username you created previously (see Account Creation).
+Since its structure is similar to Beluga, please look at the Beluga
+documentation, as well as relevant parts of the Digital Research Alliance of
+Canada Documentation.
+
+Note
+However, we don’t have any CPU priority on Cedar, in this case you can
+use --account=def-bengioy for CPU. Thus, it might take some time before
+they start.",152,"[-0.0029961764812469482, -0.024931585416197777, -0.022512003779411316, -0.03387412801384926, -0.004068008158355951, 0.015932442620396614, -0.0558059997856617, -0.004237803164869547, -0.014057621359825134, -0.01356946025043726, 0.0018995830323547125, 0.00532378489151597, -0.011284301057457924, -0.005617388989776373, -0.01294687855988741, 0.0004342156753409654, 0.0031146793626248837, -0.015635300427675247, -0.006986361928284168, 0.008900094777345657, -0.029516052454710007, -0.013038850389420986, 0.003813315648585558, -0.02081405185163021, -0.013279394246637821, 0.011687564663589, 0.01498441956937313, 0.002718490781262517, -0.0022904654033482075, 0.023120436817407608, 0.0031235229689627886, -0.010633420199155807, 0.0004819705500267446, -0.0022621662355959415, -0.03526078909635544, 0.004570318851619959, 0.005599701777100563, -0.016470126807689667, 0.007117245811969042, -0.010265530087053776, 0.016073938459157944, 0.008893019519746304, -0.010781990364193916, 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+32,"Niagara
+Niagara is a cluster located at University of Toronto. It uses SLURM to schedule
+jobs. Its full documentation can be found here, and its current status here.
+You can access Niagara via ssh:
+ssh <user>@niagara.computecanada.ca
+Where <user> is the username you created previously (see Account Creation).
+Since its structure is similar to Beluga, please look at the Beluga
+documentation, as well as relevant parts of the Digital Research Alliance of
+Canada Documentation.",108,"[-0.00123020785395056, -0.03160620108246803, -0.00421180808916688, -0.024960432201623917, 0.010406059212982655, 0.015379802323877811, -0.05717335641384125, 0.0010820537572726607, -0.01571843959391117, -0.026554850861430168, 0.015859538689255714, -0.009651178494095802, 0.014187514781951904, -8.868299482855946e-05, -0.0033299389760941267, -0.027091028168797493, 0.003619191935285926, -0.035444095730781555, -0.0005370584549382329, -0.012099248357117176, -0.015704330056905746, 0.003929609898477793, 0.013404414989054203, -0.02335190214216709, -0.027867073193192482, 0.0064376466907560825, 0.007217219099402428, -0.012346171773970127, 0.0010406059445813298, 0.0047374023124575615, -0.0003796447708737105, -0.002769069978967309, -0.0026050421874970198, -0.01086463127285242, -0.019570445641875267, 0.004183588549494743, 0.003206477267667651, -0.01727052964270115, -0.013785382732748985, 0.0074006482027471066, 0.021771591156721115, 0.0011111554922536016, 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+33,"FAQ
+
+What to do with  ImportError: /lib64/libm.so.6: version GLIBC_2.23 not found?
+The structure of the file system is different than a classical Linux, so your
+code has trouble finding libraries. See how to install binary packages.
+
+
+Disk quota exceeded error on /project file systems
+You have files in /project with the wrong permissions. See how to change
+permissions.",92,"[0.012188717722892761, 0.007562497165054083, -0.004967730026692152, -0.05283890292048454, -0.024842170998454094, 0.025757556781172752, -0.023870453238487244, -0.01042131893336773, -0.01867387630045414, -0.006178855895996094, 0.020546896383166313, -0.008435634896159172, -0.001554396003484726, 0.004752966109663248, -0.010203034617006779, 0.007759657688438892, 0.012125344946980476, 0.010252323932945728, -0.005569772329181433, -0.01032273843884468, 0.010864928364753723, 0.02610962837934494, -0.006354891695082188, -0.025349153205752373, 0.0018184497021138668, 0.008506049402058125, 0.009287647902965546, -0.01974417455494404, 0.003207372035831213, 0.02441968396306038, 0.0064851585775613785, -0.016138961538672447, -0.011428243480622768, 0.003686189418658614, -0.027377085760235786, -0.029996497556567192, 0.006773856934159994, -0.020011749118566513, 0.001957518048584461, -0.018786538392305374, 0.028982531279325485, -0.006513324100524187, -0.005238824989646673, 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+34,"What to do with  ImportError: /lib64/libm.so.6: version GLIBC_2.23 not found?
+The structure of the file system is different than a classical Linux, so your
+code has trouble finding libraries. See how to install binary packages.",58,"[0.0077850413508713245, 0.008312370628118515, -0.0015299479709938169, -0.03355477750301361, -0.01691615954041481, 0.015861501917243004, -0.020829495042562485, -0.006407741457223892, -0.013911771588027477, -0.000915887241717428, 0.014348899945616722, -0.0010390463285148144, -0.015389680862426758, -0.005696541164070368, -0.012773851864039898, 0.01951117254793644, 0.011143294163048267, 0.03000224567949772, 0.014008911326527596, 0.0008738223114050925, 0.013460766524076462, 0.008180538192391396, 0.009859664365649223, -0.029641442000865936, -0.0011656746501103044, 0.00846501812338829, 0.009221319109201431, -0.019358525052666664, 0.019289139658212662, 0.016222305595874786, 0.011906534433364868, -0.026463590562343597, -0.007722594775259495, -0.018137343227863312, -0.02772640436887741, -0.03064059093594551, 0.007771164178848267, -0.007708717603236437, -0.002353899646550417, -0.019025474786758423, 0.03380456566810608, -0.009679262526333332, 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+35,"Disk quota exceeded error on /project file systems
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+36,"Roles and authorizations
+There are mainly two types of researchers statuses at Mila :
+
+Core researchers
+Affiliated researchers
+
+This is determined by Mila policy. Core researchers have access to the Mila
+computing cluster. See your supervisor’s Mila status to know what is your own
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+37,"Overview of available computing resources at Mila
+The Mila cluster is to be used for regular development and relatively small
+number of jobs (< 5). It is a heterogeneous cluster. It uses
+SLURM to schedule jobs.
+
+Mila cluster versus Digital Research Alliance of Canada clusters
+There are a lot of commonalities between the Mila cluster and the clusters from
+Digital Research Alliance of Canada (the Alliance). At the time being, the
+Alliance clusters where we have a large allocation of resources are beluga,
+cedar, graham and narval. We also have comparable computational resources
+in the Mila cluster, with more to come.
+The main distinguishing factor is that we have more control over our own
+cluster than we have over the ones at the Alliance. Notably, also, the compute
+nodes in the Mila cluster all have unrestricted access to the Internet, which
+is not the case in general for the Alliance clusters (although cedar does
+allow it).
+At the current time of this writing (June 2021), Mila students are advised to
+use a healthy diet of a mix of Mila and Alliance clusters. This is especially
+true in times when your favorite cluster is oversubscribed, because you can
+easily switch over to a different one if you are used to it.
+
+
+Guarantees about one GPU as absolute minimum
+There are certain guarantees that the Mila cluster tries to honor when it comes
+to giving at minimum one GPU per student, all the time, to be used in
+interactive mode. This is strictly better than “one GPU per student on average”
+because it’s a floor meaning that, at any time, you should be able to ask for
+your GPU, right now, and get it (although it might take a minute for the
+request to be processed by SLURM).
+Interactive sessions are possible on the Alliance clusters, and there are
+generally special rules that allow you to get resources more easily if you
+request them for a very short duration (for testing code before queueing long
+jobs). You do not get the same guarantee as on the Mila cluster, however.",447,"[-0.0027316929772496223, -0.018620111048221588, -0.014884939417243004, -0.034620027989149094, -0.03434128314256668, 0.017505135387182236, -0.02918451651930809, -0.005780457518994808, -0.014661944471299648, -0.04289872944355011, 0.008898908272385597, -0.00122124794870615, 0.012898887507617474, -0.01608354039490223, -0.027219370007514954, -0.011721192859113216, 0.02425074391067028, -0.015930229797959328, 0.009233401156961918, -0.0033327352721244097, -0.02094762586057186, 0.0035714099649339914, -0.0047316825948655605, -0.015735110267996788, -0.030104372650384903, -0.00044533738400787115, 0.01659921556711197, -0.003172457218170166, -0.0018553909612819552, 0.01116370502859354, 0.008006926625967026, -0.005850143730640411, -0.0064633809961378574, 0.0034668808802962303, -0.022661902010440826, -0.0010461617494001985, 0.027665359899401665, -0.004372799303382635, -0.021086998283863068, -8.966634050011635e-05, 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+38,"Mila cluster versus Digital Research Alliance of Canada clusters
+There are a lot of commonalities between the Mila cluster and the clusters from
+Digital Research Alliance of Canada (the Alliance). At the time being, the
+Alliance clusters where we have a large allocation of resources are beluga,
+cedar, graham and narval. We also have comparable computational resources
+in the Mila cluster, with more to come.
+The main distinguishing factor is that we have more control over our own
+cluster than we have over the ones at the Alliance. Notably, also, the compute
+nodes in the Mila cluster all have unrestricted access to the Internet, which
+is not the case in general for the Alliance clusters (although cedar does
+allow it).
+At the current time of this writing (June 2021), Mila students are advised to
+use a healthy diet of a mix of Mila and Alliance clusters. This is especially
+true in times when your favorite cluster is oversubscribed, because you can
+easily switch over to a different one if you are used to it.",225,"[0.007346095517277718, -0.020403482019901276, -0.014818485826253891, -0.044708043336868286, -0.03061925619840622, 0.030899910256266594, -0.030310537666082382, 0.0011041946709156036, -0.007739010266959667, -0.03614812344312668, -0.002011932199820876, -0.004308025352656841, 0.005283295176923275, -0.02187691256403923, -0.029636971652507782, -0.004522023722529411, 0.02654978632926941, -0.026086708530783653, 0.011275239288806915, 0.0019593096803873777, -0.02378535270690918, 0.013127550482749939, -0.007760059088468552, -0.015295595861971378, -0.014958811923861504, 0.009296635165810585, 0.014888648875057697, -0.0033801160752773285, -0.0013085452374070883, 0.002673221053555608, 0.009212438948452473, 0.001205931417644024, -0.00757061829790473, 0.006644462700933218, -0.013822167180478573, -0.009647451341152191, 0.01758291944861412, -0.021034952253103256, -0.014846550300717354, -0.002364502754062414, 0.009156308136880398, 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+39,"Guarantees about one GPU as absolute minimum
+There are certain guarantees that the Mila cluster tries to honor when it comes
+to giving at minimum one GPU per student, all the time, to be used in
+interactive mode. This is strictly better than “one GPU per student on average”
+because it’s a floor meaning that, at any time, you should be able to ask for
+your GPU, right now, and get it (although it might take a minute for the
+request to be processed by SLURM).
+Interactive sessions are possible on the Alliance clusters, and there are
+generally special rules that allow you to get resources more easily if you
+request them for a very short duration (for testing code before queueing long
+jobs). You do not get the same guarantee as on the Mila cluster, however.",173,"[-0.011539354920387268, -0.00905919261276722, -0.006082302890717983, -0.035764358937740326, -0.021300218999385834, 0.0005627259379252791, -0.032596420496702194, -0.002457583788782358, -0.02161979116499424, -0.032763153314590454, 0.019535621628165245, 0.02300923690199852, 0.009524657391011715, -0.008718778379261494, -0.020536022260785103, -0.005175689235329628, 0.02439868450164795, 0.0019834351260215044, 0.013310899958014488, 0.0007520380313508213, -0.01335952989757061, 0.014109831303358078, -0.001164530054666102, -0.017548711970448494, -0.032679785043001175, -0.012407759204506874, 0.02081391215324402, -0.005825255531817675, 0.00540494779124856, 0.007211228366941214, 0.016548311337828636, -0.007954582571983337, -0.026149386540055275, -0.01439466793090105, -0.026330014690756798, 0.0034041444305330515, 0.018368486315011978, 0.008371416479349136, -0.02238398604094982, -0.006172616966068745, 0.024259740486741066, 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+40,"Node profile description
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Name
+GPU
+CPUs
+Sockets
+Cores/Socket
+Threads/Core
+Memory (GB)
+TmpDisk (TB)
+Arch
+Slurm Features
+
+Model
+Mem
+#
+GPU Arch and Memory
+
+
+
+GPU Compute Nodes
+
+cn-a[001-011]
+RTX8000
+48
+8
+40
+2
+20
+1
+384
+3.6
+x86_64
+turing,48gb
+
+cn-b[001-005]
+V100
+32
+8
+40
+2
+20
+1
+384
+3.6
+x86_64
+volta,nvlink,32gb
+
+cn-c[001-040]
+RTX8000
+48
+8
+64
+2
+32
+1
+384
+3
+x86_64
+turing,48gb
+
+cn-g[001-026]
+A100
+80
+4
+64
+2
+32
+1
+1024
+7
+x86_64
+ampere,nvlink,80gb
+
+DGX Systems
+
+cn-d[001-002]
+A100
+40
+8
+128
+2
+64
+1
+1024
+14
+x86_64
+ampere,nvlink,40gb
+
+cn-d[003-004]
+A100
+80
+8
+128
+2
+64
+1
+2048
+28
+x86_64
+ampere,nvlink,80gb
+
+cn-e[002-003]
+V100
+32
+8
+40
+2
+20
+1
+512
+7
+x86_64
+volta,32gb
+
+CPU Compute Nodes
+
+cn-f[001-004]
+
+
+
+
+
+
+
+
+
+
+
+
+32
+1
+32
+1
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+10
+x86_64
+rome
+
+cn-h[001-004]
+
+
+
+
+
+
+
+
+
+
+
+
+64
+2
+32
+1
+768
+7
+x86_64
+milan
+
+Legacy GPU Compute Nodes
+
+kepler5
+V100
+16
+2
+16
+2
+4
+2
+256
+3.6
+x86_64
+volta,16gb
+
+TITAN RTX
+
+rtx[1,3-5,7]
+titanrtx
+24
+2
+20
+1
+10
+2
+128
+0.93
+x86_64
+turing,24gb
+
+
+
+
+Special nodes and outliers
+
+DGX A100
+DGX A100 nodes are NVIDIA appliances with 8 NVIDIA A100 Tensor Core GPUs. Each
+GPU has 40 GB of memory, for a total of 320 GB per appliance. The GPUs are
+interconnected via 6 NVSwitches which allows 4.8 TB/s bi-directional bandwidth.
+In order to run jobs on a DGX A100, add the flags below to your Slurm
+commands:
+--gres=gpu:a100:<number> --reservation=DGXA100
+
+
+
+
+MIG
+MIG (Multi-Instance GPU)
+is an NVIDIA technology allowing certain GPUs to be
+partitioned into multiple instances, each of which has a roughly proportional
+amount of compute resources, device memory and bandwidth to that memory.
+NVIDIA supports MIG on its A100 GPUs and allows slicing the A100 into up to 7
+instances. Although this can theoretically be done dynamically, the SLURM job
+scheduler does not support doing so in practice as it does not model
+reconfigurable resources very well. Therefore, the A100s must currently be
+statically partitioned into the required number of instances of every size
+expected to be used.
+The cn-g series of nodes include A100-80GB GPUs. One third have been
+configured to offer regular (non-MIG mode) a100l GPUs. The other two-thirds
+have been configured in MIG mode, and offer the following profiles:
+
+
+
+
+
+
+
+
+
+Name
+GPU
+Cluster-wide
+
+Model
+Memory
+Compute
+#
+
+
+
+a100l.1g.10gb
+a100l.1
+A100
+10GB 
+(1/8th)
+1/7th
+of full
+72
+
+a100l.2g.20gb
+a100l.2
+A100
+20GB 
+(2/8th)
+2/7th
+of full
+108
+
+a100l.3g.40gb
+a100l.3
+A100
+40GB 
+(4/8th)
+3/7th
+of full
+72
+
+
+
+And can be requested using a SLURM flag such as --gres=gpu:a100l.1
+The partitioning may be revised as needs and SLURM capabilities evolve. Other
+MIG profiles exist and could be introduced.
+
+Warning
+MIG has a number of important limitations,
+most notably that a GPU in MIG mode does not support graphics APIs
+(OpenGL/Vulkan), nor P2P over NVLink and PCIe. We have therefore chosen to
+limit every MIG job to exactly one MIG slice and no more. Thus,
+--gres=gpu:a100l.3 will work (and request a size-3 slice of an
+a100l GPU) but --gres=gpu:a100l.1:3 (with :3 requesting
+three size-1 slices) will not.
+
+
+
+AMD
+
+Warning
+As of August 20 2019 the GPUs had to return back to AMD.  Mila will get
+more samples. You can join the amd slack channels to get the latest
+information
+
+Mila has a few node equipped with MI50 GPUs.
+srun --gres=gpu -c 8 --reservation=AMD --pty bash
+
+ first time setup of AMD stack
+conda create -n rocm python=3.6
+conda activate rocm
+
+pip install tensorflow-rocm
+pip install /wheels/pytorch/torch-1.1.0a0+d8b9d32-cp36-cp36m-linux_x86_64.whl",1239,"[-0.010523217730224133, -0.006171110551804304, 0.008128670044243336, -0.021032223477959633, -0.004956073127686977, 0.013727789744734764, -0.027285048738121986, 0.0019735475070774555, -0.028947729617357254, -0.03700534626841545, 0.016541561111807823, 0.019483229145407677, -0.02519603632390499, -0.011091656051576138, -0.01479361206293106, 0.009485817514359951, -0.00388669827952981, 0.004092757124453783, 0.014040431007742882, -0.025906585156917572, -0.006405591499060392, -0.005876232869923115, -0.018460040912032127, -0.007588653825223446, -0.0024744838010519743, 0.017294742166996002, 0.023035969585180283, -0.01359989121556282, 0.005005811341106892, 0.014409915544092655, 0.003398196306079626, 0.015603636391460896, -0.02687292918562889, -0.027597688138484955, -0.014210962690412998, 0.030212506651878357, 0.030098818242549896, -0.0009459171560592949, 0.02378915064036846, 0.004476453177630901, 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+41,"Special nodes and outliers
+
+DGX A100
+DGX A100 nodes are NVIDIA appliances with 8 NVIDIA A100 Tensor Core GPUs. Each
+GPU has 40 GB of memory, for a total of 320 GB per appliance. The GPUs are
+interconnected via 6 NVSwitches which allows 4.8 TB/s bi-directional bandwidth.
+In order to run jobs on a DGX A100, add the flags below to your Slurm
+commands:
+--gres=gpu:a100:<number> --reservation=DGXA100
+
+
+
+
+MIG
+MIG (Multi-Instance GPU)
+is an NVIDIA technology allowing certain GPUs to be
+partitioned into multiple instances, each of which has a roughly proportional
+amount of compute resources, device memory and bandwidth to that memory.
+NVIDIA supports MIG on its A100 GPUs and allows slicing the A100 into up to 7
+instances. Although this can theoretically be done dynamically, the SLURM job
+scheduler does not support doing so in practice as it does not model
+reconfigurable resources very well. Therefore, the A100s must currently be
+statically partitioned into the required number of instances of every size
+expected to be used.
+The cn-g series of nodes include A100-80GB GPUs. One third have been
+configured to offer regular (non-MIG mode) a100l GPUs. The other two-thirds
+have been configured in MIG mode, and offer the following profiles:
+
+
+
+
+
+
+
+
+
+Name
+GPU
+Cluster-wide
+
+Model
+Memory
+Compute
+#
+
+
+
+a100l.1g.10gb
+a100l.1
+A100
+10GB 
+(1/8th)
+1/7th
+of full
+72
+
+a100l.2g.20gb
+a100l.2
+A100
+20GB 
+(2/8th)
+2/7th
+of full
+108
+
+a100l.3g.40gb
+a100l.3
+A100
+40GB 
+(4/8th)
+3/7th
+of full
+72
+
+
+
+And can be requested using a SLURM flag such as --gres=gpu:a100l.1
+The partitioning may be revised as needs and SLURM capabilities evolve. Other
+MIG profiles exist and could be introduced.
+
+Warning
+MIG has a number of important limitations,
+most notably that a GPU in MIG mode does not support graphics APIs
+(OpenGL/Vulkan), nor P2P over NVLink and PCIe. We have therefore chosen to
+limit every MIG job to exactly one MIG slice and no more. Thus,
+--gres=gpu:a100l.3 will work (and request a size-3 slice of an
+a100l GPU) but --gres=gpu:a100l.1:3 (with :3 requesting
+three size-1 slices) will not.
+
+
+
+AMD
+
+Warning
+As of August 20 2019 the GPUs had to return back to AMD.  Mila will get
+more samples. You can join the amd slack channels to get the latest
+information
+
+Mila has a few node equipped with MI50 GPUs.
+srun --gres=gpu -c 8 --reservation=AMD --pty bash
+
+ first time setup of AMD stack
+conda create -n rocm python=3.6
+conda activate rocm
+
+pip install tensorflow-rocm
+pip install /wheels/pytorch/torch-1.1.0a0+d8b9d32-cp36-cp36m-linux_x86_64.whl",746,"[-0.005199730861932039, -0.008026232942938805, -0.0009968300582841039, -0.016671568155288696, -0.004510609898716211, 0.01237469632178545, -0.019310127943754196, 0.005678799003362656, -0.014431004412472248, -0.03584903106093407, 0.02567804791033268, 0.03198700770735741, -0.007093892432749271, -0.015949280932545662, -0.009146515280008316, 0.012971688993275166, -0.002848612377420068, 0.01690741814672947, 0.01869102567434311, -0.011387079022824764, -0.014666853472590446, 0.014991145581007004, -0.009522398933768272, -0.005266063380986452, 0.002233194187283516, 0.01112911943346262, 0.015094329603016376, -0.016406238079071045, 0.00034433018299750984, 0.017968738451600075, -0.0012529472587630153, 0.013465497642755508, -0.02267097495496273, -0.005476116668432951, -0.013126464560627937, 0.022862602025270462, 0.026282411068677902, -0.00813678652048111, 0.009198106825351715, -0.014578409492969513, 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+42,"DGX A100
+DGX A100 nodes are NVIDIA appliances with 8 NVIDIA A100 Tensor Core GPUs. Each
+GPU has 40 GB of memory, for a total of 320 GB per appliance. The GPUs are
+interconnected via 6 NVSwitches which allows 4.8 TB/s bi-directional bandwidth.
+In order to run jobs on a DGX A100, add the flags below to your Slurm
+commands:
+--gres=gpu:a100:<number> --reservation=DGXA100",108,"[0.0011326802195981145, -0.013014649972319603, -0.0020505122374743223, -0.01509781926870346, -0.00522167282178998, 0.005269798915833235, -0.02264672890305519, 0.0032038178760558367, 0.007218902464956045, -0.021986715495586395, 0.017847876995801926, 0.031433165073394775, 0.005713245831429958, -0.019195405766367912, -0.0039016450755298138, 0.023196740075945854, -0.007191401906311512, 0.01170837227255106, 0.009308947250247002, -0.022756731137633324, -0.009879584424197674, 0.00685108220204711, -0.010491471737623215, 0.0004834086576011032, -0.0012933866819366813, 0.006672328803688288, 0.022137967869639397, -0.012595266103744507, -0.00349944899789989, 0.01766912452876568, -0.00011429929145378992, 0.014135299250483513, -0.02371925115585327, -0.0026795880403369665, -0.0031281912233680487, 0.019649164751172066, 0.001854570466093719, -0.015414075925946236, 0.0031127221882343292, -0.010697726160287857, 0.02657931298017502, 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+43,"MIG
+MIG (Multi-Instance GPU)
+is an NVIDIA technology allowing certain GPUs to be
+partitioned into multiple instances, each of which has a roughly proportional
+amount of compute resources, device memory and bandwidth to that memory.
+NVIDIA supports MIG on its A100 GPUs and allows slicing the A100 into up to 7
+instances. Although this can theoretically be done dynamically, the SLURM job
+scheduler does not support doing so in practice as it does not model
+reconfigurable resources very well. Therefore, the A100s must currently be
+statically partitioned into the required number of instances of every size
+expected to be used.
+The cn-g series of nodes include A100-80GB GPUs. One third have been
+configured to offer regular (non-MIG mode) a100l GPUs. The other two-thirds
+have been configured in MIG mode, and offer the following profiles:
+
+
+
+
+
+
+
+
+
+Name
+GPU
+Cluster-wide
+
+Model
+Memory
+Compute
+#
+
+
+
+a100l.1g.10gb
+a100l.1
+A100
+10GB 
+(1/8th)
+1/7th
+of full
+72
+
+a100l.2g.20gb
+a100l.2
+A100
+20GB 
+(2/8th)
+2/7th
+of full
+108
+
+a100l.3g.40gb
+a100l.3
+A100
+40GB 
+(4/8th)
+3/7th
+of full
+72
+
+
+
+And can be requested using a SLURM flag such as --gres=gpu:a100l.1
+The partitioning may be revised as needs and SLURM capabilities evolve. Other
+MIG profiles exist and could be introduced.
+
+Warning
+MIG has a number of important limitations,
+most notably that a GPU in MIG mode does not support graphics APIs
+(OpenGL/Vulkan), nor P2P over NVLink and PCIe. We have therefore chosen to
+limit every MIG job to exactly one MIG slice and no more. Thus,
+--gres=gpu:a100l.3 will work (and request a size-3 slice of an
+a100l GPU) but --gres=gpu:a100l.1:3 (with :3 requesting
+three size-1 slices) will not.",487,"[-0.0166629608720541, -0.0032609880436211824, 0.00907926820218563, -0.02819017507135868, -0.004821817856281996, 0.005664842203259468, -0.025340117514133453, 0.012881696224212646, -0.03163281828165054, -0.06411218643188477, 0.017495403066277504, 0.035103682428598404, -0.0012742368271574378, -0.0034179529175162315, 0.0012980460887774825, 0.008444353938102722, 0.004412651062011719, 0.01842661015689373, 0.007590747904032469, 0.0035572811029851437, -0.013220317661762238, 0.009297960437834263, 0.0056295692920684814, -0.003495553508400917, -0.022151438519358635, 0.004663089290261269, 0.008169224485754967, -0.01626790314912796, 0.003142823465168476, 0.02031724341213703, 0.00724507262930274, 0.0069558341056108475, -0.014885202050209045, -0.0039787935093045235, -0.016225576400756836, 0.007470819633454084, 0.037389371544122696, -0.0027054385282099247, -0.0013685920275747776, -0.0064584845677018166, 0.030645174905657768, -0.003493789816275239, 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+44,"AMD
+
+Warning
+As of August 20 2019 the GPUs had to return back to AMD.  Mila will get
+more samples. You can join the amd slack channels to get the latest
+information
+
+Mila has a few node equipped with MI50 GPUs.
+srun --gres=gpu -c 8 --reservation=AMD --pty bash
+
+ first time setup of AMD stack
+conda create -n rocm python=3.6
+conda activate rocm
+
+pip install tensorflow-rocm
+pip install /wheels/pytorch/torch-1.1.0a0+d8b9d32-cp36-cp36m-linux_x86_64.whl",143,"[-0.018515771254897118, -0.017366820946335793, -0.010163788683712482, -0.018987135961651802, -0.015186762437224388, 0.014737493358552456, -0.03326062858104706, -0.010988675989210606, -0.019222818315029144, -0.011047596111893654, 0.05299900099635124, 0.008838077075779438, 0.002509645652025938, -0.01900186575949192, -0.020150816068053246, 0.008521378971636295, 0.005623226519674063, 0.003907166421413422, 0.013971527107059956, -0.007873253896832466, -0.010664612986147404, 0.009383091703057289, -0.007725952193140984, 0.0031835490372031927, -0.010583597235381603, 0.006462844088673592, 0.013846321031451225, -0.024437284097075462, 0.0011066008592024446, -0.01764669455587864, 0.007748047821223736, 0.0037653890904039145, -0.014317684806883335, -0.0240248404443264, -0.025085408240556717, -0.003973452374339104, 0.012726831249892712, -0.0191049762070179, 0.016512474045157433, -0.01713113859295845, 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+45,"Data sharing policies
+
+Note
+/network/scratch aims to support
+Access Control Lists (ACLs)
+to allow collaborative work on rapidly changing data, e.g. work in process
+datasets, model checkpoints, etc…
+
+/network/projects aims to offer a collaborative
+space for long-term projects. Data that should be kept for a longer period then
+90 days can be stored in that location but first a request to Mila’s helpdesk has to be made to create the project
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+46,"Monitoring
+Every compute node on the Mila cluster has a Netdata
+monitoring daemon allowing you to get a sense of the state of the node.
+This information is exposed in two ways:
+
+For every node, there is a web interface from Netdata itself at <node>.server.mila.quebec:19999.
+This is accessible only when using the Mila wifi or through SSH tunnelling.
+
+SSH tunnelling: on your local machine, run
+
+ssh -L 19999:<node>.server.mila.quebec:19999 -p 2222
+login.server.mila.quebec
+or ssh -L 19999:<node>.server.mila.quebec:19999 mila if you have
+already setup your SSH Login,
+
+
+then open http://localhost:19999 in your browser.
+
+
+The Mila dashboard at dashboard.server.mila.quebec
+exposes aggregated statistics with the use of grafana.
+These are collected internally to an instance of prometheus.
+
+In both cases, those graphs are not editable by individual users,
+but they provide valuable insight into the state of the whole cluster
+or the individual nodes.
+One of the important uses is to collect data about the health
+of the Mila cluster and to sound the alarm if outages occur
+(e.g. if the nodes crash or if GPUs mysteriously become unavailable for SLURM).
+
+Example with Netdata on cn-c001
+For example, if we have a job running on cn-c001, we can type
+cn-c001.server.mila.quebec:19999 in a browser address bar and the following
+page will appear.
+
+
+
+Example watching the CPU/RAM/GPU usage
+Given that compute nodes are generally shared
+with other users who are also running jobs at the same time and
+consuming resources, this is not generally a good way to profile your code
+in fine details.
+However, it can still be a very useful source of information
+for getting an idea of whether the machine that you requested is being
+used in its full capacity.
+Given how expensive the GPUs are, it generally makes sense to try to
+make sure that this resources is always kept busy.
+
+
+CPU
+iowait (pink line): High values means your model is waiting on IO a lot (disk or network).
+
+
+
+
+
+
+
+
+CPU RAM
+You can see how much CPU RAM is being used by your script in practice,
+considering the amount that you requested (e.g. `sbatch --mem=8G ...`).
+GPU usage is generally more important to monitor than CPU RAM.
+You should not cut it so close to the limit that your experiments randomly fail
+because they run out of RAM. However, you should not request blindly 32GB of RAM
+when you actually require only 8GB.
+
+
+
+
+
+
+
+
+GPU
+Monitors the GPU usage using an nvidia-smi plugin for Netdata.
+Under the plugin interface, select the GPU number which was allocated to
+you. You can figure this out by running echo $SLURM_JOB_GPUS on the
+allocated node or, if you have the job ID,
+scontrol show -d job YOUR_JOB_ID | grep 'GRES' and checking IDX
+You should make sure you use the GPUs to their fullest capacity.
+Select the biggest batch size if possible to increase GPU memory usage and
+the GPU computational load.
+Spawn multiple experiments if you can fit many on a single GPU.
+Running 10 independent MNIST experiments on a single GPU will probably take
+less than 10x the time to run a single one. This assumes that you have more
+experiments to run, because nothing is gained by gratuitously running experiments.
+You can request a less powerful GPU and leave the more powerful GPUs
+to other researchers who have experiments that can make best use of them.
+Sometimes you really just need a k80 and not a v100.
+
+
+
+
+
+
+
+
+Other users or jobs
+If the node seems unresponsive or slow,
+it may be useful to check what other tasks are
+running at the same time on that node.
+This should not be an issue in general,
+but in practice it is useful to be able to
+inspect this to diagnose certain problems.
+
+
+
+
+
+
+
+
+Example with Mila dashboard",864,"[-0.004716416355222464, 0.006024593953043222, 0.019674992188811302, -0.03332190215587616, -0.0034099831245839596, 0.0067536854185163975, -0.03683828189969063, -0.015181837603449821, -0.01486089825630188, -0.03396378085017204, 0.01957731507718563, 0.010423559695482254, -0.0068060122430324554, 0.006450187880545855, -0.011065438389778137, 0.004231518600136042, 0.021475045010447502, -0.007360679563134909, 0.014902760274708271, -0.026135645806789398, -0.018084248527884483, 0.015335330739617348, -0.00916770938783884, -0.006390884052962065, -0.02319137379527092, 0.006512980442494154, 0.01643768884241581, -0.03558243066072464, -0.018879620358347893, 0.006293206475675106, 0.010611937381327152, -0.013528301380574703, -0.004719905089586973, -0.005044333171099424, -0.008818862028419971, 0.02857757732272148, 0.037982501089572906, -0.01733073778450489, 0.005372249521315098, 0.01890752837061882, 0.018167970702052116, 0.015628362074494362, 0.010995669290423393, 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+47,"Example with Netdata on cn-c001
+For example, if we have a job running on cn-c001, we can type
+cn-c001.server.mila.quebec:19999 in a browser address bar and the following
+page will appear.",54,"[-0.014151128008961678, 0.0012711969902738929, 0.01999969594180584, -0.009886695072054863, -0.018641864880919456, 0.01455423329025507, -0.03425690159201622, -0.025374436751008034, -0.028684144839644432, -0.0012544009368866682, 0.021399956196546555, 0.024158047512173653, 0.005360597278922796, -0.014278423972427845, -0.008182336576282978, -0.004603890236467123, 0.000803117174655199, -0.02352156490087509, 0.01766592636704445, -0.04308279603719711, -0.024285344406962395, 0.0145259452983737, 0.017255747690796852, -0.016689985990524292, -0.03470951318740845, -0.0003082962066400796, -0.0014895459171384573, -0.013535861857235432, -0.010806058533489704, 0.009907911531627178, -0.011081867851316929, -0.004904451314359903, -0.015827199444174767, -0.02004212699830532, 0.002786379074677825, 0.024455072358250618, 0.014377432875335217, -0.00530048506334424, 0.005975863430649042, -0.004667538218200207, 0.032220158725976944, 0.012022447772324085, 0.022503193467855453, 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+48,"Example watching the CPU/RAM/GPU usage
+Given that compute nodes are generally shared
+with other users who are also running jobs at the same time and
+consuming resources, this is not generally a good way to profile your code
+in fine details.
+However, it can still be a very useful source of information
+for getting an idea of whether the machine that you requested is being
+used in its full capacity.
+Given how expensive the GPUs are, it generally makes sense to try to
+make sure that this resources is always kept busy.
+
+
+CPU
+iowait (pink line): High values means your model is waiting on IO a lot (disk or network).
+
+
+
+
+
+
+
+
+CPU RAM
+You can see how much CPU RAM is being used by your script in practice,
+considering the amount that you requested (e.g. `sbatch --mem=8G ...`).
+GPU usage is generally more important to monitor than CPU RAM.
+You should not cut it so close to the limit that your experiments randomly fail
+because they run out of RAM. However, you should not request blindly 32GB of RAM
+when you actually require only 8GB.
+
+
+
+
+
+
+
+
+GPU
+Monitors the GPU usage using an nvidia-smi plugin for Netdata.
+Under the plugin interface, select the GPU number which was allocated to
+you. You can figure this out by running echo $SLURM_JOB_GPUS on the
+allocated node or, if you have the job ID,
+scontrol show -d job YOUR_JOB_ID | grep 'GRES' and checking IDX
+You should make sure you use the GPUs to their fullest capacity.
+Select the biggest batch size if possible to increase GPU memory usage and
+the GPU computational load.
+Spawn multiple experiments if you can fit many on a single GPU.
+Running 10 independent MNIST experiments on a single GPU will probably take
+less than 10x the time to run a single one. This assumes that you have more
+experiments to run, because nothing is gained by gratuitously running experiments.
+You can request a less powerful GPU and leave the more powerful GPUs
+to other researchers who have experiments that can make best use of them.
+Sometimes you really just need a k80 and not a v100.
+
+
+
+
+
+
+
+
+Other users or jobs
+If the node seems unresponsive or slow,
+it may be useful to check what other tasks are
+running at the same time on that node.
+This should not be an issue in general,
+but in practice it is useful to be able to
+inspect this to diagnose certain problems.",517,"[-0.008570767939090729, -0.013921541161835194, 0.014377649873495102, -0.026318179443478584, -0.0031706395093351603, -0.013764966279268265, -0.019183816388249397, 0.0011096387170255184, -0.025215348228812218, -0.04618275538086891, 0.011777147650718689, 0.018516670912504196, -0.006582948844879866, -0.008121466264128685, -0.0046257637441158295, 0.0031263900455087423, 0.030198512598872185, 0.0011955846566706896, 0.008768187835812569, -0.021239710971713066, -0.02119886502623558, -0.002306074369698763, -0.02461628057062626, -0.00846184603869915, -0.01838051900267601, 0.022151928395032883, 0.01644716039299965, -0.03597135469317436, -0.015630248934030533, 0.002033770317211747, 0.006317452527582645, -0.010490510612726212, 0.001478100079111755, -0.0194697342813015, -0.011075964197516441, 0.005582231562584639, 0.02566464990377426, 0.015780014917254448, 0.015711938962340355, 0.0018686860566958785, 0.018503054976463318, 0.016297392547130585, 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+49,Example with Mila dashboard,7,"[0.0016101578949019313, 0.004171281587332487, 0.010871266946196556, -0.024970054626464844, -0.019696516916155815, 0.024970054626464844, -0.023168982937932014, -0.027188973501324654, -0.007168264128267765, -0.028139939531683922, 0.0306326225399971, 0.009524066001176834, 0.001506596221588552, -0.004549506586045027, -0.021944254636764526, -0.0032203157898038626, 0.015676526352763176, 0.01399792730808258, 0.02005673199892044, -0.029883377254009247, -0.006476652808487415, 0.020647482946515083, -0.003378810128197074, -0.0020604257006198168, -0.028067898005247116, -0.0003748479939531535, 0.0101004084572196, -0.02044576220214367, -0.010554278269410133, -0.005849880166351795, 0.017419962212443352, -0.015676526352763176, -0.006458641961216927, -0.009624925442039967, -0.008609121665358543, -0.0031752889044582844, 0.019004905596375465, -0.02629564329981804, 0.002364806830883026, 0.018875228241086006, 0.012153630144894123, 0.01726146787405014, 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0.022232426330447197, 0.004308162722736597, -0.003796658478677273, 0.013198251836001873, -0.008097616955637932, -0.0032527348957955837, 0.008198477327823639, 0.005042999982833862, 0.00747804855927825, 0.018990498036146164, -0.01446620561182499, 0.040574535727500916, -0.0047116028144955635, -0.013169433921575546, 0.011534061282873154, -0.007715790066868067, 0.009488044306635857, 0.029595205560326576, -0.0041892919689416885, -0.026554998010396957, -0.01574856787919998, -0.0009437614353373647, 0.00800396129488945, -0.014221260324120522, -0.00020115716324653476, -0.030863160267472267, 0.005107838660478592, -0.03642486780881882, -0.019984688609838486, 0.01203836128115654, -0.00020622268493752927, -0.001955963671207428, -0.0069737485609948635, 0.002914133481681347, 0.03691475838422775, 0.0012742580147460103, -0.024523388594388962, -0.02047457918524742, 0.010410193353891373, -0.008868475444614887, 0.011944705620408058, 0.005871492903679609, 0.008825249969959259, -0.0017668510554358363, 0.020085548982024193, 0.012535457499325275, 0.023558015003800392, 0.013234272599220276, 0.04025754705071449, 0.016497815027832985, -0.03034445084631443, 0.030171548947691917, 0.015172225423157215, -0.018486198037862778, -0.013025349006056786, -0.013947497121989727, 0.011534061282873154, 0.009034174494445324, 0.01766490936279297, -0.0015363139100372791, 0.0075644999742507935, -0.01656985655426979, -0.013486422598361969, 0.040286365896463394, 0.02658381499350071, 0.014999322593212128, -0.02407672256231308, 0.014343732967972755, 0.03143950179219246, 0.018140392377972603, -0.01808275654911995, -0.007535682991147041, 0.012204060330986977, 0.0030960417352616787, -0.013500831089913845, -0.010215677320957184, -0.0005250123213045299, -0.012362554669380188, 0.01825566031038761, 0.02824079990386963, 0.0011914087226614356, 0.004898914135992527, 0.006919716484844685, 0.030978428199887276, 0.010107613168656826, 0.0018253858434036374, -0.0005921022384427488, -0.0181115735322237, 0.003097842913120985, -0.004315366968512535, -0.010964922606945038, -0.01965329237282276, 0.012744381092488766, 0.0004619748215191066, -0.01059029996395111, 0.00028749601915478706, -0.001303075230680406, 0.03132423385977745, -0.029062088578939438, 0.020935654640197754, 0.008162455633282661, -0.023154573515057564, -0.032073479145765305, 0.02590661123394966, -0.0028114723972976208, -0.010741589590907097, 0.025402311235666275, -0.013183843344449997, 0.014667925424873829, 0.01850060559809208, 0.018053939566016197, -0.008609121665358543, 0.016353728249669075, 0.004819666966795921, 0.020431354641914368, -0.0047188070602715015, -0.00741320988163352, -0.02381736785173416, -0.021641673520207405, -0.004787248093634844, -0.04483947530388832, 0.014538249000906944, -0.0012319328961893916, 0.08512584120035172, 0.02155522257089615, -0.006001169793307781, 0.01852942258119583, -0.017146199941635132, 0.024264033883810043, 0.016656309366226196, -0.0090485829859972, -0.014653516933321953, -0.010554278269410133, 0.018399745225906372, 0.020244043320417404, 0.020978879183530807, -0.02727542631328106, -0.0030420096591115, 0.017088565975427628, -0.00727632874622941, 0.01002836599946022, -0.01629609428346157, -0.02520059235394001, 0.014581474475562572, 0.020906837657094002, 0.003634562250226736, 0.0033355841878801584, -0.017016522586345673, -0.005439235828816891, 0.007499661296606064, 0.007917510345578194, -0.018053939566016197, 8.307441748911515e-05, 0.018731143325567245, 0.0040452065877616405, -0.022578231990337372, -0.0215696319937706, 0.009243099018931389, -0.003991174511611462, -0.024393711239099503, 0.01863028295338154, 0.014019540511071682, 0.006865684408694506, 0.004236119799315929, 0.026122739538550377, -0.028788326308131218, -0.019437162205576897, 0.02239092066884041, -0.029321443289518356, 0.023471562191843987, -0.019048132002353668, -0.02422080934047699]"
+50,"Storage
+
+
+
+
+
+
+
+
+
+
+Path
+Performance
+Usage
+Quota (Space/Files)
+Backup
+Auto-cleanup
+
+
+
+/network/datasets/
+High
+
+Curated raw datasets (read only)
+
+
+
+
+
+
+$HOME or /home/mila/<u>/<username>/
+Low
+
+Personal user space
+Specific libraries, code, binaries
+
+
+100GB/1000K
+Daily
+no
+
+$SCRATCH or /network/scratch/<u>/<username>/
+High
+
+Temporary job results
+Processed datasets
+Optimized for small Files
+
+
+no
+no
+90 days
+
+$SLURM_TMPDIR
+Highest
+
+High speed disk for temporary job
+results
+
+
+4TB/-
+no
+at job end
+
+/network/projects/<groupname>/
+Fair
+
+Shared space to facilitate
+collaboration between researchers
+Long-term project storage
+
+
+200GB/1000K
+Daily
+no
+
+$ARCHIVE or /network/archive/<u>/<username>/
+Low
+
+Long-term personal storage
+
+
+500GB
+no
+no
+
+
+
+
+Note
+The $HOME file system is backed up once a day. For any file
+restoration request, file a request to Mila’s IT support with the path to the file or directory to
+restore, with the required date.
+
+
+Warning
+Currently there is no backup system for any other file systems of
+the Mila cluster. Storage local to personal computers, Google Drive and other
+related solutions should be used to backup important data
+
+
+$HOME
+$HOME is appropriate for codes and libraries which are small and read once,
+as well as the experimental results that would be needed at a later time (e.g.
+the weights of a network referenced in a paper).
+Quotas are enabled on $HOME for both disk capacity (blocks) and number of
+files (inodes). The limits for blocks and inodes are respectively 100GiB and 1
+million per user. The command to check the quota usage from a login node is:
+beegfs-ctl --cfgFile=/etc/beegfs/home.d/beegfs-client.conf --getquota --uid $USER
+
+
+$SCRATCH
+$SCRATCH can be used to store processed datasets, work in progress datasets
+or temporary job results. Its block size is optimized for small files which
+minimizes the performance hit of working on extracted datasets.
+
+Note
+Auto-cleanup: this file system is cleared on a weekly basis,
+files not used for more than 90 days will be deleted.
+
+
+
+$SLURM_TMPDIR
+$SLURM_TMPDIR points to the local disk of the node on which a job is
+running. It should be used to copy the data on the node at the beginning of the
+job and write intermediate checkpoints. This folder is cleared after each job.
+
+
+projects
+projects can be used for collaborative projects. It aims to ease the
+sharing of data between users working on a long-term project.
+Quotas are enabled on projects for both disk capacity (blocks) and number
+of files (inodes). The limits for blocks and inodes are respectively 200GiB and
+1 million per user and per group.
+
+Note
+It is possible to request higher quota limits if the project requires
+it. File a request to Mila’s IT support.
+
+
+
+$ARCHIVE
+$ARCHIVE purpose is to store data other than datasets that has to be kept
+long-term (e.g.  generated samples, logs, data relevant for paper submission).
+$ARCHIVE is only available on the login nodes. Because this file system
+is tuned for large files, it is recommended to archive your directories. For
+example, to archive the results of an experiment in
+$SCRATCH/my_experiment_results/, run the commands below from a login node:
+cd $SCRATCH
+tar cJf $ARCHIVE/my_experiment_results.tar.xz --xattrs my_experiment_results
+Disk capacity quotas are enabled on $ARCHIVE. The soft limit per user is
+500GB, the hard limit is 550GB. The grace time is 7 days. This means that one
+can use more than 500GB for 7 days before the file system enforces quota.
+However, it is not possible to use more than 550GB.
+The command to check the quota usage from a login node is df:
+df -h $ARCHIVE
+
+Note
+There is NO backup of this file system.
+
+
+
+datasets
+datasets contains curated datasets to the benefit of the Mila community.
+To request the addition of a dataset or a preprocessed dataset you think could
+benefit the research of others, you can fill this form. Datasets can also be browsed from the
+web : Mila Datasets
+Datasets in datasets/restricted are restricted and require an explicit
+request to gain access. Please submit a support ticket mentioning the dataset’s
+access group (ex.: scannet_users), your cluster’s username and the
+approbation of the group owner. You can find the dataset’s access group by
+listing the content of /network/datasets/restricted with the ls command.
+Those datasets are mirrored to the Alliance clusters in
+~/projects/rrg-bengioy-ad/data/curated/ if they follow Digital Research
+Alliance of Canada’s good practices on data.
+To list the local datasets on an Alliance cluster, you can execute the
+following command:
+ssh [CLUSTER_LOGIN] -C ""projects/rrg-bengioy-ad/data/curated/list_datasets_cc.sh""",1124,"[0.009135674685239792, -0.004360368009656668, 0.007658775895833969, -0.03811805695295334, -0.034911077469587326, 0.006691758520901203, -0.01972011663019657, -0.01917155459523201, -0.006857030559331179, -0.02903161197900772, 0.029931817203760147, -0.0196638535708189, 0.0037449935916811228, 0.0018724967958405614, -0.014290754683315754, -0.005126948934048414, 0.02019835077226162, -0.019129356369376183, 0.012785724364221096, -0.01597863994538784, 0.006589782424271107, 0.027146808803081512, -0.02474157325923443, -0.020240547135472298, 0.00038680684519931674, -0.006621430162340403, 0.014740857295691967, -0.02111262083053589, 0.014248558320105076, 0.003998176194727421, 0.004093119874596596, -0.020184284076094627, -0.0027955586556345224, 0.0021837004460394382, -0.023574119433760643, 0.0036219186149537563, 0.022125352174043655, -0.014192295260727406, 0.010401587933301926, 0.01132289133965969, 0.003468954237177968, 0.02296929433941841, 0.00043054239358752966, -0.02226600982248783, -0.011519811116158962, 0.013285057619214058, -0.001149871270172298, -0.0058759478852152824, 0.0046979449689388275, -0.005559469573199749, 0.01524722296744585, 0.02204095758497715, -0.047204501926898956, 0.01052817888557911, -0.03797740116715431, -0.007996352389454842, 0.005035521928220987, 0.014670529402792454, -0.004712010733783245, -0.0037203787360340357, -0.003502360312268138, -0.006695275194942951, -0.029537977650761604, 0.0015032720984891057, -0.008333929814398289, -0.006832415703684092, -0.027231203392148018, 0.0024913877714425325, 0.007848662324249744, -0.032688695937395096, 0.02377104014158249, 0.02566991001367569, 0.012082439847290516, -0.014178229495882988, 0.01576765440404415, -0.01645687408745289, 0.006371764000505209, 0.0004892227589152753, 0.0015164586948230863, 0.006695275194942951, 0.020887570455670357, 0.0005621886230073869, -0.004223227500915527, 0.02226600982248783, 0.021815907210111618, 0.026781100779771805, 0.013510108925402164, 0.032294854521751404, -0.026176273822784424, -0.014543937519192696, 0.01911529153585434, -0.011871454305946827, 0.026738902553915977, 0.005471558775752783, 0.016203690320253372, 0.019860774278640747, -0.02111262083053589, 1.6620606402284466e-05, 0.0012307490687817335, -0.02057812549173832, -0.023756973445415497, 0.018327612429857254, -0.031929146498441696, -0.015176894143223763, -0.02810327522456646, -0.007630644366145134, 0.03266056254506111, -0.008355027996003628, 0.0006496597197838128, -0.010746197775006294, -0.030410051345825195, 0.015317551791667938, -0.010549277998507023, -0.042703475803136826, 0.011540910229086876, 0.0196638535708189, 0.04689505696296692, -0.004268941003829241, -0.0031419266015291214, 0.005809135735034943, 0.02534639835357666, -0.014178229495882988, 0.025965288281440735, -0.010746197775006294, -0.009220069274306297, -0.002823689952492714, 0.01716015860438347, -0.015205025672912598, 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+51,"$HOME
+$HOME is appropriate for codes and libraries which are small and read once,
+as well as the experimental results that would be needed at a later time (e.g.
+the weights of a network referenced in a paper).
+Quotas are enabled on $HOME for both disk capacity (blocks) and number of
+files (inodes). The limits for blocks and inodes are respectively 100GiB and 1
+million per user. The command to check the quota usage from a login node is:
+beegfs-ctl --cfgFile=/etc/beegfs/home.d/beegfs-client.conf --getquota --uid $USER",132,"[0.0048737479373812675, -0.014893949031829834, 0.00030439073452726007, -0.04710124060511589, -0.028836924582719803, -0.00015612861898262054, -0.04100381210446358, -0.016684019938111305, -0.004191982559859753, -0.031242331489920616, 0.027284596115350723, -0.010929222218692303, -0.005380701273679733, -0.00016989503637887537, -0.012894104234874249, 0.013551396317780018, 0.011236890219151974, -0.008859452791512012, 0.00048204301856458187, -0.00548558821901679, -0.007398027461022139, 0.03347992151975632, -0.022319946438074112, -0.025242796167731285, 0.003601119155064225, -0.01580297015607357, 0.02066972479224205, -0.016684019938111305, 0.018334241583943367, 0.009285992942750454, -0.0017315089935436845, -0.025005053728818893, -0.008397949859499931, 0.018753789365291595, -0.03322819247841835, 0.007083366625010967, -0.0012822431745007634, -0.026865048334002495, 0.026235727593302727, 0.00030417219386436045, 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+52,"$SCRATCH
+$SCRATCH can be used to store processed datasets, work in progress datasets
+or temporary job results. Its block size is optimized for small files which
+minimizes the performance hit of working on extracted datasets.
+
+Note
+Auto-cleanup: this file system is cleared on a weekly basis,
+files not used for more than 90 days will be deleted.",78,"[-0.013857276178896427, 0.015203411690890789, 0.007882450707256794, -0.03138583153486252, -0.038296472281217575, 0.012827878817915916, -0.00775287626311183, -0.02329462207853794, -0.007306563667953014, -0.03512909635901451, 0.012892666272819042, 0.001378529006615281, 5.407374555943534e-05, 0.010272380895912647, -0.012604722753167152, -0.005373743828386068, 0.0032807551324367523, -0.006075605750083923, 0.006338354200124741, -0.028333630412817, -0.005161385517567396, 0.012323977425694466, -0.028420014306902885, -0.021048663184046745, -0.003518308512866497, -0.006561510264873505, -0.002436720998957753, -0.016887880861759186, -0.001945417607203126, 0.005560907069593668, 0.011064224876463413, -0.03939065709710121, 0.006730677094310522, -0.02201327309012413, -0.029715759679675102, 0.010473941452801228, -0.0026490793097764254, -0.013979651965200901, 0.005078601650893688, 0.022330010309815407, 0.009257380850613117, 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+53,"$SLURM_TMPDIR
+$SLURM_TMPDIR points to the local disk of the node on which a job is
+running. It should be used to copy the data on the node at the beginning of the
+job and write intermediate checkpoints. 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+54,"projects
+projects can be used for collaborative projects. It aims to ease the
+sharing of data between users working on a long-term project.
+Quotas are enabled on projects for both disk capacity (blocks) and number
+of files (inodes). The limits for blocks and inodes are respectively 200GiB and
+1 million per user and per group.
+
+Note
+It is possible to request higher quota limits if the project requires
+it. File a request to Mila’s IT support.",102,"[0.003945981152355671, -0.02248307690024376, -0.01136636734008789, -0.04363464564085007, -0.027184966951608658, 0.033454153686761856, -0.053315822035074234, -0.01693512499332428, -0.026241814717650414, -0.02156766504049301, 0.028793873265385628, 0.0018273568712174892, 0.03198394551873207, -0.024591298773884773, -0.017600879073143005, 0.01871046982705593, 0.010513369925320148, -0.025756368413567543, -0.0008547314209863544, -0.015340087935328484, 0.010409345850348473, 0.010326126590371132, -0.02534027211368084, -0.00667834747582674, -0.021470576524734497, -0.0067962417379021645, 0.027725892141461372, -0.028141988441348076, 0.012039056979119778, -0.0023821520153433084, 0.015603615902364254, -0.014369197189807892, -0.003928643651306629, 0.013266541063785553, -0.031234972178936005, -0.018433071672916412, 0.01951492205262184, -0.023551058024168015, 0.004372479859739542, 0.002503513591364026, 0.018918517976999283, 0.0003309267049189657, 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+55,"$ARCHIVE
+$ARCHIVE purpose is to store data other than datasets that has to be kept
+long-term (e.g.  generated samples, logs, data relevant for paper submission).
+$ARCHIVE is only available on the login nodes. Because this file system
+is tuned for large files, it is recommended to archive your directories. For
+example, to archive the results of an experiment in
+$SCRATCH/my_experiment_results/, run the commands below from a login node:
+cd $SCRATCH
+tar cJf $ARCHIVE/my_experiment_results.tar.xz --xattrs my_experiment_results
+Disk capacity quotas are enabled on $ARCHIVE. The soft limit per user is
+500GB, the hard limit is 550GB. The grace time is 7 days. This means that one
+can use more than 500GB for 7 days before the file system enforces quota.
+However, it is not possible to use more than 550GB.
+The command to check the quota usage from a login node is df:
+df -h $ARCHIVE
+
+Note
+There is NO backup of this file system.",231,"[0.011372470296919346, -0.011699385941028595, 0.008214610628783703, -0.04866165295243263, -0.027113081887364388, 0.005919249262660742, -0.02843465469777584, -0.00925795640796423, -0.0006812179344706237, -0.029714491218328476, 0.02031046710908413, -0.012054122984409332, -0.0015511076198890805, -0.006642635446041822, -0.012346260249614716, -0.0020953863859176636, 0.013139203190803528, -0.0033700072672218084, -0.003575198585167527, -0.0222163125872612, 0.011476805433630943, -0.0042220731265842915, -0.034750375896692276, -0.0028605067636817694, -0.011998478323221207, 0.004510732367634773, 0.0008581519941799343, -0.020213088020682335, 0.016164906322956085, 0.00026192329823970795, -0.00033908741897903383, -0.015371963381767273, 0.013083557598292828, -0.010934265330433846, -0.033609651029109955, 0.007449490018188953, -0.0067087141796946526, -0.02160421572625637, 0.026695745065808296, -0.007331244181841612, 0.004319452214986086, 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+56,"datasets
+datasets contains curated datasets to the benefit of the Mila community.
+To request the addition of a dataset or a preprocessed dataset you think could
+benefit the research of others, you can fill this form. Datasets can also be browsed from the
+web : Mila Datasets
+Datasets in datasets/restricted are restricted and require an explicit
+request to gain access. Please submit a support ticket mentioning the dataset’s
+access group (ex.: scannet_users), your cluster’s username and the
+approbation of the group owner. You can find the dataset’s access group by
+listing the content of /network/datasets/restricted with the ls command.
+Those datasets are mirrored to the Alliance clusters in
+~/projects/rrg-bengioy-ad/data/curated/ if they follow Digital Research
+Alliance of Canada’s good practices on data.
+To list the local datasets on an Alliance cluster, you can execute the
+following command:
+ssh [CLUSTER_LOGIN] -C ""projects/rrg-bengioy-ad/data/curated/list_datasets_cc.sh""",227,"[-0.018440071493387222, -0.007025305647403002, 0.013211769051849842, -0.04714873060584068, -0.020349161699414253, 0.0313553549349308, -0.0039230347611010075, -0.006981917191296816, -0.013197306543588638, -0.04321485012769699, 0.02124585583806038, -0.004722104407846928, 3.135490260319784e-05, -0.023429738357663155, -0.0223161019384861, 0.003660896560177207, 0.02314048260450363, -0.019192136824131012, 0.019929740577936172, -0.004906505346298218, 0.001793387345969677, 0.00522830244153738, -0.010630158707499504, -0.026278909295797348, -0.015547511167824268, -0.0034240682143718004, 0.0055030956864356995, -0.01653098128736019, 0.0016008513048291206, 0.0053693149238824844, 0.014556809328496456, -0.00453408807516098, -0.010904951952397823, -0.005485017318278551, -0.014057843014597893, 0.00870660599321127, 0.022894615307450294, 0.00021400426339823753, -0.011888423003256321, -0.0027171988040208817, 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+57,"this is the favored method since the bandwidth can
+be limited to prevent impacting the usage of the cluster: rsync
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+58,"Quick Start
+Users first need login access to the cluster. It is
+recommended to install milatools which will help in the set up of the
+ssh configuration needed to securely and easily connect to the
+cluster.
+
+mila code
+milatools also makes it easy to run and debug code on the Mila cluster. Using
+the mila code command will allow you to use VSCode on the server. Simply run:
+mila code path/on/cluster
+
+
+The details of the command can be found on the github page of the package. Note that you need to
+first setup your ssh configuration using mila init before the mila code
+command can be used. The initialisation of the ssh configuration is explained
+here and on the github page of the package.",162,"[-0.001446299604140222, 0.00430583069100976, 0.0038776842411607504, -0.056974805891513824, -0.03522635996341705, 0.014884180389344692, -0.030408840626478195, -0.02921142242848873, 0.0008188735810108483, -0.018740979954600334, 0.021609213203191757, -0.019896626472473145, -0.01218302920460701, -0.016443608328700066, -0.02458883449435234, -0.008374962024390697, 0.014153199270367622, -0.014605711214244366, 0.027791230008006096, -0.0066902232356369495, -0.02687228098511696, 0.01485633384436369, 0.009279987774789333, -0.031773339956998825, -0.0023704692721366882, -0.005301358178257942, 0.0069164796732366085, -0.011319774203002453, -0.00992046669125557, 0.004901058506220579, 0.010630562901496887, -0.015942363068461418, -0.009579341858625412, -0.010428672656416893, -0.010804606601595879, 0.013192480430006981, 0.032831523567438126, -0.02280663140118122, -0.028417786583304405, -0.0053814179264009, 0.018532127141952515, 0.009328719228506088, 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+59,"mila code
+milatools also makes it easy to run and debug code on the Mila cluster. Using
+the mila code command will allow you to use VSCode on the server. Simply run:
+mila code path/on/cluster
+
+
+The details of the command can be found on the github page of the package. Note that you need to
+first setup your ssh configuration using mila init before the mila code
+command can be used. The initialisation of the ssh configuration is explained
+here and on the github page of the package.",116,"[-0.0061481851153075695, 0.004416301846504211, -0.0006918872823007405, -0.04422536492347717, -0.019688047468662262, 0.02736375294625759, -0.030342591926455498, -0.030785953626036644, -0.0017786439275369048, -0.02139221876859665, 0.02680955082178116, -0.02229279838502407, -0.001258213073015213, -0.023747580125927925, -0.018039293587207794, -0.01853807643055916, 0.010903935879468918, -0.010897008702158928, 0.02964983880519867, -0.015337556600570679, -0.015074309892952442, 0.007592575158923864, 0.00857628509402275, -0.02992693893611431, -0.021558480337262154, 0.003250744426622987, 0.008728690445423126, -0.01748509146273136, -0.004405910614877939, 0.006671213544905186, 0.014464687556028366, -0.03818455711007118, -0.012635818682610989, -0.011222601868212223, -0.00989251583814621, -0.004755750764161348, 0.02680955082178116, -0.017734482884407043, -0.02647702768445015, 0.0005446771974675357, 0.025036102160811424, 0.01197077613323927, 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+60,"Logging in to the cluster
+To access the Mila Cluster clusters, you will need a Mila account. Please contact
+Mila systems administrators if you don’t have it already. Our IT support service
+is available here: https://it-support.mila.quebec/
+You will also need to complete and return an IT Onboarding Training to get
+access to the cluster.  Please refer to the Mila Intranet for more
+informations:
+https://sites.google.com/mila.quebec/mila-intranet/it-infrastructure/it-onboarding-training
+IMPORTANT : Your access to the Cluster is granted based on your status at
+Mila (for students, your status is the same as your main supervisor’ status),
+and on the duration of your stay, set during the creation of your account. The
+following have access to the cluster : Current Students of Core Professors -
+Core Professors - Staff
+
+SSH Login
+You can access the Mila cluster via ssh:
+# Generic login, will send you to one of the 4 login nodes to spread the load
+ssh <user>@login.server.mila.quebec -p 2222
+
+# To connect to a specific login node, X in [1, 2, 3, 4]
+ssh <user>@login-X.login.server.mila.quebec -p 2222
+Four login nodes are available and accessible behind a load balancer. At each
+connection, you will be redirected to the least loaded login-node.
+The ECDSA, RSA and ED25519 fingerprints for Mila’s login nodes are:
+SHA256:baEGIa311fhnxBWsIZJ/zYhq2WfCttwyHRKzAb8zlp8 (ECDSA)
+SHA256:Xr0/JqV/+5DNguPfiN5hb8rSG+nBAcfVCJoSyrR0W0o (RSA)
+SHA256:gfXZzaPiaYHcrPqzHvBi6v+BWRS/lXOS/zAjOKeoBJg (ED25519)
+
+
+
+Important
+Login nodes are merely entry points to the cluster. They give you access
+to the compute nodes and to the filesystem, but they are not meant to run
+anything heavy. Do not run compute-heavy programs on these nodes,
+because in doing so you could bring them down, impeding cluster access for
+everyone.
+This means no training or experiments, no compiling programs, no Python
+scripts, but also no zip of a large folder or anything that demands a
+sustained amount of computation.
+Rule of thumb: never run a program that takes more than a few seconds on
+a login node.
+
+Note
+In a similar vein, you should not run VSCode remote SSH instances directly
+on login nodes, because even though they are typically not very
+computationally expensive, when many people do it, they add up! See
+Visual Studio Code for specific instructions.
+
+
+
+
+mila init
+To make it easier to set up a productive environment, Mila publishes the
+milatools package, which defines a mila init command which will
+automatically perform some of the below steps for you. You can install it with
+pip and use it, provided your Python version is at least 3.8:
+$ pip install milatools
+$ mila init
+
+
+
+
+SSH Config
+The login nodes support the following authentication mechanisms:
+publickey,keyboard-interactive.  If you would like to set an entry in your
+.ssh/config file, please use the following recommendation:
+Host mila
+    User YOUR-USERNAME
+    Hostname login.server.mila.quebec
+    PreferredAuthentications publickey,keyboard-interactive
+    Port 2222
+    ServerAliveInterval 120
+    ServerAliveCountMax 5
+
+
+Then you can simply write ssh mila to connect to a login node. You will also
+be able to use mila with scp, rsync and other such programs.
+
+Tip
+You can run commands on the login node with ssh directly, for example
+ssh mila squeue -u '$USER' (remember to put single quotes around any
+$VARIABLE you want to evaluate on the remote side, otherwise it will be
+evaluated locally before ssh is even executed).
+
+
+
+Passwordless login
+To save you some repetitive typing it is highly recommended to set up public
+key authentication, which means you won’t have to enter your password every time
+you connect to the cluster.
+# ON YOUR LOCAL MACHINE
+# You might already have done this in the past, but if you haven't:
+ssh-keygen  # Press ENTER 3x
+
+# Copy your public key over to the cluster
+# You will need to enter your password
+ssh-copy-id mila
+
+
+
+
+Connecting to compute nodes
+If (and only if) you have a job running on compute node “cnode”, you are
+allowed to SSH to it directly, if for some reason you need a second terminal.
+That session will be automatically ended when your job is relinquished.
+First, however, you need to have
+password-less ssh either with a key present in your home or with an
+ssh-agent. To generate a key pair on the login node:
+# ON A LOGIN NODE
+ssh-keygen  # Press ENTER 3x
+cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
+chmod 600 ~/.ssh/authorized_keys
+chmod 700 ~/.ssh
+
+
+Then from the login node you can write ssh <node>. From your local
+machine, you can use ssh -J mila USERNAME@<node> (-J represents a “jump”
+through the login node, necessary because the compute nodes are behind a
+firewall).
+If you wish, you may also add the following wildcard rule in your .ssh/config:
+Host *.server.mila.quebec !*login.server.mila.quebec
+    HostName %h
+    User YOUR-USERNAME
+    ProxyJump mila
+
+
+This will let you connect to a compute node with ssh <node>.server.mila.quebec.",1269,"[-0.0010284606833010912, -0.00547266798093915, 0.006620875094085932, -0.05209735035896301, -0.02978544495999813, 0.02083078771829605, -0.03331838920712471, -0.03171497583389282, 0.0036994011607021093, -0.015232428908348083, 0.028453797101974487, -0.02743467688560486, -0.0013732624938711524, -0.013486338779330254, -0.015789546072483063, -0.020192140713334084, 0.02047749236226082, -0.02739391289651394, 0.032013919204473495, -0.003407253883779049, -0.014512251131236553, 0.006811110302805901, 0.019499139860272408, -0.017284253612160683, -0.016523312777280807, -0.002420407021418214, 0.01069395337253809, -0.014118191786110401, -0.006641257554292679, 0.0004632744239643216, 0.002792385406792164, -0.01585748791694641, -0.014974252320826054, 0.008356774225831032, -0.01795007847249508, 0.006491786800324917, 0.04734146595001221, -0.028127677738666534, -0.03291074186563492, 0.0018259211210533977, 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+61,"SSH Login
+You can access the Mila cluster via ssh:
+# Generic login, will send you to one of the 4 login nodes to spread the load
+ssh <user>@login.server.mila.quebec -p 2222
+
+# To connect to a specific login node, X in [1, 2, 3, 4]
+ssh <user>@login-X.login.server.mila.quebec -p 2222
+Four login nodes are available and accessible behind a load balancer. At each
+connection, you will be redirected to the least loaded login-node.
+The ECDSA, RSA and ED25519 fingerprints for Mila’s login nodes are:
+SHA256:baEGIa311fhnxBWsIZJ/zYhq2WfCttwyHRKzAb8zlp8 (ECDSA)
+SHA256:Xr0/JqV/+5DNguPfiN5hb8rSG+nBAcfVCJoSyrR0W0o (RSA)
+SHA256:gfXZzaPiaYHcrPqzHvBi6v+BWRS/lXOS/zAjOKeoBJg (ED25519)
+
+
+
+Important
+Login nodes are merely entry points to the cluster. They give you access
+to the compute nodes and to the filesystem, but they are not meant to run
+anything heavy. Do not run compute-heavy programs on these nodes,
+because in doing so you could bring them down, impeding cluster access for
+everyone.
+This means no training or experiments, no compiling programs, no Python
+scripts, but also no zip of a large folder or anything that demands a
+sustained amount of computation.
+Rule of thumb: never run a program that takes more than a few seconds on
+a login node.
+
+Note
+In a similar vein, you should not run VSCode remote SSH instances directly
+on login nodes, because even though they are typically not very
+computationally expensive, when many people do it, they add up! See
+Visual Studio Code for specific instructions.",426,"[-0.0021864166483283043, -0.007259916514158249, 0.010467786341905594, -0.04192517325282097, -0.03849444165825844, 0.015222188085317612, -0.044734593480825424, -0.021921571344137192, 0.00079690245911479, -0.02340732142329216, 0.022583404555916786, -0.003300729440525174, -0.022326774895191193, -0.022488858550786972, -0.020854532718658447, -0.008570077829062939, 0.020841024816036224, -0.024244744330644608, 0.03838638588786125, -0.0064427535980939865, -0.0121493861079216, 0.01059610117226839, 0.013554095290601254, -0.019801000133156776, -0.01339876651763916, 0.0018808249151334167, 0.016491828486323357, -0.013979559764266014, -0.012892261147499084, -0.005980145186185837, 0.020638423040509224, -0.008846968412399292, -0.023744992911815643, 0.005534420255571604, -0.016316240653395653, 0.02267795242369175, 0.04046643525362015, -0.018585385754704475, -0.033199768513441086, -0.011203908361494541, 0.02454189397394657, 0.006520417984575033, 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+62,"mila init
+To make it easier to set up a productive environment, Mila publishes the
+milatools package, which defines a mila init command which will
+automatically perform some of the below steps for you. You can install it with
+pip and use it, provided your Python version is at least 3.8:
+$ pip install milatools
+$ mila init",82,"[-0.000789464043918997, -0.012887466698884964, 0.008861911483108997, -0.020981252193450928, -0.021692480891942978, 0.025931404903531075, -0.014494843780994415, -0.03962967172265053, -0.01926007680594921, 0.0074821277521550655, 0.043299611657857895, -0.015718158334493637, -0.000880145700648427, 0.006258814129978418, -0.015547462739050388, -0.012617199681699276, 0.015874627977609634, 0.007809292990714312, 0.011991318315267563, 0.022218789905309677, -0.007958650588989258, 0.02752455696463585, 0.0013726716861128807, -0.02216189168393612, -0.034707967191934586, -0.008499184623360634, 0.008883248083293438, -0.008157795295119286, -0.002837803214788437, -0.0018082993337884545, 0.004171357490122318, -0.035902831703424454, -0.014750886708498001, -0.017097942531108856, -0.012119339779019356, 0.00487547367811203, 0.02567536197602749, -0.023982636630535126, -0.02210499346256256, 0.01674232818186283, 0.022745098918676376, 0.02376926876604557, 0.014004096388816833, 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+63,"SSH Config
+The login nodes support the following authentication mechanisms:
+publickey,keyboard-interactive.  If you would like to set an entry in your
+.ssh/config file, please use the following recommendation:
+Host mila
+    User YOUR-USERNAME
+    Hostname login.server.mila.quebec
+    PreferredAuthentications publickey,keyboard-interactive
+    Port 2222
+    ServerAliveInterval 120
+    ServerAliveCountMax 5
+
+
+Then you can simply write ssh mila to connect to a login node. You will also
+be able to use mila with scp, rsync and other such programs.
+
+Tip
+You can run commands on the login node with ssh directly, for example
+ssh mila squeue -u '$USER' (remember to put single quotes around any
+$VARIABLE you want to evaluate on the remote side, otherwise it will be
+evaluated locally before ssh is even executed).",195,"[-0.007288321852684021, 0.0018439926207065582, 0.019215278327465057, -0.03870024532079697, -0.037432711571455, 0.015790240839123726, -0.03670455515384674, -0.02675306238234043, 0.00016950319695752114, -0.02036145329475403, 0.019889499992132187, -0.0013846800429746509, -0.013046163134276867, -0.0075175566598773, -0.018932105973362923, -0.023611195385456085, 0.017246555536985397, -0.015250863507390022, 0.03327951580286026, -0.008535629138350487, 0.0018979301676154137, 0.01062571257352829, -0.004004868678748608, -0.029369037598371506, -0.026550795882940292, 0.008805317804217339, 0.01731397770345211, -0.028263315558433533, 0.004254330415278673, -0.004877984058111906, 0.014778909273445606, -0.001002902747131884, 0.005363422445952892, 0.0177859328687191, -0.023044850677251816, 0.003103099064901471, 0.031068071722984314, -0.026928359642624855, -0.018689386546611786, 0.0025468673557043076, 0.016370069235563278, 0.007059086579829454, 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+64,"Passwordless login
+To save you some repetitive typing it is highly recommended to set up public
+key authentication, which means you won’t have to enter your password every time
+you connect to the cluster.
+# ON YOUR LOCAL MACHINE
+# You might already have done this in the past, but if you haven't:
+ssh-keygen  # Press ENTER 3x
+
+# Copy your public key over to the cluster
+# You will need to enter your password
+ssh-copy-id mila",101,"[-0.025274498388171196, 0.006204107776284218, 0.013371502980589867, -0.03861232101917267, -0.024250583723187447, 0.012300435453653336, -0.022970693185925484, -0.04014819115400314, -0.0009169752011075616, -0.020154930651187897, 0.03144492581486702, -0.006143481470644474, -0.01615358330309391, -0.01434826198965311, -0.021663855761289597, -0.019360050559043884, 0.018484333530068398, -0.010225662961602211, 0.037399791181087494, 0.009080496616661549, -0.01518356055021286, 0.009390365332365036, -0.0018709999276325107, -0.03424721583724022, -0.01588413305580616, 0.004860221408307552, 0.016759848222136497, -0.022687768563628197, 0.005793195217847824, -0.013755470514297485, 0.015601209364831448, -0.01141798309981823, -0.005675310268998146, -0.010636575520038605, -0.04661501199007034, -0.0021673960145562887, 0.02294374816119671, -0.03079824149608612, -0.016382617875933647, -0.02364431880414486, -0.0018709999276325107, 0.0011805319227278233, 0.0029690125957131386, 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+65,"Connecting to compute nodes
+If (and only if) you have a job running on compute node “cnode”, you are
+allowed to SSH to it directly, if for some reason you need a second terminal.
+That session will be automatically ended when your job is relinquished.
+First, however, you need to have
+password-less ssh either with a key present in your home or with an
+ssh-agent. To generate a key pair on the login node:
+# ON A LOGIN NODE
+ssh-keygen  # Press ENTER 3x
+cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
+chmod 600 ~/.ssh/authorized_keys
+chmod 700 ~/.ssh
+
+
+Then from the login node you can write ssh <node>. From your local
+machine, you can use ssh -J mila USERNAME@<node> (-J represents a “jump”
+through the login node, necessary because the compute nodes are behind a
+firewall).
+If you wish, you may also add the following wildcard rule in your .ssh/config:
+Host *.server.mila.quebec !*login.server.mila.quebec
+    HostName %h
+    User YOUR-USERNAME
+    ProxyJump mila
+
+
+This will let you connect to a compute node with ssh <node>.server.mila.quebec.",270,"[-0.00836926233023405, -0.01904125139117241, 0.011708888225257397, -0.03961765021085739, -0.014328069984912872, 0.009264767169952393, -0.036439619958400726, -0.03072993829846382, -0.011331832967698574, -0.024885594844818115, 0.026191819459199905, 0.003243678482249379, -0.014812854118645191, -0.0008134444942697883, -0.018542999401688576, -0.01676545850932598, 0.010685454122722149, -0.03277680650353432, 0.025060655549168587, -0.01777542568743229, -0.025114521384239197, -0.001231318456120789, 0.009796682745218277, -0.013600893318653107, -0.02180182747542858, 0.0022337110713124275, 0.02795589528977871, -0.018287140876054764, -0.009857281111180782, -0.009823615662753582, 0.0022589601576328278, 0.009089705534279346, -0.01633453741669655, -0.0010554158361628652, -0.03169950842857361, 0.004514554049819708, 0.025181852281093597, -0.022461673244833946, -0.012584192678332329, -0.013977947644889355, 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+66,"Running your code
+
+SLURM commands guide
+
+Basic Usage
+The SLURM documentation
+provides extensive information on the available commands to query the cluster
+status or submit jobs.
+Below are some basic examples of how to use SLURM.
+
+
+Submitting jobs
+
+Batch job
+In order to submit a batch job, you have to create a script containing the main
+command(s) you would like to execute on the allocated resources/nodes.
+ 1#!/bin/bash
+ 2#SBATCH --job-name=test
+ 3#SBATCH --output=job_output.txt
+ 4#SBATCH --error=job_error.txt
+ 5#SBATCH --ntasks=1
+ 6#SBATCH --time=10:00
+ 7#SBATCH --mem=100Gb
+ 8
+ 9module load python/3.5
+10python my_script.py
+
+
+Your job script is then submitted to SLURM with sbatch (ref.)
+sbatch job_script
+sbatch: Submitted batch job 4323674
+The working directory of the job will be the one where your executed sbatch.
+
+Tip
+Slurm directives can be specified on the command line alongside sbatch or
+inside the job script with a line starting with #SBATCH.
+
+
+
+Interactive job
+Workload managers usually run batch jobs to avoid having to watch its
+progression and let the scheduler run it as soon as resources are available. If
+you want to get access to a shell while leveraging cluster resources, you can
+submit an interactive jobs where the main executable is a shell with the
+srun/salloc (srun/salloc) commands
+salloc
+Will start an interactive job on the first node available with the default
+resources set in SLURM (1 task/1 CPU).  srun accepts the same arguments as
+sbatch with the exception that the environment is not passed.
+
+Tip
+To pass your current environment to an interactive job, add
+--preserve-env to srun.
+
+salloc can also be used and is mostly a wrapper around srun if provided
+without more info but it gives more flexibility if for example you want to get
+an allocation on multiple nodes.
+
+
+
+Job submission arguments
+In order to accurately select the resources for your job, several arguments are
+available. The most important ones are:
+
+
+
+
+
+
+Argument
+Description
+
+
+
+-n, –ntasks=<number>
+The number of task in your script, usually =1
+
+-c, –cpus-per-task=<ncpus>
+The number of cores for each task
+
+-t, –time=<time>
+Time requested for your job
+
+–mem=<size[units]>
+Memory requested for all your tasks
+
+–gres=<list>
+Select generic resources such as GPUs for your job: --gres=gpu:GPU_MODEL
+
+
+
+
+Tip
+Always consider requesting the adequate amount of resources to improve the
+scheduling of your job (small jobs always run first).
+
+
+
+Checking job status
+To display jobs currently in queue, use squeue and to get only your jobs type
+squeue -u $USER
+JOBID   USER          NAME    ST  START_TIME         TIME NODES CPUS TRES_PER_NMIN_MEM NODELIST (REASON) COMMENT
+133     my_username   myjob   R   2019-03-28T18:33   0:50     1    2        N/A  7000M node1 (None) (null)
+
+Note
+The maximum number of jobs able to be submitted to the system per user is 1000 (MaxSubmitJobs=1000)
+at any given time from the given association. If this limit is reached, new submission requests
+will be denied until existing jobs in this association complete.
+
+
+
+Removing a job
+To cancel your job simply use scancel
+scancel 4323674
+
+
+
+Partitioning
+Since we don’t have many GPUs on the cluster, resources must be shared as fairly
+as possible.  The --partition=/-p flag of SLURM allows you to set the
+priority you need for a job.  Each job assigned with a priority can preempt jobs
+with a lower priority: unkillable > main > long. Once preempted, your job is
+killed without notice and is automatically re-queued on the same partition until
+resources are available. (To leverage a different preemption mechanism, see the
+Handling preemption)
+
+
+
+
+
+
+
+
+Flag
+Max Resource Usage
+Max Time
+Note
+
+
+
+--partition=unkillable
+6  CPUs, mem=32G,  1 GPU
+2 days
+
+
+--partition=unkillable-cpu
+2  CPUs, mem=16G
+2 days
+CPU-only jobs
+
+--partition=short-unkillable
+24 CPUs, mem=128G, 4 GPUs
+3 hours (!)
+Large but short jobs
+
+--partition=main
+8  CPUs, mem=48G,  2 GPUs
+5 days
+
+
+--partition=main-cpu
+8  CPUs, mem=64G
+5 days
+CPU-only jobs
+
+--partition=long
+no limit of resources
+7 days
+
+
+--partition=long-cpu
+no limit of resources
+7 days
+CPU-only jobs
+
+
+
+
+Warning
+Historically, before the 2022 introduction of CPU-only nodes (e.g. the cn-f
+series), CPU jobs ran side-by-side with the GPU jobs on GPU nodes. To prevent
+them obstructing any GPU job, they were always lowest-priority and preemptible.
+This was implemented by automatically assigning them to one of the now-obsolete
+partitions cpu_jobs, cpu_jobs_low or cpu_jobs_low-grace.
+Do not use these partition names anymore. Prefer the *-cpu partition
+names defined above.
+For backwards-compatibility purposes, the legacy partition names are translated
+to their effective equivalent long-cpu, but they will eventually be removed
+entirely.
+
+
+Note
+As a convenience, should you request the unkillable, main or long
+partition for a CPU-only job, the partition will be translated to its -cpu
+equivalent automatically.
+
+For instance, to request an unkillable job with 1 GPU, 4 CPUs, 10G of RAM and
+12h of computation do:
+sbatch --gres=gpu:1 -c 4 --mem=10G -t 12:00:00 --partition=unkillable <job.sh>
+You can also make it an interactive job using salloc:
+salloc --gres=gpu:1 -c 4 --mem=10G -t 12:00:00 --partition=unkillable
+The Mila cluster has many different types of nodes/GPUs. To request a specific
+type of node/GPU, you can add specific feature requirements to your job
+submission command.
+To access those special nodes you need to request them explicitly by adding the
+flag --constraint=<name>.  The full list of nodes in the Mila Cluster can be
+accessed Node profile description.
+Example:
+To request a machine with 2 GPUs using NVLink, you can use
+sbatch -c 4 --gres=gpu:2 --constraint=nvlink
+
+
+
+
+
+
+Feature
+Particularities
+
+
+
+12GB/16GB/24GB/32GB/48GB
+Request a specific amount of GPU memory
+
+volta/turing/ampere
+Request a specific GPU architecture
+
+nvlink
+Machine with GPUs using the NVLink interconnect technology
+
+
+
+
+Information on partitions/nodes
+sinfo (ref.) provides most of the
+information about available nodes and partitions/queues to submit jobs to.
+Partitions are a group of nodes usually sharing similar features. On a
+partition, some job limits can be applied which will override those asked for a
+job (i.e. max time, max CPUs, etc…)
+To display available partitions, simply use
+sinfo
+PARTITION AVAIL TIMELIMIT NODES STATE  NODELIST
+batch     up     infinite     2 alloc  node[1,3,5-9]
+batch     up     infinite     6 idle   node[10-15]
+cpu       up     infinite     6 idle   cpu_node[1-15]
+gpu       up     infinite     6 idle   gpu_node[1-15]
+To display available nodes and their status, you can use
+sinfo -N -l
+NODELIST    NODES PARTITION STATE  CPUS MEMORY TMP_DISK WEIGHT FEATURES REASON
+node[1,3,5-9]   2 batch     allocated 2    246    16000     0  (null)   (null)
+node[2,4]       2 batch     drain     2    246    16000     0  (null)   (null)
+node[10-15]     6 batch     idle      2    246    16000     0  (null)   (null)
+...
+And to get statistics on a job running or terminated, use sacct with some of
+the fields you want to display
+sacct --format=User,JobID,Jobname,partition,state,time,start,end,elapsed,nnodes,ncpus,nodelist,workdir -u $USER
+     User        JobID    JobName  Partition      State  Timelimit               Start                 End    Elapsed   NNodes      NCPUS        NodeList              WorkDir
+--------- ------------ ---------- ---------- ---------- ---------- ------------------- ------------------- ---------- -------- ---------- --------------- --------------------
+my_usern+ 2398         run_extra+      batch    RUNNING 130-05:00+ 2019-03-27T18:33:43             Unknown 1-01:07:54        1         16 node9           /home/mila/my_usern+
+my_usern+ 2399         run_extra+      batch    RUNNING 130-05:00+ 2019-03-26T08:51:38             Unknown 2-10:49:59        1         16 node9           /home/mila/my_usern+
+Or to get the list of all your previous jobs, use the --start=YYYY-MM-DD flag. You can check sacct(1) for further information about additional time formats.
+sacct -u $USER --start=2019-01-01
+scontrol (ref.) can be used to
+provide specific information on a job (currently running or recently terminated)
+scontrol show job 43123
+JobId=43123 JobName=python_script.py
+UserId=my_username(1500000111) GroupId=student(1500000000) MCS_label=N/A
+Priority=645895 Nice=0 Account=my_username QOS=normal
+JobState=RUNNING Reason=None Dependency=(null)
+Requeue=1 Restarts=3 BatchFlag=1 Reboot=0 ExitCode=0:0
+RunTime=2-10:41:57 TimeLimit=130-05:00:00 TimeMin=N/A
+SubmitTime=2019-03-26T08:47:17 EligibleTime=2019-03-26T08:49:18
+AccrueTime=2019-03-26T08:49:18
+StartTime=2019-03-26T08:51:38 EndTime=2019-08-03T13:51:38 Deadline=N/A
+PreemptTime=None SuspendTime=None SecsPreSuspend=0
+LastSchedEval=2019-03-26T08:49:18
+Partition=slurm_partition AllocNode:Sid=login-node-1:14586
+ReqNodeList=(null) ExcNodeList=(null)
+NodeList=node2
+BatchHost=node2
+NumNodes=1 NumCPUs=16 NumTasks=1 CPUs/Task=16 ReqB:S:C:T=0:0:*:*
+TRES=cpu=16,mem=32000M,node=1,billing=3
+Socks/Node=* NtasksPerN:B:S:C=1:0:*:* CoreSpec=*
+MinCPUsNode=16 MinMemoryNode=32000M MinTmpDiskNode=0
+Features=(null) DelayBoot=00:00:00
+OverSubscribe=OK Contiguous=0 Licenses=(null) Network=(null)
+WorkDir=/home/mila/my_username
+StdErr=/home/mila/my_username/slurm-43123.out
+StdIn=/dev/null
+StdOut=/home/mila/my_username/slurm-43123.out
+Power=
+Or more info on a node and its resources
+scontrol show node node9
+NodeName=node9 Arch=x86_64 CoresPerSocket=4
+CPUAlloc=16 CPUTot=16 CPULoad=1.38
+AvailableFeatures=(null)
+ActiveFeatures=(null)
+Gres=(null)
+NodeAddr=10.252.232.4 NodeHostName=mila20684000000 Port=0 Version=18.08
+OS=Linux 4.15.0-1036 #38-Ubuntu SMP Fri Dec 7 02:47:47 UTC 2018
+RealMemory=32000 AllocMem=32000 FreeMem=23262 Sockets=2 Boards=1
+State=ALLOCATED+CLOUD ThreadsPerCore=2 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A
+Partitions=slurm_partition
+BootTime=2019-03-26T08:50:01 SlurmdStartTime=2019-03-26T08:51:15
+CfgTRES=cpu=16,mem=32000M,billing=3
+AllocTRES=cpu=16,mem=32000M
+CapWatts=n/a
+CurrentWatts=0 LowestJoules=0 ConsumedJoules=0
+ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s
+
+
+
+Useful Commands
+
+sallocGet an interactive job and give you a shell. (ssh like) CPU only
+
+salloc --gres=gpu:1 -c 2 --mem=12000Get an interactive job with one GPU, 2 CPUs and 12000 MB RAM
+
+sbatchstart a batch job (same options as salloc)
+
+sattach --pty <jobid>.0Re-attach a dropped interactive job
+
+sinfostatus of all nodes
+
+sinfo -Ogres:27,nodelist,features -tidle,mix,allocList GPU type and FEATURES that you can request
+
+savail(Custom) List available gpu
+
+scancel <jobid>Cancel a job
+
+squeuesummary status of all active jobs
+
+squeue -u $USERsummary status of all YOUR active jobs
+
+squeue -j <jobid>summary status of a specific job
+
+squeue -Ojobid,name,username,partition,state,timeused,nodelist,gres,tresstatus of all jobs including requested resources (see the SLURM squeue doc for all output options)
+
+scontrol show job <jobid>Detailed status of a running job
+
+sacct -j <job_id> -o NodeListGet the node where a finished job ran
+
+sacct -u $USER -S <start_time> -E <stop_time>Find info about old jobs
+
+sacct -oJobID,JobName,User,Partition,Node,StateList of current and recent jobs
+
+
+
+
+Special GPU requirements
+Specific GPU architecture and memory can be easily requested through the
+--gres flag by using either
+
+--gres=gpu:architecture:number
+--gres=gpu:memory:number
+--gres=gpu:model:number
+
+Example:
+To request 1 GPU with at least 16GB of memory use
+sbatch -c 4 --gres=gpu:16gb:1
+The full list of GPU and their features can be accessed here.
+
+
+Example script
+Here is a sbatch script that follows good practices on the Mila cluster:
+ 1#!/bin/bash
+ 2
+ 3#SBATCH --partition=unkillable                           # Ask for unkillable job
+ 4#SBATCH --cpus-per-task=2                                # Ask for 2 CPUs
+ 5#SBATCH --gres=gpu:1                                     # Ask for 1 GPU
+ 6#SBATCH --mem=10G                                        # Ask for 10 GB of RAM
+ 7#SBATCH --time=3:00:00                                   # The job will run for 3 hours
+ 8#SBATCH -o /network/scratch/<u>/<username>/slurm-%j.out  # Write the log on scratch
+ 9
+10# 1. Load the required modules
+11module --quiet load anaconda/3
+12
+13# 2. Load your environment
+14conda activate ""<env_name>""
+15
+16# 3. Copy your dataset on the compute node
+17cp /network/datasets/<dataset> $SLURM_TMPDIR
+18
+19# 4. Launch your job, tell it to save the model in $SLURM_TMPDIR
+20#    and look for the dataset into $SLURM_TMPDIR
+21python main.py --path $SLURM_TMPDIR --data_path $SLURM_TMPDIR
+22
+23# 5. Copy whatever you want to save on $SCRATCH
+24cp $SLURM_TMPDIR/<to_save> /network/scratch/<u>/<username>/",3623,"[0.0028114602901041508, -0.0005426594289019704, -0.008118218742311, -0.02147180587053299, -0.02455863170325756, 0.009369268082082272, -0.017269913107156754, -0.0025564911775290966, -0.019078494980931282, -0.0241914764046669, 0.031085843220353127, -0.0032143115531653166, -0.0030817275401204824, 0.01400630734860897, 0.004552050027996302, -0.017691461369395256, 0.021063854917883873, -0.012456094846129417, 0.016413217410445213, -0.012354107573628426, -0.009444058872759342, -0.007186731789261103, -0.007050747983157635, -0.007866648957133293, -0.023416370153427124, 0.028393369168043137, 0.016875561326742172, -0.008981714956462383, -0.0016555999172851443, -0.007574284914880991, -0.011096259579062462, -0.002311720745638013, -0.013068021275103092, -0.0022097330074757338, -0.02602725476026535, 0.0016403017798438668, 0.008471776731312275, -0.008172612637281418, -0.009008911438286304, -0.012748459354043007, 0.024055493995547295, 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+67,"SLURM commands guide
+
+Basic Usage
+The SLURM documentation
+provides extensive information on the available commands to query the cluster
+status or submit jobs.
+Below are some basic examples of how to use SLURM.
+
+
+Submitting jobs
+
+Batch job
+In order to submit a batch job, you have to create a script containing the main
+command(s) you would like to execute on the allocated resources/nodes.
+ 1#!/bin/bash
+ 2#SBATCH --job-name=test
+ 3#SBATCH --output=job_output.txt
+ 4#SBATCH --error=job_error.txt
+ 5#SBATCH --ntasks=1
+ 6#SBATCH --time=10:00
+ 7#SBATCH --mem=100Gb
+ 8
+ 9module load python/3.5
+10python my_script.py
+
+
+Your job script is then submitted to SLURM with sbatch (ref.)
+sbatch job_script
+sbatch: Submitted batch job 4323674
+The working directory of the job will be the one where your executed sbatch.
+
+Tip
+Slurm directives can be specified on the command line alongside sbatch or
+inside the job script with a line starting with #SBATCH.
+
+
+
+Interactive job
+Workload managers usually run batch jobs to avoid having to watch its
+progression and let the scheduler run it as soon as resources are available. If
+you want to get access to a shell while leveraging cluster resources, you can
+submit an interactive jobs where the main executable is a shell with the
+srun/salloc (srun/salloc) commands
+salloc
+Will start an interactive job on the first node available with the default
+resources set in SLURM (1 task/1 CPU).  srun accepts the same arguments as
+sbatch with the exception that the environment is not passed.
+
+Tip
+To pass your current environment to an interactive job, add
+--preserve-env to srun.
+
+salloc can also be used and is mostly a wrapper around srun if provided
+without more info but it gives more flexibility if for example you want to get
+an allocation on multiple nodes.
+
+
+
+Job submission arguments
+In order to accurately select the resources for your job, several arguments are
+available. The most important ones are:
+
+
+
+
+
+
+Argument
+Description
+
+
+
+-n, –ntasks=<number>
+The number of task in your script, usually =1
+
+-c, –cpus-per-task=<ncpus>
+The number of cores for each task
+
+-t, –time=<time>
+Time requested for your job
+
+–mem=<size[units]>
+Memory requested for all your tasks
+
+–gres=<list>
+Select generic resources such as GPUs for your job: --gres=gpu:GPU_MODEL
+
+
+
+
+Tip
+Always consider requesting the adequate amount of resources to improve the
+scheduling of your job (small jobs always run first).
+
+
+
+Checking job status
+To display jobs currently in queue, use squeue and to get only your jobs type
+squeue -u $USER
+JOBID   USER          NAME    ST  START_TIME         TIME NODES CPUS TRES_PER_NMIN_MEM NODELIST (REASON) COMMENT
+133     my_username   myjob   R   2019-03-28T18:33   0:50     1    2        N/A  7000M node1 (None) (null)
+
+Note
+The maximum number of jobs able to be submitted to the system per user is 1000 (MaxSubmitJobs=1000)
+at any given time from the given association. If this limit is reached, new submission requests
+will be denied until existing jobs in this association complete.
+
+
+
+Removing a job
+To cancel your job simply use scancel
+scancel 4323674",778,"[-0.0008809684659354389, -0.0027041311841458082, -0.006415083073079586, 0.00024894162197597325, -0.03300740569829941, 0.003195636672899127, -0.0204765647649765, -0.008251851424574852, -0.01766018755733967, -0.023673901334404945, 0.01918402500450611, -0.0018112575635313988, -0.003449042560532689, 0.01610913872718811, 0.00284018786624074, -0.006054532248526812, 0.018068358302116394, -0.013204323127865791, 0.00710897333920002, 0.0025816799607127905, -0.0057177916169166565, -0.007877695374190807, -0.0031123016960918903, -0.007700820919126272, -0.02946992591023445, 0.029551560059189796, 0.021238483488559723, -0.009285883978009224, -0.0054626851342618465, -0.006336850579828024, -0.013918621465563774, 0.0026463069953024387, -0.0024830386973917484, 0.00405109440907836, -0.023347364738583565, 0.00432320823892951, -0.004884443245828152, -0.0012245122343301773, -0.006336850579828024, -0.012217910028994083, 0.02144256979227066, 0.030476747080683708, 0.006915092468261719, 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+68,"Basic Usage
+The SLURM documentation
+provides extensive information on the available commands to query the cluster
+status or submit jobs.
+Below are some basic examples of how to use SLURM.",42,"[-0.002638529986143112, -0.001455569756217301, 0.0016406138893216848, -0.023104073479771614, -0.027677305042743683, 0.02014336735010147, -0.020288759842514992, -0.0068598478101193905, -0.009291855618357658, -0.027915218845009804, 0.014076566323637962, -0.0010326119372621179, 5.163059904589318e-05, 0.00805602502077818, -0.014116218313574791, -0.015081090852618217, 0.017513098195195198, -0.014089783653616905, -0.0021907894406467676, 0.007540545426309109, -0.005101928953081369, -0.0030284442473202944, -0.009741248562932014, -0.016468921676278114, -0.017116576433181763, 0.020487021654844284, 0.011618123389780521, -0.017037270590662956, -0.00010274488886352628, -0.002740965224802494, -0.005253929644823074, -0.0014150914503261447, -0.0059247142635285854, -0.0011912211775779724, -0.02005084604024887, 0.004979667719453573, 0.013772564940154552, -0.007659502327442169, -0.019244583323597908, -0.008208026178181171, 0.01564943976700306, 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+69,"Submitting jobs
+
+Batch job
+In order to submit a batch job, you have to create a script containing the main
+command(s) you would like to execute on the allocated resources/nodes.
+ 1#!/bin/bash
+ 2#SBATCH --job-name=test
+ 3#SBATCH --output=job_output.txt
+ 4#SBATCH --error=job_error.txt
+ 5#SBATCH --ntasks=1
+ 6#SBATCH --time=10:00
+ 7#SBATCH --mem=100Gb
+ 8
+ 9module load python/3.5
+10python my_script.py
+
+
+Your job script is then submitted to SLURM with sbatch (ref.)
+sbatch job_script
+sbatch: Submitted batch job 4323674
+The working directory of the job will be the one where your executed sbatch.
+
+Tip
+Slurm directives can be specified on the command line alongside sbatch or
+inside the job script with a line starting with #SBATCH.
+
+
+
+Interactive job
+Workload managers usually run batch jobs to avoid having to watch its
+progression and let the scheduler run it as soon as resources are available. If
+you want to get access to a shell while leveraging cluster resources, you can
+submit an interactive jobs where the main executable is a shell with the
+srun/salloc (srun/salloc) commands
+salloc
+Will start an interactive job on the first node available with the default
+resources set in SLURM (1 task/1 CPU).  srun accepts the same arguments as
+sbatch with the exception that the environment is not passed.
+
+Tip
+To pass your current environment to an interactive job, add
+--preserve-env to srun.
+
+salloc can also be used and is mostly a wrapper around srun if provided
+without more info but it gives more flexibility if for example you want to get
+an allocation on multiple nodes.",390,"[-0.009859878569841385, -0.010809497907757759, -0.016769874840974808, 0.005583223421126604, -0.025579111650586128, 0.0008334426092915237, -0.028798388317227364, -0.008452286943793297, -0.016217613592743874, -0.0018840853590518236, 0.01584046147763729, 0.014628180302679539, 0.0030963656026870012, 0.010836437344551086, -0.001223224331624806, -0.009671301580965519, 0.017322136089205742, -0.0152073809877038, 0.008283914066851139, -0.008317588828504086, -0.02109367400407791, -0.0004445027152542025, 0.001964904135093093, -0.007866350933909416, -0.021834513172507286, 0.025121139362454414, 0.01011580415070057, -0.00755654601380229, 0.0004457655013538897, -0.0002923363063018769, -0.023922327905893326, 0.005172395147383213, 0.0005295306909829378, -0.013887342996895313, -0.027586108073592186, 0.00846575666218996, -0.012264234013855457, -0.011105832643806934, 0.0029970258474349976, -0.006751726847141981, 0.022144317626953125, 0.02307373285293579, 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+70,"Batch job
+In order to submit a batch job, you have to create a script containing the main
+command(s) you would like to execute on the allocated resources/nodes.
+ 1#!/bin/bash
+ 2#SBATCH --job-name=test
+ 3#SBATCH --output=job_output.txt
+ 4#SBATCH --error=job_error.txt
+ 5#SBATCH --ntasks=1
+ 6#SBATCH --time=10:00
+ 7#SBATCH --mem=100Gb
+ 8
+ 9module load python/3.5
+10python my_script.py
+
+
+Your job script is then submitted to SLURM with sbatch (ref.)
+sbatch job_script
+sbatch: Submitted batch job 4323674
+The working directory of the job will be the one where your executed sbatch.
+
+Tip
+Slurm directives can be specified on the command line alongside sbatch or
+inside the job script with a line starting with #SBATCH.",196,"[-0.012385226786136627, -0.014706618152558804, -0.01936281844973564, 0.002692075911909342, -0.020033741369843483, 0.004002051893621683, -0.02681005746126175, -0.01263346802443266, -0.00935265701264143, -0.0012848163023591042, 0.019268888980150223, 0.01619606465101242, -0.0030023774597793818, 0.001722593093290925, -0.003790711285546422, -0.0007443044451065361, 0.013203751295804977, -0.009634444490075111, 0.009976615197956562, -0.007004429120570421, -0.01941649243235588, 0.0049514067359268665, -0.006353634409606457, -0.002101664198562503, -0.02031552791595459, 0.016679128631949425, 0.007675351575016975, -0.008943394757807255, 0.008205380290746689, 0.006866890005767345, -0.02014108933508396, -0.006501237396150827, 0.0044616335071623325, -0.010050416924059391, -0.02847394347190857, 0.008829337544739246, -0.011539864353835583, -0.011942417360842228, 0.011928998865187168, -0.006762897130101919, 0.02175130322575569, 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+71,"Interactive job
+Workload managers usually run batch jobs to avoid having to watch its
+progression and let the scheduler run it as soon as resources are available. If
+you want to get access to a shell while leveraging cluster resources, you can
+submit an interactive jobs where the main executable is a shell with the
+srun/salloc (srun/salloc) commands
+salloc
+Will start an interactive job on the first node available with the default
+resources set in SLURM (1 task/1 CPU).  srun accepts the same arguments as
+sbatch with the exception that the environment is not passed.
+
+Tip
+To pass your current environment to an interactive job, add
+--preserve-env to srun.
+
+salloc can also be used and is mostly a wrapper around srun if provided
+without more info but it gives more flexibility if for example you want to get
+an allocation on multiple nodes.",189,"[-0.014191504567861557, -0.01634894870221615, -0.0013116283807903528, -0.002052374416962266, -0.03157714754343033, -0.0023062946274876595, -0.025539103895425797, -0.003887603525072336, -0.026463722810149193, -0.007929310202598572, 0.01824021525681019, 0.014807917177677155, 0.013161814771592617, 0.014934001490473747, -0.0013291401555761695, -0.01507409568876028, 0.01695135235786438, -0.01725955866277218, 0.010107769630849361, -0.01695135235786438, -0.018044084310531616, 0.013070753775537014, 0.013771222904324532, -0.010941327549517155, -0.010633121244609356, 0.012503373436629772, 0.011774885468184948, -0.008279545232653618, 0.0037930402904748917, -0.01325287576764822, -0.020607801154255867, 0.007179808337241411, 0.002530444646254182, -0.016026733443140984, -0.02758447453379631, -0.0021626984234899282, -0.008580747060477734, -0.020705867558717728, -0.014807917177677155, -0.012636463157832623, 0.02717820182442665, 0.017707860097289085, 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+72,"Job submission arguments
+In order to accurately select the resources for your job, several arguments are
+available. The most important ones are:
+
+
+
+
+
+
+Argument
+Description
+
+
+
+-n, –ntasks=<number>
+The number of task in your script, usually =1
+
+-c, –cpus-per-task=<ncpus>
+The number of cores for each task
+
+-t, –time=<time>
+Time requested for your job
+
+–mem=<size[units]>
+Memory requested for all your tasks
+
+–gres=<list>
+Select generic resources such as GPUs for your job: --gres=gpu:GPU_MODEL
+
+
+
+
+Tip
+Always consider requesting the adequate amount of resources to improve the
+scheduling of your job (small jobs always run first).",149,"[0.0016386127099394798, -0.023764993995428085, 0.004891991149634123, -0.03493890166282654, -0.01896839030086994, -0.0006340681575238705, -0.021911760792136192, -0.004449123051017523, -0.018954763188958168, -0.025073157623410225, 0.0324588380753994, 0.01821891963481903, -0.0022943983785808086, -0.023056404665112495, -0.013803863897919655, 0.0044763763435184956, 0.015711603686213493, 0.004939684644341469, -0.0014793503796681762, -0.006820171605795622, -0.009913436137139797, -0.006748631596565247, -0.0033589855302125216, -0.004483189899474382, -0.010696972720324993, 0.03869987651705742, 0.020821623504161835, -0.009790795855224133, 0.021857254207134247, -0.0024630289990454912, -0.013190661557018757, 0.016706354916095734, -0.0065476372838020325, -0.02264760248363018, -0.03270411863923073, 0.0026282528415322304, -0.009606835432350636, -0.018450574949383736, 0.014730480499565601, -0.00041923444950953126, 0.011160280555486679, 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+73,"Checking job status
+To display jobs currently in queue, use squeue and to get only your jobs type
+squeue -u $USER
+JOBID   USER          NAME    ST  START_TIME         TIME NODES CPUS TRES_PER_NMIN_MEM NODELIST (REASON) COMMENT
+133     my_username   myjob   R   2019-03-28T18:33   0:50     1    2        N/A  7000M node1 (None) (null)
+
+Note
+The maximum number of jobs able to be submitted to the system per user is 1000 (MaxSubmitJobs=1000)
+at any given time from the given association. If this limit is reached, new submission requests
+will be denied until existing jobs in this association complete.",168,"[-0.011936421506106853, -0.02893262356519699, -0.005028929095715284, -0.023694586008787155, -0.05068008601665497, -0.011360511183738708, -0.0363646075129509, 0.004161635879427195, -0.023680873215198517, 0.0003766554582398385, 0.006890352349728346, 0.0010686902096495032, -0.004740973934531212, -0.008919749408960342, -0.012964832596480846, -0.020239124074578285, 0.018689652904868126, -0.008631794713437557, 0.010428085923194885, -0.025532012805342674, -0.00466212909668684, 0.00692463293671608, -0.02702663652598858, -0.01970435120165348, -0.018689652904868126, 0.025449739769101143, 0.015316464938223362, 0.009790470823645592, 0.021404657512903214, 0.001787720830179751, -0.0029395408928394318, 0.018991319462656975, -0.017633818089962006, -0.0036062938161194324, -0.03422551229596138, -0.020266549661755562, 0.00098041829187423, -0.0063452948816120625, 0.0027355728670954704, -0.00699319364503026, 0.0297005046159029, 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+74,"Removing a job
+To cancel your job simply use scancel
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+75,"Partitioning
+Since we don’t have many GPUs on the cluster, resources must be shared as fairly
+as possible.  The --partition=/-p flag of SLURM allows you to set the
+priority you need for a job.  Each job assigned with a priority can preempt jobs
+with a lower priority: unkillable > main > long. Once preempted, your job is
+killed without notice and is automatically re-queued on the same partition until
+resources are available. (To leverage a different preemption mechanism, see the
+Handling preemption)
+
+
+
+
+
+
+
+
+Flag
+Max Resource Usage
+Max Time
+Note
+
+
+
+--partition=unkillable
+6  CPUs, mem=32G,  1 GPU
+2 days
+
+
+--partition=unkillable-cpu
+2  CPUs, mem=16G
+2 days
+CPU-only jobs
+
+--partition=short-unkillable
+24 CPUs, mem=128G, 4 GPUs
+3 hours (!)
+Large but short jobs
+
+--partition=main
+8  CPUs, mem=48G,  2 GPUs
+5 days
+
+
+--partition=main-cpu
+8  CPUs, mem=64G
+5 days
+CPU-only jobs
+
+--partition=long
+no limit of resources
+7 days
+
+
+--partition=long-cpu
+no limit of resources
+7 days
+CPU-only jobs
+
+
+
+
+Warning
+Historically, before the 2022 introduction of CPU-only nodes (e.g. the cn-f
+series), CPU jobs ran side-by-side with the GPU jobs on GPU nodes. To prevent
+them obstructing any GPU job, they were always lowest-priority and preemptible.
+This was implemented by automatically assigning them to one of the now-obsolete
+partitions cpu_jobs, cpu_jobs_low or cpu_jobs_low-grace.
+Do not use these partition names anymore. Prefer the *-cpu partition
+names defined above.
+For backwards-compatibility purposes, the legacy partition names are translated
+to their effective equivalent long-cpu, but they will eventually be removed
+entirely.
+
+
+Note
+As a convenience, should you request the unkillable, main or long
+partition for a CPU-only job, the partition will be translated to its -cpu
+equivalent automatically.
+
+For instance, to request an unkillable job with 1 GPU, 4 CPUs, 10G of RAM and
+12h of computation do:
+sbatch --gres=gpu:1 -c 4 --mem=10G -t 12:00:00 --partition=unkillable <job.sh>
+You can also make it an interactive job using salloc:
+salloc --gres=gpu:1 -c 4 --mem=10G -t 12:00:00 --partition=unkillable
+The Mila cluster has many different types of nodes/GPUs. To request a specific
+type of node/GPU, you can add specific feature requirements to your job
+submission command.
+To access those special nodes you need to request them explicitly by adding the
+flag --constraint=<name>.  The full list of nodes in the Mila Cluster can be
+accessed Node profile description.
+Example:
+To request a machine with 2 GPUs using NVLink, you can use
+sbatch -c 4 --gres=gpu:2 --constraint=nvlink
+
+
+
+
+
+
+Feature
+Particularities
+
+
+
+12GB/16GB/24GB/32GB/48GB
+Request a specific amount of GPU memory
+
+volta/turing/ampere
+Request a specific GPU architecture
+
+nvlink
+Machine with GPUs using the NVLink interconnect technology
+
+
+
+
+Information on partitions/nodes
+sinfo (ref.) provides most of the
+information about available nodes and partitions/queues to submit jobs to.
+Partitions are a group of nodes usually sharing similar features. On a
+partition, some job limits can be applied which will override those asked for a
+job (i.e. max time, max CPUs, etc…)
+To display available partitions, simply use
+sinfo
+PARTITION AVAIL TIMELIMIT NODES STATE  NODELIST
+batch     up     infinite     2 alloc  node[1,3,5-9]
+batch     up     infinite     6 idle   node[10-15]
+cpu       up     infinite     6 idle   cpu_node[1-15]
+gpu       up     infinite     6 idle   gpu_node[1-15]
+To display available nodes and their status, you can use
+sinfo -N -l
+NODELIST    NODES PARTITION STATE  CPUS MEMORY TMP_DISK WEIGHT FEATURES REASON
+node[1,3,5-9]   2 batch     allocated 2    246    16000     0  (null)   (null)
+node[2,4]       2 batch     drain     2    246    16000     0  (null)   (null)
+node[10-15]     6 batch     idle      2    246    16000     0  (null)   (null)
+...
+And to get statistics on a job running or terminated, use sacct with some of
+the fields you want to display
+sacct --format=User,JobID,Jobname,partition,state,time,start,end,elapsed,nnodes,ncpus,nodelist,workdir -u $USER
+     User        JobID    JobName  Partition      State  Timelimit               Start                 End    Elapsed   NNodes      NCPUS        NodeList              WorkDir
+--------- ------------ ---------- ---------- ---------- ---------- ------------------- ------------------- ---------- -------- ---------- --------------- --------------------
+my_usern+ 2398         run_extra+      batch    RUNNING 130-05:00+ 2019-03-27T18:33:43             Unknown 1-01:07:54        1         16 node9           /home/mila/my_usern+
+my_usern+ 2399         run_extra+      batch    RUNNING 130-05:00+ 2019-03-26T08:51:38             Unknown 2-10:49:59        1         16 node9           /home/mila/my_usern+
+Or to get the list of all your previous jobs, use the --start=YYYY-MM-DD flag. You can check sacct(1) for further information about additional time formats.
+sacct -u $USER --start=2019-01-01
+scontrol (ref.) can be used to
+provide specific information on a job (currently running or recently terminated)
+scontrol show job 43123
+JobId=43123 JobName=python_script.py
+UserId=my_username(1500000111) GroupId=student(1500000000) MCS_label=N/A
+Priority=645895 Nice=0 Account=my_username QOS=normal
+JobState=RUNNING Reason=None Dependency=(null)
+Requeue=1 Restarts=3 BatchFlag=1 Reboot=0 ExitCode=0:0
+RunTime=2-10:41:57 TimeLimit=130-05:00:00 TimeMin=N/A
+SubmitTime=2019-03-26T08:47:17 EligibleTime=2019-03-26T08:49:18
+AccrueTime=2019-03-26T08:49:18
+StartTime=2019-03-26T08:51:38 EndTime=2019-08-03T13:51:38 Deadline=N/A
+PreemptTime=None SuspendTime=None SecsPreSuspend=0
+LastSchedEval=2019-03-26T08:49:18
+Partition=slurm_partition AllocNode:Sid=login-node-1:14586
+ReqNodeList=(null) ExcNodeList=(null)
+NodeList=node2
+BatchHost=node2
+NumNodes=1 NumCPUs=16 NumTasks=1 CPUs/Task=16 ReqB:S:C:T=0:0:*:*
+TRES=cpu=16,mem=32000M,node=1,billing=3
+Socks/Node=* NtasksPerN:B:S:C=1:0:*:* CoreSpec=*
+MinCPUsNode=16 MinMemoryNode=32000M MinTmpDiskNode=0
+Features=(null) DelayBoot=00:00:00
+OverSubscribe=OK Contiguous=0 Licenses=(null) Network=(null)
+WorkDir=/home/mila/my_username
+StdErr=/home/mila/my_username/slurm-43123.out
+StdIn=/dev/null
+StdOut=/home/mila/my_username/slurm-43123.out
+Power=
+Or more info on a node and its resources
+scontrol show node node9
+NodeName=node9 Arch=x86_64 CoresPerSocket=4
+CPUAlloc=16 CPUTot=16 CPULoad=1.38
+AvailableFeatures=(null)
+ActiveFeatures=(null)
+Gres=(null)
+NodeAddr=10.252.232.4 NodeHostName=mila20684000000 Port=0 Version=18.08
+OS=Linux 4.15.0-1036 #38-Ubuntu SMP Fri Dec 7 02:47:47 UTC 2018
+RealMemory=32000 AllocMem=32000 FreeMem=23262 Sockets=2 Boards=1
+State=ALLOCATED+CLOUD ThreadsPerCore=2 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A
+Partitions=slurm_partition
+BootTime=2019-03-26T08:50:01 SlurmdStartTime=2019-03-26T08:51:15
+CfgTRES=cpu=16,mem=32000M,billing=3
+AllocTRES=cpu=16,mem=32000M
+CapWatts=n/a
+CurrentWatts=0 LowestJoules=0 ConsumedJoules=0
+ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s",2111,"[0.006247207056730986, -0.006557627115398645, 0.010046327486634254, -0.026992449536919594, -0.012430918402969837, 0.0074430303648114204, -0.016607481986284256, 0.01055428758263588, -0.03219904378056526, -0.03614984452724457, 0.025426238775253296, 0.049780115485191345, -0.0003697263600770384, 0.003003668272867799, -0.005538179073482752, 0.015619780868291855, 0.021038025617599487, -0.008698821067810059, 0.023450836539268494, -0.010321472771465778, 0.008176750503480434, 0.013185804709792137, -0.014575640670955181, 0.008649435825645924, -0.013863084837794304, 0.016254732385277748, 0.020868705585598946, -0.004275333136320114, 0.005411189049482346, 0.023845916613936424, -0.008155586197972298, 0.015450460836291313, -0.03188862279057503, -0.007845165207982063, -0.03871786594390869, 0.017369423061609268, 0.017679842188954353, -0.01751052215695381, 0.0045257858000695705, -0.02249135635793209, 0.022801775485277176, 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+76,"Information on partitions/nodes
+sinfo (ref.) provides most of the
+information about available nodes and partitions/queues to submit jobs to.
+Partitions are a group of nodes usually sharing similar features. On a
+partition, some job limits can be applied which will override those asked for a
+job (i.e. max time, max CPUs, etc…)
+To display available partitions, simply use
+sinfo
+PARTITION AVAIL TIMELIMIT NODES STATE  NODELIST
+batch     up     infinite     2 alloc  node[1,3,5-9]
+batch     up     infinite     6 idle   node[10-15]
+cpu       up     infinite     6 idle   cpu_node[1-15]
+gpu       up     infinite     6 idle   gpu_node[1-15]
+To display available nodes and their status, you can use
+sinfo -N -l
+NODELIST    NODES PARTITION STATE  CPUS MEMORY TMP_DISK WEIGHT FEATURES REASON
+node[1,3,5-9]   2 batch     allocated 2    246    16000     0  (null)   (null)
+node[2,4]       2 batch     drain     2    246    16000     0  (null)   (null)
+node[10-15]     6 batch     idle      2    246    16000     0  (null)   (null)
+...
+And to get statistics on a job running or terminated, use sacct with some of
+the fields you want to display
+sacct --format=User,JobID,Jobname,partition,state,time,start,end,elapsed,nnodes,ncpus,nodelist,workdir -u $USER
+     User        JobID    JobName  Partition      State  Timelimit               Start                 End    Elapsed   NNodes      NCPUS        NodeList              WorkDir
+--------- ------------ ---------- ---------- ---------- ---------- ------------------- ------------------- ---------- -------- ---------- --------------- --------------------
+my_usern+ 2398         run_extra+      batch    RUNNING 130-05:00+ 2019-03-27T18:33:43             Unknown 1-01:07:54        1         16 node9           /home/mila/my_usern+
+my_usern+ 2399         run_extra+      batch    RUNNING 130-05:00+ 2019-03-26T08:51:38             Unknown 2-10:49:59        1         16 node9           /home/mila/my_usern+
+Or to get the list of all your previous jobs, use the --start=YYYY-MM-DD flag. You can check sacct(1) for further information about additional time formats.
+sacct -u $USER --start=2019-01-01
+scontrol (ref.) can be used to
+provide specific information on a job (currently running or recently terminated)
+scontrol show job 43123
+JobId=43123 JobName=python_script.py
+UserId=my_username(1500000111) GroupId=student(1500000000) MCS_label=N/A
+Priority=645895 Nice=0 Account=my_username QOS=normal
+JobState=RUNNING Reason=None Dependency=(null)
+Requeue=1 Restarts=3 BatchFlag=1 Reboot=0 ExitCode=0:0
+RunTime=2-10:41:57 TimeLimit=130-05:00:00 TimeMin=N/A
+SubmitTime=2019-03-26T08:47:17 EligibleTime=2019-03-26T08:49:18
+AccrueTime=2019-03-26T08:49:18
+StartTime=2019-03-26T08:51:38 EndTime=2019-08-03T13:51:38 Deadline=N/A
+PreemptTime=None SuspendTime=None SecsPreSuspend=0
+LastSchedEval=2019-03-26T08:49:18
+Partition=slurm_partition AllocNode:Sid=login-node-1:14586
+ReqNodeList=(null) ExcNodeList=(null)
+NodeList=node2
+BatchHost=node2
+NumNodes=1 NumCPUs=16 NumTasks=1 CPUs/Task=16 ReqB:S:C:T=0:0:*:*
+TRES=cpu=16,mem=32000M,node=1,billing=3
+Socks/Node=* NtasksPerN:B:S:C=1:0:*:* CoreSpec=*
+MinCPUsNode=16 MinMemoryNode=32000M MinTmpDiskNode=0
+Features=(null) DelayBoot=00:00:00
+OverSubscribe=OK Contiguous=0 Licenses=(null) Network=(null)
+WorkDir=/home/mila/my_username
+StdErr=/home/mila/my_username/slurm-43123.out
+StdIn=/dev/null
+StdOut=/home/mila/my_username/slurm-43123.out
+Power=
+Or more info on a node and its resources
+scontrol show node node9
+NodeName=node9 Arch=x86_64 CoresPerSocket=4
+CPUAlloc=16 CPUTot=16 CPULoad=1.38
+AvailableFeatures=(null)
+ActiveFeatures=(null)
+Gres=(null)
+NodeAddr=10.252.232.4 NodeHostName=mila20684000000 Port=0 Version=18.08
+OS=Linux 4.15.0-1036 #38-Ubuntu SMP Fri Dec 7 02:47:47 UTC 2018
+RealMemory=32000 AllocMem=32000 FreeMem=23262 Sockets=2 Boards=1
+State=ALLOCATED+CLOUD ThreadsPerCore=2 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A
+Partitions=slurm_partition
+BootTime=2019-03-26T08:50:01 SlurmdStartTime=2019-03-26T08:51:15
+CfgTRES=cpu=16,mem=32000M,billing=3
+AllocTRES=cpu=16,mem=32000M
+CapWatts=n/a
+CurrentWatts=0 LowestJoules=0 ConsumedJoules=0
+ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s",1362,"[0.006787808146327734, 0.0031490011606365442, 0.016924360767006874, -0.027331870049238205, -0.03662776201963425, 0.0015519208973273635, -0.02182936854660511, 0.016840990632772446, -0.01347140222787857, -0.03562730923295021, 0.01778586395084858, 0.031792230904102325, -0.015159670263528824, 0.008580289781093597, 0.0004309685027692467, 0.004227616358548403, 0.015479260124266148, -0.016785409301519394, 0.01075488980859518, -0.0038454984314739704, -0.001848062383942306, 0.002179810544475913, -0.02121797949075699, -0.0030361025128513575, -0.010365824215114117, 0.040101565420627594, 0.015812745317816734, -0.002945783780887723, 0.0035484882537275553, 0.016312971711158752, -0.00984475389122963, 0.0030065751634538174, -0.024872418493032455, -0.02321888878941536, -0.017438484355807304, 0.00624589528888464, 0.012519581243395805, -0.024552829563617706, 0.008747031912207603, -0.021162400022149086, 0.01718837022781372, 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+77,"Useful Commands
+
+sallocGet an interactive job and give you a shell. (ssh like) CPU only
+
+salloc --gres=gpu:1 -c 2 --mem=12000Get an interactive job with one GPU, 2 CPUs and 12000 MB RAM
+
+sbatchstart a batch job (same options as salloc)
+
+sattach --pty <jobid>.0Re-attach a dropped interactive job
+
+sinfostatus of all nodes
+
+sinfo -Ogres:27,nodelist,features -tidle,mix,allocList GPU type and FEATURES that you can request
+
+savail(Custom) List available gpu
+
+scancel <jobid>Cancel a job
+
+squeuesummary status of all active jobs
+
+squeue -u $USERsummary status of all YOUR active jobs
+
+squeue -j <jobid>summary status of a specific job
+
+squeue -Ojobid,name,username,partition,state,timeused,nodelist,gres,tresstatus of all jobs including requested resources (see the SLURM squeue doc for all output options)
+
+scontrol show job <jobid>Detailed status of a running job
+
+sacct -j <job_id> -o NodeListGet the node where a finished job ran
+
+sacct -u $USER -S <start_time> -E <stop_time>Find info about old jobs
+
+sacct -oJobID,JobName,User,Partition,Node,StateList of current and recent jobs",307,"[-0.012057056650519371, 0.0006974077550694346, 0.012245121411979198, -0.02439272776246071, -0.029226696118712425, -0.011625204235315323, -0.004799140617251396, -0.0040747420862317085, -0.032792966812849045, -0.029310280457139015, 0.03368453308939934, 0.010608259588479996, -0.005704638548195362, 0.017301980406045914, 0.0030369015876203775, -0.028725188225507736, 0.021341895684599876, -0.021258311346173286, -0.0020791632123291492, 0.003446117276325822, -0.006366348825395107, 0.004642419517040253, -0.003216259879991412, 0.001859753974713385, -0.0013704367447644472, 0.02088218182325363, 0.00812858808785677, -0.003775231074541807, -0.00719522824510932, 0.006188732106238604, -0.004461320117115974, 0.00766887329518795, -0.0032110358588397503, -0.012711801566183567, -0.03630351275205612, 0.015727806836366653, 0.013011313043534756, -0.001974682556465268, -0.006049424409866333, 0.0037717483937740326, 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+78,"Special GPU requirements
+Specific GPU architecture and memory can be easily requested through the
+--gres flag by using either
+
+--gres=gpu:architecture:number
+--gres=gpu:memory:number
+--gres=gpu:model:number
+
+Example:
+To request 1 GPU with at least 16GB of memory use
+sbatch -c 4 --gres=gpu:16gb:1
+The full list of GPU and their features can be accessed here.",94,"[0.0016877787420526147, 0.0009171421988867223, 0.025906937196850777, -0.03308825567364693, 0.005182742606848478, 0.003495810553431511, -0.00585006270557642, -0.005230166483670473, -0.01689641736447811, -0.017858443781733513, 0.033657338470220566, 0.016666073352098465, -0.00285897683352232, -0.023982886224985123, -0.00921376422047615, 0.005182742606848478, 0.0029047068674117327, 0.018034588545560837, 0.027383849024772644, 0.00849563255906105, -0.00904439389705658, -0.015487253665924072, -0.0088546983897686, 0.012689250521361828, -0.004244429059326649, 0.0006808700854890049, 0.008536281995475292, -0.016760921105742455, -0.004583170171827078, 0.006378499325364828, -0.0025456412695348263, 0.0042105549946427345, -0.01849527657032013, -0.012018542736768723, -0.03799322992563248, 0.012506330385804176, 0.004369763191789389, 0.010792299173772335, 0.024321628734469414, -0.003055446781218052, 0.017736496403813362, 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+79,"Example script
+Here is a sbatch script that follows good practices on the Mila cluster:
+ 1#!/bin/bash
+ 2
+ 3#SBATCH --partition=unkillable                           # Ask for unkillable job
+ 4#SBATCH --cpus-per-task=2                                # Ask for 2 CPUs
+ 5#SBATCH --gres=gpu:1                                     # Ask for 1 GPU
+ 6#SBATCH --mem=10G                                        # Ask for 10 GB of RAM
+ 7#SBATCH --time=3:00:00                                   # The job will run for 3 hours
+ 8#SBATCH -o /network/scratch/<u>/<username>/slurm-%j.out  # Write the log on scratch
+ 9
+10# 1. Load the required modules
+11module --quiet load anaconda/3
+12
+13# 2. Load your environment
+14conda activate ""<env_name>""
+15
+16# 3. Copy your dataset on the compute node
+17cp /network/datasets/<dataset> $SLURM_TMPDIR
+18
+19# 4. Launch your job, tell it to save the model in $SLURM_TMPDIR
+20#    and look for the dataset into $SLURM_TMPDIR
+21python main.py --path $SLURM_TMPDIR --data_path $SLURM_TMPDIR
+22
+23# 5. Copy whatever you want to save on $SCRATCH
+24cp $SLURM_TMPDIR/<to_save> /network/scratch/<u>/<username>/",324,"[-0.013378001749515533, -0.0004840043548028916, 0.015805350616574287, -0.019542915746569633, -0.010771360248327255, 0.0011197180720046163, -0.015874309465289116, -0.017474152147769928, -0.016398396342992783, -0.02681116946041584, 0.02937643602490425, 0.010716192424297333, -0.0005589970387518406, -0.00018855913367588073, -0.01327456347644329, 0.00200670026242733, 0.0251423679292202, -0.014895094558596611, 0.012309140525758266, -0.010185210034251213, -0.004475424066185951, 0.016591480001807213, -0.00026376728783361614, -0.01348833553493023, -0.01563984900712967, 0.002129102125763893, 0.005040885880589485, -0.01730865053832531, 0.002123930025845766, 0.019404998049139977, -0.008357803337275982, -0.01377796195447445, 0.002384249586611986, 0.0009050838416442275, -0.03740323707461357, 0.0021032425574958324, -0.00180844368878752, -0.0077302781865000725, 0.006137330550700426, -0.0106955049559474, 0.02478378266096115, 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+80,"print(torch.__file__)'
+cvmfs/ai.mila.quebec/apps/x86_64/debian/pytorch/python3.7-cuda10.1-cudnn7.6-v1.5.0/lib/python3.7/site-packages/torch/__init__.py'
+
+
+
+On using containers
+Another option for creating portable code is Using containers on clusters.
+Containers are a popular approach at deploying applications by packaging a lot
+of the required dependencies together. The most popular tool for this is
+Docker, but Docker cannot be used on the Mila
+cluster (nor the other clusters from Digital Research Alliance of Canada).
+One popular mechanism for containerisation on a computational cluster is called
+Singularity.
+This is the recommended approach for running containers on the
+Mila cluster. See section Singularity for more details.",170,"[-0.006494851782917976, -0.015869194641709328, 0.007536445278674364, -0.03725563734769821, -0.008624252863228321, 0.01931036449968815, -0.02634911984205246, -0.01611093059182167, -0.00784927885979414, -0.011759699322283268, 0.05741918459534645, 0.02275153435766697, 0.005623894743621349, 0.00863136351108551, -0.018784234300255775, 0.005101320333778858, 0.007195172365754843, -0.011240679770708084, 0.001445966656319797, -0.010060443542897701, -0.005769646726548672, 0.013231438584625721, 0.001662817201577127, -0.0207323357462883, -0.021656615659594536, 0.01871313713490963, 0.014440113678574562, -0.032762207090854645, -0.004802706651389599, -0.006014936603605747, 0.013572711497545242, -0.005382159724831581, -0.02475651167333126, -0.025439059361815453, -0.029548553749918938, 0.012015653774142265, 0.020774994045495987, -0.026989007368683815, -0.014141499996185303, -0.022708874195814133, 0.029463235288858414, 0.015542141161859035, 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+82,"Virtual environments
+A virtual environment in Python is a local, isolated environment in which you
+can install or uninstall Python packages without interfering with the global
+environment (or other virtual environments). It usually lives in a directory
+(location varies depending on whether you use venv, conda or poetry). In order
+to use a virtual environment, you have to activate it. Activating an
+environment essentially sets environment variables in your shell so that:
+
+python points to the right Python version for that environment (different
+virtual environments can use different versions of Python!)
+python looks for packages in the virtual environment
+pip install installs packages into the virtual environment
+Any shell commands installed via pip install are made available
+
+To run experiments within a virtual environment, you can simply activate it
+in the script given to sbatch.
+
+Pip/Virtualenv
+Pip is the preferred package manager for Python and each cluster provides
+several Python versions through the associated module which comes with pip. In
+order to install new packages, you will first have to create a personal space
+for them to be stored.  The preferred solution (as it is the preferred solution
+on Digital Research Alliance of Canada clusters) is to use virtual
+environments.
+First, load the Python module you want to use:
+module load python/3.8
+Then, create a virtual environment in your home directory:
+python -m venv $HOME/<env>
+Where <env> is the name of your environment. Finally, activate the environment:
+source $HOME/<env>/bin/activate
+You can now install any Python package you wish using the pip command, e.g.
+pytorch:
+pip install torch torchvision
+Or Tensorflow:
+pip install tensorflow-gpu
+
+
+Conda
+Another solution for Python is to use miniconda or anaconda which are also available through the module
+command: (the use of Conda is not recommended for Digital Research Alliance of
+Canada clusters due to the availability of custom-built packages for pip)
+module load miniconda/3
+=== Module miniconda/3 loaded ===]
+o enable conda environment functions, first use:
+To create an environment (see here
+for details) using a specific Python version, you may write:
+conda create -n <env> python=3.9
+Where <env> is the name of your environment. You can now activate it by doing:
+conda activate <env>
+You are now ready to install any Python package you want in this environment.
+For instance, to install PyTorch, you can find the Conda command of any version
+you want on pytorch’s website, e.g:
+conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
+If you make a lot of environments and install/uninstall a lot of packages, it
+can be good to periodically clean up Conda’s cache:
+conda clean --all",592,"[-0.005768963135778904, -0.014696724712848663, 0.017383364960551262, -0.009915301576256752, -0.021386727690696716, 0.011823880486190319, 0.0015877117402851582, -0.030377663671970367, -0.013266952708363533, -1.8070222722599283e-06, 0.012701693922281265, 0.013060799799859524, -0.017330164089798927, 0.0007414863212034106, -0.004841274116188288, -0.005802213679999113, 0.011956882663071156, 0.005998391658067703, -0.009270241484045982, 0.018726686015725136, -0.024552175775170326, 0.019245393574237823, 0.0006616851314902306, -0.015268633142113686, -0.036442555487155914, 0.008412378840148449, -0.0011729117250069976, -0.004817998502403498, 0.004182913806289434, -0.010094854049384594, 0.011624378152191639, -0.011345073580741882, -0.014483921229839325, -0.025523090735077858, -0.022104937583208084, 0.022557144984602928, 0.004036611877381802, -0.03362291306257248, 0.004455568268895149, 0.008977637626230717, 0.026228001341223717, 0.028595436364412308, -0.013446505181491375, 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+83,"Pip/Virtualenv
+Pip is the preferred package manager for Python and each cluster provides
+several Python versions through the associated module which comes with pip. In
+order to install new packages, you will first have to create a personal space
+for them to be stored.  The preferred solution (as it is the preferred solution
+on Digital Research Alliance of Canada clusters) is to use virtual
+environments.
+First, load the Python module you want to use:
+module load python/3.8
+Then, create a virtual environment in your home directory:
+python -m venv $HOME/<env>
+Where <env> is the name of your environment. Finally, activate the environment:
+source $HOME/<env>/bin/activate
+You can now install any Python package you wish using the pip command, e.g.
+pytorch:
+pip install torch torchvision
+Or Tensorflow:
+pip install tensorflow-gpu",187,"[-0.01617649756371975, -0.020919274538755417, 0.01052009779959917, -0.030579490587115288, -0.01205847691744566, 0.036410555243492126, 0.0038728199433535337, -0.022625599056482315, 0.008182298392057419, -0.005142487119883299, 0.013469218276441097, -0.0018977825529873371, -0.003112027421593666, 0.006452460773289204, -0.01007672119885683, -0.007221650332212448, 0.009398221969604492, 0.008518188260495663, -0.0051156156696379185, 0.013905876316130161, -0.01613618992269039, 0.013731212355196476, 0.021308908239006996, -0.020502770319581032, -0.03342784568667412, 0.009136227890849113, 0.00567655311897397, 0.000859880237840116, 0.008659263141453266, 0.0004463148070499301, 0.018809881061315536, -0.013361732475459576, 9.551892435410991e-05, -0.027811752632260323, -0.03079446218907833, 0.006862247362732887, 0.01009687501937151, -0.021940382197499275, -0.010231231339275837, -0.011554640717804432, 0.027758009731769562, 0.008833926171064377, -0.0026669723447412252, 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+84,"Conda
+Another solution for Python is to use miniconda or anaconda which are also available through the module
+command: (the use of Conda is not recommended for Digital Research Alliance of
+Canada clusters due to the availability of custom-built packages for pip)
+module load miniconda/3
+=== Module miniconda/3 loaded ===]
+o enable conda environment functions, first use:
+To create an environment (see here
+for details) using a specific Python version, you may write:
+conda create -n <env> python=3.9
+Where <env> is the name of your environment. You can now activate it by doing:
+conda activate <env>
+You are now ready to install any Python package you want in this environment.
+For instance, to install PyTorch, you can find the Conda command of any version
+you want on pytorch’s website, e.g:
+conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
+If you make a lot of environments and install/uninstall a lot of packages, it
+can be good to periodically clean up Conda’s cache:
+conda clean --all",240,"[-0.005291816778481007, -0.01600024104118347, 0.01169663481414318, -0.018496062606573105, -0.01560900453478098, 0.024351125583052635, 0.0023575397208333015, -0.023932907730340958, 0.0004506813420448452, 0.013605601154267788, 0.02522803656756878, 0.007042264565825462, -0.0002306105598108843, 0.00971346814185381, -0.010664578527212143, 0.011507761664688587, 0.0132615827023983, 0.028816623613238335, -0.006128254346549511, 0.018482571467757225, -0.0034317548852413893, 0.020087992772459984, 0.015069367364048958, -0.024823307991027832, -0.03248615562915802, 0.015244749374687672, 0.0032749229576438665, -0.005753880832344294, 0.006900609936565161, 0.00701528275385499, 0.03140688315033913, -0.009092886000871658, -0.009760687127709389, -0.031676698476076126, -0.022799668833613396, 0.007986629381775856, 0.00884330365806818, -0.011406579986214638, -0.0008946171728894114, -0.0010177219519391656, 0.023190906271338463, 0.011291907168924809, -0.002701558405533433, 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+85,"print(torch.__file__)'
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+86,"The module command
+For a list of available modules, simply use:
+module avail
+-------------------------------------------------------------------------------------------------------------- Global Aliases ---------------------------------------------------------------------------------------------------------------
+  cuda/10.0 -> cudatoolkit/10.0    cuda/9.2      -> cudatoolkit/9.2                                 pytorch/1.4.1       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.4.1    tensorflow/1.15 -> python/3.7/tensorflow/1.15
+  cuda/10.1 -> cudatoolkit/10.1    mujoco-py     -> python/3.7/mujoco-py/2.0                        pytorch/1.5.0       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.0    tensorflow/2.2  -> python/3.7/tensorflow/2.2
+  cuda/10.2 -> cudatoolkit/10.2    mujoco-py/2.0 -> python/3.7/mujoco-py/2.0                        pytorch/1.5.1       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.1
+  cuda/11.0 -> cudatoolkit/11.0    pytorch       -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.1    tensorflow          -> python/3.7/tensorflow/2.2
+  cuda/9.0  -> cudatoolkit/9.0     pytorch/1.4.0 -> python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.4.0    tensorflow-cpu/1.15 -> python/3.7/tensorflow/1.15
+
+-------------------------------------------------------------------------------------------------- /cvmfs/config.mila.quebec/modules/Core ---------------------------------------------------------------------------------------------------
+  Mila       (S,L)    anaconda/3 (D)    go/1.13.5        miniconda/2        mujoco/1.50        python/2.7    python/3.6        python/3.8           singularity/3.0.3    singularity/3.2.1    singularity/3.5.3 (D)
+  anaconda/2          go/1.12.4         go/1.14   (D)    miniconda/3 (D)    mujoco/2.0  (D)    python/3.5    python/3.7 (D)    singularity/2.6.1    singularity/3.1.1    singularity/3.4.2
+
+------------------------------------------------------------------------------------------------ /cvmfs/config.mila.quebec/modules/Compiler -------------------------------------------------------------------------------------------------
+  python/3.7/mujoco-py/2.0
+
+-------------------------------------------------------------------------------------------------- /cvmfs/config.mila.quebec/modules/Cuda ---------------------------------------------------------------------------------------------------
+  cuda/10.0/cudnn/7.3        cuda/10.0/nccl/2.4         cuda/10.1/nccl/2.4     cuda/11.0/nccl/2.7        cuda/9.0/nccl/2.4     cudatoolkit/9.0     cudatoolkit/10.1        cudnn/7.6/cuda/10.0/tensorrt/7.0
+  cuda/10.0/cudnn/7.5        cuda/10.1/cudnn/7.5        cuda/10.2/cudnn/7.6    cuda/9.0/cudnn/7.3        cuda/9.2/cudnn/7.6    cudatoolkit/9.2     cudatoolkit/10.2        cudnn/7.6/cuda/10.1/tensorrt/7.0
+  cuda/10.0/cudnn/7.6 (D)    cuda/10.1/cudnn/7.6 (D)    cuda/10.2/nccl/2.7     cuda/9.0/cudnn/7.5 (D)    cuda/9.2/nccl/2.4     cudatoolkit/10.0    cudatoolkit/11.0 (D)    cudnn/7.6/cuda/9.0/tensorrt/7.0
+
+------------------------------------------------------------------------------------------------ /cvmfs/config.mila.quebec/modules/Pytorch --------------------------------------------------------------------------------------------------
+  python/3.7/cuda/10.1/cudnn/7.6/pytorch/1.4.1    python/3.7/cuda/10.1/cudnn/7.6/pytorch/1.5.1 (D)    python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.0
+  python/3.7/cuda/10.1/cudnn/7.6/pytorch/1.5.0    python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.4.1        python/3.7/cuda/10.2/cudnn/7.6/pytorch/1.5.1 (D)
+
+----------------------------------------------------------------------------------------------- /cvmfs/config.mila.quebec/modules/Tensorflow ------------------------------------------------------------------------------------------------
+  python/3.7/tensorflow/1.15    python/3.7/tensorflow/2.0    python/3.7/tensorflow/2.2 (D)
+Modules can be loaded using the load command:
+module load <module>
+To search for a module or a software, use the command spider:
+module spider search_term
+E.g.: by default, python2 will refer to the os-shipped installation of python2.7 and python3 to python3.6.
+If you want to use python3.7 you can type:
+module load python3.7",1311,"[-0.009266162291169167, -0.010914240032434464, -0.0018190399277955294, -0.02348339557647705, -0.0345480814576149, 0.008356641978025436, -0.014661735855042934, -0.03432925045490265, -0.004065485671162605, -0.011953691020607948, 0.04869009181857109, -0.0007030828855931759, -0.008534442633390427, -0.0014018916990607977, -0.01691843941807747, -0.004858751315623522, 0.011816920712590218, -0.0017044951673597097, 0.02565803751349449, -0.01149551197886467, -0.00738557567819953, -0.0009394384105689824, -0.012056268751621246, -0.026246147230267525, -0.03452072665095329, 0.02400312013924122, 0.004964747931808233, -0.018928956240415573, -0.004086001310497522, 0.0014967757742851973, 0.0027234309818595648, 0.01153654232621193, -0.01762964203953743, -0.028475496917963028, -0.022977346554398537, 0.014935275539755821, 0.014579673297703266, -0.010572315193712711, 0.00020259043958503753, 0.009573894552886486, 0.028858453035354614, 0.021541262045502663, 0.00410993630066514, 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+87,"Available Software
+Modules are divided in 5 main sections:
+
+
+
+
+
+
+Section
+Description
+
+
+
+Core
+Base interpreter and software (Python, go, etc…)
+
+Compiler
+Interpreter-dependent software (see the note below)
+
+Cuda
+Toolkits, cudnn and related libraries
+
+Pytorch/Tensorflow
+Pytorch/TF built with a specific Cuda/Cudnn
+version for Mila’s GPUs (see the related paragraph)
+
+
+
+
+Note
+Modules which are nested (../../..) usually depend on other software/module
+loaded alongside the main module.  No need to load the dependent software,
+the complex naming scheme allows an automatic detection of the dependent
+module(s):
+i.e.: Loading cudnn/7.6/cuda/9.0/tensorrt/7.0 will load cudnn/7.6 and
+cuda/9.0 alongside
+python/3.X is a particular dependency which can be served through
+python/3.X or anaconda/3 and is not automatically loaded to let the
+user pick his favorite flavor.",212,"[-0.022183023393154144, -0.009789557196199894, 0.005524412263184786, -0.03881673514842987, -0.011561068706214428, 0.010621954686939716, -0.006765893194824457, -0.020062901079654694, -0.013161832466721535, -0.004446565173566341, 0.04541899263858795, 0.01815621368587017, -0.016263756901025772, 0.0007052252185530961, -0.028657222166657448, 0.0029205039609223604, 0.0022446259390562773, 0.012002168223261833, 0.013197405263781548, -0.03050699271261692, 0.008508946746587753, 0.02585410699248314, -0.011333405040204525, -0.044792916625738144, -0.013396610505878925, 0.01200928259640932, 0.002941847313195467, -0.039215147495269775, -0.0029329543467611074, -0.015068518929183483, 0.018341191112995148, -0.004812961909919977, -0.0183696486055851, -0.0578266866505146, -0.016078779473900795, -0.009334228932857513, 0.007470229174941778, -0.017885863780975342, 4.9245707486988977e-05, 0.013311237096786499, 0.03221447393298149, 0.015338870696723461, 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+88,"print(torch.__file__)'
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+89,"On using containers
+Another option for creating portable code is Using containers on clusters.
+Containers are a popular approach at deploying applications by packaging a lot
+of the required dependencies together. The most popular tool for this is
+Docker, but Docker cannot be used on the Mila
+cluster (nor the other clusters from Digital Research Alliance of Canada).
+One popular mechanism for containerisation on a computational cluster is called
+Singularity.
+This is the recommended approach for running containers on the
+Mila cluster. See section Singularity for more details.",110,"[-0.012206011451780796, -0.010396954603493214, 0.010786072351038456, -0.02960025891661644, -0.011782759800553322, 0.022473258897662163, -0.026091372594237328, -0.00895653571933508, -0.00023871865414548665, -0.02260979264974594, 0.03678187355399132, 0.022664405405521393, 0.015482792630791664, 0.0062907375395298, -0.02349725365638733, -0.0004034110461361706, 0.019933754578232765, -0.0015999909956008196, 0.005918686743825674, -0.01496396865695715, -0.01257464848458767, 0.010205809026956558, 0.01759904809296131, -0.017994992434978485, -0.028071094304323196, 0.018650347366929054, 0.02080756239593029, -0.029873324558138847, 0.00592210004106164, -0.0026760383043438196, 0.028071094304323196, -0.006376070436090231, -0.01825440302491188, -0.016861772164702415, -0.02510833740234375, -0.0007189292809925973, 0.019018985331058502, -0.026787688955664635, -0.029327193275094032, -0.01905994489789009, 0.02071199007332325, 0.005526155233383179, 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+90,"Singularity
+
+Overview
+
+What is Singularity?
+Running Docker on SLURM is a security problem (e.g. running as root, being able
+to mount any directory).  The alternative is to use Singularity, which is a
+popular solution in the world of HPC.
+There is a good level of compatibility between Docker and Singularity,
+and we can find many exaggerated claims about able to convert containers
+from Docker to Singularity without any friction.
+Oftentimes, Docker images from DockerHub are 100% compatible with Singularity,
+and they can indeed be used without friction, but things get messy when
+we try to convert our own Docker build files to Singularity recipes.
+
+
+Links to official documentation
+
+official Singularity user guide (this is the one you
+will use most often)
+official Singularity admin guide
+
+
+
+Overview of the steps used in practice
+Most often, the process to create and use a Singularity container is:
+
+on your Linux computer (at home or work)
+
+select a Docker image from DockerHub (e.g. pytorch/pytorch)
+make a recipe file for Singularity that starts with that DockerHub image
+build the recipe file, thus creating the image file (e.g. my-pytorch-image.sif)
+test your singularity container before send it over to the cluster
+rsync -av my-pytorch-image.sif <login-node>:Documents/my-singularity-images
+
+
+on the login node for that cluster
+
+queue your jobs with sbatch ...
+(note that your jobs will copy over the my-pytorch-image.sif to $SLURM_TMPDIR
+and will then launch Singularity with that image)
+do something else while you wait for them to finish
+queue more jobs with the same my-pytorch-image.sif,
+reusing it many times over
+
+
+
+In the following sections you will find specific examples or tips to accomplish
+in practice the steps highlighted above.
+
+
+Nope, not on MacOS
+Singularity does not work on MacOS, as of the time of this writing in 2021.
+Docker does not actually run on MacOS, but there Docker silently installs a
+virtual machine running Linux, which makes it a pleasant experience,
+and the user does not need to care about the details of how Docker does it.
+Given its origins in HPC, Singularity does not provide that kind of seamless
+experience on MacOS, even though it’s technically possible to run it
+inside a Linux virtual machine on MacOS.
+
+
+Where to build images
+Building Singularity images is a rather heavy task, which can take 20 minutes
+if you have a lot of steps in your recipe. This makes it a bad task to run on
+the login nodes of our clusters, especially if it needs to be run regularly.
+On the Mila cluster, we are lucky to have unrestricted internet access on the compute
+nodes, which means that anyone can request an interactive CPU node (no need for GPU)
+and build their images there without problem.
+
+Warning
+Do not build Singularity images from scratch every time your run a
+job in a large batch.  This will be a colossal waste of GPU time as well as
+internet bandwidth.  If you setup your workflow properly (e.g. using bind
+paths for your code and data), you can spend months reusing the same
+Singularity image my-pytorch-image.sif.
+
+
+
+
+Building the containers
+Building a container is like creating a new environment except that containers
+are much more powerful since they are self-contained systems.  With
+singularity, there are two ways to build containers.
+The first one is by yourself, it’s like when you got a new Linux laptop and you
+don’t really know what you need, if you see that something is missing, you
+install it. Here you can get a vanilla container with Ubuntu called a sandbox,
+you log in and you install each packages by yourself.  This procedure can take
+time but will allow you to understand how things work and what you need. This is
+recommended if you need to figure out how things will be compiled or if you want
+to install packages on the fly. We’ll refer to this procedure as singularity
+sandboxes.
+The second way is more like you know what you want, so you write a list of
+everything you need, you send it to singularity and it will install everything
+for you. Those lists are called singularity recipes.
+
+First way: Build and use a sandbox
+You might ask yourself: On which machine should I build a container?
+First of all, you need to choose where you’ll build your container. This
+operation requires memory and high cpu usage.
+
+Warning
+Do NOT build containers on any login nodes !
+
+
+(Recommended for beginner) If you need to use apt-get, you should build
+the container on your laptop with sudo privileges. You’ll only need to
+install singularity on your laptop. Windows/Mac users can look there and
+Ubuntu/Debian users can use directly:
+
+sudo apt-get install singularity-container
+
+
+If you can’t install singularity on your laptop and you don’t need
+apt-get, you can reserve a cpu node on the Mila cluster to build your
+container.
+
+In this case, in order to avoid too much I/O over the network, you should define
+the singularity cache locally:
+
+export SINGULARITY_CACHEDIR=$SLURM_TMPDIR
+
+
+If you can’t install singularity on your laptop and you want to use
+apt-get, you can use singularity-hub to build your containers and read
+Recipe_section.
+
+
+Download containers from the web
+Hopefully, you may not need to create containers from scratch as many have been
+already built for the most common deep learning software. You can find most of
+them on dockerhub.
+Go on dockerhub and select the container you want to pull.
+For example, if you want to get the latest PyTorch version with GPU support
+(Replace runtime by devel if you need the full Cuda toolkit):
+singularity pull docker://pytorch/pytorch:1.0.1-cuda10.0-cudnn7-runtime
+Or the latest TensorFlow:
+singularity pull docker://tensorflow/tensorflow:latest-gpu-py3
+Currently the pulled image pytorch.simg or tensorflow.simg is read-only
+meaning that you won’t be able to install anything on it.  Starting now, PyTorch
+will be taken as example. If you use TensorFlow, simply replace every
+pytorch occurrences by tensorflow.
+
+
+How to add or install stuff in a container
+The first step is to transform your read only container
+pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg in a writable version that will
+allow you to add packages.
+
+Warning
+Depending on the version of singularity you are using, singularity
+will build a container with the extension .simg or .sif. If you’re using
+.sif files, replace every occurences of .simg by .sif.
+
+
+Tip
+If you want to use apt-get you have to put sudo ahead of the
+following commands
+
+This command will create a writable image in the folder pytorch.
+singularity build --sandbox pytorch pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg
+Then you’ll need the following command to log inside the container.
+singularity shell --writable -H $HOME:/home pytorch
+Once you get into the container, you can use pip and install anything you need
+(Or with apt-get if you built the container with sudo).
+
+Warning
+Singularity mounts your home folder, so if you install things into
+the $HOME of your container, they will be installed in your real
+$HOME!
+
+You should install your stuff in /usr/local instead.
+
+
+Creating useful directories
+One of the benefits of containers is that you’ll be able to use them across
+different clusters. However for each cluster the datasets and experiments
+folder location can be different. In order to be invariant to those locations,
+we will create some useful mount points inside the container:
+mkdir /dataset
+mkdir /tmp_log
+mkdir /final_log
+From now, you won’t need to worry anymore when you write your code to specify
+where to pick up your dataset. Your dataset will always be in /dataset
+independently of the cluster you are using.
+
+
+Testing
+If you have some code that you want to test before finalizing your container,
+you have two choices.  You can either log into your container and run Python
+code inside it with:
+singularity shell --nv pytorch
+Or you can execute your command directly with
+singularity exec --nv pytorch Python YOUR_CODE.py
+
+Tip
+—nv allows the container to use gpus. You don’t need this if you
+don’t plan to use a gpu.
+
+
+Warning
+Don’t forget to clear the cache of the packages you installed in
+the containers.
+
+
+
+Creating a new image from the sandbox
+Once everything you need is installed inside the container, you need to convert
+it back to a read-only singularity image with:
+singularity build pytorch_final.simg pytorch
+
+
+
+Second way: Use recipes
+A singularity recipe is a file including specifics about installation software,
+environment variables, files to add, and container metadata.  It is a starting
+point for designing any custom container. Instead of pulling a container and
+installing your packages manually, you can specify in this file the packages
+you want and then build your container from this file.
+Here is a toy example of a singularity recipe installing some stuff:
+################# Header: Define the base system you want to use ################
+# Reference of the kind of base you want to use (e.g., docker, debootstrap, shub).
+Bootstrap: docker
+# Select the docker image you want to use (Here we choose tensorflow)
+From: tensorflow/tensorflow:latest-gpu-py3
+
+################# Section: Defining the system #################################
+# Commands in the %post section are executed within the container.
+%post
+        echo ""Installing Tools with apt-get""
+        apt-get update
+        apt-get install -y cmake libcupti-dev libyaml-dev wget unzip
+        apt-get clean
+        echo ""Installing things with pip""
+        pip install tqdm
+        echo ""Creating mount points""
+        mkdir /dataset
+        mkdir /tmp_log
+        mkdir /final_log
+
+
+# Environment variables that should be sourced at runtime.
+%environment
+        # use bash as default shell
+        SHELL=/bin/bash
+        export SHELL
+
+
+A recipe file contains two parts: the header and sections. In the
+header you specify which base system you want to use, it can be any docker
+or singularity container. In sections, you can list the things you want to
+install in the subsection post or list the environment’s variable you need
+to source at each runtime in the subsection environment. For a more detailed
+description, please look at the singularity documentation.
+In order to build a singularity container from a singularity recipe file, you
+should use:
+sudo singularity build <NAME_CONTAINER> <YOUR_RECIPE_FILES>
+
+Warning
+You always need to use sudo when you build a container from a
+recipe. As there is no access to sudo on the cluster, a personal computer or
+the use singularity hub is needed to build a container
+
+
+Build recipe on singularity hub
+Singularity hub allows users to build containers from recipes directly on
+singularity-hub’s cloud meaning that you don’t need to build containers by
+yourself.  You need to register on singularity-hub and link your
+singularity-hub account to your GitHub account, then:
+
+
+Create a new github repository.
+Add a collection on singularity-hub and select the github repository your created.
+Clone the github repository on your computer.
+$ git clone <url>
+
+
+
+Write the singularity recipe and save it as a file named Singularity.
+Git add Singularity, commit and push on the master branch
+$ git add Singularity
+$ git commit
+$ git push origin master
+
+
+
+
+
+At this point, robots from singularity-hub will build the container for you, you
+will be able to download your container from the website or directly with:
+singularity pull shub://<github_username>/<repository_name>
+
+
+Example: Recipe with OpenAI gym, MuJoCo and Miniworld
+Here is an example on how you can use a singularity recipe to install complex
+environment such as OpenAI gym, MuJoCo and Miniworld on a PyTorch based
+container. In order to use MuJoCo, you’ll need to copy the key stored on the
+Mila cluster in /ai/apps/mujoco/license/mjkey.txt to your current directory.
+#This is a dockerfile that sets up a full Gym install with test dependencies
+Bootstrap: docker
+
+# Here we ll build our container upon the pytorch container
+From: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime
+
+# Now we'll copy the mjkey file located in the current directory inside the container's root
+# directory
+%files
+        mjkey.txt
+
+# Then we put everything we need to install
+%post
+        export PATH=$PATH:/opt/conda/bin
+        apt -y update && \
+        apt install -y keyboard-configuration && \
+        apt install -y \
+        python3-dev \
+        python-pyglet \
+        python3-opengl \
+        libhdf5-dev \
+        libjpeg-dev \
+        libboost-all-dev \
+        libsdl2-dev \
+        libosmesa6-dev \
+        patchelf \
+        ffmpeg \
+        xvfb \
+        libhdf5-dev \
+        openjdk-8-jdk \
+        wget \
+        git \
+        unzip && \
+        apt clean && \
+        rm -rf /var/lib/apt/lists/*
+        pip install h5py
+
+        # Download Gym and MuJoCo
+        mkdir /Gym && cd /Gym
+        git clone https://github.com/openai/gym.git || true && \
+        mkdir /Gym/.mujoco && cd /Gym/.mujoco
+        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
+        unzip mjpro150_linux.zip && \
+        wget https://www.roboti.us/download/mujoco200_linux.zip && \
+        unzip mujoco200_linux.zip && \
+        mv mujoco200_linux mujoco200
+
+        # Export global environment variables
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
+        # Install Python dependencies
+        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
+        pip install -r requirements.txt
+        # Install Gym and MuJoCo
+        cd /Gym/gym
+        pip install -e '.[all]'
+        # Change permission to use mujoco_py as non sudoer user
+        chmod -R 777 /opt/conda/lib/python3.6/site-packages/mujoco_py/
+        pip install --upgrade minerl
+
+# Export global environment variables
+%environment
+        export SHELL=/bin/sh
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        export PATH=/Gym/gym/.tox/py3/bin:$PATH
+
+%runscript
+        exec /bin/sh ""$@""
+
+
+Here is the same recipe but written for TensorFlow:
+#This is a dockerfile that sets up a full Gym install with test dependencies
+Bootstrap: docker
+
+# Here we ll build our container upon the tensorflow container
+From: tensorflow/tensorflow:latest-gpu-py3
+
+# Now we'll copy the mjkey file located in the current directory inside the container's root
+# directory
+%files
+        mjkey.txt
+
+# Then we put everything we need to install
+%post
+        apt -y update && \
+        apt install -y keyboard-configuration && \
+        apt install -y \
+        python3-setuptools \
+        python3-dev \
+        python-pyglet \
+        python3-opengl \
+        libjpeg-dev \
+        libboost-all-dev \
+        libsdl2-dev \
+        libosmesa6-dev \
+        patchelf \
+        ffmpeg \
+        xvfb \
+        wget \
+        git \
+        unzip && \
+        apt clean && \
+        rm -rf /var/lib/apt/lists/*
+
+        # Download Gym and MuJoCo
+        mkdir /Gym && cd /Gym
+        git clone https://github.com/openai/gym.git || true && \
+        mkdir /Gym/.mujoco && cd /Gym/.mujoco
+        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
+        unzip mjpro150_linux.zip && \
+        wget https://www.roboti.us/download/mujoco200_linux.zip && \
+        unzip mujoco200_linux.zip && \
+        mv mujoco200_linux mujoco200
+
+        # Export global environment variables
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
+
+        # Install Python dependencies
+        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
+        pip install -r requirements.txt
+        # Install Gym and MuJoCo
+        cd /Gym/gym
+        pip install -e '.[all]'
+        # Change permission to use mujoco_py as non sudoer user
+        chmod -R 777 /usr/local/lib/python3.5/dist-packages/mujoco_py/
+
+        # Then install miniworld
+        cd /usr/local/
+        git clone https://github.com/maximecb/gym-miniworld.git
+        cd gym-miniworld
+        pip install -e .
+
+# Export global environment variables
+%environment
+        export SHELL=/bin/bash
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        export PATH=/Gym/gym/.tox/py3/bin:$PATH
+
+%runscript
+        exec /bin/bash ""$@""
+
+
+Keep in mind that those environment variables are sourced at runtime and not at
+build time. This is why, you should also define them in the %post section
+since they are required to install MuJoCo.
+
+
+
+
+Using containers on clusters
+
+How to use containers on clusters
+On every cluster with Slurm, datasets and intermediate results should go in
+$SLURM_TMPDIR while the final experiment results should go in $SCRATCH.
+In order to use the container you built, you need to copy it on the cluster you
+want to use.
+
+Warning
+You should always store your container in $SCRATCH !
+
+Then reserve a node with srun/sbatch, copy the container and your dataset on the
+node given by SLURM (i.e in $SLURM_TMPDIR) and execute the code
+<YOUR_CODE> within the container <YOUR_CONTAINER> with:
+singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ $SLURM_TMPDIR/<YOUR_CONTAINER> python <YOUR_CODE>
+Remember that /dataset, /tmp_log and /final_log were created in the
+previous section. Now each time, we’ll use singularity, we are explicitly
+telling it to mount $SLURM_TMPDIR on the cluster’s node in the folder
+/dataset inside the container with the option -B such that each dataset
+downloaded by PyTorch in /dataset will be available in $SLURM_TMPDIR.
+This will allow us to have code and scripts that are invariant to the cluster
+environment. The option -H specify what will be the container’s home. For
+example, if you have your code in $HOME/Project12345/Version35/ you can
+specify -H $HOME/Project12345/Version35:/home, thus the container will only
+have access to the code inside Version35.
+If you want to run multiple commands inside the container you can use:
+singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ \
+   -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ \
+   $SLURM_TMPDIR/<YOUR_CONTAINER> bash -c 'pwd && ls && python <YOUR_CODE>'
+
+Example: Interactive case (srun/salloc)
+Once you get an interactive session with SLURM, copy <YOUR_CONTAINER> and
+<YOUR_DATASET> to $SLURM_TMPDIR
+0. Get an interactive session
+srun --gres=gpu:1
+1. Copy your container on the compute node
+rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
+2. Copy your dataset on the compute node
+rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
+Then use singularity shell to get a shell inside the container
+3. Get a shell in your environment
+singularity shell --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER>
+4. Execute your code
+python <YOUR_CODE>
+or use singularity exec to execute <YOUR_CODE>.
+3. Execute your code
+singularity exec --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER> \
+        python <YOUR_CODE>
+You can create also the following alias to make your life easier.
+alias my_env='singularity exec --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER>'
+This will allow you to run any code with:
+my_env python <YOUR_CODE>
+
+
+Example: sbatch case
+You can also create a sbatch script:
+:linenos:
+
+#!/bin/bash
+#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
+#SBATCH --gres=gpu:1              # Ask for 1 GPU
+#SBATCH --mem=10G                 # Ask for 10 GB of RAM
+#SBATCH --time=0:10:00            # The job will run for 10 minutes
+
+# 1. Copy your container on the compute node
+rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
+# 2. Copy your dataset on the compute node
+rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
+# 3. Executing your code with singularity
+singularity exec --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER> \
+        python ""<YOUR_CODE>""
+# 4. Copy whatever you want to save on $SCRATCH
+rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH
+
+
+
+
+Issue with PyBullet and OpenGL libraries
+If you are running certain gym environments that require pyglet, you may
+encounter a problem when running your singularity instance with the Nvidia
+drivers using the --nv flag. This happens because the --nv flag also
+provides the OpenGL libraries:
+libGL.so.1 => /.singularity.d/libs/libGL.so.1
+libGLX.so.0 => /.singularity.d/libs/libGLX.so.0
+
+
+If you don’t experience those problems with pyglet, you probably don’t need
+to address this. Otherwise, you can resolve those problems by apt-get install
+-y libosmesa6-dev mesa-utils mesa-utils-extra libgl1-mesa-glx, and then making
+sure that your LD_LIBRARY_PATH points to those libraries before the ones in
+/.singularity.d/libs.
+%environment
+        # ...
+        export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/mesa:$LD_LIBRARY_PATH
+
+
+
+
+Mila cluster
+On the Mila cluster $SCRATCH is not yet defined, you should add the
+experiment results you want to keep in /network/scratch/<u>/<username>/. In
+order to use the sbatch script above and to match other cluster environment’s
+names, you can define $SCRATCH as an alias for
+/network/scratch/<u>/<username> with:
+echo ""export SCRATCH=/network/scratch/${USER:0:1}/$USER"" >> ~/.bashrc
+Then, you can follow the general procedure explained above.
+
+
+Digital Research Alliance of Canada
+Using singularity on Digital Research Alliance of Canada is similar except that
+you need to add Yoshua’s account name and load singularity. Here is an example
+of a sbatch script using singularity on compute Canada cluster:
+
+Warning
+You should use singularity/2.6 or singularity/3.4. There is a bug
+in singularity/3.2 which makes gpu unusable.
+
+ 1#!/bin/bash
+ 2#SBATCH --account=rpp-bengioy     # Yoshua pays for your job
+ 3#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
+ 4#SBATCH --gres=gpu:1              # Ask for 1 GPU
+ 5#SBATCH --mem=32G                 # Ask for 32 GB of RAM
+ 6#SBATCH --time=0:10:00            # The job will run for 10 minutes
+ 7#SBATCH --output=""/scratch/<user>/slurm-%j.out"" # Modify the output of sbatch
+ 8
+ 9# 1. You have to load singularity
+10module load singularity
+11# 2. Then you copy the container to the local disk
+12rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
+13# 3. Copy your dataset on the compute node
+14rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
+15# 4. Executing your code with singularity
+16singularity exec --nv \
+17        -H $HOME:/home \
+18        -B $SLURM_TMPDIR:/dataset/ \
+19        -B $SLURM_TMPDIR:/tmp_log/ \
+20        -B $SCRATCH:/final_log/ \
+21        $SLURM_TMPDIR/<YOUR_CONTAINER> \
+22        python ""<YOUR_CODE>""
+23# 5. Copy whatever you want to save on $SCRATCH
+24rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH",6074,"[0.0033816518262028694, -0.022014964371919632, 0.006865985691547394, -0.012219126336276531, 0.004432428162544966, -0.004223641939461231, -0.011924772523343563, -0.005726218223571777, -0.012013763189315796, -0.023890618234872818, 0.04994439706206322, 0.005442132242023945, -0.01126760896295309, 0.018537476658821106, 0.007769584655761719, 0.0006225080578587949, 0.026231760159134865, -0.019153567031025887, 0.005298377480357885, -0.019879184663295746, -0.005079323425889015, -0.008310374803841114, -0.0022778231650590897, -0.009898519143462181, -0.03269386664032936, 0.02825801447033882, 0.02212449163198471, -0.015114751644432545, 0.00038612610660493374, 0.004117537755519152, 0.0016009793616831303, -0.006814644671976566, -0.017866622656583786, 0.000549775140825659, -0.02212449163198471, -0.003792378818616271, 0.0032995063811540604, -0.0260537788271904, -0.0007029420812614262, -0.02562936209142208, 0.021494710817933083, 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+91,"Overview
+
+What is Singularity?
+Running Docker on SLURM is a security problem (e.g. running as root, being able
+to mount any directory).  The alternative is to use Singularity, which is a
+popular solution in the world of HPC.
+There is a good level of compatibility between Docker and Singularity,
+and we can find many exaggerated claims about able to convert containers
+from Docker to Singularity without any friction.
+Oftentimes, Docker images from DockerHub are 100% compatible with Singularity,
+and they can indeed be used without friction, but things get messy when
+we try to convert our own Docker build files to Singularity recipes.
+
+
+Links to official documentation
+
+official Singularity user guide (this is the one you
+will use most often)
+official Singularity admin guide
+
+
+
+Overview of the steps used in practice
+Most often, the process to create and use a Singularity container is:
+
+on your Linux computer (at home or work)
+
+select a Docker image from DockerHub (e.g. pytorch/pytorch)
+make a recipe file for Singularity that starts with that DockerHub image
+build the recipe file, thus creating the image file (e.g. my-pytorch-image.sif)
+test your singularity container before send it over to the cluster
+rsync -av my-pytorch-image.sif <login-node>:Documents/my-singularity-images
+
+
+on the login node for that cluster
+
+queue your jobs with sbatch ...
+(note that your jobs will copy over the my-pytorch-image.sif to $SLURM_TMPDIR
+and will then launch Singularity with that image)
+do something else while you wait for them to finish
+queue more jobs with the same my-pytorch-image.sif,
+reusing it many times over
+
+
+
+In the following sections you will find specific examples or tips to accomplish
+in practice the steps highlighted above.
+
+
+Nope, not on MacOS
+Singularity does not work on MacOS, as of the time of this writing in 2021.
+Docker does not actually run on MacOS, but there Docker silently installs a
+virtual machine running Linux, which makes it a pleasant experience,
+and the user does not need to care about the details of how Docker does it.
+Given its origins in HPC, Singularity does not provide that kind of seamless
+experience on MacOS, even though it’s technically possible to run it
+inside a Linux virtual machine on MacOS.
+
+
+Where to build images
+Building Singularity images is a rather heavy task, which can take 20 minutes
+if you have a lot of steps in your recipe. This makes it a bad task to run on
+the login nodes of our clusters, especially if it needs to be run regularly.
+On the Mila cluster, we are lucky to have unrestricted internet access on the compute
+nodes, which means that anyone can request an interactive CPU node (no need for GPU)
+and build their images there without problem.
+
+Warning
+Do not build Singularity images from scratch every time your run a
+job in a large batch.  This will be a colossal waste of GPU time as well as
+internet bandwidth.  If you setup your workflow properly (e.g. using bind
+paths for your code and data), you can spend months reusing the same
+Singularity image my-pytorch-image.sif.",694,"[0.005968751851469278, -0.02496994473040104, 0.003029441926628351, -0.010595535859465599, 0.006332618650048971, -0.0029743611812591553, -0.023834947496652603, -0.002638869220390916, -0.010815858840942383, -0.022018952295184135, 0.03995191305875778, 0.014808379113674164, -0.010308448225259781, 0.017251962795853615, 0.007110425736755133, 0.00291093485429883, 0.025196945294737816, -0.02049672044813633, -0.0011875745840370655, -0.019108017906546593, -0.01114968117326498, -0.011036181822419167, -0.0002812457096297294, -0.0055815172381699085, -0.04473225399851799, 0.028628643602132797, 0.02599811926484108, -0.02123112976551056, -0.0021181057672947645, 0.00035301761818118393, -0.0023083847481757402, -0.00968753732740879, -0.02233942225575447, 0.00021615026344079524, -0.013780205510556698, 0.009867802262306213, 0.008552540093660355, -0.02431565336883068, -0.02229936234652996, -0.031138990074396133, 0.02342100813984871, 0.0018944443436339498, 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+92,"What is Singularity?
+Running Docker on SLURM is a security problem (e.g. running as root, being able
+to mount any directory).  The alternative is to use Singularity, which is a
+popular solution in the world of HPC.
+There is a good level of compatibility between Docker and Singularity,
+and we can find many exaggerated claims about able to convert containers
+from Docker to Singularity without any friction.
+Oftentimes, Docker images from DockerHub are 100% compatible with Singularity,
+and they can indeed be used without friction, but things get messy when
+we try to convert our own Docker build files to Singularity recipes.",138,"[0.01754847541451454, -0.02661094255745411, 0.0029577023815363646, -0.0005467141163535416, -0.00929707195609808, 2.668111483217217e-05, -0.034533899277448654, 0.0006510296370834112, -0.018178557977080345, -0.02277682162821293, 0.04721599072217941, 0.01848689652979374, -0.0031705228611826897, 0.017173107713460922, 0.014237189665436745, 0.011830809526145458, 0.029814980924129486, -0.02043076790869236, -0.0075073689222335815, -0.00977298617362976, -0.010858872905373573, -0.005576903000473976, 0.0037637390196323395, -0.0022455076687037945, -0.03195994347333908, 0.021382596343755722, 0.029922228306531906, -0.02301812916994095, 0.005322188604623079, -0.005834968760609627, 0.009846718981862068, -0.0011889460729435086, -0.01805790327489376, 0.00174780935049057, -0.005466303322464228, 0.0015207449905574322, -0.005707611329853535, -0.023138783872127533, -0.023876115679740906, -0.01451871544122696, 0.010275711305439472, 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+93,"Links to official documentation
+
+official Singularity user guide (this is the one you
+will use most often)
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+94,"Overview of the steps used in practice
+Most often, the process to create and use a Singularity container is:
+
+on your Linux computer (at home or work)
+
+select a Docker image from DockerHub (e.g. pytorch/pytorch)
+make a recipe file for Singularity that starts with that DockerHub image
+build the recipe file, thus creating the image file (e.g. my-pytorch-image.sif)
+test your singularity container before send it over to the cluster
+rsync -av my-pytorch-image.sif <login-node>:Documents/my-singularity-images
+
+
+on the login node for that cluster
+
+queue your jobs with sbatch ...
+(note that your jobs will copy over the my-pytorch-image.sif to $SLURM_TMPDIR
+and will then launch Singularity with that image)
+do something else while you wait for them to finish
+queue more jobs with the same my-pytorch-image.sif,
+reusing it many times over
+
+
+
+In the following sections you will find specific examples or tips to accomplish
+in practice the steps highlighted above.",224,"[0.014468112960457802, -0.010657438077032566, 0.007261720020323992, -0.014813908375799656, 0.008333687670528889, -0.009972762316465378, -0.02637733332812786, -0.00021331301832105964, -0.015768306329846382, -0.032698486000299454, 0.02381844073534012, 0.00822994951158762, -0.022255443036556244, 0.011217628605663776, 0.014274466782808304, -0.006383397616446018, 0.011736322194337845, -0.024510033428668976, 0.000864058209117502, -0.017815420404076576, -0.03908880054950714, 0.0037311408668756485, 0.00451609818264842, -0.009716873057186604, -0.031840912997722626, 0.011833145283162594, 0.03214521333575249, -0.021065903827548027, 0.002845902694389224, 0.013610538095235825, -0.0029306227806955576, -0.009703041054308414, -0.010076500475406647, -0.013292405754327774, -0.033307090401649475, 0.011791650205850601, 0.008956121280789375, -0.01758027821779251, -0.010636691004037857, -0.04404060170054436, 0.02751154452562332, -0.00799480825662613, 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+95,"Nope, not on MacOS
+Singularity does not work on MacOS, as of the time of this writing in 2021.
+Docker does not actually run on MacOS, but there Docker silently installs a
+virtual machine running Linux, which makes it a pleasant experience,
+and the user does not need to care about the details of how Docker does it.
+Given its origins in HPC, Singularity does not provide that kind of seamless
+experience on MacOS, even though it’s technically possible to run it
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+96,"Where to build images
+Building Singularity images is a rather heavy task, which can take 20 minutes
+if you have a lot of steps in your recipe. This makes it a bad task to run on
+the login nodes of our clusters, especially if it needs to be run regularly.
+On the Mila cluster, we are lucky to have unrestricted internet access on the compute
+nodes, which means that anyone can request an interactive CPU node (no need for GPU)
+and build their images there without problem.
+
+Warning
+Do not build Singularity images from scratch every time your run a
+job in a large batch.  This will be a colossal waste of GPU time as well as
+internet bandwidth.  If you setup your workflow properly (e.g. using bind
+paths for your code and data), you can spend months reusing the same
+Singularity image my-pytorch-image.sif.",185,"[0.00529184564948082, -0.013783249072730541, 0.004802830051630735, -0.02116738259792328, 0.012993838638067245, -0.012022794224321842, -0.038562361150979996, 0.003339276881888509, -0.004561815410852432, -0.03336482495069504, 0.032582398504018784, 0.021237241104245186, -0.025107448920607567, -0.007908077910542488, 0.005438550375401974, -0.011282284744083881, 0.020175380632281303, -0.01033219788223505, -0.007621654774993658, -0.024366941303014755, -0.0241853054612875, -0.004460519179701805, 0.005581761710345745, 0.00016809905355330557, -0.03434285521507263, 0.019253237172961235, 0.01721334271132946, -0.03372809290885925, -0.010290281847119331, -0.001966540701687336, 0.007467964198440313, -0.027384864166378975, -0.009067743085324764, -0.011051748879253864, -0.035432662814855576, 0.02316536009311676, 0.02192186377942562, -0.014684434980154037, -0.0187921654433012, -0.02598767727613449, 0.030011575669050217, -0.010388084687292576, 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+97,"Building the containers
+Building a container is like creating a new environment except that containers
+are much more powerful since they are self-contained systems.  With
+singularity, there are two ways to build containers.
+The first one is by yourself, it’s like when you got a new Linux laptop and you
+don’t really know what you need, if you see that something is missing, you
+install it. Here you can get a vanilla container with Ubuntu called a sandbox,
+you log in and you install each packages by yourself.  This procedure can take
+time but will allow you to understand how things work and what you need. This is
+recommended if you need to figure out how things will be compiled or if you want
+to install packages on the fly. We’ll refer to this procedure as singularity
+sandboxes.
+The second way is more like you know what you want, so you write a list of
+everything you need, you send it to singularity and it will install everything
+for you. Those lists are called singularity recipes.
+
+First way: Build and use a sandbox
+You might ask yourself: On which machine should I build a container?
+First of all, you need to choose where you’ll build your container. This
+operation requires memory and high cpu usage.
+
+Warning
+Do NOT build containers on any login nodes !
+
+
+(Recommended for beginner) If you need to use apt-get, you should build
+the container on your laptop with sudo privileges. You’ll only need to
+install singularity on your laptop. Windows/Mac users can look there and
+Ubuntu/Debian users can use directly:
+
+sudo apt-get install singularity-container
+
+
+If you can’t install singularity on your laptop and you don’t need
+apt-get, you can reserve a cpu node on the Mila cluster to build your
+container.
+
+In this case, in order to avoid too much I/O over the network, you should define
+the singularity cache locally:
+
+export SINGULARITY_CACHEDIR=$SLURM_TMPDIR
+
+
+If you can’t install singularity on your laptop and you want to use
+apt-get, you can use singularity-hub to build your containers and read
+Recipe_section.
+
+
+Download containers from the web
+Hopefully, you may not need to create containers from scratch as many have been
+already built for the most common deep learning software. You can find most of
+them on dockerhub.
+Go on dockerhub and select the container you want to pull.
+For example, if you want to get the latest PyTorch version with GPU support
+(Replace runtime by devel if you need the full Cuda toolkit):
+singularity pull docker://pytorch/pytorch:1.0.1-cuda10.0-cudnn7-runtime
+Or the latest TensorFlow:
+singularity pull docker://tensorflow/tensorflow:latest-gpu-py3
+Currently the pulled image pytorch.simg or tensorflow.simg is read-only
+meaning that you won’t be able to install anything on it.  Starting now, PyTorch
+will be taken as example. If you use TensorFlow, simply replace every
+pytorch occurrences by tensorflow.
+
+
+How to add or install stuff in a container
+The first step is to transform your read only container
+pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg in a writable version that will
+allow you to add packages.
+
+Warning
+Depending on the version of singularity you are using, singularity
+will build a container with the extension .simg or .sif. If you’re using
+.sif files, replace every occurences of .simg by .sif.
+
+
+Tip
+If you want to use apt-get you have to put sudo ahead of the
+following commands
+
+This command will create a writable image in the folder pytorch.
+singularity build --sandbox pytorch pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg
+Then you’ll need the following command to log inside the container.
+singularity shell --writable -H $HOME:/home pytorch
+Once you get into the container, you can use pip and install anything you need
+(Or with apt-get if you built the container with sudo).
+
+Warning
+Singularity mounts your home folder, so if you install things into
+the $HOME of your container, they will be installed in your real
+$HOME!
+
+You should install your stuff in /usr/local instead.
+
+
+Creating useful directories
+One of the benefits of containers is that you’ll be able to use them across
+different clusters. However for each cluster the datasets and experiments
+folder location can be different. In order to be invariant to those locations,
+we will create some useful mount points inside the container:
+mkdir /dataset
+mkdir /tmp_log
+mkdir /final_log
+From now, you won’t need to worry anymore when you write your code to specify
+where to pick up your dataset. Your dataset will always be in /dataset
+independently of the cluster you are using.
+
+
+Testing
+If you have some code that you want to test before finalizing your container,
+you have two choices.  You can either log into your container and run Python
+code inside it with:
+singularity shell --nv pytorch
+Or you can execute your command directly with
+singularity exec --nv pytorch Python YOUR_CODE.py
+
+Tip
+—nv allows the container to use gpus. You don’t need this if you
+don’t plan to use a gpu.
+
+
+Warning
+Don’t forget to clear the cache of the packages you installed in
+the containers.
+
+
+
+Creating a new image from the sandbox
+Once everything you need is installed inside the container, you need to convert
+it back to a read-only singularity image with:
+singularity build pytorch_final.simg pytorch
+
+
+
+Second way: Use recipes
+A singularity recipe is a file including specifics about installation software,
+environment variables, files to add, and container metadata.  It is a starting
+point for designing any custom container. Instead of pulling a container and
+installing your packages manually, you can specify in this file the packages
+you want and then build your container from this file.
+Here is a toy example of a singularity recipe installing some stuff:
+################# Header: Define the base system you want to use ################
+# Reference of the kind of base you want to use (e.g., docker, debootstrap, shub).
+Bootstrap: docker
+# Select the docker image you want to use (Here we choose tensorflow)
+From: tensorflow/tensorflow:latest-gpu-py3
+
+################# Section: Defining the system #################################
+# Commands in the %post section are executed within the container.
+%post
+        echo ""Installing Tools with apt-get""
+        apt-get update
+        apt-get install -y cmake libcupti-dev libyaml-dev wget unzip
+        apt-get clean
+        echo ""Installing things with pip""
+        pip install tqdm
+        echo ""Creating mount points""
+        mkdir /dataset
+        mkdir /tmp_log
+        mkdir /final_log
+
+
+# Environment variables that should be sourced at runtime.
+%environment
+        # use bash as default shell
+        SHELL=/bin/bash
+        export SHELL
+
+
+A recipe file contains two parts: the header and sections. In the
+header you specify which base system you want to use, it can be any docker
+or singularity container. In sections, you can list the things you want to
+install in the subsection post or list the environment’s variable you need
+to source at each runtime in the subsection environment. For a more detailed
+description, please look at the singularity documentation.
+In order to build a singularity container from a singularity recipe file, you
+should use:
+sudo singularity build <NAME_CONTAINER> <YOUR_RECIPE_FILES>
+
+Warning
+You always need to use sudo when you build a container from a
+recipe. As there is no access to sudo on the cluster, a personal computer or
+the use singularity hub is needed to build a container
+
+
+Build recipe on singularity hub
+Singularity hub allows users to build containers from recipes directly on
+singularity-hub’s cloud meaning that you don’t need to build containers by
+yourself.  You need to register on singularity-hub and link your
+singularity-hub account to your GitHub account, then:
+
+
+Create a new github repository.
+Add a collection on singularity-hub and select the github repository your created.
+Clone the github repository on your computer.
+$ git clone <url>
+
+
+
+Write the singularity recipe and save it as a file named Singularity.
+Git add Singularity, commit and push on the master branch
+$ git add Singularity
+$ git commit
+$ git push origin master
+
+
+
+
+
+At this point, robots from singularity-hub will build the container for you, you
+will be able to download your container from the website or directly with:
+singularity pull shub://<github_username>/<repository_name>
+
+
+Example: Recipe with OpenAI gym, MuJoCo and Miniworld
+Here is an example on how you can use a singularity recipe to install complex
+environment such as OpenAI gym, MuJoCo and Miniworld on a PyTorch based
+container. In order to use MuJoCo, you’ll need to copy the key stored on the
+Mila cluster in /ai/apps/mujoco/license/mjkey.txt to your current directory.
+#This is a dockerfile that sets up a full Gym install with test dependencies
+Bootstrap: docker
+
+# Here we ll build our container upon the pytorch container
+From: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime
+
+# Now we'll copy the mjkey file located in the current directory inside the container's root
+# directory
+%files
+        mjkey.txt
+
+# Then we put everything we need to install
+%post
+        export PATH=$PATH:/opt/conda/bin
+        apt -y update && \
+        apt install -y keyboard-configuration && \
+        apt install -y \
+        python3-dev \
+        python-pyglet \
+        python3-opengl \
+        libhdf5-dev \
+        libjpeg-dev \
+        libboost-all-dev \
+        libsdl2-dev \
+        libosmesa6-dev \
+        patchelf \
+        ffmpeg \
+        xvfb \
+        libhdf5-dev \
+        openjdk-8-jdk \
+        wget \
+        git \
+        unzip && \
+        apt clean && \
+        rm -rf /var/lib/apt/lists/*
+        pip install h5py
+
+        # Download Gym and MuJoCo
+        mkdir /Gym && cd /Gym
+        git clone https://github.com/openai/gym.git || true && \
+        mkdir /Gym/.mujoco && cd /Gym/.mujoco
+        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
+        unzip mjpro150_linux.zip && \
+        wget https://www.roboti.us/download/mujoco200_linux.zip && \
+        unzip mujoco200_linux.zip && \
+        mv mujoco200_linux mujoco200
+
+        # Export global environment variables
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
+        # Install Python dependencies
+        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
+        pip install -r requirements.txt
+        # Install Gym and MuJoCo
+        cd /Gym/gym
+        pip install -e '.[all]'
+        # Change permission to use mujoco_py as non sudoer user
+        chmod -R 777 /opt/conda/lib/python3.6/site-packages/mujoco_py/
+        pip install --upgrade minerl
+
+# Export global environment variables
+%environment
+        export SHELL=/bin/sh
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        export PATH=/Gym/gym/.tox/py3/bin:$PATH
+
+%runscript
+        exec /bin/sh ""$@""
+
+
+Here is the same recipe but written for TensorFlow:
+#This is a dockerfile that sets up a full Gym install with test dependencies
+Bootstrap: docker
+
+# Here we ll build our container upon the tensorflow container
+From: tensorflow/tensorflow:latest-gpu-py3
+
+# Now we'll copy the mjkey file located in the current directory inside the container's root
+# directory
+%files
+        mjkey.txt
+
+# Then we put everything we need to install
+%post
+        apt -y update && \
+        apt install -y keyboard-configuration && \
+        apt install -y \
+        python3-setuptools \
+        python3-dev \
+        python-pyglet \
+        python3-opengl \
+        libjpeg-dev \
+        libboost-all-dev \
+        libsdl2-dev \
+        libosmesa6-dev \
+        patchelf \
+        ffmpeg \
+        xvfb \
+        wget \
+        git \
+        unzip && \
+        apt clean && \
+        rm -rf /var/lib/apt/lists/*
+
+        # Download Gym and MuJoCo
+        mkdir /Gym && cd /Gym
+        git clone https://github.com/openai/gym.git || true && \
+        mkdir /Gym/.mujoco && cd /Gym/.mujoco
+        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
+        unzip mjpro150_linux.zip && \
+        wget https://www.roboti.us/download/mujoco200_linux.zip && \
+        unzip mujoco200_linux.zip && \
+        mv mujoco200_linux mujoco200
+
+        # Export global environment variables
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
+
+        # Install Python dependencies
+        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
+        pip install -r requirements.txt
+        # Install Gym and MuJoCo
+        cd /Gym/gym
+        pip install -e '.[all]'
+        # Change permission to use mujoco_py as non sudoer user
+        chmod -R 777 /usr/local/lib/python3.5/dist-packages/mujoco_py/
+
+        # Then install miniworld
+        cd /usr/local/
+        git clone https://github.com/maximecb/gym-miniworld.git
+        cd gym-miniworld
+        pip install -e .
+
+# Export global environment variables
+%environment
+        export SHELL=/bin/bash
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        export PATH=/Gym/gym/.tox/py3/bin:$PATH
+
+%runscript
+        exec /bin/bash ""$@""
+
+
+Keep in mind that those environment variables are sourced at runtime and not at
+build time. This is why, you should also define them in the %post section
+since they are required to install MuJoCo.",3496,"[0.008545185439288616, -0.010117878206074238, 0.006331267300993204, -0.017900342121720314, -0.009206661954522133, -0.01129908487200737, -0.01490345224738121, -0.016644887626171112, 0.0003712363541126251, -0.02734999544918537, 0.04924618825316429, 0.012797529809176922, -0.015267938375473022, 0.009969383478164673, 0.008106452412903309, 0.0020266131032258272, 0.02174770087003708, -0.0012790779583156109, -0.00886242464184761, -0.021653203293681145, -0.015659423545002937, -0.004009352531284094, 0.00959139782935381, -0.022719664499163628, -0.027039505541324615, 0.025892049074172974, 0.017333362251520157, -0.011643322184681892, -0.007721716072410345, 0.008059203624725342, 0.007168236654251814, -0.018750810995697975, -0.0330737829208374, -0.014781956560909748, -0.027525488287210464, 0.013836991041898727, 0.015429932624101639, -0.014633461833000183, 0.006314392667263746, -0.02130221761763096, 0.020208757370710373, 0.008356193080544472, 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+98,"First way: Build and use a sandbox
+You might ask yourself: On which machine should I build a container?
+First of all, you need to choose where you’ll build your container. This
+operation requires memory and high cpu usage.
+
+Warning
+Do NOT build containers on any login nodes !
+
+
+(Recommended for beginner) If you need to use apt-get, you should build
+the container on your laptop with sudo privileges. You’ll only need to
+install singularity on your laptop. Windows/Mac users can look there and
+Ubuntu/Debian users can use directly:
+
+sudo apt-get install singularity-container
+
+
+If you can’t install singularity on your laptop and you don’t need
+apt-get, you can reserve a cpu node on the Mila cluster to build your
+container.
+
+In this case, in order to avoid too much I/O over the network, you should define
+the singularity cache locally:
+
+export SINGULARITY_CACHEDIR=$SLURM_TMPDIR
+
+
+If you can’t install singularity on your laptop and you want to use
+apt-get, you can use singularity-hub to build your containers and read
+Recipe_section.
+
+
+Download containers from the web
+Hopefully, you may not need to create containers from scratch as many have been
+already built for the most common deep learning software. You can find most of
+them on dockerhub.
+Go on dockerhub and select the container you want to pull.
+For example, if you want to get the latest PyTorch version with GPU support
+(Replace runtime by devel if you need the full Cuda toolkit):
+singularity pull docker://pytorch/pytorch:1.0.1-cuda10.0-cudnn7-runtime
+Or the latest TensorFlow:
+singularity pull docker://tensorflow/tensorflow:latest-gpu-py3
+Currently the pulled image pytorch.simg or tensorflow.simg is read-only
+meaning that you won’t be able to install anything on it.  Starting now, PyTorch
+will be taken as example. If you use TensorFlow, simply replace every
+pytorch occurrences by tensorflow.
+
+
+How to add or install stuff in a container
+The first step is to transform your read only container
+pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg in a writable version that will
+allow you to add packages.
+
+Warning
+Depending on the version of singularity you are using, singularity
+will build a container with the extension .simg or .sif. If you’re using
+.sif files, replace every occurences of .simg by .sif.
+
+
+Tip
+If you want to use apt-get you have to put sudo ahead of the
+following commands
+
+This command will create a writable image in the folder pytorch.
+singularity build --sandbox pytorch pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg
+Then you’ll need the following command to log inside the container.
+singularity shell --writable -H $HOME:/home pytorch
+Once you get into the container, you can use pip and install anything you need
+(Or with apt-get if you built the container with sudo).
+
+Warning
+Singularity mounts your home folder, so if you install things into
+the $HOME of your container, they will be installed in your real
+$HOME!
+
+You should install your stuff in /usr/local instead.
+
+
+Creating useful directories
+One of the benefits of containers is that you’ll be able to use them across
+different clusters. However for each cluster the datasets and experiments
+folder location can be different. In order to be invariant to those locations,
+we will create some useful mount points inside the container:
+mkdir /dataset
+mkdir /tmp_log
+mkdir /final_log
+From now, you won’t need to worry anymore when you write your code to specify
+where to pick up your dataset. Your dataset will always be in /dataset
+independently of the cluster you are using.
+
+
+Testing
+If you have some code that you want to test before finalizing your container,
+you have two choices.  You can either log into your container and run Python
+code inside it with:
+singularity shell --nv pytorch
+Or you can execute your command directly with
+singularity exec --nv pytorch Python YOUR_CODE.py
+
+Tip
+—nv allows the container to use gpus. You don’t need this if you
+don’t plan to use a gpu.
+
+
+Warning
+Don’t forget to clear the cache of the packages you installed in
+the containers.
+
+
+
+Creating a new image from the sandbox
+Once everything you need is installed inside the container, you need to convert
+it back to a read-only singularity image with:
+singularity build pytorch_final.simg pytorch",994,"[0.014942701905965805, -0.010020559653639793, 0.015390169806778431, -0.02768874540925026, -0.008203570730984211, 0.0013627419248223305, -0.023431025445461273, -0.00736287422478199, -0.010095138102769852, -0.013308768160641193, 0.05231301859021187, 0.025966674089431763, -0.02358018048107624, 0.001830548862926662, 0.014522354118525982, -0.00701032392680645, 0.023620858788490295, -0.012034163810312748, 0.004786546342074871, -0.0263056643307209, -0.02138352207839489, -0.004311959259212017, 0.009939202107489109, -0.025993792340159416, -0.033438023179769516, 0.026264986023306847, 0.020298752933740616, -0.017939377576112747, -0.003230579663068056, 0.008800193667411804, 0.0027949768118560314, -0.020814018324017525, -0.02173607237637043, -0.02358018048107624, -0.028339607641100883, 0.0188478734344244, 0.011146008037030697, -0.01113244891166687, 0.0018797024386003613, -0.022495411336421967, 0.03140408173203468, 0.004691628739237785, 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+99,"Download containers from the web
+Hopefully, you may not need to create containers from scratch as many have been
+already built for the most common deep learning software. You can find most of
+them on dockerhub.
+Go on dockerhub and select the container you want to pull.
+For example, if you want to get the latest PyTorch version with GPU support
+(Replace runtime by devel if you need the full Cuda toolkit):
+singularity pull docker://pytorch/pytorch:1.0.1-cuda10.0-cudnn7-runtime
+Or the latest TensorFlow:
+singularity pull docker://tensorflow/tensorflow:latest-gpu-py3
+Currently the pulled image pytorch.simg or tensorflow.simg is read-only
+meaning that you won’t be able to install anything on it.  Starting now, PyTorch
+will be taken as example. If you use TensorFlow, simply replace every
+pytorch occurrences by tensorflow.",196,"[-0.012158196419477463, -0.024535460397601128, 0.020222585648298264, -0.026110000908374786, 0.0012348168529570103, 0.0062057459726929665, -0.03683057427406311, -0.006380314938724041, -0.002052037976682186, -0.020756561309099197, 0.04964596778154373, 0.039486754685640335, -0.015060828998684883, -0.007263427134603262, -0.009632084518671036, -0.0018312599277123809, 0.006886906456202269, -0.019483236595988274, -0.011672142893075943, -0.0484137199819088, -0.010179751552641392, -0.004744160920381546, 0.01039197202771902, -0.010159214027225971, -0.04813988506793976, 0.019154636189341545, 0.01203497126698494, -0.019811835139989853, -0.020455343648791313, -0.009967530146241188, 0.019893985241651535, -0.010166059248149395, -0.02940969169139862, -0.021975118666887283, -0.018702810630202293, 0.003789167618378997, 0.0036009072791785, -0.014691154472529888, 0.0037857447750866413, -0.03485897183418274, 0.003902123775333166, -0.0029830709099769592, 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+100,"How to add or install stuff in a container
+The first step is to transform your read only container
+pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg in a writable version that will
+allow you to add packages.
+
+Warning
+Depending on the version of singularity you are using, singularity
+will build a container with the extension .simg or .sif. If you’re using
+.sif files, replace every occurences of .simg by .sif.
+
+
+Tip
+If you want to use apt-get you have to put sudo ahead of the
+following commands
+
+This command will create a writable image in the folder pytorch.
+singularity build --sandbox pytorch pytorch-1.0.1-cuda10.0-cudnn7-runtime.simg
+Then you’ll need the following command to log inside the container.
+singularity shell --writable -H $HOME:/home pytorch
+Once you get into the container, you can use pip and install anything you need
+(Or with apt-get if you built the container with sudo).
+
+Warning
+Singularity mounts your home folder, so if you install things into
+the $HOME of your container, they will be installed in your real
+$HOME!
+
+You should install your stuff in /usr/local instead.",275,"[0.009190776385366917, -0.020408624783158302, 0.0005110774654895067, -0.0054744756780564785, 0.004884969908744097, -0.0013858559541404247, -0.01933303475379944, -0.01960882730782032, -0.009011510759592056, -0.0037197477649897337, 0.05518602579832077, 0.029013343155384064, -0.015389206819236279, 0.007508443668484688, 0.005829558242112398, -0.006543170660734177, 0.008177239447832108, -0.0010454249568283558, -0.023594025522470474, -0.034501608461141586, -0.01940198242664337, 0.0021115343552082777, 0.0038059328217059374, -0.022187484428286552, -0.02764817140996456, 0.00802555400878191, 0.010680054314434528, -0.014437722973525524, -0.00012529155355878174, -0.00558823999017477, 0.008687455207109451, -0.012327912263572216, -0.030337145552039146, -0.033067487180233, -0.02057410031557083, 0.014086088165640831, 0.008356504142284393, -0.0154857337474823, 0.0018667686963453889, -0.02078094519674778, 0.010321523994207382, 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+101,"Creating useful directories
+One of the benefits of containers is that you’ll be able to use them across
+different clusters. However for each cluster the datasets and experiments
+folder location can be different. In order to be invariant to those locations,
+we will create some useful mount points inside the container:
+mkdir /dataset
+mkdir /tmp_log
+mkdir /final_log
+From now, you won’t need to worry anymore when you write your code to specify
+where to pick up your dataset. Your dataset will always be in /dataset
+independently of the cluster you are using.",119,"[-0.001933228108100593, -0.0022973865270614624, 0.014834840781986713, -0.04739093780517578, -0.00907543208450079, 0.016257911920547485, -0.021480312570929527, -0.0180971622467041, 0.014942241832613945, -0.010639467276632786, 0.02279598079621792, 0.011431553401052952, -0.004772657062858343, 0.005534536670893431, -0.011505392380058765, 0.009927932173013687, 0.026890665292739868, -0.001020314870402217, -0.01773468218743801, -0.006893837824463844, -0.014042754657566547, 0.00434305053204298, 0.008296770974993706, -0.0194262582808733, -0.015774603933095932, -0.008887479081749916, 0.0036785034462809563, -0.008256494998931885, -0.001824148348532617, 0.005594950169324875, 0.015076493844389915, -0.011257026344537735, -0.0029988528694957495, -0.03544251620769501, -0.02173539064824581, 0.012049112468957901, 0.016660667955875397, -0.027870705351233482, 0.0024450637865811586, -0.025037989020347595, 0.002438351046293974, -0.0023208805359899998, 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+102,"Testing
+If you have some code that you want to test before finalizing your container,
+you have two choices.  You can either log into your container and run Python
+code inside it with:
+singularity shell --nv pytorch
+Or you can execute your command directly with
+singularity exec --nv pytorch Python YOUR_CODE.py
+
+Tip
+—nv allows the container to use gpus. You don’t need this if you
+don’t plan to use a gpu.
+
+
+Warning
+Don’t forget to clear the cache of the packages you installed in
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+103,"Creating a new image from the sandbox
+Once everything you need is installed inside the container, you need to convert
+it back to a read-only singularity image with:
+singularity build pytorch_final.simg pytorch",46,"[0.0029666260816156864, -0.009213080629706383, 0.009522764012217522, -0.01793348416686058, -0.008115112781524658, -0.0021607454400509596, -0.042933378368616104, -0.016385067254304886, 0.0009308097651228309, -0.0052012731321156025, 0.060866862535476685, 0.04023068770766258, -0.01190873421728611, -0.009016009978950024, 0.016314683482050896, 0.002225849311798811, 0.003856965806335211, 0.005929733160883188, -0.020143497735261917, -0.01914406381547451, -0.016483603045344353, 0.010177322663366795, 0.004117381293326616, -0.02184675633907318, -0.0037021241150796413, 0.01741265319287777, 0.0019214446656405926, -0.029222851619124413, -0.022480199113488197, -0.001028465572744608, -0.001055738888680935, -0.004427064675837755, -0.02172006666660309, -0.03414963185787201, -0.03203815594315529, -0.004226474557071924, 0.009199004620313644, -0.039977312088012695, 0.020551715046167374, -0.018806228414177895, 0.01130344346165657, -0.006158476695418358, 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+104,"Second way: Use recipes
+A singularity recipe is a file including specifics about installation software,
+environment variables, files to add, and container metadata.  It is a starting
+point for designing any custom container. Instead of pulling a container and
+installing your packages manually, you can specify in this file the packages
+you want and then build your container from this file.
+Here is a toy example of a singularity recipe installing some stuff:
+################# Header: Define the base system you want to use ################
+# Reference of the kind of base you want to use (e.g., docker, debootstrap, shub).
+Bootstrap: docker
+# Select the docker image you want to use (Here we choose tensorflow)
+From: tensorflow/tensorflow:latest-gpu-py3
+
+################# Section: Defining the system #################################
+# Commands in the %post section are executed within the container.
+%post
+        echo ""Installing Tools with apt-get""
+        apt-get update
+        apt-get install -y cmake libcupti-dev libyaml-dev wget unzip
+        apt-get clean
+        echo ""Installing things with pip""
+        pip install tqdm
+        echo ""Creating mount points""
+        mkdir /dataset
+        mkdir /tmp_log
+        mkdir /final_log
+
+
+# Environment variables that should be sourced at runtime.
+%environment
+        # use bash as default shell
+        SHELL=/bin/bash
+        export SHELL
+
+
+A recipe file contains two parts: the header and sections. In the
+header you specify which base system you want to use, it can be any docker
+or singularity container. In sections, you can list the things you want to
+install in the subsection post or list the environment’s variable you need
+to source at each runtime in the subsection environment. For a more detailed
+description, please look at the singularity documentation.
+In order to build a singularity container from a singularity recipe file, you
+should use:
+sudo singularity build <NAME_CONTAINER> <YOUR_RECIPE_FILES>
+
+Warning
+You always need to use sudo when you build a container from a
+recipe. As there is no access to sudo on the cluster, a personal computer or
+the use singularity hub is needed to build a container
+
+
+Build recipe on singularity hub
+Singularity hub allows users to build containers from recipes directly on
+singularity-hub’s cloud meaning that you don’t need to build containers by
+yourself.  You need to register on singularity-hub and link your
+singularity-hub account to your GitHub account, then:
+
+
+Create a new github repository.
+Add a collection on singularity-hub and select the github repository your created.
+Clone the github repository on your computer.
+$ git clone <url>
+
+
+
+Write the singularity recipe and save it as a file named Singularity.
+Git add Singularity, commit and push on the master branch
+$ git add Singularity
+$ git commit
+$ git push origin master
+
+
+
+
+
+At this point, robots from singularity-hub will build the container for you, you
+will be able to download your container from the website or directly with:
+singularity pull shub://<github_username>/<repository_name>
+
+
+Example: Recipe with OpenAI gym, MuJoCo and Miniworld
+Here is an example on how you can use a singularity recipe to install complex
+environment such as OpenAI gym, MuJoCo and Miniworld on a PyTorch based
+container. In order to use MuJoCo, you’ll need to copy the key stored on the
+Mila cluster in /ai/apps/mujoco/license/mjkey.txt to your current directory.
+#This is a dockerfile that sets up a full Gym install with test dependencies
+Bootstrap: docker
+
+# Here we ll build our container upon the pytorch container
+From: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime
+
+# Now we'll copy the mjkey file located in the current directory inside the container's root
+# directory
+%files
+        mjkey.txt
+
+# Then we put everything we need to install
+%post
+        export PATH=$PATH:/opt/conda/bin
+        apt -y update && \
+        apt install -y keyboard-configuration && \
+        apt install -y \
+        python3-dev \
+        python-pyglet \
+        python3-opengl \
+        libhdf5-dev \
+        libjpeg-dev \
+        libboost-all-dev \
+        libsdl2-dev \
+        libosmesa6-dev \
+        patchelf \
+        ffmpeg \
+        xvfb \
+        libhdf5-dev \
+        openjdk-8-jdk \
+        wget \
+        git \
+        unzip && \
+        apt clean && \
+        rm -rf /var/lib/apt/lists/*
+        pip install h5py
+
+        # Download Gym and MuJoCo
+        mkdir /Gym && cd /Gym
+        git clone https://github.com/openai/gym.git || true && \
+        mkdir /Gym/.mujoco && cd /Gym/.mujoco
+        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
+        unzip mjpro150_linux.zip && \
+        wget https://www.roboti.us/download/mujoco200_linux.zip && \
+        unzip mujoco200_linux.zip && \
+        mv mujoco200_linux mujoco200
+
+        # Export global environment variables
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
+        # Install Python dependencies
+        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
+        pip install -r requirements.txt
+        # Install Gym and MuJoCo
+        cd /Gym/gym
+        pip install -e '.[all]'
+        # Change permission to use mujoco_py as non sudoer user
+        chmod -R 777 /opt/conda/lib/python3.6/site-packages/mujoco_py/
+        pip install --upgrade minerl
+
+# Export global environment variables
+%environment
+        export SHELL=/bin/sh
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        export PATH=/Gym/gym/.tox/py3/bin:$PATH
+
+%runscript
+        exec /bin/sh ""$@""
+
+
+Here is the same recipe but written for TensorFlow:
+#This is a dockerfile that sets up a full Gym install with test dependencies
+Bootstrap: docker
+
+# Here we ll build our container upon the tensorflow container
+From: tensorflow/tensorflow:latest-gpu-py3
+
+# Now we'll copy the mjkey file located in the current directory inside the container's root
+# directory
+%files
+        mjkey.txt
+
+# Then we put everything we need to install
+%post
+        apt -y update && \
+        apt install -y keyboard-configuration && \
+        apt install -y \
+        python3-setuptools \
+        python3-dev \
+        python-pyglet \
+        python3-opengl \
+        libjpeg-dev \
+        libboost-all-dev \
+        libsdl2-dev \
+        libosmesa6-dev \
+        patchelf \
+        ffmpeg \
+        xvfb \
+        wget \
+        git \
+        unzip && \
+        apt clean && \
+        rm -rf /var/lib/apt/lists/*
+
+        # Download Gym and MuJoCo
+        mkdir /Gym && cd /Gym
+        git clone https://github.com/openai/gym.git || true && \
+        mkdir /Gym/.mujoco && cd /Gym/.mujoco
+        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
+        unzip mjpro150_linux.zip && \
+        wget https://www.roboti.us/download/mujoco200_linux.zip && \
+        unzip mujoco200_linux.zip && \
+        mv mujoco200_linux mujoco200
+
+        # Export global environment variables
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
+
+        # Install Python dependencies
+        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
+        pip install -r requirements.txt
+        # Install Gym and MuJoCo
+        cd /Gym/gym
+        pip install -e '.[all]'
+        # Change permission to use mujoco_py as non sudoer user
+        chmod -R 777 /usr/local/lib/python3.5/dist-packages/mujoco_py/
+
+        # Then install miniworld
+        cd /usr/local/
+        git clone https://github.com/maximecb/gym-miniworld.git
+        cd gym-miniworld
+        pip install -e .
+
+# Export global environment variables
+%environment
+        export SHELL=/bin/bash
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        export PATH=/Gym/gym/.tox/py3/bin:$PATH
+
+%runscript
+        exec /bin/bash ""$@""
+
+
+Keep in mind that those environment variables are sourced at runtime and not at
+build time. This is why, you should also define them in the %post section
+since they are required to install MuJoCo.",2285,"[-0.0020977051462978125, -0.013706615194678307, -0.004519459791481495, -0.008894131518900394, 0.004881430417299271, -0.011017692275345325, -0.012141523882746696, -0.01603701524436474, -0.009859385900199413, -0.019594669342041016, 0.05609508231282234, 0.004991745110601187, -0.009969701059162617, 0.013927244581282139, -0.016050804406404495, -0.007080832030624151, 0.008445978164672852, -7.530278526246548e-05, 0.0012212194269523025, -0.02011866308748722, -0.0023700448218733072, 0.006708519533276558, -0.0007855620933696628, -0.01991182379424572, -0.019677404314279556, 0.020435819402337074, 0.0204909760504961, -0.013534248806536198, -0.0023252293467521667, 0.016712693497538567, 0.02555166743695736, -0.02036687172949314, -0.028709430247545242, -0.009631861932575703, -0.030998462811112404, 0.01828468032181263, 0.0039782277308404446, -0.008108139038085938, 0.0001556688512209803, -0.002704436657950282, 0.022476643323898315, 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+105,"Build recipe on singularity hub
+Singularity hub allows users to build containers from recipes directly on
+singularity-hub’s cloud meaning that you don’t need to build containers by
+yourself.  You need to register on singularity-hub and link your
+singularity-hub account to your GitHub account, then:
+
+
+Create a new github repository.
+Add a collection on singularity-hub and select the github repository your created.
+Clone the github repository on your computer.
+$ git clone <url>
+
+
+
+Write the singularity recipe and save it as a file named Singularity.
+Git add Singularity, commit and push on the master branch
+$ git add Singularity
+$ git commit
+$ git push origin master
+
+
+
+
+
+At this point, robots from singularity-hub will build the container for you, you
+will be able to download your container from the website or directly with:
+singularity pull shub://<github_username>/<repository_name>",194,"[0.013846042566001415, -0.031613562256097794, 0.0012137109879404306, -0.00784295704215765, 0.002830313751474023, -0.01638425886631012, -0.02323341742157936, -0.011656997725367546, -0.0036025226581841707, -0.033359427005052567, 0.043646592646837234, -0.0020564261358231306, -0.030378028750419617, -0.012744804844260216, -0.005694201681762934, -0.011186957359313965, 0.014812982641160488, -0.020372886210680008, 0.005378603469580412, -0.039295364171266556, -0.025529898703098297, 0.004284081049263477, -0.0007503856322728097, -0.010367744602262974, -0.04711146280169487, 0.02025201916694641, 0.02465696819126606, -0.030270591378211975, 0.016008228063583374, 0.013846042566001415, 0.012476210482418537, -0.011442122049629688, -0.013241704553365707, -0.007305768318474293, -0.01494727935642004, 0.011052660644054413, 0.005378603469580412, -0.0037099604960530996, -0.008138410747051239, -0.013564018532633781, 0.03301025554537773, 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+106,"Example: Recipe with OpenAI gym, MuJoCo and Miniworld
+Here is an example on how you can use a singularity recipe to install complex
+environment such as OpenAI gym, MuJoCo and Miniworld on a PyTorch based
+container. In order to use MuJoCo, you’ll need to copy the key stored on the
+Mila cluster in /ai/apps/mujoco/license/mjkey.txt to your current directory.
+#This is a dockerfile that sets up a full Gym install with test dependencies
+Bootstrap: docker
+
+# Here we ll build our container upon the pytorch container
+From: pytorch/pytorch:1.0-cuda10.0-cudnn7-runtime
+
+# Now we'll copy the mjkey file located in the current directory inside the container's root
+# directory
+%files
+        mjkey.txt
+
+# Then we put everything we need to install
+%post
+        export PATH=$PATH:/opt/conda/bin
+        apt -y update && \
+        apt install -y keyboard-configuration && \
+        apt install -y \
+        python3-dev \
+        python-pyglet \
+        python3-opengl \
+        libhdf5-dev \
+        libjpeg-dev \
+        libboost-all-dev \
+        libsdl2-dev \
+        libosmesa6-dev \
+        patchelf \
+        ffmpeg \
+        xvfb \
+        libhdf5-dev \
+        openjdk-8-jdk \
+        wget \
+        git \
+        unzip && \
+        apt clean && \
+        rm -rf /var/lib/apt/lists/*
+        pip install h5py
+
+        # Download Gym and MuJoCo
+        mkdir /Gym && cd /Gym
+        git clone https://github.com/openai/gym.git || true && \
+        mkdir /Gym/.mujoco && cd /Gym/.mujoco
+        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
+        unzip mjpro150_linux.zip && \
+        wget https://www.roboti.us/download/mujoco200_linux.zip && \
+        unzip mujoco200_linux.zip && \
+        mv mujoco200_linux mujoco200
+
+        # Export global environment variables
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
+        # Install Python dependencies
+        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
+        pip install -r requirements.txt
+        # Install Gym and MuJoCo
+        cd /Gym/gym
+        pip install -e '.[all]'
+        # Change permission to use mujoco_py as non sudoer user
+        chmod -R 777 /opt/conda/lib/python3.6/site-packages/mujoco_py/
+        pip install --upgrade minerl
+
+# Export global environment variables
+%environment
+        export SHELL=/bin/sh
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        export PATH=/Gym/gym/.tox/py3/bin:$PATH
+
+%runscript
+        exec /bin/sh ""$@""
+
+
+Here is the same recipe but written for TensorFlow:
+#This is a dockerfile that sets up a full Gym install with test dependencies
+Bootstrap: docker
+
+# Here we ll build our container upon the tensorflow container
+From: tensorflow/tensorflow:latest-gpu-py3
+
+# Now we'll copy the mjkey file located in the current directory inside the container's root
+# directory
+%files
+        mjkey.txt
+
+# Then we put everything we need to install
+%post
+        apt -y update && \
+        apt install -y keyboard-configuration && \
+        apt install -y \
+        python3-setuptools \
+        python3-dev \
+        python-pyglet \
+        python3-opengl \
+        libjpeg-dev \
+        libboost-all-dev \
+        libsdl2-dev \
+        libosmesa6-dev \
+        patchelf \
+        ffmpeg \
+        xvfb \
+        wget \
+        git \
+        unzip && \
+        apt clean && \
+        rm -rf /var/lib/apt/lists/*
+
+        # Download Gym and MuJoCo
+        mkdir /Gym && cd /Gym
+        git clone https://github.com/openai/gym.git || true && \
+        mkdir /Gym/.mujoco && cd /Gym/.mujoco
+        wget https://www.roboti.us/download/mjpro150_linux.zip  && \
+        unzip mjpro150_linux.zip && \
+        wget https://www.roboti.us/download/mujoco200_linux.zip && \
+        unzip mujoco200_linux.zip && \
+        mv mujoco200_linux mujoco200
+
+        # Export global environment variables
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        cp /mjkey.txt /Gym/.mujoco/mjkey.txt
+
+        # Install Python dependencies
+        wget https://raw.githubusercontent.com/openai/mujoco-py/master/requirements.txt
+        pip install -r requirements.txt
+        # Install Gym and MuJoCo
+        cd /Gym/gym
+        pip install -e '.[all]'
+        # Change permission to use mujoco_py as non sudoer user
+        chmod -R 777 /usr/local/lib/python3.5/dist-packages/mujoco_py/
+
+        # Then install miniworld
+        cd /usr/local/
+        git clone https://github.com/maximecb/gym-miniworld.git
+        cd gym-miniworld
+        pip install -e .
+
+# Export global environment variables
+%environment
+        export SHELL=/bin/bash
+        export MUJOCO_PY_MJKEY_PATH=/Gym/.mujoco/mjkey.txt
+        export MUJOCO_PY_MUJOCO_PATH=/Gym/.mujoco/mujoco150/
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mjpro150/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/Gym/.mujoco/mujoco200/bin
+        export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/bin
+        export PATH=/Gym/gym/.tox/py3/bin:$PATH
+
+%runscript
+        exec /bin/bash ""$@""
+
+
+Keep in mind that those environment variables are sourced at runtime and not at
+build time. This is why, you should also define them in the %post section
+since they are required to install MuJoCo.",1628,"[-0.0014408923452720046, -0.017131298780441284, 0.005926522426307201, -0.018661636859178543, -0.010960347019135952, 0.00708136148750782, -0.018293222412467003, -0.011165808886289597, -0.007481658831238747, -0.024017823860049248, 0.04602353647351265, 0.0035601642448455095, 0.016819562762975693, -0.002228202298283577, -0.008997827768325806, 0.013829732313752174, 0.002339789643883705, -0.011746770702302456, -0.006968003232032061, -0.02801370993256569, -0.01069820486009121, 0.015473430044949055, 0.003595588728785515, -0.03650142624974251, -0.007198262494057417, 0.01802399568259716, 0.01544509083032608, -0.024570446461439133, -0.009415837936103344, 0.013482572510838509, 0.030266709625720978, -0.018576618283987045, -0.02323848567903042, -0.022544164210557938, -0.013432977721095085, -0.004488286562263966, 0.023677749559283257, -0.014467374421656132, 0.021892353892326355, -0.0066208429634571075, 0.015941035002470016, 0.018845845013856888, 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+107,"Using containers on clusters
+
+How to use containers on clusters
+On every cluster with Slurm, datasets and intermediate results should go in
+$SLURM_TMPDIR while the final experiment results should go in $SCRATCH.
+In order to use the container you built, you need to copy it on the cluster you
+want to use.
+
+Warning
+You should always store your container in $SCRATCH !
+
+Then reserve a node with srun/sbatch, copy the container and your dataset on the
+node given by SLURM (i.e in $SLURM_TMPDIR) and execute the code
+<YOUR_CODE> within the container <YOUR_CONTAINER> with:
+singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ $SLURM_TMPDIR/<YOUR_CONTAINER> python <YOUR_CODE>
+Remember that /dataset, /tmp_log and /final_log were created in the
+previous section. Now each time, we’ll use singularity, we are explicitly
+telling it to mount $SLURM_TMPDIR on the cluster’s node in the folder
+/dataset inside the container with the option -B such that each dataset
+downloaded by PyTorch in /dataset will be available in $SLURM_TMPDIR.
+This will allow us to have code and scripts that are invariant to the cluster
+environment. The option -H specify what will be the container’s home. For
+example, if you have your code in $HOME/Project12345/Version35/ you can
+specify -H $HOME/Project12345/Version35:/home, thus the container will only
+have access to the code inside Version35.
+If you want to run multiple commands inside the container you can use:
+singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ \
+   -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ \
+   $SLURM_TMPDIR/<YOUR_CONTAINER> bash -c 'pwd && ls && python <YOUR_CODE>'
+
+Example: Interactive case (srun/salloc)
+Once you get an interactive session with SLURM, copy <YOUR_CONTAINER> and
+<YOUR_DATASET> to $SLURM_TMPDIR
+0. Get an interactive session
+srun --gres=gpu:1
+1. Copy your container on the compute node
+rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
+2. Copy your dataset on the compute node
+rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
+Then use singularity shell to get a shell inside the container
+3. Get a shell in your environment
+singularity shell --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER>
+4. Execute your code
+python <YOUR_CODE>
+or use singularity exec to execute <YOUR_CODE>.
+3. Execute your code
+singularity exec --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER> \
+        python <YOUR_CODE>
+You can create also the following alias to make your life easier.
+alias my_env='singularity exec --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER>'
+This will allow you to run any code with:
+my_env python <YOUR_CODE>
+
+
+Example: sbatch case
+You can also create a sbatch script:
+:linenos:
+
+#!/bin/bash
+#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
+#SBATCH --gres=gpu:1              # Ask for 1 GPU
+#SBATCH --mem=10G                 # Ask for 10 GB of RAM
+#SBATCH --time=0:10:00            # The job will run for 10 minutes
+
+# 1. Copy your container on the compute node
+rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
+# 2. Copy your dataset on the compute node
+rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
+# 3. Executing your code with singularity
+singularity exec --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER> \
+        python ""<YOUR_CODE>""
+# 4. Copy whatever you want to save on $SCRATCH
+rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH
+
+
+
+
+Issue with PyBullet and OpenGL libraries
+If you are running certain gym environments that require pyglet, you may
+encounter a problem when running your singularity instance with the Nvidia
+drivers using the --nv flag. This happens because the --nv flag also
+provides the OpenGL libraries:
+libGL.so.1 => /.singularity.d/libs/libGL.so.1
+libGLX.so.0 => /.singularity.d/libs/libGLX.so.0
+
+
+If you don’t experience those problems with pyglet, you probably don’t need
+to address this. Otherwise, you can resolve those problems by apt-get install
+-y libosmesa6-dev mesa-utils mesa-utils-extra libgl1-mesa-glx, and then making
+sure that your LD_LIBRARY_PATH points to those libraries before the ones in
+/.singularity.d/libs.
+%environment
+        # ...
+        export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/mesa:$LD_LIBRARY_PATH
+
+
+
+
+Mila cluster
+On the Mila cluster $SCRATCH is not yet defined, you should add the
+experiment results you want to keep in /network/scratch/<u>/<username>/. In
+order to use the sbatch script above and to match other cluster environment’s
+names, you can define $SCRATCH as an alias for
+/network/scratch/<u>/<username> with:
+echo ""export SCRATCH=/network/scratch/${USER:0:1}/$USER"" >> ~/.bashrc
+Then, you can follow the general procedure explained above.
+
+
+Digital Research Alliance of Canada
+Using singularity on Digital Research Alliance of Canada is similar except that
+you need to add Yoshua’s account name and load singularity. Here is an example
+of a sbatch script using singularity on compute Canada cluster:
+
+Warning
+You should use singularity/2.6 or singularity/3.4. There is a bug
+in singularity/3.2 which makes gpu unusable.
+
+ 1#!/bin/bash
+ 2#SBATCH --account=rpp-bengioy     # Yoshua pays for your job
+ 3#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
+ 4#SBATCH --gres=gpu:1              # Ask for 1 GPU
+ 5#SBATCH --mem=32G                 # Ask for 32 GB of RAM
+ 6#SBATCH --time=0:10:00            # The job will run for 10 minutes
+ 7#SBATCH --output=""/scratch/<user>/slurm-%j.out"" # Modify the output of sbatch
+ 8
+ 9# 1. You have to load singularity
+10module load singularity
+11# 2. Then you copy the container to the local disk
+12rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
+13# 3. Copy your dataset on the compute node
+14rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
+15# 4. Executing your code with singularity
+16singularity exec --nv \
+17        -H $HOME:/home \
+18        -B $SLURM_TMPDIR:/dataset/ \
+19        -B $SLURM_TMPDIR:/tmp_log/ \
+20        -B $SCRATCH:/final_log/ \
+21        $SLURM_TMPDIR/<YOUR_CONTAINER> \
+22        python ""<YOUR_CODE>""
+23# 5. Copy whatever you want to save on $SCRATCH
+24rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH",1879,"[0.0014658313011750579, -0.009101789444684982, 0.014303785748779774, -0.033352773636579514, -0.005989454686641693, 0.007213253527879715, -0.008045027032494545, -0.010928965173661709, 0.0006089165108278394, -0.021571582183241844, 0.03496177867054939, 0.0076836831867694855, -0.009299506433308125, 0.02038528211414814, -0.0003104238712694496, -0.013478829525411129, 0.012858408503234386, -0.015667350962758064, 0.009667668491601944, -0.02712128683924675, -0.013874263502657413, -0.0059178676456213, 0.012783411890268326, -0.011413030326366425, -0.029889322817325592, 0.008760898374021053, 0.018844453617930412, -0.014153794385492802, 0.004441809840500355, 0.007751861121505499, -0.00383161474019289, -0.0018766048597171903, -0.021803388372063637, -0.01150166243314743, -0.033625487238168716, 0.0007133143953979015, 0.001997621264308691, -0.018817182630300522, 0.008324557915329933, -0.015203738585114479, 0.019185345619916916, -0.00809275172650814, 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+108,"How to use containers on clusters
+On every cluster with Slurm, datasets and intermediate results should go in
+$SLURM_TMPDIR while the final experiment results should go in $SCRATCH.
+In order to use the container you built, you need to copy it on the cluster you
+want to use.
+
+Warning
+You should always store your container in $SCRATCH !
+
+Then reserve a node with srun/sbatch, copy the container and your dataset on the
+node given by SLURM (i.e in $SLURM_TMPDIR) and execute the code
+<YOUR_CODE> within the container <YOUR_CONTAINER> with:
+singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ $SLURM_TMPDIR/<YOUR_CONTAINER> python <YOUR_CODE>
+Remember that /dataset, /tmp_log and /final_log were created in the
+previous section. Now each time, we’ll use singularity, we are explicitly
+telling it to mount $SLURM_TMPDIR on the cluster’s node in the folder
+/dataset inside the container with the option -B such that each dataset
+downloaded by PyTorch in /dataset will be available in $SLURM_TMPDIR.
+This will allow us to have code and scripts that are invariant to the cluster
+environment. The option -H specify what will be the container’s home. For
+example, if you have your code in $HOME/Project12345/Version35/ you can
+specify -H $HOME/Project12345/Version35:/home, thus the container will only
+have access to the code inside Version35.
+If you want to run multiple commands inside the container you can use:
+singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ \
+   -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ \
+   $SLURM_TMPDIR/<YOUR_CONTAINER> bash -c 'pwd && ls && python <YOUR_CODE>'
+
+Example: Interactive case (srun/salloc)
+Once you get an interactive session with SLURM, copy <YOUR_CONTAINER> and
+<YOUR_DATASET> to $SLURM_TMPDIR
+0. Get an interactive session
+srun --gres=gpu:1
+1. Copy your container on the compute node
+rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
+2. Copy your dataset on the compute node
+rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
+Then use singularity shell to get a shell inside the container
+3. Get a shell in your environment
+singularity shell --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER>
+4. Execute your code
+python <YOUR_CODE>
+or use singularity exec to execute <YOUR_CODE>.
+3. Execute your code
+singularity exec --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER> \
+        python <YOUR_CODE>
+You can create also the following alias to make your life easier.
+alias my_env='singularity exec --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER>'
+This will allow you to run any code with:
+my_env python <YOUR_CODE>
+
+
+Example: sbatch case
+You can also create a sbatch script:
+:linenos:
+
+#!/bin/bash
+#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
+#SBATCH --gres=gpu:1              # Ask for 1 GPU
+#SBATCH --mem=10G                 # Ask for 10 GB of RAM
+#SBATCH --time=0:10:00            # The job will run for 10 minutes
+
+# 1. Copy your container on the compute node
+rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
+# 2. Copy your dataset on the compute node
+rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
+# 3. Executing your code with singularity
+singularity exec --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER> \
+        python ""<YOUR_CODE>""
+# 4. Copy whatever you want to save on $SCRATCH
+rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH
+
+
+
+
+Issue with PyBullet and OpenGL libraries
+If you are running certain gym environments that require pyglet, you may
+encounter a problem when running your singularity instance with the Nvidia
+drivers using the --nv flag. This happens because the --nv flag also
+provides the OpenGL libraries:
+libGL.so.1 => /.singularity.d/libs/libGL.so.1
+libGLX.so.0 => /.singularity.d/libs/libGLX.so.0
+
+
+If you don’t experience those problems with pyglet, you probably don’t need
+to address this. Otherwise, you can resolve those problems by apt-get install
+-y libosmesa6-dev mesa-utils mesa-utils-extra libgl1-mesa-glx, and then making
+sure that your LD_LIBRARY_PATH points to those libraries before the ones in
+/.singularity.d/libs.
+%environment
+        # ...
+        export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/mesa:$LD_LIBRARY_PATH
+
+
+
+
+Mila cluster
+On the Mila cluster $SCRATCH is not yet defined, you should add the
+experiment results you want to keep in /network/scratch/<u>/<username>/. In
+order to use the sbatch script above and to match other cluster environment’s
+names, you can define $SCRATCH as an alias for
+/network/scratch/<u>/<username> with:
+echo ""export SCRATCH=/network/scratch/${USER:0:1}/$USER"" >> ~/.bashrc
+Then, you can follow the general procedure explained above.
+
+
+Digital Research Alliance of Canada
+Using singularity on Digital Research Alliance of Canada is similar except that
+you need to add Yoshua’s account name and load singularity. Here is an example
+of a sbatch script using singularity on compute Canada cluster:
+
+Warning
+You should use singularity/2.6 or singularity/3.4. There is a bug
+in singularity/3.2 which makes gpu unusable.
+
+ 1#!/bin/bash
+ 2#SBATCH --account=rpp-bengioy     # Yoshua pays for your job
+ 3#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
+ 4#SBATCH --gres=gpu:1              # Ask for 1 GPU
+ 5#SBATCH --mem=32G                 # Ask for 32 GB of RAM
+ 6#SBATCH --time=0:10:00            # The job will run for 10 minutes
+ 7#SBATCH --output=""/scratch/<user>/slurm-%j.out"" # Modify the output of sbatch
+ 8
+ 9# 1. You have to load singularity
+10module load singularity
+11# 2. Then you copy the container to the local disk
+12rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
+13# 3. Copy your dataset on the compute node
+14rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
+15# 4. Executing your code with singularity
+16singularity exec --nv \
+17        -H $HOME:/home \
+18        -B $SLURM_TMPDIR:/dataset/ \
+19        -B $SLURM_TMPDIR:/tmp_log/ \
+20        -B $SCRATCH:/final_log/ \
+21        $SLURM_TMPDIR/<YOUR_CONTAINER> \
+22        python ""<YOUR_CODE>""
+23# 5. Copy whatever you want to save on $SCRATCH
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+109,"Example: Interactive case (srun/salloc)
+Once you get an interactive session with SLURM, copy <YOUR_CONTAINER> and
+<YOUR_DATASET> to $SLURM_TMPDIR
+0. Get an interactive session
+srun --gres=gpu:1
+1. Copy your container on the compute node
+rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
+2. Copy your dataset on the compute node
+rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
+Then use singularity shell to get a shell inside the container
+3. Get a shell in your environment
+singularity shell --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER>
+4. Execute your code
+python <YOUR_CODE>
+or use singularity exec to execute <YOUR_CODE>.
+3. Execute your code
+singularity exec --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER> \
+        python <YOUR_CODE>
+You can create also the following alias to make your life easier.
+alias my_env='singularity exec --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER>'
+This will allow you to run any code with:
+my_env python <YOUR_CODE>",399,"[-0.005500020459294319, -0.0020174901001155376, 0.024853238835930824, -0.014545508660376072, -0.018293773755431175, -0.01090213842689991, -0.01672733575105667, -0.013132915832102299, -0.025035057216882706, -0.02535673789680004, 0.031804315745830536, 0.014713341370224953, -0.005426593590527773, 0.015454602427780628, 0.00012838772090617567, -0.010014023631811142, 0.013202846050262451, -0.026615483686327934, 0.01267836894840002, -0.01784621924161911, -0.01868538372218609, -0.002611897885799408, 0.01843363419175148, -0.016811251640319824, -0.016433628275990486, -0.003437075763940811, 0.0026940659154206514, -0.016559502109885216, 0.017552513629198074, 0.0022482601925730705, 0.005604915786534548, -0.006951074581593275, -0.006440583150833845, -0.010167869739234447, -0.01812594197690487, 0.011041998863220215, -0.0011958086397498846, -0.0175385270267725, -0.006814710795879364, -0.005160858388990164, 0.009195838123559952, 0.006709815002977848, 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+110,"Example: sbatch case
+You can also create a sbatch script:
+:linenos:
+
+#!/bin/bash
+#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
+#SBATCH --gres=gpu:1              # Ask for 1 GPU
+#SBATCH --mem=10G                 # Ask for 10 GB of RAM
+#SBATCH --time=0:10:00            # The job will run for 10 minutes
+
+# 1. Copy your container on the compute node
+rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
+# 2. Copy your dataset on the compute node
+rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
+# 3. Executing your code with singularity
+singularity exec --nv \
+        -H $HOME:/home \
+        -B $SLURM_TMPDIR:/dataset/ \
+        -B $SLURM_TMPDIR:/tmp_log/ \
+        -B $SCRATCH:/final_log/ \
+        $SLURM_TMPDIR/<YOUR_CONTAINER> \
+        python ""<YOUR_CODE>""
+# 4. Copy whatever you want to save on $SCRATCH
+rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH",270,"[-0.009656386449933052, -0.0061024450697004795, 0.013622277416288853, -0.014439195394515991, -0.01073862798511982, -0.014774340204894543, -0.015570311807096004, -0.014648661017417908, -0.009425973519682884, -0.01932673715054989, 0.037955254316329956, 0.021114179864525795, -0.009265382774174213, 0.00971224345266819, 0.0006785828154534101, -0.0053902603685855865, 0.014830198138952255, -0.010864307172596455, 0.011129630729556084, -0.019117271527647972, -0.01869833841919899, -0.008818522095680237, 0.003178647719323635, 0.0002973981900140643, -0.02337641455233097, 0.011737082153558731, 0.00803651474416256, -0.02179843559861183, 0.0014505526050925255, 0.007484921254217625, 0.006818120833486319, -0.014173870906233788, 0.002462971955537796, -0.010333660058677197, -0.03276049345731735, -0.006992675829678774, 0.000928632915019989, -0.013782868161797523, 0.0026218171697109938, -0.01867040991783142, 0.019857384264469147, 0.03019104339182377, 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+111,"Issue with PyBullet and OpenGL libraries
+If you are running certain gym environments that require pyglet, you may
+encounter a problem when running your singularity instance with the Nvidia
+drivers using the --nv flag. This happens because the --nv flag also
+provides the OpenGL libraries:
+libGL.so.1 => /.singularity.d/libs/libGL.so.1
+libGLX.so.0 => /.singularity.d/libs/libGLX.so.0
+
+
+If you don’t experience those problems with pyglet, you probably don’t need
+to address this. Otherwise, you can resolve those problems by apt-get install
+-y libosmesa6-dev mesa-utils mesa-utils-extra libgl1-mesa-glx, and then making
+sure that your LD_LIBRARY_PATH points to those libraries before the ones in
+/.singularity.d/libs.
+%environment
+        # ...
+        export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/mesa:$LD_LIBRARY_PATH",203,"[-0.006877453066408634, -0.009704811498522758, -0.007882969453930855, -0.04495745152235031, 0.013621770776808262, 0.011449575424194336, 0.005507567897439003, -0.01364979986101389, -0.0161583349108696, 0.008086174726486206, 0.046639151871204376, 0.005854418501257896, -0.0020443168468773365, 0.012556694447994232, -0.003832874819636345, 0.009410513564944267, -0.0007103431271389127, 0.0012551438994705677, 0.0361565537750721, -0.0005675738211721182, -0.0026153940707445145, -0.011078199371695518, 0.016102278605103493, -0.01276690699160099, -0.0002975820971187204, -0.012206340208649635, 0.007126204203814268, -0.03276512399315834, -0.004011555574834347, 0.031840190291404724, 0.02926158346235752, -3.476170604699291e-05, -0.01917138136923313, -0.0007536994526162744, -0.019325537607073784, -0.009417520835995674, -0.0017587781185284257, -0.01656474731862545, 0.020516742020845413, 0.006859935354441404, 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+112,"Mila cluster
+On the Mila cluster $SCRATCH is not yet defined, you should add the
+experiment results you want to keep in /network/scratch/<u>/<username>/. In
+order to use the sbatch script above and to match other cluster environment’s
+names, you can define $SCRATCH as an alias for
+/network/scratch/<u>/<username> with:
+echo ""export SCRATCH=/network/scratch/${USER:0:1}/$USER"" >> ~/.bashrc
+Then, you can follow the general procedure explained above.",117,"[-0.005913616623729467, -0.01070469431579113, 0.006292304489761591, -0.03210731968283653, -0.022586025297641754, 0.016188906505703926, -0.01790652610361576, -0.01916431076824665, -0.01077231764793396, -0.01309854257851839, 0.025304464623332024, -0.016026610508561134, 0.0023414406459778547, -0.003061623778194189, -0.026629870757460594, -0.035867150872945786, 0.011218627914786339, -0.01119834091514349, 0.013294649310410023, -0.011306537315249443, -0.006741996388882399, 0.016702840104699135, -0.00917641818523407, -0.02453356422483921, -0.019015541300177574, -0.002420897362753749, -0.0028638269286602736, -0.010278670117259026, -0.00011738901230273768, 0.022748321294784546, -0.015012268908321857, -0.011407971382141113, 0.0035299118608236313, -0.010346293449401855, -0.04814745485782623, 0.013571902178227901, 0.00034487643279135227, -0.021449962630867958, 0.001465724897570908, 0.00028908762033097446, 0.023870859295129776, 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+113,"Digital Research Alliance of Canada
+Using singularity on Digital Research Alliance of Canada is similar except that
+you need to add Yoshua’s account name and load singularity. Here is an example
+of a sbatch script using singularity on compute Canada cluster:
+
+Warning
+You should use singularity/2.6 or singularity/3.4. There is a bug
+in singularity/3.2 which makes gpu unusable.
+
+ 1#!/bin/bash
+ 2#SBATCH --account=rpp-bengioy     # Yoshua pays for your job
+ 3#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
+ 4#SBATCH --gres=gpu:1              # Ask for 1 GPU
+ 5#SBATCH --mem=32G                 # Ask for 32 GB of RAM
+ 6#SBATCH --time=0:10:00            # The job will run for 10 minutes
+ 7#SBATCH --output=""/scratch/<user>/slurm-%j.out"" # Modify the output of sbatch
+ 8
+ 9# 1. You have to load singularity
+10module load singularity
+11# 2. Then you copy the container to the local disk
+12rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
+13# 3. Copy your dataset on the compute node
+14rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
+15# 4. Executing your code with singularity
+16singularity exec --nv \
+17        -H $HOME:/home \
+18        -B $SLURM_TMPDIR:/dataset/ \
+19        -B $SLURM_TMPDIR:/tmp_log/ \
+20        -B $SCRATCH:/final_log/ \
+21        $SLURM_TMPDIR/<YOUR_CONTAINER> \
+22        python ""<YOUR_CODE>""
+23# 5. Copy whatever you want to save on $SCRATCH
+24rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH",432,"[-0.009171411395072937, -0.01838463917374611, -0.0131926154717803, -0.024573251605033875, -0.002383451908826828, 0.009422301314771175, -0.021130487322807312, -0.0148582449182868, -0.01099036168307066, -0.032113879919052124, 0.037967972457408905, 0.006003929302096367, -0.012865065596997738, -0.0037563759833574295, 0.016196323558688164, -0.001030564191751182, 0.018161624670028687, -0.029995255172252655, 0.007275800686329603, -0.014231020584702492, -0.020893534645438194, 0.004575252067297697, -0.0005218156729824841, 0.008641756139695644, -0.022552194073796272, 0.015736358240246773, 0.007415183819830418, -0.00033386622089892626, 0.0031291518826037645, 0.00604574428871274, -0.000522686867043376, -0.006833259481936693, 0.005868030712008476, -0.0001606173173058778, -0.024461744353175163, -0.012035735882818699, -0.001932198996655643, -0.0006041388842277229, -0.013826808892190456, -0.021506821736693382, 0.024712633341550827, 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+114,"Sharing Data with ACLs
+Regular permissions bits are extremely blunt tools: They control access through
+only three sets of bits owning user, owning group and all others. Therefore,
+access is either too narrow (0700 allows access only by oneself) or too wide
+(770 gives all permissions to everyone in the same group, and 777 to
+literally everyone).
+ACLs (Access Control Lists) are an expansion of the permissions bits that allow
+more fine-grained, granular control of accesses to a file. They can be used to
+permit specific users access to files and folders even if conservative default
+permissions would have denied them such access.
+As an illustrative example, to use ACLs to allow $USER (oneself) to
+share with $USER2 (another person) a “playground” folder hierarchy in
+Mila’s scratch filesystem at a location
+
+/network/scratch/${USER:0:1}/$USER/X/Y/Z/...
+
+in a safe and secure fashion that allows both users to read, write, execute,
+search and delete each others’ files:
+
+
+1. Grant oneself permissions to access any future files/folders created
+by the other (or oneself)
+(-d renders this permission a “default” / inheritable one)
+
+setfacl -Rdm user:${USER}:rwx  /network/scratch/${USER:0:1}/$USER/X/Y/Z/
+
+
+
+
+Note
+The importance of doing this seemingly-redundant step first is that files
+and folders are always owned by only one person, almost always their
+creator (the UID will be the creator’s, the GID typically as well). If that
+user is not yourself, you will not have access to those files unless the
+other person specifically gives them to you – or these files inherited a
+default ACL allowing you full access.
+This is the inherited, default ACL serving that purpose.
+
+
+2. Grant the other permission to access any future files/folders created
+by the other (or oneself)
+(-d renders this permission a “default” / inheritable one)
+
+setfacl -Rdm user:${USER2}:rwx /network/scratch/${USER:0:1}/$USER/X/Y/Z/
+
+
+
+
+3. Grant the other permission to access any existing files/folders created
+by oneself.
+Such files and folders were created before the new default ACLs were added
+above and thus did not inherit them from their parent folder at the moment of
+their creation.
+
+setfacl -Rm  user:${USER2}:rwx /network/scratch/${USER:0:1}/$USER/X/Y/Z/
+
+
+
+Note
+The purpose of granting permissions first for future files and then for
+existing files is to prevent a race condition whereby after the first
+setfacl command the other person could create files to which the
+second setfacl command does not apply.
+
+
+
+4. Grant another permission to search through one’s hierarchy down to the
+shared location in question.
+
+
+Non-recursive (!!!!)
+May also grant :rx in unlikely event others listing your folders on the
+path is not troublesome or desirable.
+
+setfacl -m   user:${USER2}:x   /network/scratch/${USER:0:1}/$USER/X/Y/
+setfacl -m   user:${USER2}:x   /network/scratch/${USER:0:1}/$USER/X/
+setfacl -m   user:${USER2}:x   /network/scratch/${USER:0:1}/$USER/
+
+
+
+Note
+In order to access a file, all folders from the root (/) down to the
+parent folder in question must be searchable (+x) by the concerned user.
+This is already the case for all users for folders such as /,
+/network and /network/scratch, but users must explicitly grant access
+to some or all users either through base permissions or by adding ACLs, for
+at least /network/scratch/${USER:0:1}/$USER, $HOME and subfolders.
+To bluntly allow all users to search through a folder (think twice!),
+the following command can be used:
+chmod a+x /network/scratch/${USER:0:1}/$USER/
+
+
+
+
+Note
+For more information on setfacl and path resolution/access checking,
+consider the following documentation viewing commands:
+
+man setfacl
+man path_resolution
+
+
+
+Viewing and Verifying ACLs
+getfacl /path/to/folder/or/file
+           1:  # file: somedir/
+           2:  # owner: lisa
+           3:  # group: staff
+           4:  # flags: -s-
+           5:  user::rwx
+           6:  user:joe:rwx               #effective:r-x
+           7:  group::rwx                 #effective:r-x
+           8:  group:cool:r-x
+           9:  mask::r-x
+          10:  other::r-x
+          11:  default:user::rwx
+          12:  default:user:joe:rwx       #effective:r-x
+          13:  default:group::r-x
+          14:  default:mask::r-x
+          15:  default:other::---
+
+
+
+Note
+
+man getfacl",1102,"[0.00020359196059871465, 0.01278620958328247, 0.006951528135687113, -0.06909561902284622, -0.03024287149310112, 0.021321630105376244, -0.034249983727931976, -0.005530086345970631, -0.02573486976325512, -0.013740605674684048, 0.027251074090600014, -0.022364020347595215, 0.010918028652668, 0.013693224638700485, -0.0019680203404277563, 0.02064475230872631, 0.019453449174761772, -0.008758789859712124, 0.009686112403869629, 0.008833246305584908, -0.004030803218483925, 0.010945103131234646, -0.04099167883396149, -0.0014722078340128064, -0.027887338772416115, 0.011872424744069576, 0.014620546251535416, -0.00546916713938117, 0.017436355352401733, -0.01715206727385521, 0.01903378590941429, 0.0032913151662796736, -0.014255032874643803, 0.0057906839065253735, -0.017639419063925743, 0.002873343415558338, -0.006535248830914497, -0.025843169540166855, -0.0008554034866392612, 0.02064475230872631, 0.020387539640069008, -0.0017209601355716586, -0.010972178541123867, 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+115,"Viewing and Verifying ACLs
+getfacl /path/to/folder/or/file
+           1:  # file: somedir/
+           2:  # owner: lisa
+           3:  # group: staff
+           4:  # flags: -s-
+           5:  user::rwx
+           6:  user:joe:rwx               #effective:r-x
+           7:  group::rwx                 #effective:r-x
+           8:  group:cool:r-x
+           9:  mask::r-x
+          10:  other::r-x
+          11:  default:user::rwx
+          12:  default:user:joe:rwx       #effective:r-x
+          13:  default:group::r-x
+          14:  default:mask::r-x
+          15:  default:other::---
+
+
+
+Note
+
+man getfacl",206,"[0.00094050751067698, 0.0094239367172122, -0.006409511901438236, -0.04477398842573166, -0.011419836431741714, 0.01580258458852768, -0.047160837799310684, -0.01753099262714386, -0.02731158398091793, -0.010171541944146156, 0.025569459423422813, -0.029712149873375893, -0.011316955089569092, 0.01438967976719141, -0.013559769839048386, 0.017215488478541374, 0.0188478734344244, -0.005106347147375345, 0.010281282477080822, -0.0019084501545876265, -0.00588824599981308, 0.03525402769446373, -0.03388227894902229, -0.010507620871067047, -0.01006180141121149, 0.010301858186721802, 0.0003973794518969953, -0.018532371148467064, 0.0165844839066267, -0.010109812952578068, 0.02359413541853428, -0.0045610759407281876, -0.015212730504572392, -0.008491145446896553, -0.01858724094927311, 0.0029149733018130064, -0.01011667214334011, -0.04337480291724205, -0.0070165120996534824, 0.021948033943772316, 0.014705182984471321, 0.0019667495507746935, -0.018422631546854973, 0.010185259394347668, 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+116,"Contributing datasets
+If a dataset could help the research of others at Mila, this form can be filled to request its addition
+to /network/datasets.
+
+Publicly share a Mila dataset
+Mila offers two ways to publicly share a Mila dataset:
+
+Academic Torrent
+Google Drive
+
+Note that these options are not mutually exclusive and both can be used.
+
+Academic Torrent
+Mila hosts/seeds some datasets created by the Mila community through Academic
+Torrent. The first step is to create an
+account and a torrent file.
+Then drop the dataset in /network/scratch/.transit_datasets and send the
+Academic Torrent URL to Mila’s helpdesk. If
+the dataset does not reside on the Mila cluster, only the Academic Torrent URL
+would be needed to proceed with the initial download. Then you can delete /
+stop sharing your copy.
+
+Note
+
+Avoid mentioning dataset in the name of the dataset
+Avoid capital letters, special charaters (including spaces) in files and
+directories names. Spaces can be replaced by hyphens (-).
+Multiple archives can be provided to spread the data (e.g. dataset splits,
+raw data, extra data, …)
+
+
+
+Generate a .torrent file to be uploaded to Academic Torrent
+The command line / Python utility torrentool can be used to create a
+DATASET_NAME.torrent file:
+# Install torrentool
+python3 -m pip install torrentool click
+# Change Directory to the location of the dataset to be hosted by Mila
+cd /network/scratch/.transit_datasets
+torrent create --tracker https://academictorrents.com/announce.php DATASET_NAME
+
+
+The resulting DATASET_NAME.torrent can then be used to register a new dataset
+on Academic Torrent.
+
+Warning
+
+The creation of a DATASET_NAME.torrent file requires the computation of
+checksums for the dataset content which can quickly become CPU-heavy. This
+process should not be executed on a login node
+
+
+
+
+Download a dataset from Academic Torrent
+Academic Torrent provides a Python API to easily download a dataset
+from it’s registered list:
+# Install the Python API with:
+# python3 -m pip install academictorrents
+import academictorrents as at
+mnist_path = at.get(""323a0048d87ca79b68f12a6350a57776b6a3b7fb"", datastore=""~/scratch/.academictorrents-datastore"") # Download the mnist dataset
+
+
+
+Note
+Current needs have been evaluated to be for a download speed of about 10
+MB/s. This speed can be higher if more users also seeds the dataset.
+
+
+
+
+Google Drive
+Only a member of the staff team can upload to Mila’s Google Drive
+which requires to first drop the dataset in
+/network/scratch/.transit_datasets. Then, contact Mila’s helpdesk and provide the following informations:
+
+directory containing the archived dataset (zip is favored) in
+/network/scratch/.transit_datasets
+the name of the dataset
+a licence in .txt format. One of the the creative common licenses can be used. It is
+recommended to at least have the Attribution option. The No Derivatives
+option is discouraged unless the dataset should not be modified by others.
+MD5 checksum of the archive
+the arXiv and GitHub URLs (those can be sent later if the article is still in
+the submission process)
+instructions to know if the dataset needs to be unziped, untared or
+else before uploading to Google Drive
+
+
+Note
+
+Avoid mentioning dataset in the name of the dataset
+Avoid capital letters, special charaters (including spaces) in files and
+directories names. Spaces can be replaced by hyphens (-).
+Multiple archives can be provided to spread the data (e.g. dataset splits,
+raw data, extra data, …)
+
+
+
+Download a dataset from Mila’s Google Drive with  gdown
+The utility gdown is a simple utility to
+download data from Google Drive from the command line shell or in a Python
+script and requires no setup.
+
+Warning
+A limitation however is that it uses a shared client id which can cause a
+quota block when too many users uses it in the same day. It is described in
+a GitHub issue.
+
+
+
+Download a dataset from Mila’s Google Drive with rclone
+Rclone is a command line program to manage files on
+cloud storage. In the context of a Google Drive remote, it allows to specify a
+client id to avoid sharing with other users which avoid quota limits. Rclone
+describes the creation of a client id in its documentaton. Once this is done, a
+remote for Mila’s Google Drive can be configured from the command line:
+rclone config create mila-gdrive drive client_id XXXXXXXXXXXX-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.apps.googleusercontent.com \
+    client_secret XXXXXXXXXXXXX-XXXXXXXXXX \
+    scope 'drive.readonly' \
+    root_folder_id 1peJ6VF9wQ-LeETgcdGxu1e4fo28JbtUt \
+    config_is_local false \
+    config_refresh_token false
+
+
+The remote can then be used to download a dataset:
+rclone copy --progress mila-gdrive:DATASET_NAME/ ~/scratch/datasets/DATASET_NAME/
+
+
+Rclone is available from the conda channel conda-forge.
+
+
+
+Digital Object Identifier (DOI)
+It is recommended to get a DOI to reference the dataset. A DOI is a permanent
+id/URL which prevents losing references of online scientific data.
+https://figshare.com can be used to create a DOI:
+
+Go in My Data
+Create an item by clicking Create new item
+Check Metadata record only at the top
+Fill the metadata fields
+
+Then reference the dataset using https://doi.org like this:
+https://doi.org/10.6084/m9.figshare.2066037",1216,"[-0.007763088680803776, 0.0014167112531140447, 0.02133975923061371, -0.04080687463283539, -0.01970469020307064, 0.026776017621159554, -0.016728023067116737, -0.02147950977087021, -0.014813454821705818, -0.04061122611165047, 0.020892560482025146, 0.009488996118307114, 0.016336724162101746, 0.0001875701273093, -0.02203850820660591, 0.006194399669766426, 0.023449977859854698, -0.017901919782161713, 0.028984060510993004, -0.002943475265055895, 0.01297574769705534, -0.008035600185394287, -0.016238899901509285, -0.009160584770143032, -0.0306331068277359, 0.025560196489095688, 0.018167443573474884, -0.01097034104168415, 0.003930456470698118, 0.003098946763202548, 0.002587113995105028, -0.017049448564648628, -0.009342258796095848, -0.009943181648850441, -0.011801850982010365, -0.004908703733235598, 0.03217035159468651, -0.008175349794328213, 0.004957615863531828, -0.002538201631978154, 0.0195928905159235, 0.027670415118336678, 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+117,"Publicly share a Mila dataset
+Mila offers two ways to publicly share a Mila dataset:
+
+Academic Torrent
+Google Drive
+
+Note that these options are not mutually exclusive and both can be used.
+
+Academic Torrent
+Mila hosts/seeds some datasets created by the Mila community through Academic
+Torrent. The first step is to create an
+account and a torrent file.
+Then drop the dataset in /network/scratch/.transit_datasets and send the
+Academic Torrent URL to Mila’s helpdesk. If
+the dataset does not reside on the Mila cluster, only the Academic Torrent URL
+would be needed to proceed with the initial download. Then you can delete /
+stop sharing your copy.
+
+Note
+
+Avoid mentioning dataset in the name of the dataset
+Avoid capital letters, special charaters (including spaces) in files and
+directories names. Spaces can be replaced by hyphens (-).
+Multiple archives can be provided to spread the data (e.g. dataset splits,
+raw data, extra data, …)
+
+
+
+Generate a .torrent file to be uploaded to Academic Torrent
+The command line / Python utility torrentool can be used to create a
+DATASET_NAME.torrent file:
+# Install torrentool
+python3 -m pip install torrentool click
+# Change Directory to the location of the dataset to be hosted by Mila
+cd /network/scratch/.transit_datasets
+torrent create --tracker https://academictorrents.com/announce.php DATASET_NAME
+
+
+The resulting DATASET_NAME.torrent can then be used to register a new dataset
+on Academic Torrent.
+
+Warning
+
+The creation of a DATASET_NAME.torrent file requires the computation of
+checksums for the dataset content which can quickly become CPU-heavy. This
+process should not be executed on a login node
+
+
+
+
+Download a dataset from Academic Torrent
+Academic Torrent provides a Python API to easily download a dataset
+from it’s registered list:
+# Install the Python API with:
+# python3 -m pip install academictorrents
+import academictorrents as at
+mnist_path = at.get(""323a0048d87ca79b68f12a6350a57776b6a3b7fb"", datastore=""~/scratch/.academictorrents-datastore"") # Download the mnist dataset
+
+
+
+Note
+Current needs have been evaluated to be for a download speed of about 10
+MB/s. This speed can be higher if more users also seeds the dataset.
+
+
+
+
+Google Drive
+Only a member of the staff team can upload to Mila’s Google Drive
+which requires to first drop the dataset in
+/network/scratch/.transit_datasets. Then, contact Mila’s helpdesk and provide the following informations:
+
+directory containing the archived dataset (zip is favored) in
+/network/scratch/.transit_datasets
+the name of the dataset
+a licence in .txt format. One of the the creative common licenses can be used. It is
+recommended to at least have the Attribution option. The No Derivatives
+option is discouraged unless the dataset should not be modified by others.
+MD5 checksum of the archive
+the arXiv and GitHub URLs (those can be sent later if the article is still in
+the submission process)
+instructions to know if the dataset needs to be unziped, untared or
+else before uploading to Google Drive
+
+
+Note
+
+Avoid mentioning dataset in the name of the dataset
+Avoid capital letters, special charaters (including spaces) in files and
+directories names. Spaces can be replaced by hyphens (-).
+Multiple archives can be provided to spread the data (e.g. dataset splits,
+raw data, extra data, …)
+
+
+
+Download a dataset from Mila’s Google Drive with  gdown
+The utility gdown is a simple utility to
+download data from Google Drive from the command line shell or in a Python
+script and requires no setup.
+
+Warning
+A limitation however is that it uses a shared client id which can cause a
+quota block when too many users uses it in the same day. It is described in
+a GitHub issue.
+
+
+
+Download a dataset from Mila’s Google Drive with rclone
+Rclone is a command line program to manage files on
+cloud storage. In the context of a Google Drive remote, it allows to specify a
+client id to avoid sharing with other users which avoid quota limits. Rclone
+describes the creation of a client id in its documentaton. Once this is done, a
+remote for Mila’s Google Drive can be configured from the command line:
+rclone config create mila-gdrive drive client_id XXXXXXXXXXXX-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.apps.googleusercontent.com \
+    client_secret XXXXXXXXXXXXX-XXXXXXXXXX \
+    scope 'drive.readonly' \
+    root_folder_id 1peJ6VF9wQ-LeETgcdGxu1e4fo28JbtUt \
+    config_is_local false \
+    config_refresh_token false
+
+
+The remote can then be used to download a dataset:
+rclone copy --progress mila-gdrive:DATASET_NAME/ ~/scratch/datasets/DATASET_NAME/
+
+
+Rclone is available from the conda channel conda-forge.
+
+
+
+Digital Object Identifier (DOI)
+It is recommended to get a DOI to reference the dataset. A DOI is a permanent
+id/URL which prevents losing references of online scientific data.
+https://figshare.com can be used to create a DOI:
+
+Go in My Data
+Create an item by clicking Create new item
+Check Metadata record only at the top
+Fill the metadata fields
+
+Then reference the dataset using https://doi.org like this:
+https://doi.org/10.6084/m9.figshare.2066037",1181,"[-0.010033246129751205, 0.0051741586066782475, 0.019506366923451424, -0.031983159482479095, -0.0144932447001338, 0.015543480403721333, -0.024827558547258377, -0.02757217362523079, -0.016019586473703384, -0.035988058894872665, 0.022363007068634033, 0.011650608852505684, 0.01262382697314024, 0.0010992460884153843, -0.01739189401268959, 0.009473120793700218, 0.019940465688705444, -0.018316101282835007, 0.035371918231248856, -0.003878868417814374, 0.020024484023451805, 0.0025818280410021544, -0.02409939654171467, -0.013709069229662418, -0.033579517155885696, 0.015641503036022186, 0.02219497039914131, -0.0049956184811890125, 0.006392431445419788, 0.0022282488644123077, 0.00012263683311175555, -0.026241876184940338, -0.00421494385227561, -0.009438113309442997, -0.014248190447688103, 0.0020619616843760014, 0.027992267161607742, -0.0035883034579455853, 0.00015709767467342317, 0.003369504353031516, 0.012546809390187263, 0.012091707438230515, 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+118,"Academic Torrent
+Mila hosts/seeds some datasets created by the Mila community through Academic
+Torrent. The first step is to create an
+account and a torrent file.
+Then drop the dataset in /network/scratch/.transit_datasets and send the
+Academic Torrent URL to Mila’s helpdesk. If
+the dataset does not reside on the Mila cluster, only the Academic Torrent URL
+would be needed to proceed with the initial download. Then you can delete /
+stop sharing your copy.
+
+Note
+
+Avoid mentioning dataset in the name of the dataset
+Avoid capital letters, special charaters (including spaces) in files and
+directories names. Spaces can be replaced by hyphens (-).
+Multiple archives can be provided to spread the data (e.g. dataset splits,
+raw data, extra data, …)
+
+
+
+Generate a .torrent file to be uploaded to Academic Torrent
+The command line / Python utility torrentool can be used to create a
+DATASET_NAME.torrent file:
+# Install torrentool
+python3 -m pip install torrentool click
+# Change Directory to the location of the dataset to be hosted by Mila
+cd /network/scratch/.transit_datasets
+torrent create --tracker https://academictorrents.com/announce.php DATASET_NAME
+
+
+The resulting DATASET_NAME.torrent can then be used to register a new dataset
+on Academic Torrent.
+
+Warning
+
+The creation of a DATASET_NAME.torrent file requires the computation of
+checksums for the dataset content which can quickly become CPU-heavy. This
+process should not be executed on a login node
+
+
+
+
+Download a dataset from Academic Torrent
+Academic Torrent provides a Python API to easily download a dataset
+from it’s registered list:
+# Install the Python API with:
+# python3 -m pip install academictorrents
+import academictorrents as at
+mnist_path = at.get(""323a0048d87ca79b68f12a6350a57776b6a3b7fb"", datastore=""~/scratch/.academictorrents-datastore"") # Download the mnist dataset
+
+
+
+Note
+Current needs have been evaluated to be for a download speed of about 10
+MB/s. This speed can be higher if more users also seeds the dataset.",471,"[-0.004834022838622332, -0.011273466981947422, 0.021793950349092484, -0.03131281957030296, -0.007721650414168835, 0.026695456355810165, -0.025729363784193993, -0.02838612161576748, -0.011934105306863785, -0.03489305078983307, 0.024337051436305046, 0.007970277220010757, 0.009440729394555092, 0.010612829588353634, -0.004741675686091185, 0.00535969203338027, 0.019336091354489326, -0.014541138894855976, 0.0278746597468853, -0.004805608652532101, 0.006030985154211521, 0.007735857740044594, -0.022007059305906296, -0.014235682785511017, -0.023200469091534615, 0.003079425310716033, 0.017801707610487938, -0.019321884959936142, -0.007245706859976053, 0.005579904653131962, -0.0010682089487090707, -0.024493331089615822, -0.008566983044147491, -0.020799441263079643, -0.00948335137218237, 0.0021683843806385994, 0.033784884959459305, 0.0005625190096907318, 0.017730670049786568, 0.01954920031130314, 0.018071645870804787, 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+119,"Generate a .torrent file to be uploaded to Academic Torrent
+The command line / Python utility torrentool can be used to create a
+DATASET_NAME.torrent file:
+# Install torrentool
+python3 -m pip install torrentool click
+# Change Directory to the location of the dataset to be hosted by Mila
+cd /network/scratch/.transit_datasets
+torrent create --tracker https://academictorrents.com/announce.php DATASET_NAME
+
+
+The resulting DATASET_NAME.torrent can then be used to register a new dataset
+on Academic Torrent.
+
+Warning
+
+The creation of a DATASET_NAME.torrent file requires the computation of
+checksums for the dataset content which can quickly become CPU-heavy. This
+process should not be executed on a login node",158,"[0.0010754105169326067, -0.017816422507166862, 0.014752636663615704, -0.03240933641791344, 0.015841659158468246, 0.023639066144824028, -0.02764667198061943, -0.0361555740237236, -0.007514261174947023, -0.024553846567869186, 0.017192048951983452, 0.014781677164137363, -0.0057500433176755905, 0.027036819607019424, 0.0049114953726530075, -0.0008104149019345641, 0.012494727969169617, -0.011565428227186203, 0.029998963698744774, 0.007870009168982506, 0.008153154514729977, 0.005357994697988033, -0.027995159849524498, -0.014389628544449806, -0.014781677164137363, -0.0017270095413550735, 0.014542091637849808, -0.026949698105454445, 0.0012968452647328377, 0.020604321733117104, 0.017380813136696815, -0.010345721617341042, -0.006926188711076975, -0.02340674214065075, -0.012959377840161324, 0.002553759841248393, 0.031102506443858147, 0.0029349178075790405, 0.013002938590943813, 0.01958790048956871, 0.026717372238636017, 0.00951080396771431, 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+120,"Download a dataset from Academic Torrent
+Academic Torrent provides a Python API to easily download a dataset
+from it’s registered list:
+# Install the Python API with:
+# python3 -m pip install academictorrents
+import academictorrents as at
+mnist_path = at.get(""323a0048d87ca79b68f12a6350a57776b6a3b7fb"", datastore=""~/scratch/.academictorrents-datastore"") # Download the mnist dataset
+
+
+
+Note
+Current needs have been evaluated to be for a download speed of about 10
+MB/s. This speed can be higher if more users also seeds the dataset.",143,"[-0.018178056925535202, -0.024415483698248863, 0.03505665063858032, -0.027822967618703842, -0.0023751312401145697, 0.03566306456923485, -0.005627400707453489, -0.016517629846930504, -0.004400129429996014, -0.03586520627140999, 0.015218166634440422, 0.019665220752358437, 0.0017930797766894102, 0.0036168412771075964, -0.001692010322585702, 0.011298117227852345, 0.01073501631617546, -0.020343830808997154, 0.010583411902189255, -0.003938097506761551, 0.017918163910508156, 0.008684750646352768, -0.024632060900330544, -0.022321902215480804, -0.013449451886117458, -0.0012985615758225322, 0.0050859577022492886, -0.02256735786795616, -0.015131535939872265, 0.003815370611846447, 0.020906930789351463, -0.036558251827955246, 0.0005175295518711209, -0.03670263662934303, -0.0015097244177013636, -0.003844247432425618, 0.013333943672478199, -0.002994181588292122, 0.03563418984413147, 0.01985292136669159, 0.004021118860691786, 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+121,"Google Drive
+Only a member of the staff team can upload to Mila’s Google Drive
+which requires to first drop the dataset in
+/network/scratch/.transit_datasets. Then, contact Mila’s helpdesk and provide the following informations:
+
+directory containing the archived dataset (zip is favored) in
+/network/scratch/.transit_datasets
+the name of the dataset
+a licence in .txt format. One of the the creative common licenses can be used. It is
+recommended to at least have the Attribution option. The No Derivatives
+option is discouraged unless the dataset should not be modified by others.
+MD5 checksum of the archive
+the arXiv and GitHub URLs (those can be sent later if the article is still in
+the submission process)
+instructions to know if the dataset needs to be unziped, untared or
+else before uploading to Google Drive
+
+
+Note
+
+Avoid mentioning dataset in the name of the dataset
+Avoid capital letters, special charaters (including spaces) in files and
+directories names. Spaces can be replaced by hyphens (-).
+Multiple archives can be provided to spread the data (e.g. dataset splits,
+raw data, extra data, …)
+
+
+
+Download a dataset from Mila’s Google Drive with  gdown
+The utility gdown is a simple utility to
+download data from Google Drive from the command line shell or in a Python
+script and requires no setup.
+
+Warning
+A limitation however is that it uses a shared client id which can cause a
+quota block when too many users uses it in the same day. It is described in
+a GitHub issue.
+
+
+
+Download a dataset from Mila’s Google Drive with rclone
+Rclone is a command line program to manage files on
+cloud storage. In the context of a Google Drive remote, it allows to specify a
+client id to avoid sharing with other users which avoid quota limits. Rclone
+describes the creation of a client id in its documentaton. Once this is done, a
+remote for Mila’s Google Drive can be configured from the command line:
+rclone config create mila-gdrive drive client_id XXXXXXXXXXXX-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.apps.googleusercontent.com \
+    client_secret XXXXXXXXXXXXX-XXXXXXXXXX \
+    scope 'drive.readonly' \
+    root_folder_id 1peJ6VF9wQ-LeETgcdGxu1e4fo28JbtUt \
+    config_is_local false \
+    config_refresh_token false
+
+
+The remote can then be used to download a dataset:
+rclone copy --progress mila-gdrive:DATASET_NAME/ ~/scratch/datasets/DATASET_NAME/
+
+
+Rclone is available from the conda channel conda-forge.",558,"[-0.005975086707621813, 0.007927065715193748, 0.0063360449858009815, -0.018545547500252724, -0.013975745998322964, 0.025862839072942734, -0.020802414044737816, -0.04300660640001297, -0.00026524305576458573, -0.03484824672341347, 0.03866108879446983, -0.0023690080270171165, -0.004983327351510525, -0.0028964276425540447, -0.041913218796253204, -0.009370898827910423, 0.016414843499660492, 0.007604655809700489, 0.04353928565979004, -0.021461250260472298, 0.015784041956067085, 0.008564875461161137, -0.02219017595052719, -0.006265955977141857, -0.025946946814656258, 0.007695771753787994, 0.008887284435331821, -0.006378098390996456, 0.010422233492136002, 0.004709980450570583, -0.002204298973083496, -0.014648600481450558, -0.0029787824023514986, -0.012721152976155281, -0.009462014771997929, -0.015447614714503288, 0.022442495450377464, -0.0059085022658109665, 0.0018538539297878742, -0.005354799330234528, 0.013197758235037327, 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+122,"Download a dataset from Mila’s Google Drive with  gdown
+The utility gdown is a simple utility to
+download data from Google Drive from the command line shell or in a Python
+script and requires no setup.
+
+Warning
+A limitation however is that it uses a shared client id which can cause a
+quota block when too many users uses it in the same day. It is described in
+a GitHub issue.",87,"[-0.02421621046960354, 0.0049215639010071754, 0.010090087540447712, -0.011896424926817417, -0.014648267067968845, 0.026629364117980003, -0.009426822885870934, -0.034263961017131805, -0.0008784726378507912, -0.045948706567287445, 0.014944619499146938, -0.007606373634189367, -0.007860389538109303, 0.0022843817714601755, -0.023750513792037964, 0.010040695779025555, 0.006505636963993311, -0.025782642886042595, 0.04007811099290848, -0.025796756148338318, 0.024921810254454613, 0.018938317894935608, -0.007662821561098099, -0.026403572410345078, -0.01858551800251007, -0.004064259119331837, 0.015876011922955513, -0.016652172431349754, 0.011367224156856537, 0.007175957318395376, 0.0021873617079108953, -0.038610462099313736, -0.001581427175551653, -0.000868329661898315, -0.014309578575193882, -0.017865804955363274, 0.00975845567882061, -0.0067349569872021675, -0.001980973407626152, -0.01337113045156002, 0.012199833057820797, 0.009631447494029999, 0.0023390657734125853, 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+123,"Download a dataset from Mila’s Google Drive with rclone
+Rclone is a command line program to manage files on
+cloud storage. In the context of a Google Drive remote, it allows to specify a
+client id to avoid sharing with other users which avoid quota limits. Rclone
+describes the creation of a client id in its documentaton. Once this is done, a
+remote for Mila’s Google Drive can be configured from the command line:
+rclone config create mila-gdrive drive client_id XXXXXXXXXXXX-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.apps.googleusercontent.com \
+    client_secret XXXXXXXXXXXXX-XXXXXXXXXX \
+    scope 'drive.readonly' \
+    root_folder_id 1peJ6VF9wQ-LeETgcdGxu1e4fo28JbtUt \
+    config_is_local false \
+    config_refresh_token false
+
+
+The remote can then be used to download a dataset:
+rclone copy --progress mila-gdrive:DATASET_NAME/ ~/scratch/datasets/DATASET_NAME/
+
+
+Rclone is available from the conda channel conda-forge.",226,"[-0.005835805553942919, -0.004188932944089174, 0.0022567620035260916, -0.01655072718858719, -0.01281280443072319, 0.02630895934998989, -0.008917711675167084, -0.04701445251703262, -0.012723969295620918, -0.019120121374726295, 0.02960270456969738, -0.00662849098443985, -0.010284410789608955, -0.0014956813538447022, -0.03053205832839012, 0.0034099144395440817, 0.01364649087190628, -0.023151883855462074, 0.029766706749796867, -0.009990571066737175, 0.02051415480673313, 0.004024928901344538, -0.019215790554881096, -0.02848201058804989, -0.02013147808611393, -0.009464391507208347, 0.007038500625640154, -0.020404817536473274, 0.019530130550265312, 0.0016528518171980977, 0.001648580888286233, -0.02136150747537613, -0.009040715172886848, -0.003512416733428836, -0.013441486284136772, -0.009293554350733757, 0.012997308745980263, -0.00751001201570034, -0.007892687804996967, -0.005712802521884441, 0.0121226217597723, 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+124,"Digital Object Identifier (DOI)
+It is recommended to get a DOI to reference the dataset. A DOI is a permanent
+id/URL which prevents losing references of online scientific data.
+https://figshare.com can be used to create a DOI:
+
+Go in My Data
+Create an item by clicking Create new item
+Check Metadata record only at the top
+Fill the metadata fields
+
+Then reference the dataset using https://doi.org like this:
+https://doi.org/10.6084/m9.figshare.2066037",108,"[-0.032070040702819824, -0.0024736954364925623, 0.018199097365140915, -0.02928020991384983, -0.021692907437682152, 0.01550052035599947, -0.029984185472130775, -0.002413401147350669, -0.009099548682570457, -0.008930073119699955, 0.00581758376210928, 0.0082000233232975, 0.0018088285578414798, 0.02029799111187458, -0.03629390150308609, 0.018603231757879257, 0.013662359677255154, -0.006785551551729441, 0.037206463515758514, -0.001465803012251854, -0.03358228877186775, 0.0166216678917408, -0.006462895777076483, -0.013108303770422935, -0.01027936115860939, 0.007144058123230934, 0.016178423538804054, -0.017716743052005768, 0.016373973339796066, -0.022344736382365227, -0.004993018694221973, -0.020167624577879906, -0.01456188503652811, -0.03193967789411545, -0.006000096444040537, 0.016400046646595, -0.024495776742696762, -0.018564121797680855, -0.0015904655447229743, -0.02622964419424534, 0.008578084409236908, 0.016282716765999794, 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+125,"Data Transmission using Globus Connect Personal
+Mila doesn’t own a Globus license but if the source or destination provides a
+Globus account, like Digital Research Alliance of Canada for example, it’s
+possible to setup Globus Connect Personal to create a personal endpoint on the
+Mila cluster by following the Globus guide to Install, Configure, and
+Uninstall Globus Connect Personal for Linux.
+This endpoint can then be used to transfer data to and from the Mila cluster.",102,"[-0.010427705943584442, -0.011922230012714863, 0.012506453320384026, -0.04263471066951752, -0.012662699446082115, 0.03407515957951546, -0.030026357620954514, 0.008253851905465126, 0.016548464074730873, -0.024061845615506172, 0.00566900335252285, 0.006460422184318304, 0.0038348142988979816, -0.010359772481024265, -0.027132414281368256, -0.000693340552970767, 0.011738811619579792, -0.012513247318565845, 0.00509157357737422, -0.006059617735445499, -0.011446699500083923, 0.02233634889125824, -0.006093584466725588, -0.01779842935502529, -0.014890899881720543, -0.0167250894010067, 0.023925980553030968, -0.005696176551282406, 0.0058490256778895855, 0.005882992409169674, -0.0059475284069776535, -0.019211433827877045, -0.012438520789146423, -0.0078666340559721, -0.010054074227809906, -0.014388196170330048, 0.03725442290306091, -0.0009018097189255059, -0.03211869299411774, 0.00674234377220273, 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+126,"JupyterHub
+JupyterHub is a platform connected to SLURM to start a JupyterLab
+session as a batch job then connects it when the allocation has been granted.
+It does not require any ssh tunnel or port redirection, the hub acts as a proxy
+server that will redirect you to a session as soon as it is available.
+It is currently available for Mila clusters and some Digital Research Alliance
+of Canada (Alliance) clusters.
+
+
+
+
+
+
+
+Cluster
+Address
+Login type
+
+
+
+Mila Local
+https://jupyterhub.server.mila.quebec
+Google Oauth
+
+Alliance
+https://docs.alliancecan.ca/wiki/JupyterHub
+DRAC login
+
+
+
+
+Warning
+Do not forget to close the JupyterLab session! Closing the window leaves
+running the session and the SLURM job it is linked to.
+To close it, use the hub menu and then Control Panel > Stop my server
+
+
+Note
+For Mila Clusters:
+mila.quebec account credentials should be used to login and start a
+JupyterLab session.
+
+
+Access Mila Storage in JupyterLab
+Unfortunately, JupyterLab does not allow the navigation to parent directories of
+$HOME. This makes some file systems like /network/datasets or
+$SLURM_TMPDIR unavailable through their absolute path in the interface. It
+is however possible to create symbolic links to those resources. To do so, you
+can use the ln -s command:
+ln -s /network/datasets $HOME
+
+
+Note that $SLURM_TMPDIR is a directory that is dynamically created for each
+job so you would need to recreate the symbolic link every time you start a
+JupyterHub session:
+ln -sf $SLURM_TMPDIR $HOME",361,"[-0.024874243885278702, -0.013851704075932503, 0.008200377225875854, -0.012388101778924465, -0.026652976870536804, 0.014972166158258915, -0.00878161657601595, -0.012584182433784008, 0.011841876432299614, -0.028851881623268127, 0.019215913489460945, -0.008641558699309826, -0.01924392580986023, 0.0061870478093624115, -0.030280468985438347, -0.00877461303025484, 0.01750721037387848, -0.019229918718338013, 0.004460837226361036, -0.02630283124744892, -0.01956605724990368, 0.009264815598726273, 0.010567352175712585, -0.025014301761984825, -0.03537856787443161, 0.007479079999029636, 0.008445478044450283, -0.017493203282356262, -0.005658330395817757, 0.016932973638176918, -0.007682163733989, 0.0077872066758573055, -0.014495969749987125, 0.0014478462981060147, -0.009117755107581615, 0.013550580479204655, 0.021919026970863342, -0.023067498579621315, -0.0032108223531395197, -0.001179985934868455, 0.02721320651471615, -0.0026260814629495144, -0.002659345045685768, 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+127,"Access Mila Storage in JupyterLab
+Unfortunately, JupyterLab does not allow the navigation to parent directories of
+$HOME. This makes some file systems like /network/datasets or
+$SLURM_TMPDIR unavailable through their absolute path in the interface. It
+is however possible to create symbolic links to those resources. To do so, you
+can use the ln -s command:
+ln -s /network/datasets $HOME
+
+
+Note that $SLURM_TMPDIR is a directory that is dynamically created for each
+job so you would need to recreate the symbolic link every time you start a
+JupyterHub session:
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+128,"Advanced SLURM usage and Multiple GPU jobs
+
+Handling preemption
+On the Mila cluster, jobs can preempt one-another depending on their priority
+(unkillable>high>low) (See the Slurm documentation)
+The default preemption mechanism is to kill and re-queue the job automatically
+without any notice. To allow a different preemption mechanism, every partition
+have been duplicated (i.e. have the same characteristics as their counterparts)
+allowing a 120sec grace period before killing your job but don’t requeue
+it automatically: those partitions are referred by the suffix: -grace
+(main-grace, long-grace, main-cpu-grace, long-cpu-grace).
+When using a partition with a grace period, a series of signals consisting of
+first SIGCONT and SIGTERM then SIGKILL will be sent to the SLURM
+job.  It’s good practice to catch those signals using the Linux trap command
+to properly terminate a job and save what’s necessary to restart the job.  On
+each cluster, you’ll be allowed a grace period before SLURM actually kills
+your job (SIGKILL).
+The easiest way to handle preemption is by trapping the SIGTERM signal
+ 1#SBATCH --ntasks=1
+ 2#SBATCH ....
+ 3
+ 4exit_script() {
+ 5    echo ""Preemption signal, saving myself""
+ 6    trap - SIGTERM # clear the trap
+ 7    # Optional: sends SIGTERM to child/sub processes
+ 8    kill -- -$$
+ 9}
+10
+11trap exit_script SIGTERM
+12
+13# The main script part
+14python3 my_script
+
+
+
+Note
+
+Requeuing:
+The Slurm scheduler on the cluster does not allow a grace period before
+preempting a job while requeuing it automatically, therefore your job will
+be cancelled at the end of the grace period.
+To automatically requeue it, you can just add the sbatch command inside
+your exit_script function.
+
+
+
+
+Packing jobs
+
+Sharing a GPU between processes
+srun, when used in a batch job is responsible for starting tasks on the
+allocated resources (see srun) SLURM batch script
+1#SBATCH --ntasks-per-node=2
+2#SBATCH --output=myjob_output_wrapper.out
+3#SBATCH --ntasks=2
+4#SBATCH --gres=gpu:1
+5#SBATCH --cpus-per-task=4
+6#SBATCH --mem=18G
+7srun -l --output=myjob_output_%t.out python script args
+
+
+This will run Python 2 times, each process with 4 CPUs with the same arguments
+--output=myjob_output_%t.out will create 2 output files appending the task
+id (%t) to the filename and 1 global log file for things happening outside
+the srun command.
+Knowing that, if you want to have 2 different arguments to the Python program,
+you can use a multi-prog configuration file: srun -l --multi-prog silly.conf
+0  python script firstarg
+1  python script secondarg
+
+
+Or by specifying a range of tasks
+0-1  python script %t
+
+
+%t being the taskid that your Python script will parse.  Note the -l on the
+srun command: this will prepend each line with the taskid (0:, 1:)
+
+
+Sharing a node with multiple GPU 1process/GPU
+On Digital Research Alliance of Canada, several nodes, especially nodes with
+largeGPU (P100) are reserved for jobs requesting the whole node, therefore
+packing multiple processes in a single job can leverage faster GPU.
+If you want different tasks to access different GPUs in a single allocation you
+need to create an allocation requesting a whole node and using srun with a
+subset of those resources (1 GPU).
+Keep in mind that every resource not specified on the srun command while
+inherit the global allocation specification so you need to split each resource
+in a subset (except –cpu-per-task which is a per-task requirement)
+Each srun represents a job step (%s).
+Example for a GPU node with 24 cores and 4 GPUs and 128G of RAM
+Requesting 1 task per GPU
+ 1#!/bin/bash
+ 2#SBATCH --nodes=1-1
+ 3#SBATCH --ntasks-per-node=4
+ 4#SBATCH --output=myjob_output_wrapper.out
+ 5#SBATCH --gres=gpu:4
+ 6#SBATCH --cpus-per-task=6
+ 7srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args1 &
+ 8srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args2 &
+ 9srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args3 &
+10srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args4 &
+11wait
+
+
+This will create 4 output files:
+
+JOBID-step-0.out
+JOBID-step-1.out
+JOBID-step-2.out
+JOBID-step-3.out
+
+
+
+Sharing a node with multiple GPU & multiple processes/GPU
+Combining both previous sections, we can create a script requesting a whole node
+with four GPUs, allocating 1 GPU per srun and sharing each GPU with multiple
+processes
+Example still with a 24 cores/4 GPUs/128G RAM
+Requesting 2 tasks per GPU
+ 1#!/bin/bash
+ 2#SBATCH --nodes=1-1
+ 3#SBATCH --ntasks-per-node=8
+ 4#SBATCH --output=myjob_output_wrapper.out
+ 5#SBATCH --gres=gpu:4
+ 6#SBATCH --cpus-per-task=3
+ 7srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+ 8srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+ 9srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+10srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+11wait
+
+
+--exclusive is important to specify subsequent step/srun to bind to different cpus.
+This will produce 8 output files, 2 for each step:
+
+JOBID-step-0-task-0.out
+JOBID-step-0-task-1.out
+JOBID-step-1-task-0.out
+JOBID-step-1-task-1.out
+JOBID-step-2-task-0.out
+JOBID-step-2-task-1.out
+JOBID-step-3-task-0.out
+JOBID-step-3-task-1.out
+
+Running nvidia-smi in silly.conf, while parsing the output, we can see 4
+GPUs allocated and 2 tasks per GPU
+cat JOBID-step-* | grep Tesla
+0: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
+0: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
+0: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
+0: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |",1858,"[-0.011224907822906971, -0.0169999897480011, 0.0005294426809996367, -0.02269556373357773, -0.01840219832956791, 0.003677187254652381, -0.029749980196356773, 0.011998291127383709, -0.043598610907793045, -0.03437582775950432, 0.04169045016169548, 0.027928553521633148, 0.005088432226330042, 0.012894549407064915, 0.0006586411618627608, -0.006584604736417532, 0.021813761442899704, -0.005771467462182045, 0.0058654299937188625, -0.012677713297307491, -0.009894976392388344, 0.009627544321119785, -0.00450658705085516, -0.008160283789038658, -0.028723619878292084, 0.01420279685407877, 0.015091827139258385, -0.016306111589074135, -0.0023996583186089993, 0.010538258589804173, -0.016956621780991554, 0.006049741059541702, -0.028839267790317535, 0.008210878819227219, -0.04030269384384155, 0.0035380504559725523, 0.007423039525747299, -0.003122447058558464, -0.009446847252547741, -0.008210878819227219, 0.018431110307574272, 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+129,"Handling preemption
+On the Mila cluster, jobs can preempt one-another depending on their priority
+(unkillable>high>low) (See the Slurm documentation)
+The default preemption mechanism is to kill and re-queue the job automatically
+without any notice. To allow a different preemption mechanism, every partition
+have been duplicated (i.e. have the same characteristics as their counterparts)
+allowing a 120sec grace period before killing your job but don’t requeue
+it automatically: those partitions are referred by the suffix: -grace
+(main-grace, long-grace, main-cpu-grace, long-cpu-grace).
+When using a partition with a grace period, a series of signals consisting of
+first SIGCONT and SIGTERM then SIGKILL will be sent to the SLURM
+job.  It’s good practice to catch those signals using the Linux trap command
+to properly terminate a job and save what’s necessary to restart the job.  On
+each cluster, you’ll be allowed a grace period before SLURM actually kills
+your job (SIGKILL).
+The easiest way to handle preemption is by trapping the SIGTERM signal
+ 1#SBATCH --ntasks=1
+ 2#SBATCH ....
+ 3
+ 4exit_script() {
+ 5    echo ""Preemption signal, saving myself""
+ 6    trap - SIGTERM # clear the trap
+ 7    # Optional: sends SIGTERM to child/sub processes
+ 8    kill -- -$$
+ 9}
+10
+11trap exit_script SIGTERM
+12
+13# The main script part
+14python3 my_script
+
+
+
+Note
+
+Requeuing:
+The Slurm scheduler on the cluster does not allow a grace period before
+preempting a job while requeuing it automatically, therefore your job will
+be cancelled at the end of the grace period.
+To automatically requeue it, you can just add the sbatch command inside
+your exit_script function.",412,"[-0.013301797211170197, -0.02218841016292572, 0.003374663181602955, -0.018138814717531204, -0.017984142526984215, 0.00542055256664753, -0.022975830361247063, 0.02637861669063568, -0.03065318986773491, -0.017337331548333168, 0.03717753663659096, 0.023580458015203476, 0.00036185351200401783, 0.01837785355746746, 0.00015357353549916297, -0.0125917112454772, 0.02356639690697193, 0.0006788872997276485, 0.004355424549430609, -0.020107368007302284, 0.013365072198212147, 0.024635041132569313, 0.002494086977094412, -0.022539937868714333, -0.023791374638676643, 0.01600152812898159, 0.010243508964776993, -0.01984020695090294, 0.0020388588309288025, 0.024761591106653214, -0.033071696758270264, 0.01179725956171751, -0.025478705763816833, 0.012247215025126934, -0.04403935372829437, 3.9739075873512775e-05, 0.0030688343103975058, -0.00998337846249342, 0.0050690253265202045, -0.01242297887802124, 0.018068509176373482, 0.010116958990693092, 0.020515140146017075, 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+130,"Packing jobs
+
+Sharing a GPU between processes
+srun, when used in a batch job is responsible for starting tasks on the
+allocated resources (see srun) SLURM batch script
+1#SBATCH --ntasks-per-node=2
+2#SBATCH --output=myjob_output_wrapper.out
+3#SBATCH --ntasks=2
+4#SBATCH --gres=gpu:1
+5#SBATCH --cpus-per-task=4
+6#SBATCH --mem=18G
+7srun -l --output=myjob_output_%t.out python script args
+
+
+This will run Python 2 times, each process with 4 CPUs with the same arguments
+--output=myjob_output_%t.out will create 2 output files appending the task
+id (%t) to the filename and 1 global log file for things happening outside
+the srun command.
+Knowing that, if you want to have 2 different arguments to the Python program,
+you can use a multi-prog configuration file: srun -l --multi-prog silly.conf
+0  python script firstarg
+1  python script secondarg
+
+
+Or by specifying a range of tasks
+0-1  python script %t
+
+
+%t being the taskid that your Python script will parse.  Note the -l on the
+srun command: this will prepend each line with the taskid (0:, 1:)
+
+
+Sharing a node with multiple GPU 1process/GPU
+On Digital Research Alliance of Canada, several nodes, especially nodes with
+largeGPU (P100) are reserved for jobs requesting the whole node, therefore
+packing multiple processes in a single job can leverage faster GPU.
+If you want different tasks to access different GPUs in a single allocation you
+need to create an allocation requesting a whole node and using srun with a
+subset of those resources (1 GPU).
+Keep in mind that every resource not specified on the srun command while
+inherit the global allocation specification so you need to split each resource
+in a subset (except –cpu-per-task which is a per-task requirement)
+Each srun represents a job step (%s).
+Example for a GPU node with 24 cores and 4 GPUs and 128G of RAM
+Requesting 1 task per GPU
+ 1#!/bin/bash
+ 2#SBATCH --nodes=1-1
+ 3#SBATCH --ntasks-per-node=4
+ 4#SBATCH --output=myjob_output_wrapper.out
+ 5#SBATCH --gres=gpu:4
+ 6#SBATCH --cpus-per-task=6
+ 7srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args1 &
+ 8srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args2 &
+ 9srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args3 &
+10srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args4 &
+11wait
+
+
+This will create 4 output files:
+
+JOBID-step-0.out
+JOBID-step-1.out
+JOBID-step-2.out
+JOBID-step-3.out
+
+
+
+Sharing a node with multiple GPU & multiple processes/GPU
+Combining both previous sections, we can create a script requesting a whole node
+with four GPUs, allocating 1 GPU per srun and sharing each GPU with multiple
+processes
+Example still with a 24 cores/4 GPUs/128G RAM
+Requesting 2 tasks per GPU
+ 1#!/bin/bash
+ 2#SBATCH --nodes=1-1
+ 3#SBATCH --ntasks-per-node=8
+ 4#SBATCH --output=myjob_output_wrapper.out
+ 5#SBATCH --gres=gpu:4
+ 6#SBATCH --cpus-per-task=3
+ 7srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+ 8srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+ 9srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+10srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+11wait
+
+
+--exclusive is important to specify subsequent step/srun to bind to different cpus.
+This will produce 8 output files, 2 for each step:
+
+JOBID-step-0-task-0.out
+JOBID-step-0-task-1.out
+JOBID-step-1-task-0.out
+JOBID-step-1-task-1.out
+JOBID-step-2-task-0.out
+JOBID-step-2-task-1.out
+JOBID-step-3-task-0.out
+JOBID-step-3-task-1.out
+
+Running nvidia-smi in silly.conf, while parsing the output, we can see 4
+GPUs allocated and 2 tasks per GPU
+cat JOBID-step-* | grep Tesla
+0: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
+0: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
+0: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
+0: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |",1434,"[-0.0033083255402743816, -0.004895759746432304, 0.006124967709183693, -0.016759373247623444, -0.019498750567436218, -0.00866064801812172, -0.02670542150735855, 0.0028289344627410173, -0.03118676319718361, -0.035738345235586166, 0.021310955286026, 0.026241835206747055, 0.013675817288458347, 0.0009903904283419251, -0.00019831953977700323, 0.012425537221133709, 0.011076919734477997, -0.00852719135582447, 0.008428854867815971, 0.00283420248888433, -0.028587866574525833, -0.020130915567278862, -0.012734594754874706, 0.004562117625027895, -0.02479488216340542, 0.012474705465137959, 0.019611135125160217, -0.006957317236810923, 0.0024057356640696526, -0.0080355079844594, -0.009995217435061932, -0.0010694109369069338, -0.015424804762005806, 0.0003652503655757755, -0.024457726627588272, -0.0011774057056754827, 0.002342519350349903, -0.0012467681663110852, 0.002698989585042, -0.023811515420675278, 0.024050332605838776, 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+131,"Sharing a GPU between processes
+srun, when used in a batch job is responsible for starting tasks on the
+allocated resources (see srun) SLURM batch script
+1#SBATCH --ntasks-per-node=2
+2#SBATCH --output=myjob_output_wrapper.out
+3#SBATCH --ntasks=2
+4#SBATCH --gres=gpu:1
+5#SBATCH --cpus-per-task=4
+6#SBATCH --mem=18G
+7srun -l --output=myjob_output_%t.out python script args
+
+
+This will run Python 2 times, each process with 4 CPUs with the same arguments
+--output=myjob_output_%t.out will create 2 output files appending the task
+id (%t) to the filename and 1 global log file for things happening outside
+the srun command.
+Knowing that, if you want to have 2 different arguments to the Python program,
+you can use a multi-prog configuration file: srun -l --multi-prog silly.conf
+0  python script firstarg
+1  python script secondarg
+
+
+Or by specifying a range of tasks
+0-1  python script %t
+
+
+%t being the taskid that your Python script will parse.  Note the -l on the
+srun command: this will prepend each line with the taskid (0:, 1:)",279,"[-0.008530627936124802, -0.003139256965368986, 0.008558735251426697, -0.016653696075081825, -0.002961828140541911, -0.011664614081382751, -0.018241770565509796, 0.007258763071149588, -0.030496641993522644, -0.032604705542325974, 0.018241770565509796, 0.022584378719329834, -0.003980725072324276, -0.00281075038947165, -0.0019042834173887968, 0.00856576208025217, 0.011826232075691223, 0.0012217981275171041, 0.017581243067979813, 0.006854717619717121, -0.0335884690284729, -0.013927267864346504, -0.005779605358839035, -0.00978843867778778, -0.033447928726673126, 0.010863550007343292, 0.017637459561228752, -0.008987374603748322, 0.0016987472772598267, -0.00502421660348773, -0.015669934451580048, 0.002036037389189005, -0.008586842566728592, -0.0028862892650067806, -0.028107503429055214, 0.0008915349026210606, -0.00043434876715764403, -0.003699650289490819, 0.013470521196722984, -0.020349832251667976, 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+132,"Sharing a node with multiple GPU 1process/GPU
+On Digital Research Alliance of Canada, several nodes, especially nodes with
+largeGPU (P100) are reserved for jobs requesting the whole node, therefore
+packing multiple processes in a single job can leverage faster GPU.
+If you want different tasks to access different GPUs in a single allocation you
+need to create an allocation requesting a whole node and using srun with a
+subset of those resources (1 GPU).
+Keep in mind that every resource not specified on the srun command while
+inherit the global allocation specification so you need to split each resource
+in a subset (except –cpu-per-task which is a per-task requirement)
+Each srun represents a job step (%s).
+Example for a GPU node with 24 cores and 4 GPUs and 128G of RAM
+Requesting 1 task per GPU
+ 1#!/bin/bash
+ 2#SBATCH --nodes=1-1
+ 3#SBATCH --ntasks-per-node=4
+ 4#SBATCH --output=myjob_output_wrapper.out
+ 5#SBATCH --gres=gpu:4
+ 6#SBATCH --cpus-per-task=6
+ 7srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args1 &
+ 8srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args2 &
+ 9srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args3 &
+10srun --gres=gpu:1 -n1 --mem=30G -l --output=%j-step-%s.out --exclusive --multi-prog python script args4 &
+11wait
+
+
+This will create 4 output files:
+
+JOBID-step-0.out
+JOBID-step-1.out
+JOBID-step-2.out
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+133,"Sharing a node with multiple GPU & multiple processes/GPU
+Combining both previous sections, we can create a script requesting a whole node
+with four GPUs, allocating 1 GPU per srun and sharing each GPU with multiple
+processes
+Example still with a 24 cores/4 GPUs/128G RAM
+Requesting 2 tasks per GPU
+ 1#!/bin/bash
+ 2#SBATCH --nodes=1-1
+ 3#SBATCH --ntasks-per-node=8
+ 4#SBATCH --output=myjob_output_wrapper.out
+ 5#SBATCH --gres=gpu:4
+ 6#SBATCH --cpus-per-task=3
+ 7srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+ 8srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+ 9srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+10srun --gres=gpu:1 -n2 --mem=30G -l --output=%j-step-%s-task-%t.out --exclusive --multi-prog silly.conf &
+11wait
+
+
+--exclusive is important to specify subsequent step/srun to bind to different cpus.
+This will produce 8 output files, 2 for each step:
+
+JOBID-step-0-task-0.out
+JOBID-step-0-task-1.out
+JOBID-step-1-task-0.out
+JOBID-step-1-task-1.out
+JOBID-step-2-task-0.out
+JOBID-step-2-task-1.out
+JOBID-step-3-task-0.out
+JOBID-step-3-task-1.out
+
+Running nvidia-smi in silly.conf, while parsing the output, we can see 4
+GPUs allocated and 2 tasks per GPU
+cat JOBID-step-* | grep Tesla
+0: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:04:00.0 Off |                    0 |
+0: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:83:00.0 Off |                    0 |
+0: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:82:00.0 Off |                    0 |
+0: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |
+1: |   0  Tesla P100-PCIE...  On   | 00000000:03:00.0 Off |                    0 |",717,"[-0.008710494264960289, 0.008550986647605896, 0.012178047560155392, -0.031568605452775955, -0.0015942075988277793, -0.0004698534612543881, -0.030153842642903328, 0.0007810663664713502, -0.044384680688381195, -0.03858693316578865, 0.025285398587584496, 0.027837518602609634, 0.006161842029541731, -0.002040655119344592, 0.01049281656742096, 0.026755640283226967, 0.009889462031424046, -0.009813175536692142, 0.005891372915357351, 0.003779633203521371, -0.024314483627676964, -0.009078054688870907, -0.01244158111512661, 0.006040477659553289, -0.017157454043626785, 0.01296864915639162, 0.02002858743071556, -0.009209821932017803, 0.006546740420162678, -0.006678507663309574, -0.00582202197983861, -0.007198640611022711, -0.01307267602533102, -0.008342932909727097, -0.016602644696831703, 0.007781189400702715, 0.003301110817119479, 0.005107705947011709, -0.008481635712087154, -0.018072888255119324, 0.0065918187610805035, 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+134,"Multiple Nodes
+
+Data Parallel
+
+Request 3 nodes with at least 4 GPUs each.
+ 1#!/bin/bash
+ 2
+ 3# Number of Nodes
+ 4#SBATCH --nodes=3
+ 5
+ 6# Number of tasks. 3 (1 per node)
+ 7#SBATCH --ntasks=3
+ 8
+ 9# Number of GPU per node
+10#SBATCH --gres=gpu:4
+11#SBATCH --gpus-per-node=4
+12
+13# 16 CPUs per node
+14#SBATCH --cpus-per-gpu=4
+15
+16# 16Go per nodes (4Go per GPU)
+17#SBATCH --mem=16G
+18
+19# we need all nodes to be ready at the same time
+20#SBATCH --wait-all-nodes=1
+21
+22# Total resources:
+23#   CPU: 16 * 3 = 48
+24#   RAM: 16 * 3 = 48 Go
+25#   GPU:  4 * 3 = 12
+26
+27# Setup our rendez-vous point
+28RDV_ADDR=$(hostname)
+29WORLD_SIZE=$SLURM_JOB_NUM_NODES
+30# -----
+31
+32srun -l torchrun \
+33   --nproc_per_node=$SLURM_GPUS_PER_NODE\
+34   --nnodes=$WORLD_SIZE\
+35   --rdzv_id=$SLURM_JOB_ID\
+36   --rdzv_backend=c10d\
+37   --rdzv_endpoint=$RDV_ADDR\
+38   training_script.py
+
+
+You can find below a pytorch script outline on what a multi-node trainer could look like.
+import os
+import torch.distributed as dist
+
+class Trainer:
+   def __init__(self):
+      self.local_rank = None
+      self.chk_path = ...
+      self.model = ...
+
+   @property
+   def device_id(self):
+      return self.local_rank
+
+   def load_checkpoint(self, path):
+      self.chk_path = path
+      # ...
+
+   def should_checkpoint(self):
+      # Note: only one worker saves its weights
+      return self.global_rank == 0 and self.local_rank == 0
+
+   def save_checkpoint(self):
+      if self.chk_path is None:
+            return
+
+      # Save your states here
+      # Note: you should save the weights of self.model not ddp_model
+      # ...
+
+   def initialize(self):
+      self.global_rank = int(os.environ.get(""RANK"", -1))
+      self.local_rank = int(os.environ.get(""LOCAL_RANK"", -1))
+
+      assert self.global_rank >= 0, 'Global rank should be set (Only Rank 0 can save checkpoints)'
+      assert self.local_rank >= 0, 'Local rank should be set'
+
+      dist.init_process_group(backend=""gloo|nccl"")
+
+   def sync_weights(self, resuming=False):
+      if resuming:
+            # in the case of resuming all workers need to load the same checkpoint
+            self.load_checkpoint()
+
+            # Wait for everybody to finish loading the checkpoint
+            dist.barrier()
+            return
+
+      # Make sure all workers have the same initial weights
+      # This makes the leader save his weights
+      if self.should_checkpoint():
+            self.save_checkpoint()
+
+      # All workers wait for the leader to finish
+      dist.barrier()
+
+      # All followers load the leader's weights
+      if not self.should_checkpoint():
+            self.load_checkpoint()
+
+      # Leader waits for the follower to load the weights
+      dist.barrier()
+
+   def dataloader(self, dataset, batch_size):
+      train_sampler = ElasticDistributedSampler(dataset)
+      train_loader = DataLoader(
+            dataset,
+            batch_size=batch_size,
+            num_workers=4,
+            pin_memory=True,
+            sampler=train_sampler,
+      )
+      return train_loader
+
+   def train_step(self):
+      # Your batch processing step here
+      # ...
+      pass
+
+   def train(self, dataset, batch_size):
+      self.sync_weights()
+
+      ddp_model = torch.nn.parallel.DistributedDataParallel(
+            self.model,
+            device_ids=[self.device_id],
+            output_device=self.device_id
+      )
+
+      loader = self.dataloader(dataset, batch_size)
+
+      for epoch in range(100):
+            for batch in iter(loader):
+               self.train_step(batch)
+
+               if self.should_checkpoint():
+                  self.save_checkpoint()
+
+def main():
+   trainer = Trainer()
+   trainer.load_checkpoint(path)
+   tainer.initialize()
+
+   trainer.train(dataset, batch_size)
+
+
+
+Note
+To bypass Python GIL (Global interpreter lock) pytorch spawn one process for each GPU.
+In the example above this means at least 12 processes are spawn, at least 4 on each node.",980,"[-0.033587753772735596, -0.0028996746987104416, -0.0028501804918050766, -0.022255301475524902, -0.00193027185741812, 0.018213847652077675, -0.016370616853237152, 0.012977708131074905, -0.027989795431494713, -0.02946438081562519, -0.002955995500087738, 0.04038722813129425, -0.013790094293653965, -0.008178481832146645, 0.012097053229808807, 0.04281756281852722, 0.0060178060084581375, -0.0030839976388961077, 0.0012126066721975803, 0.01284799911081791, -0.02550484798848629, -0.003247840330004692, 0.007591378875076771, -0.003539685159921646, -0.000521395297255367, 0.011086690239608288, 0.02960091643035412, -0.0050279232673347, 0.020179960876703262, -0.013872016221284866, 0.009632585570216179, -0.02495870552957058, 0.01124370563775301, -0.02848132513463497, -0.006778992246836424, 0.012663676403462887, 0.01092284731566906, -0.002029260154813528, 0.0054170493967831135, -0.008137521333992481, 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+135,"Data Parallel
+
+Request 3 nodes with at least 4 GPUs each.
+ 1#!/bin/bash
+ 2
+ 3# Number of Nodes
+ 4#SBATCH --nodes=3
+ 5
+ 6# Number of tasks. 3 (1 per node)
+ 7#SBATCH --ntasks=3
+ 8
+ 9# Number of GPU per node
+10#SBATCH --gres=gpu:4
+11#SBATCH --gpus-per-node=4
+12
+13# 16 CPUs per node
+14#SBATCH --cpus-per-gpu=4
+15
+16# 16Go per nodes (4Go per GPU)
+17#SBATCH --mem=16G
+18
+19# we need all nodes to be ready at the same time
+20#SBATCH --wait-all-nodes=1
+21
+22# Total resources:
+23#   CPU: 16 * 3 = 48
+24#   RAM: 16 * 3 = 48 Go
+25#   GPU:  4 * 3 = 12
+26
+27# Setup our rendez-vous point
+28RDV_ADDR=$(hostname)
+29WORLD_SIZE=$SLURM_JOB_NUM_NODES
+30# -----
+31
+32srun -l torchrun \
+33   --nproc_per_node=$SLURM_GPUS_PER_NODE\
+34   --nnodes=$WORLD_SIZE\
+35   --rdzv_id=$SLURM_JOB_ID\
+36   --rdzv_backend=c10d\
+37   --rdzv_endpoint=$RDV_ADDR\
+38   training_script.py
+
+
+You can find below a pytorch script outline on what a multi-node trainer could look like.
+import os
+import torch.distributed as dist
+
+class Trainer:
+   def __init__(self):
+      self.local_rank = None
+      self.chk_path = ...
+      self.model = ...
+
+   @property
+   def device_id(self):
+      return self.local_rank
+
+   def load_checkpoint(self, path):
+      self.chk_path = path
+      # ...
+
+   def should_checkpoint(self):
+      # Note: only one worker saves its weights
+      return self.global_rank == 0 and self.local_rank == 0
+
+   def save_checkpoint(self):
+      if self.chk_path is None:
+            return
+
+      # Save your states here
+      # Note: you should save the weights of self.model not ddp_model
+      # ...
+
+   def initialize(self):
+      self.global_rank = int(os.environ.get(""RANK"", -1))
+      self.local_rank = int(os.environ.get(""LOCAL_RANK"", -1))
+
+      assert self.global_rank >= 0, 'Global rank should be set (Only Rank 0 can save checkpoints)'
+      assert self.local_rank >= 0, 'Local rank should be set'
+
+      dist.init_process_group(backend=""gloo|nccl"")
+
+   def sync_weights(self, resuming=False):
+      if resuming:
+            # in the case of resuming all workers need to load the same checkpoint
+            self.load_checkpoint()
+
+            # Wait for everybody to finish loading the checkpoint
+            dist.barrier()
+            return
+
+      # Make sure all workers have the same initial weights
+      # This makes the leader save his weights
+      if self.should_checkpoint():
+            self.save_checkpoint()
+
+      # All workers wait for the leader to finish
+      dist.barrier()
+
+      # All followers load the leader's weights
+      if not self.should_checkpoint():
+            self.load_checkpoint()
+
+      # Leader waits for the follower to load the weights
+      dist.barrier()
+
+   def dataloader(self, dataset, batch_size):
+      train_sampler = ElasticDistributedSampler(dataset)
+      train_loader = DataLoader(
+            dataset,
+            batch_size=batch_size,
+            num_workers=4,
+            pin_memory=True,
+            sampler=train_sampler,
+      )
+      return train_loader
+
+   def train_step(self):
+      # Your batch processing step here
+      # ...
+      pass
+
+   def train(self, dataset, batch_size):
+      self.sync_weights()
+
+      ddp_model = torch.nn.parallel.DistributedDataParallel(
+            self.model,
+            device_ids=[self.device_id],
+            output_device=self.device_id
+      )
+
+      loader = self.dataloader(dataset, batch_size)
+
+      for epoch in range(100):
+            for batch in iter(loader):
+               self.train_step(batch)
+
+               if self.should_checkpoint():
+                  self.save_checkpoint()
+
+def main():
+   trainer = Trainer()
+   trainer.load_checkpoint(path)
+   tainer.initialize()
+
+   trainer.train(dataset, batch_size)
+
+
+
+Note
+To bypass Python GIL (Global interpreter lock) pytorch spawn one process for each GPU.
+In the example above this means at least 12 processes are spawn, at least 4 on each node.",975,"[-0.031103160232305527, -0.0004558548389468342, 0.0021918911952525377, -0.019945694133639336, 0.004848676733672619, 0.019876820966601372, -0.011639579199254513, 0.009697353467345238, -0.03231532871723175, -0.03314180672168732, -0.003991204779595137, 0.04162699356675148, -0.008147705346345901, -0.009256565012037754, 0.011908184736967087, 0.04253612086176872, 0.007920423522591591, -0.006105613894760609, 0.0036812752950936556, 0.017590228468179703, -0.026764145120978355, -0.0008725380175746977, 0.012658903375267982, -0.0025965215172618628, -0.010716677643358707, 0.009903973899781704, 0.02799008972942829, -0.009049945510923862, 0.01643315702676773, -0.01309969276189804, 0.004710930399596691, -0.031736794859170914, 0.01102660782635212, -0.024932116270065308, 0.0006878715939819813, 0.009373649023473263, 0.010269002057611942, 0.0039464375004172325, 0.0031285674776881933, -0.013299425132572651, 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+136,"Frequently asked questions (FAQs)
+
+Connection/SSH issues
+
+I’m getting connection refused while trying to connect to a login node
+Login nodes are protected against brute force attacks and might ban your IP if
+it detects too many connections/failures. You will be automatically unbanned
+after 1 hour. For any further problem, please submit a support ticket.
+
+
+
+Shell issues
+
+How do I change my shell ?
+By default you will be assigned /bin/bash as a shell. If you would like to
+change for another one, please submit a support ticket.
+
+
+
+SLURM issues
+
+How can I get an interactive shell on the cluster ?
+Use salloc [--slurm_options] without any executable at the end of the
+command, this will launch your default shell on an interactive session. Remember
+that an interactive session is bound to the login node where you start it so you
+could risk losing your job if the login node becomes unreachable.
+
+
+How can I reset my cluster password ?
+To reset your password, please submit a support ticket.
+Warning: your cluster password is the same as your Google Workspace account. So,
+after reset, you must use the new password for all your Google services.
+
+
+srun: error: –mem and –mem-per-cpu are mutually exclusive
+You can safely ignore this, salloc has a default memory flag in case you
+don’t provide one.
+
+
+How can I see where and if my jobs are running ?
+Use squeue -u YOUR_USERNAME to see all your job status and locations.
+To get more info on a running job, try scontrol show job #JOBID
+
+
+Unable to allocate resources: Invalid account or account/partition combination specified
+Chances are your account is not setup properly. You should submit a support ticket.
+
+
+How do I cancel a job?
+
+To cancel a specific job, use scancel #JOBID
+To cancel all your jobs (running and pending), use scancel -u YOUR_USERNAME
+To cancel all your pending jobs only, use scancel -t PD
+
+
+
+How can I access a node on which one of my jobs is running ?
+You can ssh into a node on which you have a job running, your ssh connection
+will be adopted by your job, i.e.  if your job finishes your ssh connection will
+be automatically terminated. In order to connect to a node, you need to have
+password-less ssh either with a key present in your home or with an
+ssh-agent. You can generate a key on the login node like this:
+ssh-keygen (3xENTER)
+cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
+chmod 600 ~/.ssh/authorized_keys
+chmod 700 ~/.ssh
+
+
+I’m getting Permission denied (publickey) while trying to connect to a node
+See previous question
+
+
+Where do I put my data during a job ?
+Your /home as well as the datasets are on shared file-systems, it is
+recommended to copy them to the $SLURM_TMPDIR to better process them and
+leverage higher-speed local drives. If you run a low priority job subject to
+preemption, it’s better to save any output you want to keep on the shared file
+systems, because the $SLURM_TMPDIR is deleted at the end of each job.
+
+
+slurmstepd: error: Detected 1 oom-kill event(s) in step #####.batch cgroup
+You exceeded the amount of memory allocated to your job, either you did not
+request enough memory or you have a memory leak in your process. Try increasing
+the amount of memory requested with --mem= or --mem-per-cpu=.
+
+
+fork: retry: Resource temporarily unavailable
+You exceeded the limit of 2000 tasks/PIDs in your job, it probably means there
+is an issue with a sub-process spawning too many processes in your script. For
+any help with your software, please submit a support ticket.
+
+
+
+PyTorch issues
+
+I randomly get INTERNAL ASSERT FAILED at ""../aten/src/ATen/MapAllocator.cpp"":263
+You are using PyTorch 1.10.x and hitting #67864,
+for which the solution is PR #72232
+merged in PyTorch 1.11.x. For an immediate fix, consider the following compilable Gist:
+hack.cpp.
+Compile the patch to hack.so and then export LD_PRELOAD=/absolute/path/to/hack.so
+before executing the Python process that import torch a broken PyTorch 1.10.
+For Hydra users who are using the submitit launcher plug-in, the env_set key cannot
+be used to set LD_PRELOAD in the environment as it does so too late at runtime. The
+dynamic loader reads LD_PRELOAD only once and very early during the startup of any
+process, before the variable can be set from inside the process. The hack must therefore
+be injected using the setup key in Hydra YAML config file:
+hydra:
+  launcher:
+    setup:
+      - export LD_PRELOAD=/absolute/path/to/hack.so",1071,"[-0.003647199599072337, -0.0016801803139969707, -0.017801392823457718, -0.02517228201031685, -0.04747965186834335, -0.010743418708443642, -0.01958153210580349, -0.001377695705741644, -0.033461056649684906, -0.022933201864361763, 0.03220939636230469, -0.008977186866104603, 0.002395539078861475, 0.008546059019863605, 0.009109307080507278, -0.0035707091446965933, 0.018149076029658318, -0.026368314400315285, 0.010541762225329876, -0.01862192526459694, -0.014282836578786373, -0.0013090282445773482, 0.001969626871868968, -0.005027502775192261, -0.006567740812897682, 0.00556641211733222, 0.006355653516948223, -0.020249085500836372, -0.009728183038532734, -0.0006032307865098119, 0.0016236817464232445, 0.008212283253669739, -0.029010707512497902, 0.006386945024132729, -0.035463713109493256, 0.006672045681625605, 0.004937105346471071, -0.024866322055459023, -0.0012707830173894763, -0.02799547277390957, 0.014477538876235485, 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+137,"Connection/SSH issues
+
+I’m getting connection refused while trying to connect to a login node
+Login nodes are protected against brute force attacks and might ban your IP if
+it detects too many connections/failures. You will be automatically unbanned
+after 1 hour. For any further problem, please submit a support ticket.",70,"[-0.0026325094513595104, -0.03412771224975586, 0.006893478333950043, -0.022112414240837097, -0.038337063044309616, -0.0021030104253441095, -0.022711846977472305, 0.0007538701174780726, 0.01045011356472969, -0.015065747313201427, 0.004295935854315758, 0.005618018563836813, 0.00934449303895235, -0.013707032427191734, -0.0054215374402701855, 0.013880202546715736, 0.020607171580195427, -0.01570514217019081, 0.004778812173753977, -0.023644298315048218, -0.025016333907842636, -0.00813230685889721, -0.015944914892315865, 0.002670806599780917, -0.013960126787424088, -0.012421581894159317, 0.0025059625040739775, -0.01457288023084402, 0.005534763913601637, -0.005714593920856714, 0.013207505457103252, -0.018449213355779648, -0.01642446219921112, 0.02375086396932602, -0.021752754226326942, 0.026415010914206505, 0.011249357834458351, -0.026415010914206505, -0.004322577267885208, -0.011069527827203274, 0.014799332246184349, 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+138,"I’m getting connection refused while trying to connect to a login node
+Login nodes are protected against brute force attacks and might ban your IP if
+it detects too many connections/failures. You will be automatically unbanned
+after 1 hour. For any further problem, please submit a support ticket.",63,"[-0.011293814517557621, -0.027075940743088722, 0.0021611619740724564, -0.028576470911502838, -0.03784523159265518, 0.009501145221292973, -0.0260667335242033, 0.0009087835205718875, 0.01027796883136034, -0.017767339944839478, -0.000852347700856626, 0.0043289633467793465, 0.014168724417686462, -0.015111534856259823, -0.004003627225756645, 0.013471574522554874, 0.02177760750055313, -0.004826927091926336, 0.005019472911953926, -0.026664290577173233, -0.017674388363957405, 0.004923200234770775, -0.018537525087594986, 0.0014183663297444582, -0.01979903317987919, -0.017461923882365227, -0.0017810497665777802, -0.01219014823436737, 0.015018581412732601, -0.007203873246908188, 0.007914301007986069, -0.021140214055776596, -0.013239191845059395, 0.018816383555531502, -0.029293537139892578, 0.024672435596585274, 0.008863751776516438, -0.028603028506040573, 0.002851671539247036, -0.008936786092817783, 0.01581532321870327, 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+139,"Shell issues
+
+How do I change my shell ?
+By default you will be assigned /bin/bash as a shell. If you would like to
+change for another one, please submit a support ticket.",45,"[0.01981346681714058, -0.011463318020105362, -0.003413272090256214, -0.013376069255173206, -0.030261049047112465, -0.004419115372002125, -0.03688312694430351, -0.007248668931424618, -0.0026926924474537373, -0.004854431375861168, 0.028968291357159615, -0.000315562792820856, 0.017373060807585716, -0.0038980557583272457, 0.00041676548426039517, 0.0027883299626410007, 0.009939712472259998, 0.013771810568869114, -0.005345810670405626, -0.023414716124534607, -0.0075454749166965485, 0.021277712658047676, 0.024219391867518425, -0.00517432251945138, -0.02032793127000332, -0.009168015792965889, -0.0013389260275289416, -0.010203540325164795, 0.0022326428443193436, -0.014523720368742943, 0.007387178484350443, 0.010065030306577682, -0.010375028476119041, -0.019760701805353165, -0.01863943412899971, 0.0019341874867677689, 0.00650005741044879, -0.02667299099266529, -0.002834499813616276, -0.009761628694832325, 0.011799697764217854, 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+140,"How do I change my shell ?
+By default you will be assigned /bin/bash as a shell. If you would like to
+change for another one, please submit a support ticket.",40,"[0.013889284804463387, -0.016441982239484787, -0.004061405081301928, -0.014072556048631668, -0.028825830668210983, 0.00022131548030301929, -0.02955891191959381, -0.012802752666175365, -0.007402818650007248, -0.006496284622699022, 0.031836703419685364, 0.0021665189415216446, 0.0185626819729805, -0.004722487647086382, 0.0004888576804660261, 0.002834147308021784, 0.012783116661012173, 0.014255826361477375, -0.0018376139923930168, -0.019243402406573296, -0.004225039388984442, 0.02466297149658203, 0.024165524169802666, -0.01672997698187828, -0.025828048586845398, -0.010197693482041359, 0.00042340392246842384, -0.00952352024614811, 0.009425339289009571, -0.008986799977719784, 0.003848680295050144, 0.010845686309039593, -0.00917007029056549, -0.023864436894655228, -0.0244797021150589, 0.0074093639850616455, 0.007167185191065073, -0.02667894773185253, -0.000737581925932318, -0.01737142540514469, 0.016468163579702377, 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+141,"SLURM issues
+
+How can I get an interactive shell on the cluster ?
+Use salloc [--slurm_options] without any executable at the end of the
+command, this will launch your default shell on an interactive session. Remember
+that an interactive session is bound to the login node where you start it so you
+could risk losing your job if the login node becomes unreachable.
+
+
+How can I reset my cluster password ?
+To reset your password, please submit a support ticket.
+Warning: your cluster password is the same as your Google Workspace account. So,
+after reset, you must use the new password for all your Google services.
+
+
+srun: error: –mem and –mem-per-cpu are mutually exclusive
+You can safely ignore this, salloc has a default memory flag in case you
+don’t provide one.
+
+
+How can I see where and if my jobs are running ?
+Use squeue -u YOUR_USERNAME to see all your job status and locations.
+To get more info on a running job, try scontrol show job #JOBID
+
+
+Unable to allocate resources: Invalid account or account/partition combination specified
+Chances are your account is not setup properly. You should submit a support ticket.
+
+
+How do I cancel a job?
+
+To cancel a specific job, use scancel #JOBID
+To cancel all your jobs (running and pending), use scancel -u YOUR_USERNAME
+To cancel all your pending jobs only, use scancel -t PD
+
+
+
+How can I access a node on which one of my jobs is running ?
+You can ssh into a node on which you have a job running, your ssh connection
+will be adopted by your job, i.e.  if your job finishes your ssh connection will
+be automatically terminated. In order to connect to a node, you need to have
+password-less ssh either with a key present in your home or with an
+ssh-agent. You can generate a key on the login node like this:
+ssh-keygen (3xENTER)
+cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
+chmod 600 ~/.ssh/authorized_keys
+chmod 700 ~/.ssh
+
+
+I’m getting Permission denied (publickey) while trying to connect to a node
+See previous question
+
+
+Where do I put my data during a job ?
+Your /home as well as the datasets are on shared file-systems, it is
+recommended to copy them to the $SLURM_TMPDIR to better process them and
+leverage higher-speed local drives. If you run a low priority job subject to
+preemption, it’s better to save any output you want to keep on the shared file
+systems, because the $SLURM_TMPDIR is deleted at the end of each job.
+
+
+slurmstepd: error: Detected 1 oom-kill event(s) in step #####.batch cgroup
+You exceeded the amount of memory allocated to your job, either you did not
+request enough memory or you have a memory leak in your process. Try increasing
+the amount of memory requested with --mem= or --mem-per-cpu=.
+
+
+fork: retry: Resource temporarily unavailable
+You exceeded the limit of 2000 tasks/PIDs in your job, it probably means there
+is an issue with a sub-process spawning too many processes in your script. For
+any help with your software, please submit a support ticket.",715,"[-0.0015256910119205713, -0.0025252816267311573, -0.00854869931936264, -0.017759768292307854, -0.05089201405644417, 0.01349576748907566, -0.015620867721736431, -0.02241015061736107, -0.04377155005931854, -0.013074886985123158, 0.031186537817120552, 0.010121826082468033, -0.014558318071067333, 0.009438757784664631, 0.005236854776740074, -0.021513191983103752, 0.024756040424108505, -0.02344510145485401, 0.013958046212792397, 0.0073343561962246895, -0.006485695950686932, -0.0032790713012218475, 0.00319282547570765, -0.001376485452055931, -0.006447747815400362, 0.002066453220322728, 0.017511378973722458, -0.016255637630820274, 0.0029892849270254374, -0.004229501821100712, -0.01349576748907566, 0.0052782525308430195, -0.01902930811047554, 0.001923284842632711, -0.04252960532903671, 0.019112104550004005, 0.005616336595267057, -0.016752414405345917, -0.004860822111368179, -0.027557307854294777, 0.00983893871307373, 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+142,"How can I get an interactive shell on the cluster ?
+Use salloc [--slurm_options] without any executable at the end of the
+command, this will launch your default shell on an interactive session. Remember
+that an interactive session is bound to the login node where you start it so you
+could risk losing your job if the login node becomes unreachable.",75,"[-0.0221310593187809, -0.005491217598319054, 0.01285900454968214, -0.01570502109825611, -0.04428981989622116, 0.006460662931203842, -0.033515121787786484, -0.005664332769811153, -0.03321043774485588, 0.00032091248431243, 0.02107851952314377, 0.018114784732460976, -0.0037600642535835505, 0.024665467441082, -0.01653597317636013, -0.025260984897613525, 0.02465161867439747, -0.017574666067957878, 0.022504989057779312, 0.006741109769791365, -0.004445600789040327, 4.820179310627282e-05, 0.02033066190779209, -0.016549821943044662, -0.0019336979603394866, -0.01380767673254013, 0.00815719272941351, -0.014043113216757774, -0.00030360097298398614, -0.008344157598912716, -0.008995071053504944, -0.0022678105160593987, 0.006910762749612331, -0.020635344088077545, -0.03174242004752159, 0.017076093703508377, 0.005782051011919975, -0.01506795547902584, -0.015317241661250591, -0.012699738144874573, 0.024208443239331245, 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+143,"How can I reset my cluster password ?
+To reset your password, please submit a support ticket.
+Warning: your cluster password is the same as your Google Workspace account. So,
+after reset, you must use the new password for all your Google services.",53,"[0.014951542019844055, -0.02654089778661728, -0.012098778039216995, -0.02247825637459755, -0.019714640453457832, 0.03204265609383583, -0.018581176176667213, -0.013168564066290855, 0.0008365667890757322, -0.029138950631022453, 0.025063572451472282, -0.010322167538106441, 0.0068899355828762054, 0.0033207961823791265, 0.0025439271703362465, -0.00860286783427, 0.03573596850037575, -0.014123731292784214, 0.028705941513180733, -0.009971939958631992, -0.008144387975335121, 0.0042600431479513645, 0.006660695653408766, -0.007711378857493401, -0.024172084406018257, -0.01556284911930561, 0.019803788512945175, -0.0035054616164416075, 0.007717746775597334, 0.018810415640473366, -0.0034226805437356234, 0.012665510177612305, 0.0031297628302127123, -0.0037824600003659725, -0.023357009515166283, 0.009035876952111721, 0.010175708681344986, -0.05196106806397438, -0.007055497728288174, -0.01877220906317234, 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+144,"srun: error: –mem and –mem-per-cpu are mutually exclusive
+You can safely ignore this, salloc has a default memory flag in case you
+don’t provide one.",41,"[-0.015243717469274998, -0.003035913221538067, 0.01972389966249466, -0.021416105329990387, -0.03858782723546028, 0.011900918558239937, -0.020070664584636688, -0.029350051656365395, -0.03942006081342697, -0.016034338623285294, 0.04505149647593498, 0.032623499631881714, -0.006536489352583885, -0.010409836657345295, -0.005822157021611929, -0.02706141397356987, 0.005000327713787556, -0.00916842371225357, 0.037117552012205124, 0.0057874806225299835, 0.014099398627877235, 0.005919250659644604, 0.004677837714552879, -0.006571165751665831, 0.00012667699775192887, 0.004116081167012453, 0.005093954037874937, -0.02763010561466217, -0.00945276953279972, -0.005274270661175251, 0.005114759784191847, -0.01799008436501026, -0.011991077102720737, -0.018794573843479156, -0.023413462564349174, 0.006886720657348633, -0.01292040292173624, 0.0020320334006100893, 0.018489422276616096, -0.022581230849027634, 0.012913468293845654, 0.010118555277585983, 0.0009666028781794012, 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+145,"How can I see where and if my jobs are running ?
+Use squeue -u YOUR_USERNAME to see all your job status and locations.
+To get more info on a running job, try scontrol show job #JOBID",48,"[-0.022199230268597603, -0.023132652044296265, 0.007954386062920094, -0.02094113826751709, -0.044127900153398514, -0.0023876684717833996, -0.038175638765096664, -0.006452792324125767, -0.018370842561125755, -0.008705182000994682, -0.0013248854083940387, -0.008860752917826176, -0.011295768432319164, 0.023362625390291214, 0.004224076401442289, -0.046833474189043045, 0.01818145252764225, -0.0048125386238098145, -0.0017992062494158745, -0.023565543815493584, -0.009997094050049782, 0.0042376043274998665, 0.007609425112605095, -0.03035653382539749, -0.00521499291062355, 0.025838226079940796, -0.009043378755450249, -0.007379451300948858, 0.007244172506034374, -0.001849935739301145, -0.021847505122423172, 0.011295768432319164, -0.020832914859056473, -0.023200292140245438, -0.025662362575531006, 0.01600346714258194, 0.019710101187229156, -0.014718319289386272, 0.005624210461974144, -0.01302733551710844, 0.01704511232674122, 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+146,"Unable to allocate resources: Invalid account or account/partition combination specified
+Chances are your account is not setup properly. You should submit a support ticket.",33,"[0.01642603427171707, -0.026543715968728065, 0.0032234068494290113, -0.03039356879889965, -0.037471894174814224, 0.02078920044004917, -0.041740499436855316, -0.013251596130430698, -0.00545395677909255, -0.03871465101838112, 0.03039356879889965, 0.022369666025042534, -0.0052344477735459805, -0.006798028014600277, 0.009692171588540077, 0.007659179158508778, 0.00911807082593441, -0.01872243732213974, 0.01719600521028042, -0.02435537986457348, -0.010529682971537113, 0.01104975026100874, -0.009577350690960884, -0.004423952661454678, -0.006061828229576349, -0.016358492895960808, 0.011205095797777176, -0.0023960263933986425, 0.0020144181326031685, 0.008496690541505814, 0.0008624175097793341, -0.0002906385052483529, -0.013832450844347477, -0.02603040263056755, -0.02411223016679287, 0.004184181336313486, 0.00842914916574955, -0.025409022346138954, 0.019532933831214905, -0.0045016249641776085, 0.012434346601366997, 0.02034342847764492, 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+147,"How do I cancel a job?
+
+To cancel a specific job, use scancel #JOBID
+To cancel all your jobs (running and pending), use scancel -u YOUR_USERNAME
+To cancel all your pending jobs only, use scancel -t PD",55,"[-0.003947847057133913, -0.022938642650842667, 0.019985342398285866, 0.0025183958932757378, -0.03676116466522217, 0.0070191435515880585, -0.02320834994316101, -0.021010233089327812, -0.004504119046032429, -0.0088059576228261, 0.029182376340031624, -0.001229698653332889, -0.010134268552064896, 0.012811117805540562, 0.0025352525990456343, -0.02319486439228058, 0.0024037701077759266, -0.006088652182370424, 0.009095893241465092, -0.0068236058577895164, -0.013984345830976963, 0.030315149575471878, -0.012871801853179932, 0.0015718904323875904, -0.011482806876301765, 0.02074052393436432, 0.008596934378147125, -0.009217261336743832, 0.005987511482089758, 0.015278942883014679, -0.02640438638627529, -0.002919586142525077, -0.019378500059247017, -0.002685277722775936, -0.029209347441792488, -0.01664096675813198, -0.01781419664621353, -0.009338630363345146, 0.003968074917793274, -0.0034657444339245558, 0.02732139267027378, 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+148,"How can I access a node on which one of my jobs is running ?
+You can ssh into a node on which you have a job running, your ssh connection
+will be adopted by your job, i.e.  if your job finishes your ssh connection will
+be automatically terminated. In order to connect to a node, you need to have
+password-less ssh either with a key present in your home or with an
+ssh-agent. You can generate a key on the login node like this:
+ssh-keygen (3xENTER)
+cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
+chmod 600 ~/.ssh/authorized_keys
+chmod 700 ~/.ssh",138,"[-0.009912973269820213, -0.02489018253982067, 0.006198975257575512, -0.021428722888231277, -0.03833195939660072, 0.01231040433049202, -0.04552425071597099, -0.016458768397569656, -0.02462080866098404, -0.010593143291771412, 0.02429755963385105, -0.0038015444297343493, -0.024351434782147408, -0.005761242005974054, -0.008269790560007095, -0.0025034970603883266, 0.021145880222320557, -0.01891007460653782, 0.00501709571108222, -0.019825946539640427, -0.016445299610495567, 0.0015716304769739509, 0.0028755704406648874, -0.024661215022206306, -0.003288049716502428, 0.011603296734392643, 0.022816000506281853, -0.015556364320218563, -0.003730833763256669, -0.010148676112294197, 0.011993889696896076, -0.00013552892778534442, -0.027880236506462097, -0.004639972001314163, -0.023044968023896217, 0.017226485535502434, 0.011313719674944878, -0.029227107763290405, -1.6786567584858858e-06, -0.007401057984679937, 0.02782636135816574, -0.00039943153387866914, 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+149,"I’m getting Permission denied (publickey) while trying to connect to a node
+See previous question",22,"[-0.01213471032679081, -0.005885334685444832, 0.0013929636916145682, -0.0373479425907135, -0.05932525172829628, 0.022341350093483925, -0.02521323226392269, 0.004429169464856386, -0.030067116022109985, -0.02877274714410305, 0.009256087243556976, -0.001906160730868578, 0.0020763836801052094, -0.0030758120119571686, 0.004567370284348726, 0.00886507984250784, 0.017622295767068863, -0.011393144726753235, 0.011534716002643108, 0.004304451402276754, -0.0029831163119524717, 0.006431396584957838, -0.007226894143968821, -0.009195414371788502, -0.005538146942853928, 0.0007428296958096325, 0.00697071710601449, -0.024175040423870087, 0.004385349340736866, -0.021235743537545204, 0.013739189133048058, 0.010422367602586746, -0.010820116847753525, -0.0046786051243543625, -0.03308730944991112, -0.018660487607121468, -0.016651518642902374, -0.034138984978199005, 0.009984170086681843, -0.002300538821145892, -0.001547175575979054, -0.005750504322350025, -0.002201101742684841, 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+150,"Where do I put my data during a job ?
+Your /home as well as the datasets are on shared file-systems, it is
+recommended to copy them to the $SLURM_TMPDIR to better process them and
+leverage higher-speed local drives. If you run a low priority job subject to
+preemption, it’s better to save any output you want to keep on the shared file
+systems, because the $SLURM_TMPDIR is deleted at the end of each job.",104,"[-0.02241329476237297, -0.011722519062459469, 0.002294274279847741, -0.030870884656906128, -0.014457999728620052, 0.024123817682266235, -0.02427314966917038, -0.01657579094171524, -0.0064450097270309925, -0.019766055047512054, 0.02454466186463833, 0.0050127855502069, -0.01267280988395214, 0.020417682826519012, 0.002161912154406309, -0.0164128839969635, 0.02605155110359192, -0.011145556345582008, 0.012048332951962948, -0.02449035830795765, -0.007188273128122091, -0.006183679681271315, -0.0013380438322201371, -0.029160359874367714, -0.004507094621658325, 0.0013651949120685458, 0.01964387483894825, -0.0016782819293439388, 0.019141577184200287, -0.004004797898232937, -0.017770443111658096, -0.010127387940883636, -0.006509494036436081, -0.015449018217623234, -0.0387447252869606, 0.002572573721408844, 0.0058646537363529205, -0.0043577635660767555, 0.019928961992263794, -0.01999683864414692, 0.008688375353813171, 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+151,"slurmstepd: error: Detected 1 oom-kill event(s) in step #####.batch cgroup
+You exceeded the amount of memory allocated to your job, either you did not
+request enough memory or you have a memory leak in your process. Try increasing
+the amount of memory requested with --mem= or --mem-per-cpu=.",75,"[0.007729715667665005, -0.01992078311741352, 0.0009432723745703697, -0.019666656851768494, -0.026556339114904404, 0.02785521373152733, -0.014421743340790272, -0.01671595126390457, -0.037752073258161545, -0.012685204856097698, 0.018127771094441414, 0.025511592626571655, -0.014139379374682903, -0.008463862352073193, -0.011894585564732552, 0.014767639338970184, 0.011118084192276001, -0.008612103760242462, 0.011936940252780914, 0.027756385505199432, -0.010447469539940357, 0.005407271441072226, -0.00971332285553217, 0.010299229063093662, 0.00550962844863534, 0.004997843876481056, 0.010101573541760445, -0.021530259400606155, 0.020471394062042236, 0.01079336553812027, -0.012240481562912464, -0.01056747417896986, -0.026739874854683876, 0.00135799462441355, -0.038655638694763184, 0.0038542691618204117, -0.00012640202476177365, 0.003114828374236822, 0.026273975148797035, -0.016786541789770126, 0.01901721954345703, 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+152,"fork: retry: Resource temporarily unavailable
+You exceeded the limit of 2000 tasks/PIDs in your job, it probably means there
+is an issue with a sub-process spawning too many processes in your script. For
+any help with your software, please submit a support ticket.",59,"[0.00011817013000836596, -0.0350295752286911, -0.02866813726723194, -0.021667778491973877, -0.02411234937608242, 0.011861720122396946, -0.031196044757962227, 0.00831292662769556, -0.012570089660584927, -0.02565409429371357, 0.026723593473434448, 0.025584645569324493, 0.004854416009038687, -0.0156535804271698, -0.0032796822488307953, 0.026918048039078712, 0.014077111147344112, -0.02950151450932026, -0.009139358066022396, -0.015528574585914612, -0.007951796986162663, 0.016320282593369484, -0.005896136164665222, -0.0033335043117403984, -0.021709447726607323, 0.005059287417680025, 0.020014915615320206, -0.009938010014593601, -0.00165893929079175, 0.0011241895845159888, 0.008382374420762062, -0.002932442119345069, -0.009965789504349232, 0.0015226475661620498, -0.036668550223112106, -0.007903183810412884, -0.014667419716715813, -0.03586295247077942, 0.013882656581699848, -0.0031807187478989363, 0.03577961400151253, 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+153,"PyTorch issues
+
+I randomly get INTERNAL ASSERT FAILED at ""../aten/src/ATen/MapAllocator.cpp"":263
+You are using PyTorch 1.10.x and hitting #67864,
+for which the solution is PR #72232
+merged in PyTorch 1.11.x. For an immediate fix, consider the following compilable Gist:
+hack.cpp.
+Compile the patch to hack.so and then export LD_PRELOAD=/absolute/path/to/hack.so
+before executing the Python process that import torch a broken PyTorch 1.10.
+For Hydra users who are using the submitit launcher plug-in, the env_set key cannot
+be used to set LD_PRELOAD in the environment as it does so too late at runtime. The
+dynamic loader reads LD_PRELOAD only once and very early during the startup of any
+process, before the variable can be set from inside the process. The hack must therefore
+be injected using the setup key in Hydra YAML config file:
+hydra:
+  launcher:
+    setup:
+      - export LD_PRELOAD=/absolute/path/to/hack.so",230,"[-0.015566213056445122, -0.010916379280388355, 0.0037735190708190203, -0.032133929431438446, -0.021360622718930244, 0.0286000557243824, -0.0230631772428751, 0.004091853741556406, -0.03342157602310181, -0.02736963890492916, 0.05436728894710541, -0.009120828472077847, 0.011960803531110287, 0.0017678311560302973, 0.017068468034267426, 0.0101724062114954, -0.0015889913775026798, -0.005994709208607674, 0.010372706688940525, -0.006470422726124525, -0.007575652562081814, 0.0009094002307392657, -0.0115816630423069, -0.004513915628194809, 0.003580372082069516, -0.00867015216499567, 0.011724735610187054, -0.03339296206831932, 0.0025574087630957365, 0.00250017992220819, 0.011553049087524414, -0.013985269702970982, -0.015609134919941425, -0.02918664924800396, -0.013813584111630917, -0.005490380804985762, -0.008112171664834023, -0.023077484220266342, 0.02128908596932888, -0.004771444946527481, 0.019100086763501167, -0.005268619395792484, 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+154,"I randomly get INTERNAL ASSERT FAILED at ""../aten/src/ATen/MapAllocator.cpp"":263
+You are using PyTorch 1.10.x and hitting #67864,
+for which the solution is PR #72232
+merged in PyTorch 1.11.x. For an immediate fix, consider the following compilable Gist:
+hack.cpp.
+Compile the patch to hack.so and then export LD_PRELOAD=/absolute/path/to/hack.so
+before executing the Python process that import torch a broken PyTorch 1.10.
+For Hydra users who are using the submitit launcher plug-in, the env_set key cannot
+be used to set LD_PRELOAD in the environment as it does so too late at runtime. The
+dynamic loader reads LD_PRELOAD only once and very early during the startup of any
+process, before the variable can be set from inside the process. The hack must therefore
+be injected using the setup key in Hydra YAML config file:
+hydra:
+  launcher:
+    setup:
+      - export LD_PRELOAD=/absolute/path/to/hack.so",223,"[-0.020267872139811516, -0.008940447121858597, 0.00890471413731575, -0.02798624336719513, -0.02419852465391159, 0.026599794626235962, -0.02421281673014164, 0.004441637080162764, -0.0366479717195034, -0.027857603505253792, 0.05537216737866402, -0.012084968388080597, 0.008411595597863197, -0.002256551757454872, 0.013242724351584911, 0.007589731831103563, -0.0026853501331061125, 0.0027014301158487797, 0.015236636623740196, -0.006546322256326675, -0.009269192814826965, 0.003444680478423834, -0.009119112975895405, -0.011341718025505543, 0.0005976377869956195, -0.01205638237297535, 0.01615140773355961, -0.03470408543944359, 0.003827025881037116, 0.00252991053275764, 0.004920461680740118, -0.014507679268717766, -0.011127319186925888, -0.02911541238427162, -0.01336421724408865, -0.006510589271783829, -0.008618848398327827, -0.02212599851191044, 0.027700377628207207, -0.004791822284460068, 0.01898147724568844, -0.0034464672207832336, 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