remove bare except + format

app.py

@@ -1,11 +1,11 @@
-from dashboard_utils.main_metrics import get_main_metrics
+import altair as alt
+import pandas as pd
 import streamlit as st
 import wandb
-import pandas as pd
-import altair as alt
-from streamlit_observable import observable
 
 from dashboard_utils.bubbles import get_new_bubble_data
+from dashboard_utils.main_metrics import get_main_metrics
+from streamlit_observable import observable
 
 wandb.login(anonymous="must")
 
@@ -13,30 +13,21 @@ st.title("Training transformers together dashboard")
 st.caption("Training Loss")
 
 steps, losses, alive_peers = get_main_metrics()
-source = pd.DataFrame({
-  "steps": steps, "loss":losses, "alive participants":alive_peers
-})
+source = pd.DataFrame({"steps": steps, "loss": losses, "alive participants": alive_peers})
 
-chart_loss = alt.Chart(source).mark_line().encode(
-    x='steps',
-    y='loss'
-)
-st.altair_chart(chart_loss,  use_container_width=True)
+chart_loss = alt.Chart(source).mark_line().encode(x="steps", y="loss")
+st.altair_chart(chart_loss, use_container_width=True)
 
 st.caption("Number of alive participants over time")
-chart_alive_peer = alt.Chart(source).mark_line().encode(
-    x='steps',
-    y='alive participants'
-)
-st.altair_chart(chart_alive_peer,  use_container_width=True)
+chart_alive_peer = alt.Chart(source).mark_line().encode(x="steps", y="alive participants")
+st.altair_chart(chart_alive_peer, use_container_width=True)
 
 st.header("Collaborative training participants")
 serialized_data, profiles = get_new_bubble_data()
-with st.spinner('Wait for it...'):
+with st.spinner("Wait for it..."):
     observers = observable(
         "Participants",
         notebook="d/9ae236a507f54046",  # "@huggingface/participants-bubbles-chart",
         targets=["c_noaws"],
         redefine={"serializedData": serialized_data, "profileSimple": profiles},
     )
-

dashboard_utils/bubbles.py

@@ -1,15 +1,16 @@
 import datetime
+from concurrent.futures import as_completed
 from urllib import parse
 
-from concurrent.futures import as_completed
-from requests_futures.sessions import FuturesSession
-import requests
 import wandb
-from dashboard_utils.time_tracker import simple_time_tracker, _log
+from requests_futures.sessions import FuturesSession
+
+from dashboard_utils.time_tracker import _log, simple_time_tracker
 
 URL_QUICKSEARCH = "https://huggingface.co/api/quicksearch?"
 WANDB_REPO = "learning-at-home/Worker_logs"
 
+
 @simple_time_tracker(_log)
 def get_new_bubble_data():
     serialized_data_points, latest_timestamp = get_serialized_data_points()
@@ -18,12 +19,12 @@ def get_new_bubble_data():
 
     return serialized_data, profiles
 
+
 @simple_time_tracker(_log)
 def get_profiles(serialized_data_points):
     profiles = []
-    anonymous_taken = False
     with FuturesSession() as session:
-        futures=[]
+        futures = []
         for username in serialized_data_points.keys():
             future = session.get(URL_QUICKSEARCH + parse.urlencode({"type": "user", "q": username}))
             future.username = username
@@ -35,7 +36,7 @@ def get_profiles(serialized_data_points):
             avatarUrl = None
             if response["users"]:
                 for user_candidate in response["users"]:
-                    if user_candidate['user'] == username:
+                    if user_candidate["user"] == username:
                         avatarUrl = response["users"][0]["avatarUrl"]
                         break
             if not avatarUrl:
@@ -49,6 +50,7 @@ def get_profiles(serialized_data_points):
             )
     return profiles
 
+
 @simple_time_tracker(_log)
 def get_serialized_data_points():
 
@@ -62,7 +64,7 @@ def get_serialized_data_points():
         run_name = run.name
 
         if run_name in serialized_data_points:
-            try:
+            if "_timestamp" in run_summary and "_step" in run_summary:
                 timestamp = run_summary["_timestamp"]
                 serialized_data_points[run_name]["Runs"].append(
                     {
@@ -75,12 +77,8 @@ def get_serialized_data_points():
                 )
                 if not latest_timestamp or timestamp > latest_timestamp:
                     latest_timestamp = timestamp
-            except Exception as e:
-                pass
-                # print(e)
-                # print([key for key in list(run_summary.keys()) if "gradients" not in key])
         else:
-            try:
+            if "_timestamp" in run_summary and "_step" in run_summary:
                 timestamp = run_summary["_timestamp"]
                 serialized_data_points[run_name] = {
                     "profileId": run_name,
@@ -96,13 +94,10 @@ def get_serialized_data_points():
                 }
                 if not latest_timestamp or timestamp > latest_timestamp:
                     latest_timestamp = timestamp
-            except Exception as e:
-                pass
-                # print(e)
-                # print([key for key in list(run_summary.keys()) if "gradients" not in key])
     latest_timestamp = datetime.datetime.utcfromtimestamp(latest_timestamp)
     return serialized_data_points, latest_timestamp
 
+
 @simple_time_tracker(_log)
 def get_serialized_data(serialized_data_points, latest_timestamp):
     serialized_data_points_v2 = []

dashboard_utils/main_metrics.py

@@ -1,8 +1,10 @@
-from dashboard_utils.time_tracker import simple_time_tracker, _log
 import wandb
 
+from dashboard_utils.time_tracker import _log, simple_time_tracker
+
 WANDB_REPO = "learning-at-home/Main_metrics"
 
+
 @simple_time_tracker(_log)
 def get_main_metrics():
     api = wandb.Api()
@@ -18,4 +20,4 @@ def get_main_metrics():
         losses.append(row["loss"])
         alive_peers.append(row["alive peers"])
 
-    return steps, losses, alive_peers
+    return steps, losses, alive_peers

dashboard_utils/time_tracker.py

@@ -1,5 +1,6 @@
-from time import time
 from functools import wraps
+from time import time
+
 
 def simple_time_tracker(log_fun):
     def _simple_time_tracker(fn):
@@ -13,16 +14,19 @@ def simple_time_tracker(log_fun):
                 elapsed_time = time() - start_time
 
                 # log the result
-                log_fun({
-                    'function_name': fn.__name__,
-                    'total_time': elapsed_time,
-                })
-                
+                log_fun(
+                    {
+                        "function_name": fn.__name__,
+                        "total_time": elapsed_time,
+                    }
+                )
+
             return result
 
         return wrapped_fn
+
     return _simple_time_tracker
 
 
 def _log(message):
-    print('[SimpleTimeTracker] {function_name} {total_time:.3f}'.format(**message))
+    print("[SimpleTimeTracker] {function_name} {total_time:.3f}".format(**message))

streamlit_observable/__init__.py

@@ -1,4 +1,5 @@
 import os
+
 import streamlit.components.v1 as components
 
 _RELEASE = True
@@ -22,18 +23,18 @@ def observable(key, notebook, targets=None, redefine={}, observe=[], hide=[]):
     key: str
         A unique string used to avoid constant re-renders to the iframe.
     notebook: str
-        The observablehq.com notebook id to embed. Ex. "@"d3/bar-chart" 
+        The observablehq.com notebook id to embed. Ex. "@"d3/bar-chart"
         or "d/1f434ef3b0569a00"
     targets: list or None
         An optional list of strings that are the name of the cells to embed.
-        By default, the entire notebook, including unnamed cells, will be embeded. 
+        By default, the entire notebook, including unnamed cells, will be embeded.
     observe: list or None
         An optional list of strings that are the name of cells to observe.
-        Whenever these cells change value or become fulfilled, the value will 
+        Whenever these cells change value or become fulfilled, the value will
         be passed back into Streamlit as part of the return value.
     redefine: dict or None
         An optional dict containing the cells you wish to redefine and the values
-        you wish to redefine them as. The keys are the cell names you want to 
+        you wish to redefine them as. The keys are the cell names you want to
         redefine, the values are what they will be redefined as. Keep in mind,
         there is a serialization process from Streamlit Python -> frontend JavaScript.
     hide: list or None
@@ -48,15 +49,9 @@ def observable(key, notebook, targets=None, redefine={}, observe=[], hide=[]):
 
     """
     component_value = _component_func(
-        notebook=notebook,
-        targets=targets,
-        observe=observe,
-        redefine=redefine,
-        hide=hide,
-        key=key,
-        name=key
+        notebook=notebook, targets=targets, observe=observe, redefine=redefine, hide=hide, key=key, name=key
     )
-    
+
     if component_value is None:
         return {}
 
@@ -65,12 +60,12 @@ def observable(key, notebook, targets=None, redefine={}, observe=[], hide=[]):
 
 # if not _RELEASE:
 #     import streamlit as st
-#     observers = observable("World Tour!", 
-#         notebook="@d3/world-tour", 
-#         targets=["canvas"], 
+#     observers = observable("World Tour!",
+#         notebook="@d3/world-tour",
+#         targets=["canvas"],
 #         observe=["name"]
 #     )
-    
+
 #     name = observers.get("name")
-    
+
 #     st.write(f"Current country: ** *{name}* **")