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# Quickstart Guide for Local Training
This quickstart is for local installation and usage.
If you want to use AutoTrain on Hugging Face Spaces, please refer to the *AutoTrain on Hugging Face Spaces* section.
You can install AutoTrain Advanced using pip:
```bash
$ pip install autotrain-advanced
```
It is advised to install autotrain-advanced in a virtual environment to avoid any conflicts with other packages.
Note: AutoTrain doesn't install pytorch, torchaudio, torchvision, or any other large dependencies. You will need to install them separately.
```bash
$ conda create -n autotrain python=3.10
$ conda activate autotrain
$ pip install autotrain-advanced
$ conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
$ conda install -c "nvidia/label/cuda-12.1.0" cuda-nvcc
$ conda install xformers -c xformers
$ python -m nltk.downloader punkt
$ pip install flash-attn --no-build-isolation # if you want to use flash-attn
$ pip install deepspeed # if you want to use deepspeed
````
# Running AutoTrain User Interface (UI)
To run the autotrain app locally, you can use the following command:
```bash
$ export HF_TOKEN=your_hugging_face_write_token
$ autotrain app --host 127.0.0.1 --port 8000
```
This will start the app on `http://127.0.0.1:8000`.
# Using AutoTrain Command Line Interface (CLI)
It is also possible to use the CLI:
```bash
$ export HF_TOKEN=your_hugging_face_write_token
$ autotrain --help
```
This will show the CLI commands that can be used:
```bash
usage: autotrain <command> [<args>]
positional arguments:
{
app,
llm,
setup,
api,
text-classification,
text-regression,
image-classification,
tabular,
spacerunner,
seq2seq,
token-classification
}
commands
options:
-h, --help show this help message and exit
--version, -v Display AutoTrain version
--config CONFIG Optional configuration file
For more information about a command, run: `autotrain <command> --help`
```
It is advised to use only the `autotrain --config CONFIG_FILE` command for training when using the CLI.
The autotrain commands that end users will be interested in are:
- `app`: Start the AutoTrain UI
- `llm`: Train a language model
- `text-classification`: Train a text classification model
- `text-regression`: Train a text regression model
- `image-classification`: Train an image classification model
- `tabular`: Train a tabular model
- `spacerunner`: Train any custom model using SpaceRunner
- `seq2seq`: Train a sequence-to-sequence model
- `token-classification`: Train a token classification model
Note: above commands are not required if you use preferred `autotrain --config CONFIG_FILE` command to train the models. |