Starting the UI

To run the autotrain cli locally or in colab, install autotrain-advanced python package:

$ pip install autotrain-advanced

and then run the following command:

$ export HF_TOKEN=your_hugging_face_write_token
$ autotrain --help

This will start the app on http://127.0.0.1:8000.

AutoTrain doesn’t install pytorch, torchaudio, torchvision, or any other dependencies. You will need to install them separately. It is thus recommended to use conda environment:

$ 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
$ pip install deepspeed

$ export HF_TOKEN=your_hugging_face_write_token
$ autotrain --help

This will show the CLI commands that can be used:

$ autotrain --help
usage: autotrain <command> [<args>]

positional arguments:
{
    app,
    llm,
    setup,
    dreambooth,
    api,
    text-classification,
    image-classification,
    tabular,
    spacerunner,
    seq2seq,
    token-classification
}
    
    commands

options:
  -h, --help            show this help message and exit
  --version, -v         Display AutoTrain version

For more information about a command, run: `autotrain <command> --help`

The autotrain commands that end users will be interested in are:

app: Start the AutoTrain UI
llm: Train a language model
dreambooth: Train a model using DreamBooth
text-classification: Train a text classification 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

In case of any issues, please report on the GitHub issues.

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