Skip to main content

No project description provided

Project description

🤗 AutoTrain Advanced

AutoTrain Advanced: faster and easier training and deployments of state-of-the-art machine learning models. AutoTrain Advanced is a no-code solution that allows you to train machine learning models in just a few clicks. Please note that you must upload data in correct format for project to be created. For help regarding proper data format and pricing, check out the documentation.

NOTE: AutoTrain is free! You only pay for the resources you use in case you decide to run AutoTrain on Hugging Face Spaces. When running locally, you only pay for the resources you use on your own infrastructure.

Run on Colab or Hugging Face Spaces

  • Run AutoTrain on Colab: Open In Colab

  • Deploy AutoTrain on Hugging Face Spaces: Deploy on Spaces

  • Run AutoTrain UI on Colab via ngrok: Open In Colab

Local Installation

You can Install AutoTrain-Advanced python package via PIP. Please note you will need python >= 3.10 for AutoTrain Advanced to work properly.

pip install autotrain-advanced

Please make sure that you have git lfs installed. Check out the instructions here: https://github.com/git-lfs/git-lfs/wiki/Installation

You also need to install torch, torchaudio and torchvision.

The best way to run autotrain is in a conda environment. You can create a new conda environment with the following command:

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

Once done, you can start the application using:

autotrain app --port 8080 --host 127.0.0.1

If you are not fond of UI, you can use AutoTrain Configs to train using command line or simply AutoTrain CLI.

To use config file for training, you can use the following command:

autotrain --config <path_to_config_file>

You can find sample config files in the configs directory of this repository.

Colabs

Task Colab Link
LLM Fine Tuning Open In Colab
DreamBooth Training Open In Colab

Documentation

Documentation is available at https://hf.co/docs/autotrain/

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

autotrain_advanced-0.8.16.tar.gz (251.6 kB view details)

Uploaded Source

Built Distribution

autotrain_advanced-0.8.16-py3-none-any.whl (340.6 kB view details)

Uploaded Python 3

File details

Details for the file autotrain_advanced-0.8.16.tar.gz.

File metadata

  • Download URL: autotrain_advanced-0.8.16.tar.gz
  • Upload date:
  • Size: 251.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.14

File hashes

Hashes for autotrain_advanced-0.8.16.tar.gz
Algorithm Hash digest
SHA256 4411991587c6dde70b286f1d25114ab92d86877ee1608ddcbbd9ba6ca98251d8
MD5 4f0e7ef46ceaddc213459681d4a12154
BLAKE2b-256 67e37cb913d8814348d440fd4091d82fb685daef125fd7c38344d2affd7f6489

See more details on using hashes here.

File details

Details for the file autotrain_advanced-0.8.16-py3-none-any.whl.

File metadata

File hashes

Hashes for autotrain_advanced-0.8.16-py3-none-any.whl
Algorithm Hash digest
SHA256 899f5d8ebe9d901c430696b1edbdf3b08e96f33598e8e479055cfced4e8e98e0
MD5 8cfee18ec1699e5cbe49a2b786417250
BLAKE2b-256 44a8f99a5d0a2d88dc5d189330b86f47a99042ef6caf4ba7a9d698bd122957e8

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page