An open-source AutoML Library in PyTorch
Project description
An open-source AutoML Library in PyTorch
Installation
Recommended
pip install -U gradsflow
From source
pip install git+https://github.com/gradsflow/gradsflow@main
Highlights
- 2021-8-25: Released first version 0.0.1 ✨ :tada:
- 2021-8-29: Migrated from Optuna to Ray Tune. Read more...
What is GradsFlow?
!!! attention
🚨 GradsFlow is changing fast. There will be lot of breaking changes until we reach 0.1.0
. Feel free to try and give your feedback by creating an issue or join our Slack group.
GradsFlow is an open-source AutoML Library for PyTorch that can train Deep Learning Models on your laptop or to a remote cluster directly from your laptop. Our aim is to democratize AI by enabling non ML expert to train and build AI Products. GradsFlow leverages the power of PyTorch Lightning ⚡️ and Ray️. You don't have to write any PyTorch or Hyperparameter optimization code.
GradsFlow Model API provides a simple
Keras like model training interface 🔥.
You can train any PyTorch model using model.fit(...)
and it is easily customizable for more complex tasks.
You might want to train a custom model and search hyperparameters, You can easily integrate any PyTorch Model with GradsFlow AutoModel ✨
-
gradsflow.core
: Core defines the building blocks of AutoML tasks. -
gradsflow.autotasks
: AutoTasks defines different ML/DL tasks which is provided by gradsflow AutoML API. -
gradsflow.model
: Model defines the model training functionality. -
gradsflow.tuner
: Model HyperParameter search with minimal code changes.
📑 Check out notebooks examples.
💬 Join the Slack group to chat with us.
💙 Sponsor us on ko-fi
📧 Do you need support? Contact us at admin@gradsflow.com
🤗 Contribute
Contributions of any kind are welcome. Please check the Contributing Guidelines before contributing.
Code Of Conduct
We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.
Read full Contributor Covenant Code of Conduct
Acknowledgement
GradsFlow is built with help of awesome open-source projects (including but not limited to) PyTorch Lightning and Ray 💜
It takes inspiration from multiple APIs like Keras, FastAI.
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