Studio is a model management framework written in Python to help simplify and expedite your model building experience. It was developed to minimize the overhead involved with scheduling, running, monitoring and managing artifacts of your machine learning experiments. No one wants to spend their time configuring different machines, setting up dependencies, or playing archeologist to track down previous model artifacts.
Most of the features are compatible with any Python machine learning framework (Keras, TensorFlow, PyTorch, scikit-learn, etc) without invasion of your code; some extra features are available for Keras and TensorFlow.
Use Studio to:
- Capture experiment information- Python environment, files, dependencies and logs- without modifying the experiment code. Monitor and organize experiments using a web dashboard that integrates with TensorBoard.
- Run experiments locally, remotely, or in the cloud (Google Cloud or Amazon EC2)
- Manage artifacts
- Perform hyperparameter search
- Create customizable Python environments for remote workers.
.. toctree:: :hidden: :caption: Introduction Getting Started <README> installation authentication cli
.. toctree:: :hidden: :caption: Main Documentation artifacts hyperparams model_pipelines setup_database
.. toctree:: :hidden: :caption: Remote computing remote_worker customenv
.. toctree:: :hidden: :caption: Cloud computing cloud ec2_setup gcloud_setup