studio.ml：用来简化、加快模型构建过程的模型管理框架

studio.ml：用来简化、加快模型构建过程的模型管理框架

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); 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 artifactsPerform hyperparameter searchCreate customizable Python environments for remote workers.

NOTE: studio package is compatible with Python 2 and 3!

Example usage

Start visualizer:

studio ui

Run your jobs:

studio run train_mnist_keras.py

You can see results of your job at http://localhost:5000. Run studio {ui|run} --help for a full list of ui / runner options. WARNING: because studio tries to create a reproducible environment for your experiment, if you run it in a large folder, it will take a while to archive and upload the folder.

Installation

pip install studioml from the master pypi repositry:

pip install studioml

Find more details on installation methods and the release process.

Authentication

Currently Studio supports 2 methods of authentication: email / password and using a Google account. To use studio runner and studio ui in guest mode, in studio/default_config.yaml, uncomment "guest: true" under the database section.

Alternatively, you can set up your own database and configure Studio to use it. See setting up database. This is a preferred option if you want to keep your models and artifacts private.

Further reading and cool features

Running experiments remotelyCustom Python environments for remote workers Running experiments in the cloudGoogle Cloud setup instructionsAmazon EC2 setup instructions Artifact managementHyperparameter searchPipelines for trained modelsContainerized experiments

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