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studioml 0.0.9.post139

TensorFlow model and data management tool

Latest Version: 0.0.11.post248

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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 <https:"" fchollet="" keras="">`__,
`TensorFlow <https:"" tensorflow="" tensorflow="">`__,
`PyTorch <https:"" pytorch="" pytorch="">`__,
`scikit-learn <https:"" scikit-learn="" 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 artifacts
* Perform hyperparameter search
* Create 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

You can see results of your job at Run
``studio {ui|run} --help`` for a full list of ui / runner options


pip install studioml from the master pypi repositry:


pip install studioml

Find more `details <docs installation.rst="">`__ on installation methods and the release process.


Currently Studio supports 2 methods of authentication: `email / password <docs authentication.rst#email--password-authentication="">`__ and using a `Google account. <docs authentication.rst#google-account-authentication="">`__ 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 <docs setup_database.rst="">`__. This is a
preferred option if you want to keep your models and artifacts private.

Further reading and cool features

- `Running experiments remotely <docs remote_worker.rst="">`__

- `Custom Python environments for remote workers <docs customenv.rst="">`__

- `Running experiments in the cloud <docs cloud.rst="">`__

- `Google Cloud setup instructions <docs gcloud_setup.rst="">`__

- `Amazon EC2 setup instructions <docs ec2_setup.rst="">`__

- `Artifact management <docs artifacts.rst="">`__
- `Hyperparameter search <docs hyperparams.rst="">`__
- `Pipeline for trained models <docs model_pipelines.rst="">`__

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File Type Py Version Uploaded on Size
studioml-0.0.9.post139.tar.gz (md5) Source 2017-11-15 283KB