Studio.ml, open source model management

Sep 29, 2017 · San Francisco, United States of America

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<a href="http://meet.meetup.com/wf/click?upn=DtKvkirJMjJK-2FpI8UHiQTj9xnStKDsZSBFsp528SQSE-3D_4Mb6KtWrDXK23iA6C-2F1JqQ6tgeAjd9SG8SEZyCZcH5wM-2BXHOYbfhUmzHYglsSESggMSc2DS3SjeBx-2BC96IjOXliD6t5odjuKI49xE-2BY3Y3AxPdBln4yJ3C5OqQI7j5HP2SkyUmdEng1hZRivnAfZpNkE32tpjqWIjBjUB6zHs7v-2BZCnRe2xDX88RrK9CoO-2FcVkwKmxGjZcpph5VMFfTn-2FA-3D-3D">Studio.ml is an open source project dedicated to helping machine learning (ML) professionals, academics, businesses and anyone else interested in ML model building, accelerate and simplify their experiments.

Studio.ml is an early-stage, ML model management framework written in Python that was developed to minimize the overhead involved with scheduling, running, monitoring and managing artifacts of your machine learning experiments.

Most of the features are compatible with any Python ML framework including KerasTensorFlow, PyTorch, and scikit-learn, with additional features available for Keras and TensorFlow.

So far, using Studio.ml you can:

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 execution

Access the model library to reuse models that have already been created

Speaker
Peter Zhokhov is a Senior Software Engineer in the Sentient core platform group.  He has a PhD in Physics from Texas A&M.

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