AI News, What is Studio?

What is Studio?

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.

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Status Nothing to report studioml 0.0.11.post83 Download studioml-0.0.11.post83.tar.gz TensorFlow model and data management tool

Introducing Studio.ML: an Open Source Framework that Simplifies and Expedites Machine Learning Model Development

By Arshak Navruzyan VP Distributed Artificial Intelligence Platform At Sentient, we are continuously building and iterating on machine learning models to advance our products and research.

We’re big fans of Keras, TensorFlow, PyTorch etc., but along the way we’ve found some critical missing features when it comes to running a large set of machine learning experiments in a scalable and collaborative fashion.

Today, we’re excited to introduce Studio.ml, 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 and developed to minimize the overhead involved with scheduling, running, monitoring and managing artifacts of your machine learning experiments.

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 This code is still in the early phases of development, but we encourage you to try it, report back problems, ask questions, provide feedback and contribute.

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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 This code is still in the early phases of development, but we encourage you to try it, report back problems, ask questions, provide feedback and contribute.

What is Studio?

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.

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