Scikit Learn

+ Gradient

How to use Gradient and



Use Scikit-learn to train models on Gradient

Scikit-learn is a machine learning library that contains a variety of ready-to-use algorithms. In contrast with TensorFlow and PyTorch, Scikit-learn is focused on classical machine learning as opposed to deep learning and does not support GPU acceleration.

Gradient supports any version of Scikit-learn for Notebooks, Experiments, or Jobs. In Gradient, the ML framework used to execute workloads runs within a Docker container. Containers are lightweight and portable environments that can easily be customized to include various framework versions and other libraries. Any Docker container is supported on the Gradient platform. This flexibility makes it easy to switch between different frameworks, to update them from one version to another, and to incorporate other libraries to be used alongside the framework itself.

Running workloads with Scikit-learn

When launching a workload via the web interface, CLI, or automatically via a pipeline step, you can simply pass-in a Docker image path (e.g. <inline-code>paperspace/my-docker-container:version<inline-code>.

Choose a container via Advanced Options when creating a new notebook

When using the CLI, the command would like something like this: