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Manage Python libraries in Hopsworks

Hopsworks provides a Python environment per project that is shared among all the users in the project. All common installation alternatives are supported, , in addition to libraries packaged in a .whl or .egg file and those that reside on a git repository.

Build ML models with fastai and Jupyter in Hopsworks

. Hopsworks provides Jupyter as a service in the platform, including kernels for writing PySpark/Spark and pure Python code. With an intuitive service to install Python libraries covered in a previous blog and access to a Jupyter notebook, getting started with your favourite ML library requires little effort in Hopsworks.

Connect Hopsworks to Azure

Connecting Hopsworks to your organisation’s Azure account is the first step towards using the Feature Store: 1. Connect your Azure account, 2. Create and configure a storage, 3. Add a ssh key to your resource group, 4. Enable permissions for Hopsworks to access

Connect Hopsworks to AWS

Connecting Hopsworks to your organisation’s AWS account is the first step towards using the Feature Store: 1. Connect your AWS account, 2. Create an instance profile, 3. Create a S3 bucket, 4. Create a SSH key, 5. Enable permissions for Hopsworks to access

Hopsworks.ai on AWS configure and start your cluster

Get started with hopsworks.ai

PyTorch on Hopsworks

On Hopsworks, learn how to run your first PyTorch application on a Jupyter notebook

Hopsworks' Feature Store, Petastorm and Tensorflow

On Hopsworks, learn how to: 1. Create Training/Test Datasets in Petastorm and register them with the Feature Store 2. Use Petastorm Training/Test datasets in the Feature Store to train and score a model in TensorFlow

End-to-End ML with Hopsworks' Feature Store, SKLearn, Model Serving, Inference

On Hopsworks, learn how to: 1. Register Features in the Hopsworks' Feature Store using the Python API 2. Create a Pandas DF from the Feature Store for Training Data