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.
. 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.
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
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
Get started with hopsworks.ai
On Hopsworks, learn how to run your first PyTorch application on a Jupyter notebook
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
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