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Weights and Biases

Learn how to use the Weights and Biases integration to track your data science experiments.

Connect Pachyderm to Weights and Biases to track your data science experiments. Using Pachyderm as our execution platform, we can version our executions, code, data, and models while still tracking everything in W&B.

Here we’ll use Pachyderm to manage our data and train our model.

Before You Start #

How to Use the Weights and Biases Connector #

  1. Create a Pachyderm cluster.
  2. Create a W&B Account
  3. Copy your W&B API Key into the secrets.json file. We’ll use this file to make a Pachyderm secret. This keeps our access keys from being built into our container or put in plaintext somewhere.
  4. Create the secret with pachctl create secret -f secrets.json
  5. Run make all to create a data repository and the pipeline.

Downloading the data locally and then pushing it to a remote cluster seems like an extra step, especially when dealing with a standard dataset like MNIST. However, if we think about a real-world use case where multiple teams may be manipulating the data (removing examples, adding classes, etc.) then having a history for each of these models can be very useful. In most production settings with supervised learning, the labeling environment can be directly connected to the data repository, automating this step.

About the MNIST example #