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Weights and Biases
Artifacts Quickstart With W&B

Artifacts Quickstart With W&B

wandb-examplescolabswandb-artifacts

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Weights & Biases

W&B Artifacts Quickstart

This tutorial shows how to get started with W&B Artifacts very quickly. I finetune a convnet in Keras to identify 10 types of living things in photos: plants, animals, insects, etc.

This demo will generate an experiment workflow like the following:

artifact DAG

In this example we're using Google Colab as a convenient hosted environment, but you can run your own training scripts from anywhere and visualize metrics with W&B's experiment tracking tool.

Sign up or login

Sign up or login to W&B to see and interact with your experiments in the browser.

Download sample data: Choose 1 of 3 sizes

Choose one of the three dataset size options below to run the rest of the demo. With fewer images, you'll run through the demo much faster and use less storage space. With more images, you'll get more realistic model training and more interesting results and examples to explore.

Note: for the largest dataset, this stage might take a few minutes. If you end up needing to rerun a cell, comment out the first capture line (change %%capture to #%%capture ) so you can respond to the prompt about re-downloading the dataset (and see the progress bar).

Each zipped directory contains randomly sampled images from the iNaturalist dataset, evenly distributed across 10 classes of living things like birds, insects, plants, and mammals (names given in Latin—so Aves, Insecta, Plantae, etc :).

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Step 0: Setup

Start out by installing the experiment tracking library and setting up your free W&B account:

  • pip install wandb – Install the W&B library
  • import wandb – Import the wandb library
  • wandb login – Login to your W&B account so you can log all your metrics in one place
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Step 1: Upload raw data

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Step 2: Prepare a data split

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Step 3: Train with artifacts and save model

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Step 4: Load model for inference

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More about Weights & Biases

We're always free for academics and open source projects. Here are some more resources:

  1. Documentation - Python docs
  2. Gallery - example reports in W&B
  3. Articles - blog posts and tutorials
  4. Community - join our Slack community forum