Machine Learning Visualization IDE

Original article was published by Carey on Deep Learning on Medium

Upgrade your ML workflow from tinkering with matplotlib to automatically generating interactive charts straight from your ML training script. I’ll show you how to:

  1. Log custom charts with just 3 lines of code
  2. Control the queries and visualization code
  3. Save custom presets to use from your script

Log charts in 2 minutes

In your Python script, specify a table of data to visualize. The columns can be anything you like, and you’ll use them later in the chart to specify the different features. For example, for a scatter plot you would want to specify an x and y axis, as well as an optional color axis.

To follow this tutorial, open this Google Colab and run the cells. Re-run the script multiple times to get data you can compare across runs in the dashboard.

Play with the notebook →

table = wandb.Table(data=data_1, columns=["step", "height"])
histogram = wandb.plot.histogram(table, value='height', title='Histogram')
wandb.log({'my_histo': histogram})