Tracking Jupyter Notebooks

Retain the flexibility and interactivity of Jupyter and add robust logging.

Use Weights & Biases with Jupyter to get interactive visualizations without leaving your notebook. Combine custom analysis, experiments, and prototypes, all fully logged!

Use Cases for W&B with Jupyter notebooks

  1. Iterative experimentation: Run and re-run experiments, tweaking parameters, and have all the runs you do saved automatically to W&B without having to take manual notes along the way.

  2. Code saving: When reproducing a model, it's hard to know which cells in a notebook ran, and in which order. Turn on code saving on your settings page to save a record of cell execution for each experiment.

  3. Custom analysis: Once runs are logged to W&B, it's easy to get a dataframe from the API and do custom analysis, then log those results to W&B to save and share in reports.

Getting started in a notebook

Start your notebook with the following code to install W&B and link your account:

!pip install wandb -qqq
import wandb
wandb.login()

Next, set up your experiment and save hyperparameters:

wandb.init(project="jupyter-projo",
config={
"batch_size": 128,
"learning_rate": 0.01,
"dataset": "CIFAR-100",
})

After running wandb.init() , start a new cell with %%wandb to see live graphs in the notebook. If you run this cell multiple times, data will be appended to the run.

%%wandb
# Your training loop here

Try it for yourself in this quick example notebook →

As an alternative to the %%wandb magic, after running wandb.init() you can end any cell with wandb.run to show in-line graphs:

# Initialize wandb.run first
wandb.init()
# If cell outputs wandb.run, you'll see live graphs
wandb.run

Want to know more about what you can do with W&B? Check out our guide to logging data and media, learn how to integrate us with your favorite ML toolkits, or just dive straight into the reference docs or our repo of examples.

Additional Jupyter features in W&B

  1. Easy authentication in Colab: When you call wandb.init for the first time in a Colab, we automatically authenticate your runtime if you're currently logged in to W&B in your browser. On the overview tab of your run page, you'll see a link to the Colab.

  2. Launch dockerized Jupyter: Call wandb docker --jupyter to launch a docker container, mount your code in it, ensure Jupyter is installed, and launch on port 8888.

  3. Run cells in arbitrary order without fear: By default, we wait until the next time wandb.init is called to mark a run as "finished". That allows you to run multiple cells (say, one to set up data, one to train, one to test) in whatever order you like and have them all log to the same run. If you turn on code saving in settings, you'll also log the cells that were executed, in order and in the state in which they were run, enabling you to reproduce even the most non-linear of pipelines. To mark a run as complete manually in a Jupyter notebook, call run.finish.

import wandb
run = wandb.init()
# training script and logging goes here
run.finish()

Common questions

How do I silence W&B info messages?

To disable info messages, run the following in a notebook cell:

import logging
logger = logging.getLogger("wandb")
logger.setLevel(logging.ERROR)

How do I set the WANDB_NOTEBOOK_NAME?

If you're seeing the error message "Failed to query for notebook name, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable," you can resolve it by setting the environment variable. There's multiple ways to do so:

Jupyter Magic
Pure Python
Jupyter Magic
%env "WANDB_NOTEBOOK_NAME" "notebook name here"
Pure Python
import os
os.environ["WANDB_NOTEBOOK_NAME"] = "notebook name here"