using_wandb.rst

Using Weights and Biases

Weights and Biases (WANDB) is a service for tracking experimental results and various artifacts appearing whn training ML models.

After registering for WANDB, do the following:

1. Create a project in WANDB, for example, with the name pykeen_project at https://app.wandb.ai/<your username>/new
2. Install WANDB on your machine with pip install wandb
3. Setup your computer for use with WANDB by using either of the following two instructions from https://github.com/wandb/client#running-your-script:
   1. Navigate to https://app.wandb.ai/settings, copy your API key, and set the WANDB_API_KEY environment variable
   2. Interactively run wandb login

Now you can simply specify this project name when initializing a pipeline, and everything else will work automatically!

Pipeline Example

This example shows using WANDB with the :func:`pykeen.pipeline.pipeline` function.

from pykeen.pipeline import pipeline

pipeline_result = pipeline(
    model='RotatE',
    dataset='Kinships',
    result_tracker='wandb',
    result_tracker_kwargs=dict(
        project='pykeen_project',
    ),
)

You can navigate to the created project in WANDB and observe a running experiment. Further tweaking of appearance, charts, and other settings is described in the official documentation

You can also specify optional tags which will appear on the website instead of randomly generated labels. All further keyword arguments are passed to :func:`wandb.init`.

from pykeen.pipeline import pipeline

pipeline_result = pipeline(
    model='RotatE',
    dataset='Kinships',
    result_tracker='wandb',
    result_tracker_kwargs=dict(
        project='pykeen_project',
        tags='experiment-1',
    ),
)

HPO Example

This example shows using WANDB with the :func:`pykeen.hpo.hpo_pipeline` function.

from pykeen.hpo import hpo_pipeline

pipeline_result = hpo_pipeline(
    model='RotatE',
    dataset='Kinships',
    result_tracker='wandb',
    result_tracker_kwargs=dict(
        project='pykeen_project',
        tags='new run',
        reinit=True,
    ),
)

It's safe to specify the experiment name during HPO. Several runs will be sent to the same experiment under different hashes. However, specifying the experiment name is advisable more for single runs and not for batches of multiple runs.

Additional documentation of the valid keyword arguments can be found under :class:`pykeen.trackers.WANDBResultTracker`.