Group individual runs into experiments by passing a unique group name to wandb.init().
Distributed training: Use grouping if your experiments are split up into different pieces with separate training and evaluation scripts that should be viewed as parts of a larger whole.
Multiple processes: Group multiple smaller processes together into an experiment.
K-fold cross-validation: Group together runs with different random seeds to see a larger experiment. Here's an example of k-fold cross validation with sweeps and grouping.
If you set grouping in your script, we will group the runs by default in the table in the UI. You can toggle this on and off by clicking the Group button at the top of the table. Here's an example of grouping on the project page.
Sidebar: Runs are grouped by the number of epochs.
Graphs: Each line represents the mean of the group, and the shading indicates the variance. This behavior can be change in the graph settings.
There are a few ways to use grouping:
Setting a group in your script
Pass an optional group and job_type to wandb.init(). For example:
wandb.init(group="experiment_1", job_type="eval"). Group should be unique within your project and shared by all runs in the group. You can use
wandb.util.generate_id() to generate a unique 8 character string to use in all your processes— for example:
os.environ["WANDB_RUN_GROUP"] = "experiment-" + wandb.util.generate_id()
Set a group environment variable
WANDB_RUN_GROUP to specify a group for your runs as an environment variable. For more on this, check our docs for Environment Variables.
Toggle grouping in the UI
You can dynamically group by any config column. For example, if you use
wandb.config to log batch size or learning rate, you can then group by those hyperparameters dynamically in the web app.
Click the grouping button and clear group fields at any time, which returns the table and graphs to their ungrouped state.
Click the edit button in the upper right corner of a graph and select the Advanced tab to change the line and shading. You can select the mean, minimum, or maximum value to for the line in each group. For the shading, you can turn off shading, show the min and max, the standard deviation, and the standard error.