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Metrics & Performance

Metrics

How often are system metrics collected?

By default, metrics are collected every 2 seconds and averaged over a 30-second period. If you need higher resolution metrics, email us a [email protected].

Can I just log metrics, no code or dataset examples?

Dataset Examples
By default, we don't log any of your dataset examples. You can explicitly turn this feature on to see example predictions in our web interface.
Code Logging
There are two ways to turn off code logging:
  1. 1.
    Set WANDB_DISABLE_CODE to true to turn off all code tracking. We won't pick up the git SHA or the diff patch.
  2. 2.
    Set WANDB_IGNORE_GLOBS to *.patch to turn off syncing the diff patch to our servers. You'll still have it locally and be able to apply it with the wandb restore command.

Can I log metrics on two different time scales? (For example, I want to log training accuracy per batch and validation accuracy per epoch.)

Yes, you can do this by logging your indices (e.g. batch and epoch) whenever you log your other metrics. So in one step you could call wandb.log({'train_accuracy': 0.9, 'batch': 200}) and in another step call wandb.log({'val_acuracy': 0.8, 'epoch': 4}). Then, in the UI, you can set the appropriate value as the x-axis for each chart. If you want to set the default x-axis of a particular index you can do so using by using Run.define_metric(). In our above example we could do the following:
wandb.init()
wandb.define_metric("batch")
wandb.define_metric("epoch")
wandb.define_metric("train_accuracy", step_metric="batch")
wandb.define_metric("val_accuracy", step_metric="epoch")

How can I log a metric that doesn't change over time such as a final evaluation accuracy?

Using wandb.log({'final_accuracy': 0.9} will work fine for this. By default wandb.log({'final_accuracy'}) will update wandb.settings['final_accuracy'], which is the value shown in the runs table.

How can I log additional metrics after a run completes?

There are several ways to do this.
For complicated workflows, we recommend using multiple runs and setting group parameters in wandb.init to a unique value in all the processes that are run as part of a single experiment. The runs table will automatically group the table by the group ID and the visualizations will behave as expected. This will allow you to run multiple experiments and training runs as separate processes log all the results into a single place.
For simpler workflows, you can call wandb.init with resume=True and id=UNIQUE_ID and then later call wandb.init with the same id=UNIQUE_ID. Then you can log normally with wandb.log or wandb.summary and the runs values will update.
At any point, you can always use the API to add additional evaluation metrics.

Performance

Will wandb slow down my training?

Wandb should have a negligible effect on your training performance if you use it normally. Normal use of wandb means logging less than once a second and logging less than a few megabytes of data at each step. Wandb runs in a separate process and the function calls don't block, so if the network goes down briefly or there are intermittent read write issues on disk it should not affect your performance. It is possible to log a huge amount of data quickly, and if you do that you might create disk I/O issues. If you have any questions, please don't hesitate to contact us.

How many runs to create per project?

We recommend you have roughly 10k runs per project max for performance reasons.

Best practices to organize hyperparameter searches

If 10k runs per project (approx.) is a reasonable limit then our recommendation would be to set tags in wandb.init() and have a unique tag for each search. This means that you'll easily be able to filter the project down to a given search by clicking that tag in the Project Page in the Runs Table. For example wandb.init(tags='your_tag') docs for this can be found here.
Last modified 6mo ago