Run Page

Use the run page to explore detailed information about a single version of your model.
Overview Tab
Run name, description, and tags
Run state
finished: script ended and fully synced data, or called wandb.finish()
failed: script ended with a non-zero exit status
crashed: script stopped sending heartbeats in the internal process, which can happen if the machine crashes
running: script is still running and has recently sent a heartbeat
Host name, operating system, Python version, and command that launched the run
List of config parameters saved with wandb.config​
List of summary parameters saved with wandb.log(), by default set to the last value logged
​View a live example →​
W&B Dashboard run overview tab
The Python details are private, even if you make the page itself public. Here is an example of my run page in incognito on the left and my account on the right.
Charts Tab
Search, group, and arrange visualizations
Click the pencil icon ✏️ on a graph to edit
change x-axis, metrics, and ranges
edit legends, titles, and colors of charts
View examples predictions from your validation set
To get these charts, log data with wandb.log()​
​View a live example →​
System Tab
Visualize CPU utilization, system memory, disk I/O, network traffic, GPU utilization, GPU temperature, GPU time spent accessing memory, GPU memory allocated, and GPU power usage
Lambda Labs highlighted how to use W&B system metrics in a blog post →​
​View a live example →​
Model Tab
See the layers of your model, the number of parameters, and the output shape of each layer
​View a live example →​
Logs Tab
Output printed on the command line, the stdout and stderr from the machine training the model
We show the last 1000 lines. After the run has finished, if you'd like to download the full log file, click the download button in the upper right corner.
​View a live example →​
Files Tab
Save files to sync with the run using wandb.save()​
Keep model checkpoints, validation set examples, and more
Use the diff.patch to restore the exact version of your code
View a live example →​
The W&B Artifacts system adds extra features for handling, versioning, and deduplicating large files like datasets and models. We recommend you use Artifacts for tracking inputs and outputs of runs, rather than wandb.save. Check out the Artifacts quickstart here.
Artifacts Tab
Provides a searchable list of the input and output Artifacts for this run
Click a row to see information about a particular artifact used or produced by this run
See the reference for the project-level Artifacts Tab for more on navigating and using the artifacts viewers in the web app
View a live example →​

Project Page

Workspaces

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