Pin and compare runs

• Pinned runs: Pin runs from any project in your workspace to keep them visible at the top of the runs list. You can pin up to 20 runs in a workspace, including runs from other projects.
• Baseline run: Specify a baseline run as your reference point for comparisons. The baseline run is always visible in the workspace and at the top of the runs list. In the runs table, summary metric deltas show how each run compares to the baseline. In line plots, the baseline appears with visually distinct styling to help with comparison.

• Comparing new experiments against your production model.
• Comparing runs across projects.
• Creating a baseline for your experiments to evaluate how new runs perform against it.
• Tracking multiple candidate models during experimentation.
• Evaluating whether new runs improve on your best results.

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Pin runs

1. Navigate to your workspace.
2. In the run selector or runs table, find the run you want to pin.
3. Click the action () menu, then select Pin run.

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Compare runs across projects

1. Navigate to your project’s workspace.
2. Click on the Select runs from another project button (small square box with rounded corners and a diagonal arrow) at the top of your workspace’s run selector or the runs table.

3. In the modal, select a project from the Source project dropdown.
4. Search the source project’s runs to find the run you want to compare. You can search by run name or unique ID.
5. Select the checkbox next to each run you want to compare, then click Pin runs.

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Manage and compare a baseline run

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Set a baseline run

1. Navigate to your workspace.
2. In the run selector or runs table, find the run you want to use as your baseline.
3. Click the action () menu, then select Set as baseline.

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Change the baseline run

1. Navigate to your workspace.
2. In the run selector or runs table, find the run you want to use as your new baseline.
3. Click the action () menu, then select Replace baseline.
   If the menu item is inactive, ensure that you have at least one pinning slot available. If necessary, unpin a pinned run by clicking the circular pin icon next to a pinned run.
4. The new run becomes the baseline, and the previous baseline is automatically pinned so you can find it easily. Optionally, unpin it by clicking its pin icon.

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Remove the baseline designation

1. Navigate to your workspace.
2. In the run selector or runs table, find the current baseline run.
3. Click the action () menu, then select Remove baseline.
   If the menu item is inactive, ensure that you have at least one pinning slot available. If necessary, unpin a pinned run by clicking the circular pin icon next to a pinned run.
4. The previous baseline is automatically pinned so you can find it easily. Optionally, unpin it by clicking its pin icon.

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Compare runs to the baseline

• Hover over a part of the plot to display a tooltip with values for all visible runs, including the baseline run and pinned runs.
  
• Hover over the baseline run’s legend label to display the line prominently. It appears as a heavy dashed line. Lines for other visible runs appear with reduced saturation.
  
• Hover over another run’s legend label to display that run’s line prominently and compare it with the baseline, which appears as a heavy dashed line. Lines for other visible runs appear with reduced saturation.
  

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Summary metric deltas

• If the other run outperforms (is directionally better than) the baseline, the delta is shown in dark red text with a light red background.
• If the other run underperforms (is directionally better than) the baseline, the delta is shown in dark teal text with a light teal background.

1. In the runs table, hover over the column heading for the metric.
2. Click the action () menu that appears.
3. Set Metric directionality to Higher values are best or Lower values are best.

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Hide summary metric deltas in a workspace

1. In the workspace, click Settings.
2. In the drawer that appears, click Runs.
3. In the Baseline tab, toggle Show value deltas in the runs table.
4. Close the workspace settings drawer.

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Use cases

• Track production models: Ensure that new models meet your quality bar before deployment.
  1. Set your production model as the baseline.
  2. Compare all experiments against your deployed model to identify candidates that outperform production.
• Compare hyperparameter experiments: Evaluate hyperparameter sweeps or manual experiments against your best-known configuration.
  1. Set your best known configuration as the baseline.
  2. Pin promising candidates as you discover them.
  3. Use the line plots to visually compare runs against the baseline.
  4. Continue to update the baseline as you find better configurations.

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Example workflow

1. Run this example code, which simulates a hyperparameter-tuning scenario with a series of runs. Replace placeholders surrounded with angle brackets (<>) with your own values.
   import wandb
   import random
   import math
   
   def train_model(learning_rate, batch_size, run_name, tags=None):
       """Simulate training a model with given hyperparameters."""
       config = {
           "learning_rate": learning_rate,
           "batch_size": batch_size,
           "optimizer": "adam",
           "architecture": "resnet50"
       }
       
       with wandb.init(
         # Replace with your team and project name
           project="hyperparameter-tuning",
           entity="<team>",
           name=run_name,
           config=config,
           tags=tags or []
       ) as run:
           # Simulate training loop
           for epoch in range(50):
               # Simulated metrics
               accuracy = 0.6 + 0.3 * (1 - math.exp(-learning_rate * epoch / 10))
               loss = 1.0 * math.exp(-learning_rate * epoch / 10)
               
               run.log({
                   "epoch": epoch,
                   "accuracy": accuracy,
                   "loss": loss
               })
   
   # Create baseline run with standard configuration
   train_model(
       learning_rate=0.001,
       batch_size=64,
       run_name="baseline-config",
       tags=["baseline", "production"]
   )
   
   # Experiment with different learning rates
   train_model(
       learning_rate=0.003,
       batch_size=64,
       run_name="lr-experiment-0.003",
       tags=["experiment"]
   )
   
   train_model(
       learning_rate=0.0001,
       batch_size=64,
       run_name="lr-experiment-0.0001",
       tags=["experiment"]
   )
   
   After running this code, your workspace has three runs.
2. Set baseline-config as your baseline run.
3. Pin baseline-config to keep it visible.
4. Compare the experiment runs against the baseline.

• In the runs table, review the summary metric deltas next to each run’s values to compare the run to the baseline.
• In line plots, compare the performance of one or more runs to the baseline, which is always visible.

5. Pin promising experiments for further investigation. In this example, after 50 epochs, lr-experiment-0.003 has the highest accuracy (~0.64) and the lowest loss (~0.86).

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Limitations

• Grouping: When viewing runs in the run selector or runs table, if runs are grouped by a column, pinned and baseline runs are not visually distinct from other runs.
• Reports: In a run set in a W&B Report, pinned and baseline runs are not visually distinct from other runs.
• Workspace view only: The baseline does not appear when viewing a single run’s workspace.
• Line plots only: Baseline comparison is available only for line plots, and is not yet available for other panels such as bar plots or media panels.