> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-style-guide-models-runs-20260604-113608.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
> Learn how to use pinned and baseline runs to keep track of important runs and efficiently evaluate model experiments.
# Pin and compare runs
Use the W\&B App to organize, identify, and compare important runs, such as top performers, production models, failed experiments, and reference runs. To accomplish this, you can organize and compare runs with:
* **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.
These features are useful for:
* Comparing new experiments against your production model.
* [Comparing runs across projects](/models/runs/compare-runs#compare-runs-across-projects).
* [Creating a baseline for your experiments to evaluate how new runs perform against it.](/models/runs/compare-runs#manage-and-compare-a-baseline-run)
* Tracking multiple candidate models during experimentation.
* Evaluating whether new runs improve on your best results.
See [Limitations](#limitations).
## Pin runs
Pin runs to keep them easily accessible at the top of your workspace. To hide a pinned or baseline run, click the icon. To show a hidden run, click the icon. Pinned runs appear at the top of the run selector with a circular pin icon, separated from other runs by a visual divider.
To pin a run:
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**.
You can pin up to 20 runs in a workspace. If you have a baseline run, you can pin up to 19 runs because the baseline is implicitly pinned.
To unpin a run, click the pin icon, or follow the procedure to pin the run, but select **Unpin run** instead.
Runs that you pin to a project only impact your [personal or saved workspace view](/models/track/workspaces#saved-workspace-views), including [runs pinned from another project](/models/runs/compare-runs#compare-runs-across-projects).
## Compare runs across projects
Compare runs from different projects by selecting runs from another project and [pinning](/models/runs/compare-runs#pin-runs) them to your current workspace.
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**.
View runs that you pin from another project in the run selector or runs table. Pinned runs from other projects have an open circle icon next to their name. To see which project a pinned run comes from, hover over the run name.
Artifacts, logs, and other details for pinned runs link back to the original run. W\&B does not import this data into the current project or workspace. To view the details for a run pinned from another project, click the run. W\&B opens the original run details in a new browser tab.
To compare runs from different projects, pin the runs you want to compare, then use [line plots](/models/app/features/panels/line-plot) to visually compare them.
## Manage and compare a baseline run
You can designate one run as the workspace baseline to use as a reference point for evaluating other runs.
In the runs selector and runs table, the baseline run appears at the top alongside pinned runs, and has a bookmark icon instead of a pin.
In line plots, lines for the baseline run appear bolder than other lines. When you hover over the plot or legend, the baseline run's line appears dashed.
The following sections describe how to set, change, and remove the baseline.
### Set a baseline run
Designate a run as the workspace baseline to evaluate other runs against it.
To 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**.
The baseline run appears at the top of the run selector, separated from other runs by a visual divider. The baseline run has a bookmark icon instead of a circle.
### Change the baseline run
Only one run can be the baseline at a time. To change which run is your baseline:
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 W\&B automatically pins the previous baseline so you can find it. Optionally, unpin it by clicking its pin icon.
### Remove the baseline designation
To 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. W\&B automatically pins the previous baseline so you can find it. Optionally, unpin it by clicking its pin icon.
### Compare runs to the baseline
After you set a baseline, you can compare other runs against it in line plots and the runs table.
The baseline run stays visible in line plots for metrics it has logged. Its lines appear bolder than other runs.
* 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.
### Summary metric deltas
When you set a run as the baseline, every other run that logs the same summary metric shows the delta from the baseline by default. The delta, or amount of change, appears to the right of the metric value in the run's row in the runs table.
By default, the delta uses dark gray text and a dark gray background. To turn on semantic coloring for quick visual reference, set the Metric directionality for a column. When directionality is set:
* If the other run **outperforms** (is directionally better than) the baseline, the delta appears as dark red text with a light red background.
* If the other run **underperforms** (is directionally worse than) the baseline, the delta appears as dark teal text with a light teal background.
To set the directionality for a metric:
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**.
The following screenshot shows how the runs `nanochat-train-base` and `nanochat-train-mid` compare with the baseline run `nanochat-train`. Delta metrics appear for `TOTAL_TRAINING_TIME`, `TRAIN/DT`, and `TRAIN/GRAD_NORM`.
## Hide summary metric deltas in a workspace
By default, a workspace with a baseline run always displays summary metric deltas. To 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.
## Use cases
The following scenarios show how pinned and baseline runs can help guide your experiments.
* **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 line plots to compare runs against the baseline.
4. Continue to update the baseline as you find better configurations.
## Example: Compare runs from hyperparameter tuning experiments
This section illustrates how pinned and baseline runs can help you to compare runs.
For example, the following code simulates a hyperparameter-tuning scenario with a series of runs.
```python expandable theme={null}
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="your-team-name", # Replace with your team name
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 the runs complete, navigate to the W\&B App to view the runs in your workspace. You can pin promising experiments and set the baseline run to compare the performance of new runs against it:
1. Navigate to your W\&B project.
2. Select the **Runs** tab.
3. [Filter](/models/runs/filter-runs) for the run that has the `baseline` tag and set it as your [baseline](#set-a-baseline-run) run.
4. [Pin](#pin-runs) `baseline-config` to make the run visible at the top of the runs table.
5. Compare the experiment runs against the baseline.
* Select the vertical ellipsis menu next to the `accuracy` column, then set **Metric directionality** to **Higher values are best**. The delta for each run's accuracy is now shown in the runs table.
* Select the vertical ellipsis menu next to the `loss` column, then set **Metric directionality** to **Lower values are best**. The delta for each run's loss is now shown in the runs table.
6. Use the line plots to visually compare the runs against the baseline. The baseline run appears with a bold dashed line, and you can hover over the plot to see how each run compares at a given epoch.
For example, the following image shows how the runs `lr-experiment-0.003` and `lr-experiment-0.0001` compare with the baseline run `baseline-config`.
## Limitations
The following features aren't supported for pinned and baseline runs:
* **Grouping**: When [viewing runs](/models/runs#view-logged-runs) in the run selector or runs table, if runs are grouped by a column, pinned and baseline runs aren't visually distinct from other runs.
* **Reports**: In a run set in a [W\&B Report](/models/reports), pinned and baseline runs aren't visually distinct from other runs.
* **Workspace view only**: The baseline doesn't appear when viewing a single run's workspace.
* **Line plots only**: Baseline comparison is available only for line plots, not for other panels such as bar plots or media panels.