> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Log summary metrics

> Track and customize single summary metrics like best accuracy or minimum loss on a W&B run using run.summary.

In addition to values that change over time during training, it is often important to track a single value that summarizes a model or a preprocessing step. Log this information in a W\&B Run's `summary` dictionary. A Run's summary dictionary can handle numpy arrays, PyTorch tensors or TensorFlow tensors. When a value is one of these types we persist the entire tensor in a binary file and store high level metrics in the summary object, such as min, mean, variance, percentiles, and more.

The last value logged with `wandb.Run.log()` is automatically set as the summary dictionary in a W\&B Run. If a summary metric dictionary is modified, the previous value is lost.

The following code snippet demonstrates how to provide a custom summary metric to W\&B:

```python theme={null}
import wandb
import argparse

with wandb.init(config=args) as run:
  best_accuracy = 0
  for epoch in range(1, args.epochs + 1):
      test_loss, test_accuracy = test()
      if test_accuracy > best_accuracy:
          run.summary["best_accuracy"] = test_accuracy
          best_accuracy = test_accuracy
```

You can update the summary attribute of an existing W\&B Run after training has completed. Use the [W\&B Public API](/models/ref/python/public-api/) to update the summary attribute:

```python theme={null}
api = wandb.Api()
run = api.run("username/project/run_id")
run.summary["tensor"] = np.random.random(1000)
run.summary.update()
```

## Customize summary metrics

Custom summary metrics are useful for capturing model performance at the best step of training in your `run.summary`. For example, you might want to capture the maximum accuracy or the minimum loss value, instead of the final value.

By default, the summary uses the final value from history. To customize summary metrics, pass the `summary` argument in `define_metric`. It accepts the following values:

* `"min"`
* `"max"`
* `"mean"`
* `"best"`
* `"last"`
* `"none"`

You can use `"best"` only when you also set the optional `objective` argument to `"minimize"` or `"maximize"`.

The following example adds the min and max values of loss and accuracy to the summary:

```python theme={null}
import wandb
import random

random.seed(1)

with wandb.init() as run:
    # Min and max summary values for loss
    run.define_metric("loss", summary="min")
    run.define_metric("loss", summary="max")

    # Min and max summary values for accuracy
    run.define_metric("acc", summary="min")
    run.define_metric("acc", summary="max")

    for i in range(10):
        log_dict = {
            "loss": random.uniform(0, 1 / (i + 1)),
            "acc": random.uniform(1 / (i + 1), 1),
        }
        run.log(log_dict)
```

## View summary metrics

View summary values in a run's **Overview** page or the project's runs table.

<Tabs>
  <Tab title="Run Overview">
    1. Navigate to the W\&B App.
    2. Select the **Workspace** tab from the project sidebar.
    3. Click the run that logged the summary values. The run page opens with the **Overview** tab shown by default.
    4. View the summary values in the **Summary** section.

    <Frame>
      <img src="https://mintcdn.com/wb-21fd5541/_OEDykSS2PIumrEw/images/track/customize_summary.png?fit=max&auto=format&n=_OEDykSS2PIumrEw&q=85&s=ec840a417aed257853b726878428075f" alt="Run overview" width="2416" height="2694" data-path="images/track/customize_summary.png" />
    </Frame>
  </Tab>

  <Tab title="Run Table">
    1. Navigate to the W\&B App.
    2. Select the **Runs** tab.
    3. Within the runs table, you can view the summary values within the columns based on the name of the summary value.
  </Tab>

  <Tab title="W&B Public API">
    You can use the W\&B Public API to fetch the summary values of a run.

    The following code example demonstrates one way to retrieve the summary values logged to a specific run using the W\&B Public API and pandas:

    ```python theme={null}
    import wandb
    import pandas

    entity = "<your-entity>"
    project = "<your-project>"
    run_name = "<your-run-name>" # Name of run with summary values

    all_runs = []

    for run in api.runs(f"{entity}/{project_name}"):
        print("Fetching details for run: ", run.id, run.name)
        run_data = {
                  "id": run.id,
                  "name": run.name,
                  "url": run.url,
                  "state": run.state,
                  "tags": run.tags,
                  "config": run.config,
                  "created_at": run.created_at,
                  "system_metrics": run.system_metrics,
                  "summary": run.summary,
                  "project": run.project,
                  "entity": run.entity,
                  "user": run.user,
                  "path": run.path,
                  "notes": run.notes,
                  "read_only": run.read_only,
                  "history_keys": run.history_keys,
                  "metadata": run.metadata,
              }
        all_runs.append(run_data)
      
    # Convert to DataFrame  
    df = pd.DataFrame(all_runs)

    # Get row based on the column name (run) and convert to dictionary
    df[df['name']==run_name].summary.reset_index(drop=True).to_dict()
    ```
  </Tab>
</Tabs>