> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-sdk-add-methods-properties.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Run
export const GitHubLink = ({url, compact = false}) =>
{compact ? "View source" : <>
GitHub source
}
;
## Class wandb.Run
A unit of computation logged by W\&B. Typically, this is an ML experiment.
Call [`wandb.init()`](https://docs.wandb.ai/models/ref/python/functions/init) to create a
new run. `wandb.init()` starts a new run and returns a `wandb.Run` object.
Each run is associated with a unique ID (run ID). W\&B recommends using
a context (`with` statement) manager to automatically finish the run.
For distributed training experiments, you can either track each process
separately using one run per process or track all processes to a single run.
See [Log distributed training experiments](https://docs.wandb.ai/models/track/log/distributed-training)
for more information.
You can log data to a run with `wandb.Run.log()`. Anything you log using
`wandb.Run.log()` is sent to that run. See
[Create an experiment](https://docs.wandb.ai/models/track/create-an-experiment) or
[`wandb.init`](https://docs.wandb.ai/models/ref/python/functions/init) API reference page
or more information.
There is a another `Run` object in the
[`wandb.apis.public`](https://docs.wandb.ai/models/ref/python/public-api/api)
namespace. Use this object is to interact with runs that have already been
created.
## Attributes
* `summary`: (Summary) A summary of the run, which is a dictionary-like object. For more information, see [Log summary metrics](https://docs.wandb.ai/models/track/log/log-summary).
## Examples
Create a run with `wandb.init()`:
```python theme={null}
import wandb
# Start a new run and log some data
# Use context manager (`with` statement) to automatically finish the run
with wandb.init(entity="entity", project="project") as run:
run.log({"accuracy": acc, "loss": loss})
```