> ## 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.

# Why is my W&B run slow to initialize or upload?

Slow `wandb.init()` or sluggish metric uploads are usually caused by network latency, large media payloads, high logging frequency, or slow startup of the W\&B service process.

## Slow wandb.init()

`wandb.init()` contacts the W\&B API to create the run and verify credentials. If it hangs for more than a few seconds:

* **Check connectivity**: Run `curl -I https://api.wandb.ai` to confirm your machine can reach the W\&B API. Firewall rules or proxy configurations on clusters are a common cause.

* **Increase the init timeout**: If the connection is intermittent, give `wandb.init()` more time before it gives up:

  ```python theme={null}
  import os
  os.environ["WANDB_INIT_TIMEOUT"] = "120"   # seconds
  ```

* **Use offline mode during testing**: If you do not need live syncing while iterating, run offline and sync later. Replace `[TIMESTAMP]` and `[ID]` with your run's timestamp and ID:

  ```bash theme={null}
  WANDB_MODE=offline python train.py
  wandb sync wandb/run-[TIMESTAMP]-[ID]
  ```

## Slow metric uploads during training

W\&B uploads metrics asynchronously in background threads so your training loop is not blocked. Uploads can fall behind when:

* **You log too frequently**: Calling `wandb.log()` every step on a fast GPU can generate more data than the background threads can upload. Log every N steps instead:

  ```python theme={null}
  if step % 50 == 0:
      wandb.log({"loss": loss}, step=step)
  ```

* **You log large media on every step**: `wandb.Image`, `wandb.Table`, and `wandb.Video` objects are significantly larger than scalar metrics. Log rich media every epoch or every N steps rather than every step.

* **Rate limits**: If you hit the `429 Rate limit exceeded` error, see [How do I fix rate limit exceeded errors?](/support/models/articles/rate-limit-exceeded-on-metric-logging).

## Run finalization is slow

After your script calls `wandb.finish()` (or exits), W\&B flushes any remaining buffered data. This can take time if a large backlog built up during training. Keep logging frequency reasonable throughout training rather than batching everything at the end.

## Diagnosing with debug logs

Enable debug logging to see where time is spent:

```bash theme={null}
WANDB_DEBUG=true python train.py
```

This writes detailed timing information to `wandb/debug.log` and `wandb/debug-internal.log`.

For more information, see [Experiments limits and performance](/models/track/limits) and [How do I deal with network issues?](/support/models/articles/how-do-i-deal-with-network-issues).

***

<Badge stroke shape="pill" color="orange" size="md">[Runs](/support/models/tags/runs)</Badge><Badge stroke shape="pill" color="orange" size="md">[Experiments](/support/models/tags/experiments)</Badge><Badge stroke shape="pill" color="orange" size="md">[Connectivity](/support/models/tags/connectivity)</Badge>