> ## 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.

# How do I log gradients and model weights with wandb.watch()?

`wandb.watch()` hooks into a PyTorch model's parameters and gradients and logs histograms of their values at regular intervals. This is useful for diagnosing training instability, vanishing gradients, and dead neurons.

**Basic usage**

Call `wandb.watch()` after `wandb.init()` and before the first training step:

```python theme={null}
import wandb
import torch.nn as nn

wandb.init(project="my-project")

model = MyModel()
wandb.watch(model, log="gradients", log_freq=100)

for step, batch in enumerate(dataloader):
    loss = train_step(model, batch)
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

    wandb.log({"train/loss": loss.item()}, step=step)

wandb.finish()
```

Gradient histograms are logged every `log_freq` **batches** (the [`Run.watch()`](/models/ref/python/experiments/run) default is `log_freq=1000`; the example uses `100` for faster feedback). They appear in the **Charts** tab under keys like `gradients/layer_name.weight`.

**`log` parameter options**

| Value          | What is logged                           |
| -------------- | ---------------------------------------- |
| `"gradients"`  | Gradient histograms only (default)       |
| `"parameters"` | Weight/parameter histograms only         |
| `"all"`        | Both gradients and parameters            |
| `None`         | Neither — only logs model graph topology |

```python theme={null}
wandb.watch(model, log="all", log_freq=50)
```

**Logging model graph topology**

Pass `log_graph=True` when you want the computational graph while histogram logging is off or minimal. View the graph in the run's **Overview** tab under **Model**. See [`Run.watch()`](/models/ref/python/experiments/run) for how `log`, `log_graph`, and `log_freq` interact.

```python theme={null}
wandb.watch(model, log=None, log_graph=True)  # graph focus, no histograms
```

**Watching multiple models**

Call `wandb.watch()` separately for each model (useful in GAN training):

```python theme={null}
wandb.watch(generator, log="gradients", log_freq=100)
wandb.watch(discriminator, log="gradients", log_freq=100)
```

Each model's gradients are logged with its parameter names as prefixes.

**Performance considerations**

Gradient logging adds overhead proportional to `log_freq`. Logging every step (`log_freq=1`) can significantly slow training. A value between 50 and 200 is typical for most training runs. If performance is critical, set `log="parameters"` rather than `log="gradients"` — parameter histograms are computed without a backward pass hook and are cheaper.

**Stopping the watch**

To stop logging gradients mid-training:

```python theme={null}
wandb.unwatch(model)
```

This removes the hooks without ending the run, so metric logging continues unaffected.

***

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