PyTorch Lightning ↗

Original Documentation

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.

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PyTorch Lightning provides a lightweight wrapper for organizing your PyTorch code and easily adding advanced features such as distributed training and 16-bit precision. W&B provides a lightweight wrapper for logging your ML experiments. But you don’t need to combine the two yourself: W&B is incorporated directly into the PyTorch Lightning library via the WandbLogger.

Integrate with Lightning#

    from lightning.pytorch.loggers import WandbLogger
    from lightning.pytorch import Trainer

    wandb_logger = WandbLogger(log_model="all")
    trainer = Trainer(logger=wandb_logger)
    ```

<span class="callout-start" data-callout-type="note"></span>
  **Using wandb.log():** The `WandbLogger` logs to W\&B using the Trainer's `global_step`. If you make additional calls to `wandb.log` directly in your code, **do not** use the `step` argument in `wandb.log()`.

  Instead, log the Trainer's `global_step` like your other metrics:

  ```python
      wandb.log({"accuracy":0.99, "trainer/global_step": step})
      ```
<span class="callout-end"></span>
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Fabric Logger"></span>
```python
    import lightning as L
    from wandb.integration.lightning.fabric import WandbLogger

    wandb_logger = WandbLogger(log_model="all")
    fabric = L.Fabric(loggers=[wandb_logger])
    fabric.launch()
    fabric.log_dict({"important_metric": important_metric})
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>


  <img src="https://mintcdn.com/wb-21fd5541/mVjDwbx0mC8gYx-b/images/integrations/n6P7K4M.gif?s=7d3d0917631ff3ad6467018fc770f984" alt="Interactive dashboards" data-og-width="1920" width="1920" data-og-height="1080" height="1080" data-path="images/integrations/n6P7K4M.gif" data-optimize="true" data-opv="3" />


### Sign up and create an API key

An API key authenticates your machine to W\&B. You can generate an API key from your user profile.

<span class="callout-start" data-callout-type="note"></span>
  For a more streamlined approach, create an API key by going directly to [User Settings](https://wandb.ai/settings). Copy the newly created API key immediately and save it in a secure location such as a password manager.
<span class="callout-end"></span>

1. Click your user profile icon in the upper right corner.
2. Select **User Settings**, then scroll to the **API Keys** section.

### Install the `wandb` library and log in

To install the `wandb` library locally and log in:

<span class="tab-group-start"></span>
  <span class="tab-start" data-tab-title="Command Line"></span>
1. Set the `WANDB_API_KEY` [environment variable](/models/track/environment-variables/) to your API key.

   ```bash
       export WANDB_API_KEY=<your_api_key>
       ```

2. Install the `wandb` library and log in.

   ```shell
       pip install wandb

       wandb login
       ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Python"></span>
```bash
    pip install wandb
    ```

```python
    import wandb
    wandb.login()
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Python notebook"></span>
```notebook
    !pip install wandb

    import wandb
    wandb.login()
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

## Use PyTorch Lightning's `WandbLogger`

PyTorch Lightning has multiple `WandbLogger` classes to log metrics and model weights, media, and more.

* [`PyTorch`](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.loggers.wandb.html#module-lightning.pytorch.loggers.wandb)
* [`Fabric`](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.loggers.wandb.html#module-lightning.pytorch.loggers.wandb)

To integrate with Lightning, instantiate the `WandbLogger` and pass it to Lightning's `Trainer` or `Fabric`.

<span class="tab-group-start"></span>
  <span class="tab-start" data-tab-title="PyTorch Logger"></span>
```python
    trainer = Trainer(logger=wandb_logger)
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Fabric Logger"></span>
```python
    fabric = L.Fabric(loggers=[wandb_logger])
    fabric.launch()
    fabric.log_dict({
        "important_metric": important_metric
    })
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

### Common logger arguments

Below are some of the most used parameters in `WandbLogger`. Review the PyTorch Lightning documentation for details about all logger arguments.

* [`PyTorch`](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.loggers.wandb.html#module-lightning.pytorch.loggers.wandb)
* [`Fabric`](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.loggers.wandb.html#module-lightning.pytorch.loggers.wandb)

| Parameter   | Description                                                                   |
| ----------- | ----------------------------------------------------------------------------- |
| `project`   | Define what wandb Project to log to                                           |
| `name`      | Give a name to your wandb run                                                 |
| `log_model` | Log all models if `log_model="all"` or at end of training if `log_model=True` |
| `save_dir`  | Path where data is saved                                                      |

## Log your hyperparameters

<span class="tab-group-start"></span>
  <span class="tab-start" data-tab-title="PyTorch Logger"></span>
```python
    class LitModule(LightningModule):
        def __init__(self, *args, **kwarg):
            self.save_hyperparameters()
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Fabric Logger"></span>
```python
    wandb_logger.log_hyperparams(
        {
            "hyperparameter_1": hyperparameter_1,
            "hyperparameter_2": hyperparameter_2,
        }
    )
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

## Log additional config parameters

```python
# add one parameter
wandb_logger.experiment.config["key"] = value

# add multiple parameters
wandb_logger.experiment.config.update({key1: val1, key2: val2})

# use directly wandb module
wandb.config["key"] = value
wandb.config.update()

Log gradients, parameter histogram and model topology#

You can pass your model object to wandblogger.watch() to monitor your models’s gradients and parameters as you train. See the PyTorch Lightning WandbLogger documentation

Log metrics#

You can log your metrics to W&B when using the WandbLogger by calling self.log('my_metric_name', metric_vale) within your LightningModule, such as in your training_step or validation_step methods.

The code snippet below shows how to define your LightningModule to log your metrics and your LightningModule hyperparameters. This example uses the torchmetrics library to calculate your metrics

    import torch
    from torch.nn import Linear, CrossEntropyLoss, functional as F
    from torch.optim import Adam
    from torchmetrics.functional import accuracy
    from lightning.pytorch import LightningModule


    class My_LitModule(LightningModule):
        def __init__(self, n_classes=10, n_layer_1=128, n_layer_2=256, lr=1e-3):
            """method used to define the model parameters"""
            super().__init__()

            # mnist images are (1, 28, 28) (channels, width, height)
            self.layer_1 = Linear(28 * 28, n_layer_1)
            self.layer_2 = Linear(n_layer_1, n_layer_2)
            self.layer_3 = Linear(n_layer_2, n_classes)

            self.loss = CrossEntropyLoss()
            self.lr = lr

            # save hyper-parameters to self.hparams (auto-logged by W&B)
            self.save_hyperparameters()

        def forward(self, x):
            """method used for inference input -> output"""

            # (b, 1, 28, 28) -> (b, 1*28*28)
            batch_size, channels, width, height = x.size()
            x = x.view(batch_size, -1)

            # let's do 3 x (linear + relu)
            x = F.relu(self.layer_1(x))
            x = F.relu(self.layer_2(x))
            x = self.layer_3(x)
            return x

        def training_step(self, batch, batch_idx):
            """needs to return a loss from a single batch"""
            _, loss, acc = self._get_preds_loss_accuracy(batch)

            # Log loss and metric
            self.log("train_loss", loss)
            self.log("train_accuracy", acc)
            return loss

        def validation_step(self, batch, batch_idx):
            """used for logging metrics"""
            preds, loss, acc = self._get_preds_loss_accuracy(batch)

            # Log loss and metric
            self.log("val_loss", loss)
            self.log("val_accuracy", acc)
            return preds

        def configure_optimizers(self):
            """defines model optimizer"""
            return Adam(self.parameters(), lr=self.lr)

        def _get_preds_loss_accuracy(self, batch):
            """convenience function since train/valid/test steps are similar"""
            x, y = batch
            logits = self(x)
            preds = torch.argmax(logits, dim=1)
            loss = self.loss(logits, y)
            acc = accuracy(preds, y)
            return preds, loss, acc
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Fabric Logger"></span>
```python
    import lightning as L
    import torch
    import torchvision as tv
    from wandb.integration.lightning.fabric import WandbLogger
    import wandb

    fabric = L.Fabric(loggers=[wandb_logger])
    fabric.launch()

    model = tv.models.resnet18()
    optimizer = torch.optim.SGD(model.parameters(), lr=lr)
    model, optimizer = fabric.setup(model, optimizer)

    train_dataloader = fabric.setup_dataloaders(
        torch.utils.data.DataLoader(train_dataset, batch_size=batch_size)
    )

    model.train()
    for epoch in range(num_epochs):
        for batch in train_dataloader:
            optimizer.zero_grad()
            loss = model(batch)
            loss.backward()
            optimizer.step()
            fabric.log_dict({"loss": loss})
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

## Log the min/max of a metric

Using wandb's [`define_metric`](/models/ref/python/experiments/run#define_metric) function you can define whether you'd like your W\&B summary metric to display the min, max, mean or best value for that metric. If `define`\_`metric` \_ isn't used, then the last value logged with appear in your summary metrics. See the `define_metric` [reference docs here](/models/ref/python/experiments/run#define_metric) and the [guide here](/models/track/log/customize-logging-axes/) for more.

To tell W\&B to keep track of the max validation accuracy in the W\&B summary metric, call `wandb.define_metric` only once, at the beginning of training:

<span class="tab-group-start"></span>
  <span class="tab-start" data-tab-title="PyTorch Logger"></span>
```python
    class My_LitModule(LightningModule):
        ...

        def validation_step(self, batch, batch_idx):
            if trainer.global_step == 0:
                wandb.define_metric("val_accuracy", summary="max")

            preds, loss, acc = self._get_preds_loss_accuracy(batch)

            # Log loss and metric
            self.log("val_loss", loss)
            self.log("val_accuracy", acc)
            return preds
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Fabric Logger"></span>
```python
    wandb.define_metric("val_accuracy", summary="max")
    fabric = L.Fabric(loggers=[wandb_logger])
    fabric.launch()
    fabric.log_dict({"val_accuracy": val_accuracy})
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

## Checkpoint a model

To save model checkpoints as W\&B [Artifacts](/models/artifacts/),
use the Lightning [`ModelCheckpoint`](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html) callback and set the `log_model` argument in the `WandbLogger`.

<span class="tab-group-start"></span>
  <span class="tab-start" data-tab-title="PyTorch Logger"></span>
```python
    trainer = Trainer(logger=wandb_logger, callbacks=[checkpoint_callback])
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Fabric Logger"></span>
```python
    fabric = L.Fabric(loggers=[wandb_logger], callbacks=[checkpoint_callback])
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

The *latest* and *best* aliases are automatically set to easily retrieve a model checkpoint from a W\&B [Artifact](/models/artifacts/):

```python
# reference can be retrieved in artifacts panel
# "VERSION" can be a version (ex: "v2") or an alias ("latest or "best")
checkpoint_reference = "USER/PROJECT/MODEL-RUN_ID:VERSION"

    # download checkpoint locally (if not already cached)
    wandb_logger.download_artifact(checkpoint_reference, artifact_type="model")
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Via wandb"></span>
```python
    # download checkpoint locally (if not already cached)
    run = wandb.init(project="MNIST")
    artifact = run.use_artifact(checkpoint_reference, type="model")
    artifact_dir = artifact.download()
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

<span class="tab-group-start"></span>
  <span class="tab-start" data-tab-title="PyTorch Logger"></span>
```python
    # load checkpoint
    model = LitModule.load_from_checkpoint(Path(artifact_dir) / "model.ckpt")
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Fabric Logger"></span>
```python
    # Request the raw checkpoint
    full_checkpoint = fabric.load(Path(artifact_dir) / "model.ckpt")

    model.load_state_dict(full_checkpoint["model"])
    optimizer.load_state_dict(full_checkpoint["optimizer"])
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

The model checkpoints you log are viewable through the [W\&B Artifacts](/models/artifacts/) UI, and include the full model lineage (see an example model checkpoint in the UI [here](https://wandb.ai/wandb/arttest/artifacts/model/iv3_trained/5334ab69740f9dda4fed/lineage?_gl=1*yyql5q*_ga*MTQxOTYyNzExOS4xNjg0NDYyNzk1*_ga_JH1SJHJQXJ*MTY5MjMwNzI2Mi4yNjkuMS4xNjkyMzA5NjM2LjM3LjAuMA..)).

To bookmark your best model checkpoints and centralize them across your team, you can link them to the [W\&B Model Registry](/models).

Here you can organize your best models by task, manage model lifecycle, facilitate easy tracking and auditing throughout the ML lifecyle, and [automate](/models/automations/) downstream actions with webhooks or jobs.

## Log images, text, and more

The `WandbLogger` has `log_image`, `log_text` and `log_table` methods for logging media.

You can also directly call `wandb.log` or `trainer.logger.experiment.log` to log other media types such as Audio, Molecules, Point Clouds, 3D Objects and more.

<span class="tab-group-start"></span>
  <span class="tab-start" data-tab-title="Log Images"></span>
```python
    # using tensors, numpy arrays or PIL images
    wandb_logger.log_image(key="samples", images=[img1, img2])

    # adding captions
    wandb_logger.log_image(key="samples", images=[img1, img2], caption=["tree", "person"])

    # using file path
    wandb_logger.log_image(key="samples", images=["img_1.jpg", "img_2.jpg"])

    # using .log in the trainer
    trainer.logger.experiment.log(
        {"samples": [wandb.Image(img, caption=caption) for (img, caption) in my_images]},
        step=current_trainer_global_step,
    )
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Log Text"></span>
```python
    # data should be a list of lists
    columns = ["input", "label", "prediction"]
    my_data = [["cheese", "english", "english"], ["fromage", "french", "spanish"]]

    # using columns and data
    wandb_logger.log_text(key="my_samples", columns=columns, data=my_data)

    # using a pandas DataFrame
    wandb_logger.log_text(key="my_samples", dataframe=my_dataframe)
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Log Tables"></span>
```python
    # log a W&B Table that has a text caption, an image and audio
    columns = ["caption", "image", "sound"]

    # data should be a list of lists
    my_data = [
        ["cheese", wandb.Image(img_1), wandb.Audio(snd_1)],
        ["wine", wandb.Image(img_2), wandb.Audio(snd_2)],
    ]

    # log the Table
    wandb_logger.log_table(key="my_samples", columns=columns, data=data)
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

You can use Lightning's Callbacks system to control when you log to W\&B via the `WandbLogger`, in this example we log a sample of our validation images and predictions:

```python
import torch
import wandb
import lightning.pytorch as pl
from lightning.pytorch.loggers import WandbLogger

# or
# from wandb.integration.lightning.fabric import WandbLogger


class LogPredictionSamplesCallback(Callback):
    def on_validation_batch_end(
        self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx
    ):
        """Called when the validation batch ends."""

        # `outputs` comes from `LightningModule.validation_step`
        # which corresponds to our model predictions in this case

        # Let's log 20 sample image predictions from the first batch
        if batch_idx == 0:
            n = 20
            x, y = batch
            images = [img for img in x[:n]]
            captions = [
                f"Ground Truth: {y_i} - Prediction: {y_pred}"
                for y_i, y_pred in zip(y[:n], outputs[:n])
            ]

            # Option 1: log images with `WandbLogger.log_image`
            wandb_logger.log_image(key="sample_images", images=images, caption=captions)

            # Option 2: log images and predictions as a W&B Table
            columns = ["image", "ground truth", "prediction"]
            data = [
                [wandb.Image(x_i), y_i, y_pred] or x_i,
                y_i,
                y_pred in list(zip(x[:n], y[:n], outputs[:n])),
            ]
            wandb_logger.log_table(key="sample_table", columns=columns, data=data)


trainer = pl.Trainer(callbacks=[LogPredictionSamplesCallback()])

Use multiple GPUs with Lightning and W&B#

PyTorch Lightning has Multi-GPU support through their DDP Interface. However, PyTorch Lightning’s design requires you to be careful about how you instantiate our GPUs.

Lightning assumes that each GPU (or Rank) in your training loop must be instantiated in exactly the same way - with the same initial conditions. However, only rank 0 process gets access to the wandb.run object, and for non-zero rank processes: wandb.run = None. This could cause your non-zero processes to fail. Such a situation can put you in a deadlock because rank 0 process will wait for the non-zero rank processes to join, which have already crashed.

For this reason, be careful about how we set up your training code. The recommended way to set it up would be to have your code be independent of the wandb.run object.

class MNISTClassifier(pl.LightningModule):
    def __init__(self):
        super(MNISTClassifier, self).__init__()

        self.model = nn.Sequential(
            nn.Flatten(),
            nn.Linear(28 * 28, 128),
            nn.ReLU(),
            nn.Linear(128, 10),
        )

        self.loss = nn.CrossEntropyLoss()

    def forward(self, x):
        return self.model(x)

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.forward(x)
        loss = self.loss(y_hat, y)

        self.log("train/loss", loss)
        return {"train_loss": loss}

    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.forward(x)
        loss = self.loss(y_hat, y)

        self.log("val/loss", loss)
        return {"val_loss": loss}

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=0.001)


def main():
    # Setting all the random seeds to the same value.
    # This is important in a distributed training setting.
    # Each rank will get its own set of initial weights.
    # If they don't match up, the gradients will not match either,
    # leading to training that may not converge.
    pl.seed_everything(1)

    train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=4)
    val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False, num_workers=4)

    model = MNISTClassifier()
    wandb_logger = WandbLogger(project="<project_name>")
    callbacks = [
        ModelCheckpoint(
            dirpath="checkpoints",
            every_n_train_steps=100,
        ),
    ]
    trainer = pl.Trainer(
        max_epochs=3, gpus=2, logger=wandb_logger, strategy="ddp", callbacks=callbacks
    )
    trainer.fit(model, train_loader, val_loader)

Examples#

You can follow along in a video tutorial with a Colab notebook.

Frequently Asked Questions#

How does W&B integrate with Lightning?#

The core integration is based on the Lightning loggers API, which lets you write much of your logging code in a framework-agnostic way. Loggers are passed to the Lightning Trainer and are triggered based on that API’s rich hook-and-callback system. This keeps your research code well-separated from engineering and logging code.

What does the integration log without any additional code?#

We’ll save your model checkpoints to W&B, where you can view them or download them for use in future runs. We’ll also capture system metrics, like GPU usage and network I/O, environment information, like hardware and OS information, code state (including git commit and diff patch, notebook contents and session history), and anything printed to the standard out.

What if I need to use `wandb.run` in my training setup?#

You need to expand the scope of the variable you need to access yourself. In other words, make sure that the initial conditions are the same on all processes.

if os.environ.get("LOCAL_RANK", None) is None:
    os.environ["WANDB_DIR"] = wandb.run.dir

If they are, you can use os.environ["WANDB_DIR"] to set up the model checkpoints directory. This way, any non-zero rank process can access wandb.run.dir.

Link last verified June 7, 2026. View original ↗

Source: Weights & Biases Docs

Link last verified: 2026-03-04