PyTorch Lightning
Build scalable, structured, high-performance PyTorch models with Lightning and log them with W&B.
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: Weights & Biases is incorporated directly into the PyTorch Lightning library via the
WandbLogger. from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning import Trainer
wandb_logger = WandbLogger()
trainer = Trainer(logger=wandb_logger)
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b) Pip install the
wandb library c) To login in your training script, you'll need to be signed in to you account at www.wandb.ai, then you will find your API key on the Authorize page.
Command Line
Notebook
pip install wandb
wandb login
!pip install wandb
import wandb
wandb.login()
PyTorch Lightning has a
WandbLogger class that can be used to seamlessly log metrics, model weights, media and more. Just instantiate the WandbLogger and pass it to Lightning's Trainer.wandb_logger = WandbLogger()
trainer = Trainer(logger=wandb_logger)
Below are some of the most used parameters in WandbLogger, see the PyTorch Lightning
WandbLogger documentation for a full list and descriptionParameter | 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 |
class LitModule(LightningModule):
def __init__(self, *args, **kwarg):
self.save_hyperparameters()
# 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()
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 for a full descriptionYou 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. In this example we will use the torchmetrics library to calculate our metricsimport torch
from torch.nn import Linear, CrossEntropyLoss, functional as F
from torch.optim import Adam
from torchmetrics.functional import accuracy
from pytorch_lightning 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 our 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
Using wandb's
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 definemetric isn't used, then the last value logged with appear in your summary metrics. See the define_metric reference docs here and the guide here for more.To tell W&B to keep track of the max validation accuracy in the W&B summary metric, you just need to call
wandb.define_metric once, e.g. you can call it at the beginning of training like so: 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
Custom checkpointing to W&B can be set up through the PyTorch Lightning
ModelCheckpoint when the log_model argument is used in the WandbLogger:# log model only if `val_accuracy` increases
wandb_logger = WandbLogger(log_model="all")
checkpoint_callback = ModelCheckpoint(monitor="val_accuracy", mode="max")
trainer = Trainer(logger=wandb_logger, callbacks=[checkpoint_callback])
The latest and best aliases are automatically set to easily retrieve a model checkpoint from W&B Artifacts:
# 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)
run = wandb.init(project="MNIST")
artifact = run.use_artifact(checkpoint_reference, type="model")
artifact_dir = artifact.download()
# load checkpoint
model = LitModule.load_from_checkpoint(Path(artifact_dir) / "model.ckpt")
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. When using
wandb.log or trainer.logger.experiment.log within your trainer make sure to also include"global_step": trainer.global_step in the dictionary being passed. That way, you can line up the information you're currently logging with information logged via other methods.Log Images
Log Text
Log Tables
# 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]
})
# 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)
# 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)
You can use Lightning's Callbacks system to control when you log to Weights & Biases via the WandbLogger, in this example we log a sample of our validation images and predictions:
Log Image Predictions
import torch
import wandb
import pytorch_lightning as pl
from pytorch_lightning.loggers 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] f
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()]
)
PyTorch Lightning has Multi-GPU support through their DDP Interface. However, PyTorch Lightning's design requires us to be careful about how we 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, we have to be careful about how we set up our 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)
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.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.
You will have to essentially expand the scope of the variable you need to access yourself. In other words, making 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
Then, you can use
os.environ["WANDB_DIR"] to set up the model checkpoints directory. This way, wandb.run.dir can be used by any non-zero rank processes as well.Last modified 6mo ago