Example Codes for Saving Model Weights to MLflow with PyTorch Lightning

• PyTorch: A deep learning framework for building and training neural networks.
• PyTorch Lightning: A framework simplifying PyTorch development by handling boilerplate code like training loops, logging, and callbacks.
• MLflow: An open-source platform for managing the machine learning lifecycle, including experiment tracking, model registry, and deployment.

Direct Model Weight Saving (Limited Support):

• While PyTorch Lightning's autologging doesn't directly save model weights as artifacts, you can leverage the mlflow.pytorch.save_model() function:

import mlflow.pytorch

# ... your PyTorch Lightning training code ...

with mlflow.start_run() as run:
    mlflow.pytorch.save_model(model, "model")  # Save model weights to "model" artifact

Considerations:

• This approach saves the entire model, including weights and optimizer state.
• If you only need weights, consider manually saving them using torch.save(model.state_dict(), ...) and logging the file as an artifact.

Alternative: Checkpointing with MLflow Autologging (Recommended):

• PyTorch Lightning's autologging can automatically checkpoint models during training when checkpoint_callback=True is set in the Trainer configuration.
• These checkpoints include weights, optimizer state, and potentially other training information.
• Access these checkpoints within MLflow for evaluation or deployment.

from pytorch_lightning import Trainer

# ... your PyTorch Lightning model and trainer setup ...

trainer = Trainer(checkpoint_callback=True)
trainer.fit(model)

# Checkpoints are logged to MLflow by autologging

Key Points:

• For weight-only saving, consider manual saving and artifact logging.
• Checkpointing with autologging is generally recommended for comprehensive model state management during training.
• Use MLflow's UI or API to access these models for further analysis or deployment.

Example Codes for Saving Model Weights to MLflow with PyTorch Lightning

Saving Entire Model with mlflow.pytorch.save_model():

import mlflow.pytorch
import torch

class MyModel(torch.nn.Module):
    # ... your model definition ...

# ... your PyTorch Lightning training code ...

with mlflow.start_run() as run:
    # Train your model
    # ...

    # Save the entire model (weights and optimizer state) as an artifact
    mlflow.pytorch.save_model(model, "model")  # "model" is the artifact name

Explanation:

1. Import necessary libraries (mlflow.pytorch and torch).
2. Define your PyTorch model class (MyModel).
3. Implement your PyTorch Lightning training logic (not shown here).
4. Inside an mlflow.start_run() context:
   • Train your model.
   • Use mlflow.pytorch.save_model(model, "model") to save the entire model as an artifact named "model" to the MLflow tracking server.

Saving Model Weights with Checkpointing and Autologging:

from pytorch_lightning import Trainer
from pytorch_lightning import LightningModule
import torch

class MyModel(LightningModule):
    # ... your model definition ...

    def training_step(self, batch, batch_idx):
        # ... your training logic ...
        return loss

# ... other LightningModule methods (optional) ...

# ... your training data preparation ...

trainer = Trainer(checkpoint_callback=True)  # Enable model checkpointing
trainer.fit(model, train_dataloaders)

# Checkpoints are automatically logged by autologging

1. Import Trainer and LightningModule from pytorch_lightning.
2. Define your PyTorch Lightning model class (MyModel) with training_step (and optionally other methods).
3. Prepare your training data (train_dataloaders).
4. Create a Trainer instance with checkpoint_callback=True to enable model checkpointing during training.
5. Train your model using trainer.fit(model, train_dataloaders).
6. PyTorch Lightning's autologging automatically logs these checkpoints (including weights) as artifacts to the MLflow tracking server.

Remember:

• Choose the approach that aligns with your needs. For weight-only saving, consider approach 1. For comprehensive model state management during training, approach 2 with autologging is recommended.
• These examples provide the core structure. Customize the model definition, training logic, and data preparation according to your specific use case.

• Create a custom PyTorch Lightning callback that triggers weight saving at specific points during training.
• This approach offers more granular control over when to save weights.

Example:

from pytorch_lightning.callbacks import Callback

class WeightSaveCallback(Callback):
    def on_epoch_end(self, trainer, pl_module):
        # Save weights at the end of each epoch
        torch.save(pl_module.model.state_dict(), f"weights_epoch_{trainer.current_epoch}.pth")

        # Log the saved weights as an MLflow artifact (optional)
        with mlflow.start_run() as run:
            mlflow.log_artifact(f"weights_epoch_{trainer.current_epoch}.pth")

# ... PyTorch Lightning training code ...

trainer = Trainer(callbacks=[WeightSaveCallback()])
trainer.fit(model, train_dataloaders)

Using torch.save() with Manual Logging:

• Save model weights manually using torch.save(model.state_dict(), ...) at desired points in your training code.
• Log the saved weight file as an MLflow artifact using mlflow.log_artifact().

import torch
import mlflow

# ... PyTorch Lightning training code ...

# Save weights after training
torch.save(model.state_dict(), "saved_weights.pth")

# Log the saved weights as an MLflow artifact
with mlflow.start_run() as run:
    mlflow.log_artifact("saved_weights.pth")

Third-Party Libraries:

• Explore libraries like Sacred or Weights & Biases that integrate well with PyTorch Lightning and offer features for model versioning and artifact management, potentially including weight saving.

Choosing the Right Method:

• Consider the following factors when selecting an alternate method:
  • Granularity: How often do you need to save weights?
  • Control: Do you need fine-grained control over when weights are saved?
  • Integration: How seamlessly does the method integrate with your PyTorch Lightning workflow?