wandb pytorch

Wandb (Weights & Biases) is a popular tool used for tracking and visualizing machine learning experiments. It provides a Python library that can be integrated with PyTorch, among other deep learning frameworks.

To use Wandb with PyTorch, you will need to install the wandb library first by running the following command:

pip install wandb

Once installed, you can import and initialize wandb in your PyTorch script or notebook. Here's an example:

import wandb
import torch
import torch.nn as nn
import torch.optim as optim

# Initialize wandb
wandb.init(project="your-project-name", entity="your-username")

# Define your PyTorch model
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        # Add your model layers here

    def forward(self, x):
        # Define the forward pass of your model here
        pass

# Instantiate your model
model = MyModel()

# Define your loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Training loop
for epoch in range(num_epochs):
    # Training code goes here
    
    # Log metrics to wandb
    wandb.log({"loss": loss.item(), "accuracy": accuracy})

    # Validation code goes here

# Save the trained model
torch.save(model.state_dict(), "model.pth")

# Finish wandb run
wandb.finish()

In this example, wandb.init() initializes the wandb run and connects it to your project. You can specify the project name and your username or organization in the arguments. Throughout your training loop or experiment, you can use wandb.log() to log metrics such as loss and accuracy. Finally, wandb.finish() is called to finish the wandb run.

You can then view and analyze your experiment results on the Wandb platform.