wandb

import wandb import os # Authenticate with W&B. For automated environments, use an environment variable. # wandb.login() will prompt for an API key if not set. # Set WANDB_API_KEY environment variable for CI/CD or headless environments. # For local development, running `wandb login` in your terminal is common. # os.environ.get('WANDB_API_KEY', '') # Example for fetching from env, but wandb.login() handles this. wandb.login() # Initialize a new W&B run project_name = os.environ.get('WANDB_PROJECT', 'my-awesome-project') config = { 'epochs': 10, 'lr': 0.01, 'batch_size': 32 } with wandb.init(project=project_name, config=config) as run: # Access hyperparameters epochs = run.config.epochs learning_rate = run.config.lr print(f"Starting training for {epochs} epochs with LR: {learning_rate}") # Simulate a training loop for epoch in range(epochs): # Simulate loss and accuracy metrics loss = 1.0 / (epoch + 1) + 0.1 * (epochs - epoch - 1) / epochs accuracy = 0.5 + 0.5 * (epoch + 1) / epochs # Log metrics to W&B run.log({"epoch": epoch, "loss": loss, "accuracy": accuracy}) print(f"Epoch {epoch+1}/{epochs}: Loss = {loss:.4f}, Accuracy = {accuracy:.4f}") print("Training complete!")