| import random, time | |
| import wandb | |
| def train(project="st", entity=None, epochs=10): | |
| run = wandb.init( | |
| # Set the project where this run will be logged | |
| project=project, | |
| entity=entity, | |
| # Track hyperparameters and run metadata | |
| config={ | |
| "learning_rate": 0.02, | |
| "architecture": "CNN", | |
| "dataset": "CIFAR-100", | |
| "epochs": epochs, | |
| }) | |
| # This simple block simulates a training loop logging metrics | |
| offset = random.random() / 5 | |
| for epoch in range(1, epochs+1): | |
| acc = 1 - 2 ** -epoch - random.random() / epoch - offset | |
| loss = 2 ** -epoch + random.random() / epoch + offset | |
| # 2️⃣ Log metrics from your script to W&B | |
| wandb.log({"acc": acc, "loss": loss}) | |
| time.sleep(0.1) | |
| # Mark the run as finished | |
| wandb.finish() |