Weights & Biases: Machine learning experiment tracking
ML experiment tracking platform with logging, visualization, and model versioning.
Learn more about Weights & Biases
Weights & Biases (W&B) is a machine learning operations platform that provides experiment tracking, model management, and collaboration tools for ML teams. The platform centers around logging runs with wandb.init(), capturing metrics, hyperparameters, and artifacts throughout the training process. It integrates with popular ML frameworks like PyTorch, TensorFlow, and Keras through Python APIs and automatic logging hooks. The platform supports both cloud-hosted and self-managed deployments, with options for multi-tenant, dedicated cloud, or on-premises installations.
Comprehensive MLOps
Covers the full ML lifecycle from experiment tracking and hyperparameter tuning to model versioning and production monitoring. Includes specialized tools like Weave for LLM applications.
Framework Integration
Native integrations with major ML frameworks including PyTorch, TensorFlow, Keras, and JAX. Supports automatic logging and manual instrumentation through Python APIs.
Flexible Deployment
Available as multi-tenant cloud, dedicated cloud instances, or self-managed installations on AWS, GCP, Azure, or on-premises infrastructure.
import wandb
import torch
import torch.nn as nn
import torch.optim as optim
# Initialize wandb run
wandb.init(
project="pytorch-classification",
config={
"learning_rate": 0.001,
"epochs": 10,
"batch_size": 32,
"architecture": "CNN"
}
)
# Simple model
model = nn.Sequential(
nn.Linear(784, 128),
nn.ReLU(),
nn.Linear(128, 10)
)
optimizer = optim.Adam(model.parameters(), lr=wandb.config.learning_rate)
criterion = nn.CrossEntropyLoss()
# Training loop with logging
for epoch in range(wandb.config.epochs):
# Simulate training step
loss = torch.randn(1).item() * 0.1 + 0.5 - epoch * 0.05
accuracy = min(0.9, 0.1 + epoch * 0.08)
# Log metrics to wandb
wandb.log({
"epoch": epoch,
"loss": loss,
"accuracy": accuracy,
"learning_rate": optimizer.param_groups[0]['lr']
})
wandb.finish()Adds Federated Auth support to wandb.Api() and fixes artifact download URL expiration issues.
- –wandb.Api() now supports Federated Auth (JWT based authentication)
- –Refresh presigned download url when it expires during artifact file downloads
Fixes data upload issues from v0.24.0 and adds downloadhistoryexports feature for parquet exports.
- –downloadhistoryexports in api.Run class to download exported run history in parquet file format
- –When a settings file contains an invalid setting, all settings files are ignored and an error is printed
- –After wandb login --host <invalid-url>, using wandb login --host <valid-url> works as usual
- –wandb beta sync correctly loads credentials
Removes deprecated wandb.beta.workflows module and adds live sync support with various bug fixes.
- –wandb beta sync now supports a --live option for syncing a run while it's being logged
- –Fixed Run.exit type annotations to accept None values, which are passed when no exception is raised
- –Fixed Invalid Client ID digest error when creating artifacts after calling random.seed()
- –Fixed CLI error when listing empty artifacts
- –Fixed regression for calling api.run() on a Sweeps run
Top in AI & ML
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