The AI Developer platform

Weights & Biases helps AI developers build better models faster. Quickly track experiments, version and iterate on datasets, evaluate model performance, reproduce models, and manage your ML workflows end-to-end.

Loved by
500,000+
ML practitioners

The world’s leading ML teams trust W&B

The Weights & Biases platform helps you streamline your ML workflow from end to end

Experiments

Experiment tracking

Reports

Collaborative dashboards

Artifacts

Dataset and
model versioning

Tables

Interactive data visualization

Sweeps

Hyperparameter optimization

Launch

Automate ML workflows

Models

Model lifecycle management

Integrate quickly, track & version automatically

“We’re now driving 50 or 100 times more ML experiments versus what we were doing before.”

Phil Brown, Director of Applications

Graphcore

		INTEGRATE QUICKLY
		TensorFlow
		PyTorch
		Keras
		Scikit-LEARN
		HF Transformers
		XGBoost
		LANGCHAIN
		LLAMAINDEX
		INTEGRATE QUICKLY

							
					import wandb

# 1. Start a W&B run
run = wandb.init(project="my_first_project")
# 2. Save model inputs and hyperparameters
config = wandb.config
config.learning_rate = 0.01
# 3. Log metrics to visualize performance over time
for i in range(10):
 run.log({"loss": loss})
				
			
		
		TensorFlow

							
					import wandb
# 1. Start a new run
run = wandb.init(project="gpt4")
‍
# 2. Save model inputs and hyperparameters
config = wandb.config
config.learning_rate = 0.01
‍
# Model training here
# 3. Log metrics to visualize performance over time
‍
with tf.Session() as sess:
# ...
wandb.tensorflow.log(tf.summary.merge_all())
				
			
		
		PyTorch

							
					import wandb
# 1. Start a new run
run = wandb.init(project="gpt5")
# 2. Save model inputs and hyperparameters
config = run.config
config.dropout = 0.01
# 3. Log gradients and model parameters
run.watch(model)
for batch_idx, (data, target) in enumerate(train_loader):   
...
   if batch_idx % args.log_interval == 0:  
   # 4. Log metrics to visualize performance
      run.log({"loss": loss})
				
			
		
		Keras

							
					import wandb
from wandb.keras import (
   WandbMetricsLogger,
   WandbModelCheckpoint,
)
‍
# 1. Start a new run
run = wandb.init(project="gpt-4")
‍
# 2. Save model inputs and hyperparameters
config = wandb.config
config.learning_rate = 0.01
...  # Define a model
# 3. Log layer dimensions and metrics
wandb_callbacks = [
   WandbMetricsLogger(log_freq=5),
   WandbModelCheckpoint("models"),
]
model.fit(
   X_train, y_train, validation_data=(X_test, y_test),
   callbacks=wandb_callbacks,
)
				
			
		
		Scikit-LEARN

							
					import wandb
wandb.init(project="visualize-sklearn")
‍
# Model training here
# Log classifier visualizations
wandb.sklearn.plot_classifier(clf, X_train, X_test, y_train, y_test, y_pred, y_probas, labels,
model_name="SVC", feature_names=None)
‍
# Log regression visualizations
wandb.sklearn.plot_regressor(reg, X_train, X_test, y_train, y_test,  model_name="Ridge")
‍
# Log clustering visualizations
wandb.sklearn.plot_clusterer(kmeans, X_train, cluster_labels, labels=None, model_name="KMeans")
				
			
		
		HF Transformers

							
					import wandb
‍
# 1. Define which wandb project to log to and name your run
run = wandb.init(project="gpt-5",
run_name="gpt-5-base-high-lr")
‍
# 2. Add wandb in your `TrainingArguments`
args = TrainingArguments(..., report_to="wandb")
‍
# 3. W&B logging will begin automatically when your start training your Trainer
trainer = Trainer(..., args=args)
trainer.train()
				
			
		
		XGBoost

							
					import wandb
from wandb.xgboost import wandb_callback
‍
# 1. Start a new run
run = wandb.init(project="visualize-models")
‍
# 2. Add the callback
bst = xgboost.train(param, xg_train, num_round, watchlist, callbacks=[wandb_callback()])
‍
# Get predictions
pred = bst.predict(xg_test)
				
			
		
		LANGCHAIN

							
					import wandb
import os

# 1. Set environment variables for the W&B project and tracing.
os.environ["LANGCHAIN_WANDB_TRACING"] = "true" os.environ["WANDB_PROJECT"] = "langchain-tracing"

# 2. Load llms, tools, and agents/chains

llm = OpenAI(temperature=0)
tools = load_tools(["llm-math"], llm=llm)
agent = initialize_agent(
     tools, llm,      agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,      verbose=True
)

# 3. Serve the chain/agent with all underlying complex llm interactions automatically traced and tracked

agent.run("What is 2 raised to .123243 power?")
				
			
		
	

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