AI is easy to productionize

The AI developer platform to train and fine-tune your AI models and develop your AI applications with confidence

W&B Weave: Develop GenAI applications

				
					import weave
weave.init("quickstart")
@weave.op()
def llm_app(prompt):
    pass # Track LLM calls, document retrieval, agent steps

W&B Models: Train and productionize models

				
					import wandb
run = wandb.init(project="my-model-training-project")
run.config = {"epochs": 1337, "learning_rate": 3e-4}
run.log({"metric": 42})
my_model_artifact = run.log_artifact("./my_model.pt", type="model")

세계 최고의 AI 팀이 Weights & Biases를 신뢰하고 있습니다

레코드 개발자가 사용하고 싶은 시스템

W&B Core: AI 개발자를 지원하는 기본 프레임워크

빠르게 인터그레이션 하고, 추적 및 버전 관리를 자동화하세요

“이전보다 50배 또는 100배 더 많은 ML 실험을 진행하고 있습니다.”

Phil Brown, Director of Applications

Graphcore

				
					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": 2**-i})

				
					import weave
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# Initialize Weave with your project name
weave.init("langchain_demo")

llm = ChatOpenAI()
prompt = PromptTemplate.from_template("1 + {number} = ")

llm_chain = prompt | llm

output = llm_chain.invoke({"number": 2})

print(output)

				
					import weave
from llama_index.core.chat_engine import SimpleChatEngine

# Initialize Weave with your project name
weave.init("llamaindex_demo")

chat_engine = SimpleChatEngine.from_defaults()
response = chat_engine.chat(
    "Say something profound and romantic about fourth of July"
)
print(response)

				
					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})

				
					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()

				
					from lightning.pytorch.loggers import WandbLogger

# initialise the logger
wandb_logger = WandbLogger(project="llama-4-fine-tune")

# add configs such as batch size etc to the wandb config
wandb_logger.experiment.config["batch_size"] = batch_size

# pass wandb_logger to the Trainer 
trainer = Trainer(..., logger=wandb_logger)

# train the model
trainer.fit(...)

				
					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())

				
					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,
)

				
					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")

				
					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)