Never lose your progress – Instrument W&B in 5 mins

Get started in 5 minutes

# Flexible integration for any Python script
import wandb

# 1. Start a W&B run
wandb.init(project='gpt3')

# 2. Save model inputs and hyperparameters
config = wandb.config
config.learning_rate = 0.01

# Model training here
‍
# 3. Log metrics over time to visualize performance
wandb.log({"loss": loss})

import wandb

# 1. Start a W&B run
wandb.init(project='gpt3')

# 2. Save model inputs and hyperparameters
config = wandb.config
config.learning_rate = 0.01

# Model training here
‍
# 3. Log metrics over time to visualize performance
with tf.Session() as sess:
# ...
wandb.tensorflow.log(tf.summary.merge_all())

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

import wandb
from wandb.keras import WandbCallback

# 1. Start a new run
wandb.init(project="gpt-3")
‍
# 2. Save model inputs and hyperparameters
config = wandb.config
config.learning_rate = 0.01

... Define a model

# 3. Log layer dimensions and metrics over time
model.fit(X_train, y_train, validation_data=(X_test, y_test),
callbacks=[WandbCallback()])

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')

# 1. Install the wandb library
pip install wandb
‍
# 2. Run a script with the Trainer, which automatically logs losses, evaluation metrics, model topology and gradients
python run_glue.py \
--model_name_or_path bert-base-uncased \
--task_name MRPC \
--data_dir $GLUE_DIR/$TASK_NAME \
--do_train \
--evaluate_during_training \
--max_seq_length 128 \
--per_gpu_train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 3 \
--output_dir /tmp/$TASK_NAME/ \
--overwrite_output_dir \
--logging_steps 50

import wandb

# 1. Start a new run
wandb.init(project="visualize-models", name="xgboost")

# 2. Add the callback
bst = xgboost.train(param, xg_train, num_round, watchlist, callbacks=[wandb.xgboost.wandb_callback()])

# Get predictions
pred = bst.predict(xg_test)

import wandb
import numpy as np
import xgboost as xgb
‍
# 1. Start a W&B run
wandb.init(project="visualize-models", name="xgboost")
‍
# 2. Add the wandb callback
bst = gbm = lgb.train(params,
lgb_train,
num_boost_round=20,
valid_sets=lgb_eval,
valid_names=('validation'),
callbacks=[wandb.lightgbm.callback()])

# Get prediction
pred = bst.predict(xg_test)

import wandb
fastai2.callback.wandb import WandbCallback
‍
# 1. Start a new run
wandb.init(project="gpt-3")
‍
# 2. Automatically log model metrics
learn.fit(..., cbs=WandbCallback())

import wandb

# 1. Start a W&B run
wandb.init(config=tf.flags.FLAGS, sync_tensorboard=True)

# 2. Save model inputs and hyperparameters
config = wandb.config
config.learning_rate = 0.01

# Model training here
‍
# 3. Log metrics over time to visualize performance
wandb.log({"loss": loss})

# 1. Import the wandb library
import wandb

# 2. Run a script with the Trainer, which automatically logs losses, evaluation metrics, model topology and gradients
!python run_glue.py \
--model_name_or_path bert-base-uncased \
--task_name MRPC \
--data_dir $GLUE_DIR/$TASK_NAME \
--do_train \
--evaluate_during_training \
--max_seq_length 128 \
--per_gpu_train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 3 \
--output_dir /tmp/$TASK_NAME/ \
--overwrite_output_dir \
--logging_steps 50

import wandb
import numpy as np
import xgboost as xgb
‍
# 1. Start a W&B run
wandb.init(project="visualize-models", name="xgboost")
‍
# 2. Add the wandb callback
bst = gbm = lgb.train(params,
lgb_train,
num_boost_round=20,
valid_sets=lgb_eval,
valid_names=('validation'),
callbacks=[wandb_callback()])

# Get prediction
pred = bst.predict(xg_test)

import wandb
from fastai2.callback.wandb import *
‍
# 1. Start a new run
wandb.init(project="gpt-3")
‍
# 2. Automatically log model metrics
learn.fit(..., cbs=WandbCallback())

Track your machine learning workflow

Central repository for your model pipelines

Save everything you need to compare and reproduce models — architecture, hyperparameters, weights, model predictions, GPU usage, git commits, and even datasets. You can save experiment & dataset files directly to W&B or store pointers to your own storage.

Visualize predictions

Debug performance in real time

Log model predictions to see how your model is performing in realtime, and identify problem areas during training. We support rich media including images, video, audio, bounding boxes, segmentation maps and 3D objects.

Collaborative reports

Share high level updates and detailed work logs

It's never been easier to share updates with your coworkers, stakeholders, or even the whole world. Explain how your model works, show graphs of how your model versions improved, discuss bugs, and demonstrate progress towards milestones with Reports.
‍
See an example report →

Hyperparameter optimization

Try dozens of model versions quickly with Sweeps

Optimize models with our massively scalable hyperparameter search tool. Sweeps are lightweight, fast to set up, and plug in to your existing infrastructure for running models.

System metrics

CPU and GPU usage across runs

Visualize live metrics like GPU utilization to identify training bottlenecks and avoid wasting expensive resources with automatically generated system metrics.

Fast and scalable

Robust, flexible experiment tracking at scale

We handle millions of models every month for teams doing some of the most cutting-edge deep learning research.

Never lose your progress again.

Save everything you need to compare and reproduce models — in 5 minutes.

Never lose your progress again.