Weights and Biases

Use W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production models.

Quickly identify model regressions. Use W&B to visualize results in real time, all in a central dashboard.
Focus on the interesting ML. Spend less time manually tracking results in spreadsheets and text files.
Capture dataset versions with W&B Artifacts to identify how changing data affects your resulting models.
Reproduce any model, with saved code, hyperparameters, launch commands, input data, and resulting model weights.

Features

Store hyper-parameters used in a training run
Search, compare, and visualize training runs
Analyze system usage metrics alongside runs
Collaborate with team members
Replicate historic results
Run parameter sweeps
Keep records of experiments available forever

Documentation →

If you have any questions, please don't hesitate to ask in our user forum.

🤝 Simple integration with any framework

Install wandb library and login:

pip install wandb
wandb login

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 code here ...

# 3. Log metrics over time to visualize performance
for i in range(10):
    wandb.log({"loss": loss})

Try in a colab →

If you have any questions, please don't hesitate to ask in our user forum.

Explore a W&B dashboard

Academic Researchers

If you'd like a free academic account for your research group, reach out to us →

We make it easy to cite W&B in your published paper. Learn more →

📈 Track model and data pipeline hyperparameters

Set wandb.config once at the beginning of your script to save your hyperparameters, input settings (like dataset name or model type), and any other independent variables for your experiments. This is useful for analyzing your experiments and reproducing your work in the future. Setting configs also allows you to visualize the relationships between features of your model architecture or data pipeline and the model performance (as seen in the screenshot above).

wandb.init()
wandb.config.epochs = 4
wandb.config.batch_size = 32
wandb.config.learning_rate = 0.001
wandb.config.architecture = "resnet"

🏗 Use your favorite framework

🥕 Keras

In Keras, you can use our callback to automatically save all the metrics tracked in model.fit. To get you started here's a minimal example:

# Import W&B
import wandb
from wandb.keras import WandbCallback

# Step1: Initialize W&B run
wandb.init(project="project_name")

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

# Model training code here ...

# Step 3: Add WandbCallback
model.fit(
    X_train, y_train, validation_data=(X_test, y_test), callbacks=[WandbCallback()]
)

🔥 PyTorch

W&B provides first class support for PyTorch. To automatically log gradients and store the network topology, you can call .watch and pass in your PyTorch model. Then use .log for anything else you want to track, like so:

import wandb

# 1. Start a new run
wandb.init(project="gpt-3")

# 2. Save model inputs and hyperparameters
config = wandb.config
config.dropout = 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})

🌊 TensorFlow

The simplest way to log metrics in TensorFlow is by logging tf.summary with our TensorFlow logger:

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())

Try in a colab →
Docs

💨 fastai

Visualize, compare, and iterate on fastai models using Weights & Biases with the WandbCallback.

import wandb
from fastai.callback.wandb import WandbCallback

# 1. Start a new run
wandb.init(project="gpt-3")

# 2. Automatically log model metrics
learn.fit(..., cbs=WandbCallback())

Try in a colab →
Docs

⚡️ PyTorch Lightning

Build scalable, structured, high-performance PyTorch models with Lightning and log them with W&B.

from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning import Trainer

wandb_logger = WandbLogger(project="gpt-3")
trainer = Trainer(logger=wandb_logger)

Try in a colab →
Docs

🤗 HuggingFace

Just run a script using HuggingFace's Trainer passing --report_to wandb to it in an environment where wandb is installed, and we'll automatically log losses, evaluation metrics, model topology, and gradients:

# 1. Install the wandb library
pip install wandb

# 2. Run a script that has the Trainer to automatically logs metrics, model topology and gradients
python run_glue.py \
 --report_to wandb \
 --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

Try in a colab →
Docs

🧹 Optimize hyperparameters with Sweeps

Use Weights & Biases Sweeps to automate hyperparameter optimization and explore the space of possible models.

Get started in 5 mins →

Try Sweeps in PyTorch in a Colab →

Benefits of using W&B Sweeps

Quick to set up: With just a few lines of code you can run W&B sweeps.
Transparent: We cite all the algorithms we're using, and our code is open source.
Powerful: Our sweeps are completely customizable and configurable. You can launch a sweep across dozens of machines, and it's just as easy as starting a sweep on your laptop.

Common use cases

Explore: Efficiently sample the space of hyperparameter combinations to discover promising regions and build an intuition about your model.
Optimize: Use sweeps to find a set of hyperparameters with optimal performance.
K-fold cross validation: Here's a brief code example of k-fold cross validation with W&B Sweeps.

Visualize Sweeps results

The hyperparameter importance plot surfaces which hyperparameters were the best predictors of, and highly correlated to desirable values for your metrics.

Parallel coordinates plots map hyperparameter values to model metrics. They're useful for honing in on combinations of hyperparameters that led to the best model performance.

📜 Share insights with Reports

Reports let you organize visualizations, describe your findings, and share updates with collaborators.

Common use cases

Notes: Add a graph with a quick note to yourself.
Collaboration: Share findings with your colleagues.
Work log: Track what you've tried and plan next steps.

Explore reports in The Gallery → | Read the Docs

Once you have experiments in W&B, you can visualize and document results in Reports with just a few clicks. Here's a quick demo video.

🏺 Version control datasets and models with Artifacts

Git and GitHub make code version control easy, but they're not optimized for tracking the other parts of the ML pipeline: datasets, models, and other large binary files.

W&B's Artifacts are. With just a few extra lines of code, you can start tracking you and your team's outputs, all directly linked to run.

Try Artifacts in a Colab →

Common use cases

Pipeline Management: Track and visualize the inputs and outputs of your runs as a graph
Don't Repeat Yourself™: Prevent the duplication of compute effort
Sharing Data in Teams: Collaborate on models and datasets without all the headaches

Learn about Artifacts here → | Read the Docs

💻 Run W&B Server Locally

W&B Server is a self-hosted Weights & Biases server. Securely and quickly deploy a W&B production server in Docker, Kubernetes, or in a privately-managed cloud. Learn more about setting up a production W&B deployment →.

Quickstart

On a machine with Docker and Python installed, run:

1 pip install wandb --upgrade
2 wandb server start

Generate a free license from the Deployer.
Add it to your W&B Server's localhost's settings.

Paste the license in the /system-admin page on your localhost

Docker

Running wandb server start will start our server and forward port 8080 on the host. To have other machines report metrics to this server run: wandb login --host=http://X.X.X.X:8080.

Use Docker to manually run W&B Local:

docker run --rm -d -v wandb:/vol -p 8080:8080 --name wandb-local wandb/local

Testing

To run basic test use make test. More detailed information can be found at CONTRIBUTING.md.

We use circleci for CI.

TalhaUsuf/wandb

Weights and Biases

Features

🤝 Simple integration with any framework

Try in a colab →

Academic Researchers

📈 Track model and data pipeline hyperparameters

🏗 Use your favorite framework

🥕 Keras

🔥 PyTorch

🌊 TensorFlow

💨 fastai

⚡️ PyTorch Lightning

🤗 HuggingFace

🧹 Optimize hyperparameters with Sweeps

Get started in 5 mins →

Try Sweeps in PyTorch in a Colab →

Benefits of using W&B Sweeps

Common use cases

Visualize Sweeps results

📜 Share insights with Reports

Common use cases

🏺 Version control datasets and models with Artifacts

Try Artifacts in a Colab →

Common use cases

💻 Run W&B Server Locally

Quickstart

Docker

Testing