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Track Experiments

Use the W&B Python Library to track machine learning experiments with a few lines of code. You can then review the results in an interactive dashboard or export your data to Python for programmatic access using our Public API.

Utilize W&B Integrations if you use use popular frameworks such as PyTorch, Keras, or Scikit. See our Integration guides for a for a full list of integrations and information on how to add W&B to your code.

How it worksโ€‹

W&B Experiments are composed of the following building blocks:

  1. wandb.init(): Initialize a new run at the top of your script. This returns a Run object and creates a local directory where all logs and files are saved, then streamed asynchronously to a W&B server. If you want to use a private server instead of our hosted cloud server, we offer Self-Hosting.
  2. wandb.config: Save a dictionary of hyperparameters such as learning rate or model type. The model settings you capture in config are useful later to organize and query your results.
  3. wandb.log(): Log metrics over time in a training loop, such as accuracy and loss. By default, when you call wandb.log it appends a new step to the history object and updates the summary object.
    • history: An array of dictionary-like objects that tracks metrics over time. These time series values are shown as default line plots in the UI.
    • summary: By default, the final value of a metric logged with wandb.log(). You can set the summary for a metric manually to capture the highest accuracy or lowest loss instead of the final value. These values are used in the table, and plots that compare runs โ€” for example, you could visualize at the final accuracy for all runs in your project.
  4. wandb.log_artifact: Save outputs of a run, like the model weights or a table of predictions. This lets you track not just model training, but all the pipeline steps that affect the final model.

The proceeding pseudocode demonstrates a common W&B Experiment tracking workflow:

# Flexible integration for any Python script
import wandb

# 1. Start a W&B Run

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

# Set up model and data
model, dataloader = get_model(), get_data()

# Model training goes here

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

# 4. Log an artifact to W&B

How to get startedโ€‹

Depending on your use case, explore the following resources to get started with W&B Experiments:

  • If this is your first time using W&B Experiments, we recommend you read the Quick Start. The Quickstart walks you through setting up your first experiment.
  • Explore topics about Experiments in the W&B Developer Guide such as:
    • Create an experiment
    • Configure experiments
    • Log data from experiments
    • View results from experiments
  • Explore the W&B Python Library within the W&B API Reference Guide.
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