A CLI and library for interacting with the Weights and Biases API.
Project description
Weights and Biases
The W&B client is an open source library and CLI (wandb) for organizing and analyzing your machine learning experiments. Think of it as a framework-agnostic lightweight TensorBoard that persists additional information such as the state of your code, system metrics, and configuration parameters.
Features
- Store config parameters used in a training run
- Associate version control with your training runs
- Search, compare, and visualize training runs
- Analyze system usage metrics alongside runs
- Collaborate with team members
- Run parameter sweeps
- Persist runs forever
Quickstart
pip install wandb
In your training script:
import wandb
# Your custom arguments defined here
args = ...
run = wandb.init(config=args)
run.config["more"] = "custom"
def training_loop():
while True:
# Do some machine learning
epoch, loss, val_loss = ...
# Framework agnostic / custom metrics
wandb.log({"epoch": epoch, "loss": loss, "val_loss": val_loss})
Running your script
From the directory of your training script run wandb init
to initialize a new directory. If it's your first time using wandb on the machine it will prompt you for an API key - create an account at wandb.com and you can find one in your profile page. You can check in wandb/settings directory to version control to share your project with other users. You can also set the username and API key through environment variables if you don't have easy access to a shell.
Run your script with python my_script.py
and all metadata will be synced to the cloud. Data is staged locally in a directory named wandb relative to your script. If you want to test your script without syncing to the cloud you can run wandb off
.
Detailed Usage
Framework specific and detailed usage can be found in our documentation.
Project details
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