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
Run wandb signup
from the directory of your training script. If you already have an account, you can run wandb init
to initialize a new directory. You can checkin wandb/settings to version control to share your project with other users.
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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