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A CLI and library for interacting with the Weights and Biases API.

Project description

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<img src="" width="350" /><br><br>

# Weights and Biases [![ci](]( [![pypi](](

The **Weights and Biases** client is an open source library, CLI (wandb), and local web application 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.

## Local 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

## Cloud Features

* Collaborate with team members
* Run parameter sweeps
* Persist runs forever

## Quickstart

pip install wandb

In your training script:

import wandb
from wandb.keras import WandbCallback
# 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
run.history.add({"epoch": epoch, "loss": loss, "val_loss": val_loss})
# Keras metrics, callbacks=[WandbCallback()])

Running your training script will save data in a directory named _wandb_ relative to your training script. To view your runs, call `wandb board` from the same directory as your training script.

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<img src="" alt="Runs screenshot" style="max-width:100%;">

## Cloud Usage

[Signup]( for an account, then run `wandb init` from the directory with your training script. You can checkin _wandb/settings_ to version control to enable other users on your team to share experiments. Run your script with `wandb run` and all metadata will be synced to the cloud.

## Detailed Usage

Framework specific and detailed usage can be found in our [documentation](

## Development


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