Metadata-Version: 1.1
Name: wandb-testing
Version: 0.6.1.post1
Summary: A CLI and library for interacting with the Weights and Biases API.
Home-page: https://github.com/wandb/client
Author: Weights & Biases
Author-email: support@wandb.com
License: MIT license
Description: <div align="center">
          <img src="https://app.wandb.ai/logo.svg" width="350" /><br><br>
        </div>
        
        # Weights and Biases [![ci](https://circleci.com/gh/wandb/client.svg?style=svg)](https://circleci.com/gh/wandb/client) [![pypi](https://img.shields.io/pypi/v/wandb.svg)](https://pypi.python.org/pypi/wandb)
        
        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
        
        ```shell
        pip install wandb
        ```
        
        In your training script:
        
        ```python
        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
                model.fit(..., 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.
        
        <p align="center">
            <img src="https://github.com/wandb/client/raw/master/docs/screenshot.jpg?raw=true" alt="Runs screenshot" style="max-width:100%;">
        </p>
        
        ## Cloud Usage
        
        [Signup](https://app.wandb.ai/login?invited) 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 my_script.py` and all metadata will be synced to the cloud.
        
        ## Detailed Usage
        
        Framework specific and detailed usage can be found in our [documentation](http://docs.wandb.com/).
        
        ## Development
        
        See https://github.com/wandb/client/blob/master/DEVELOPMENT.md
        
Keywords: wandb
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
