{ "info": { "author": "Weights & Biases", "author_email": "support@wandb.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 2 - Pre-Alpha", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Natural Language :: English", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5" ], "description": "
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\n\n# 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)\n\nThe **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.\n\n## Local Features\n\n* Store config parameters used in a training run\n* Associate version control with your training runs\n* Search, compare, and visualize training runs\n* Analyze system usage metrics alongside runs\n\n## Cloud Features\n\n* Collaborate with team members\n* Run parameter sweeps\n* Persist runs forever\n\n## Quickstart\n\n```shell\npip install wandb\n```\n\nIn your training script:\n\n```python\nimport wandb\nfrom wandb.keras import WandbCallback\n# Your custom arguments defined here\nargs = ...\n\nrun = wandb.init(config=args)\nrun.config[\"more\"] = \"custom\"\n\ndef training_loop():\n while True:\n # Do some machine learning\n epoch, loss, val_loss = ...\n # Framework agnostic / custom metrics\n run.history.add({\"epoch\": epoch, \"loss\": loss, \"val_loss\": val_loss})\n # Keras metrics\n model.fit(..., callbacks=[WandbCallback()])\n```\n\nRunning 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.\n\n

\n \"Runs\n

\n\n## Cloud Usage\n\n[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.\n\n## Detailed Usage\n\nFramework specific and detailed usage can be found in our [documentation](http://docs.wandb.com/).\n\n## Development\n\nSee https://github.com/wandb/client/blob/master/DEVELOPMENT.md\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/wandb/client", "keywords": "wandb", "license": "MIT license", "maintainer": "", "maintainer_email": "", "name": "wandb-testing", "package_url": "https://pypi.org/project/wandb-testing/", "platform": "", "project_url": "https://pypi.org/project/wandb-testing/", "project_urls": { "Homepage": "https://github.com/wandb/client" }, "release_url": "https://pypi.org/project/wandb-testing/0.6.1.post1/", "requires_dist": null, "requires_python": "", "summary": "A CLI and library for interacting with the Weights and Biases API.", 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