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        "description": "<div align=\"center\">\n  <img src=\"https://i.imgur.com/RUtiVzH.png\" width=\"600\" /><br><br>\n</div>\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\nUse W&B to organize and analyze machine learning experiments. It's framework-agnostic and lighter than TensorBoard. Each time you run a script instrumented with `wandb`, we save your hyperparameters and output metrics. Visualize models over the course of training, and compare versions of your models easily. We also automatically track the state of your code, system metrics, and configuration parameters.\n\n[Sign up for a free account \u2192](https://wandb.com)\n\n## Features\n\n-   Store hyper-parameters used in a training run\n-   Search, compare, and visualize training runs\n-   Analyze system usage metrics alongside runs\n-   Collaborate with team members\n-   Replicate historic results\n-   Run parameter sweeps\n-   Keep records of experiments available forever\n\n[Documentation \u2192](https://docs.wandb.com)\n\n## Quickstart\n\n```shell\npip install wandb\n```\n\nIn your training script:\n\n```python\nimport wandb\n# Your custom arguments defined here\nargs = ...\n\nwandb.init(config=args, project=\"my-project\")\nwandb.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        wandb.log({\"epoch\": epoch, \"loss\": loss, \"val_loss\": val_loss})\n```\n\nIf you're already using Tensorboard or [TensorboardX](https://github.com/lanpa/tensorboardX), you can integrate with one line:\n\n```python\nwandb.init(sync_tensorboard=True)\n```\n\n## Running your script\n\nRun `wandb login` from your terminal to signup or authenticate your machine (we store your api key in ~/.netrc). You can also set the `WANDB_API_KEY` environment variable with a key from your [settings](https://app.wandb.ai/settings).\n\nRun your script with `python my_script.py` and all metadata will be synced to the cloud. You will see a url in your terminal logs when your script starts and finishes. 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 set the environment variable `WANDB_MODE=dryrun`.\n\nIf you are using [docker](https://docker.com) to run your code, we provide a wrapper command `wandb docker` that mounts your current directory, sets environment variables, and ensures the wandb library is installed. Training your models in docker gives you the ability to restore the exact code and environment with the `wandb restore` command.\n\n## Web Interface\n\n[Sign up for a free account \u2192](https://wandb.com)\n[![Watch the video](https://i.imgur.com/PW0Ejlc.png)](https://youtu.be/EeqhOSvNX-A)\n[Introduction video \u2192](https://youtu.be/EeqhOSvNX-A)\n\n## Detailed Usage\n\nFramework specific and detailed usage can be found in our [documentation](http://docs.wandb.com/).\n\n## Testing\n\nTo run the tests we use `pytest tests`. If you want a simple mock of the wandb backend and cloud storage you can use the mock_server fixture, see tests/test_cli.py for examples.\n\nWe use [circleci](https://circleci.com) and [appveyor](https://appveyor.com) for CI.\n\n\n",
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