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"description": "# Allegro Trains\n## Auto-Magical Experiment Manager, Version Control and ML-Ops for AI\n\n\"Because it\u2019s a jungle out there\"\n\n[](https://img.shields.io/github/license/allegroai/trains.svg)\n[](https://img.shields.io/pypi/pyversions/trains.svg)\n[](https://img.shields.io/pypi/v/trains.svg)\n[](https://pypi.python.org/pypi/trains/)\n[](https://optuna.org)\n[](https://join.slack.com/t/allegroai-trains/shared_invite/zt-c0t13pty-aVUZZW1TSSSg2vyIGVPBhg)\n\n### :point_right: Help improve Trains by filling our 2-min [user survey](https://allegro.ai/lp/trains-user-survey/)\n\nTrains is our solution to a problem we share with countless other researchers and developers in the machine\nlearning/deep learning universe: Training production-grade deep learning models is a glorious but messy process.\nTrains tracks and controls the process by associating code version control, research projects,\nperformance metrics, and model provenance.\n\nWe designed Trains specifically to require effortless integration so that teams can preserve their existing methods\nand practices. Use it on a daily basis to boost collaboration and visibility, or use it to automatically collect\nyour experimentation logs, outputs, and data to one centralized server.\n\n**We have a demo server up and running at [https://demoapp.trains.allegro.ai](https://demoapp.trains.allegro.ai).**\n\n### :steam_locomotive: [Getting Started Tutorial](https://allegro.ai/blog/setting-up-allegro-ai-platform/) :rocket:\n\n**You can try out Trains and [test your code](#integrate-trains), with no additional setup.** \n\n![](\"https://github.com/allegroai/trains/blob/master/docs/webapp_screenshots.gif?raw=true\")
\n\n## Trains Automatically Logs Everything\n**With only two lines of code, this is what you are getting:**\n\n* Git repository, branch, commit id, entry point and local git diff\n* Python environment (including specific packages & versions)\n* stdout and stderr\n* Resource Monitoring (CPU/GPU utilization, temperature, IO, network, etc.)\n* Hyper-parameters\n * ArgParser for command line parameters with currently used values\n * Explicit parameters dictionary\n * Tensorflow Defines (absl-py)\n* Initial model weights file\n* Model snapshots (With optional automatic upload to central storage: Shared folder, S3, GS, Azure, Http)\n* Artifacts log & store (Shared folder, S3, GS, Azure, Http)\n* Tensorboard/TensorboardX scalars, metrics, histograms, **images, audio and video**\n* [Matplotlib & Seaborn](https://github.com/allegroai/trains/tree/master/examples/frameworks/matplotlib)\n* Supported frameworks: [PyTorch](https://github.com/allegroai/trains/tree/master/examples/frameworks/pytorch), [Tensorflow](https://github.com/allegroai/trains/tree/master/examples/frameworks/tensorflow), [Keras](https://github.com/allegroai/trains/tree/master/examples/frameworks/keras), [AutoKeras](https://github.com/allegroai/trains/tree/master/examples/frameworks/autokeras), [XGBoost](https://github.com/allegroai/trains/tree/master/examples/frameworks/xgboost) and [Scikit-Learn](https://github.com/allegroai/trains/tree/master/examples/frameworks/scikit-learn) (MxNet is coming soon)\n* Seamless integration (including version control) with **Jupyter Notebook**\n and [*PyCharm* remote debugging](https://github.com/allegroai/trains-pycharm-plugin)\n\n**Additionally, log data explicitly using [Trains Explicit Logging](https://allegro.ai/docs/examples/reporting/).**\n\n## Using Trains \n\nTrains is a two part solution:\n\n1. Trains [python package](https://pypi.org/project/trains/) auto-magically connects with your code\n\n **Trains requires only two lines of code for full integration.**\n\n To connect your code with Trains:\n\n - Install Trains \n\n pip install trains\n\t\t\n\t\tAdd optional cloud storage support (S3/GoogleStorage/Azure):
\n\n\t\t```bash\n\t\tpip install trains[s3]\n\t\tpip install trains[gs]\n\t\tpip install trains[azure]\n\t\t```\n\n\t\t \n\n - Add the following lines to your code\n\n from trains import Task\n task = Task.init(project_name=\"my project\", task_name=\"my task\")\n\n * If project_name is not provided, the repository name will be used instead\n * If task_name (experiment) is not provided, the current filename will be used instead\n\n - Run your code. When Trains connects to the server, a link is printed. For example\n\n Trains Results page:\n https://demoapp.trains.allegro.ai/projects/76e5e2d45e914f52880621fe64601e85/experiments/241f06ae0f5c4b27b8ce8b64890ce152/output/log\n\n - Open the link and view your experiment parameters, model and tensorboard metrics\n\n **See examples [here](https://allegro.ai/docs/examples/examples_overview/)**\n\n2. [Trains Server](https://github.com/allegroai/trains-server) for logging, querying, control and UI ([Web-App](https://github.com/allegroai/trains-web))\n\n **We already have a demo server up and running for you at [https://demoapp.trains.allegro.ai](https://demoapp.trains.allegro.ai).**\n\n **You can try out Trains without the need to install your own *trains-server*, just add the two lines of code, and it will automatically connect to the Trains demo-server.**\n\n *Note that the demo server resets every 24 hours and all of the logged data is deleted.*\n\n When you are ready to use your own Trains server, go ahead and [install *trains-server*](https://github.com/allegroai/trains-server).\n\n![](\"https://github.com/allegroai/trains/blob/master/docs/system_diagram.png?raw=true\")
\n\n\n## Configuring Your Own Trains server \n\n1. Install and run *trains-server* (see [Installing the Trains Server](https://github.com/allegroai/trains-server))\n\n2. Run the initial configuration wizard for your Trains installation and follow the instructions to setup Trains package\n(http://**_trains-server-ip_**:__port__ and user credentials)\n\n\t trains-init\n\nAfter installing and configuring, you can access your configuration file at `~/trains.conf`\n\nSample configuration file available [here](https://github.com/allegroai/trains/blob/master/docs/trains.conf).\n\n\n## Who We Are\n\nTrains is supported by the same team behind *allegro.ai*,\nwhere we build deep learning pipelines and infrastructure for enterprise companies.\n\nWe built Trains to track and control the glorious but messy process of training production-grade deep learning models.\nWe are committed to vigorously supporting and expanding the capabilities of Trains.\n\n## Why Are We Releasing Trains?\n\nWe believe Trains is ground-breaking. We wish to establish new standards of experiment management in\ndeep-learning and ML. Only the greater community can help us do that.\n\nWe promise to always be backwardly compatible. If you start working with Trains today,\neven though this project is currently in the beta stage, your logs and data will always upgrade with you.\n\n## License\n\nApache License, Version 2.0 (see the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0.html) for more information)\n\n## Documentation, Community & Support\n\nMore information in the [official documentation](https://allegro.ai/docs) and [on YouTube](https://www.youtube.com/c/AllegroAI).\n\nFor examples and use cases, check the [examples folder](https://github.com/allegroai/trains/tree/master/examples) and [corresponding documentation](https://allegro.ai/docs/examples/examples_overview/).\n\nIf you have any questions: post on our [Slack Channel](https://join.slack.com/t/allegroai-trains/shared_invite/enQtOTQyMTI1MzQxMzE4LTY5NTUxOTY1NmQ1MzQ5MjRhMGRhZmM4ODE5NTNjMTg2NTBlZGQzZGVkMWU3ZDg1MGE1MjQxNDEzMWU2NmVjZmY), or tag your questions on [stackoverflow](https://stackoverflow.com/questions/tagged/trains) with '**trains**' tag.\n\nFor feature requests or bug reports, please use [GitHub issues](https://github.com/allegroai/trains/issues).\n\nAdditionally, you can always find us at *trains@allegro.ai*\n\n## Contributing\n\nSee the Trains [Guidelines for Contributing](https://github.com/allegroai/trains/blob/master/docs/contributing.md).\n\n\n_May the force (and the goddess of learning rates) be with you!_\n\n\n",
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