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Python Version](https://img.shields.io/pypi/pyversions/pylzy.svg)](https://pypi.org/project/pylzy/)\n[![Telegram chat](https://img.shields.io/badge/chat-on%20Telegram-2ba2d9.svg)](https://t.me/+ad3OA-J96b9jYWJi)\n\n# ʎzy\n\nʎzy is a platform for a hybrid execution of ML workflows that transparently integrates local and remote runtimes\nwith the following properties:\n\n- Python-native SDK\n- Automatic env (pip/conda) sync\n- K8s-native runtime\n- Resources allocation on-demand\n- Env-independent results storage\n\n## Quick start\n\nʎzy allows running any python functions on a cluster by annotating them with `@op` decorator:\n\n```python\n@op(gpu_count=1, gpu_type=GpuType.V100.name)\ndef train(data_set: Bunch) -\u003e CatBoostClassifier:\n    cb_model = CatBoostClassifier(iterations=1000, task_type=\"GPU\", devices='0:1', train_dir='/tmp/catboost')\n    cb_model.fit(data_set.data, data_set.target, verbose=True)\n    return cb_model\n\n\n# local python function call\nmodel = train(data_set)\n\n# remote call on a cluster\nlzy = Lzy()\nwith lzy.workflow(\"training\"):\n    model = train(data_set)\n```\n\nPlease read the [tutorial](https://github.com/lambdazy/lzy/tree/master/docs/tutorials/0-contents.md) for details.\n\n## Runtime\n\nCheck out our [key concepts](https://github.com/lambdazy/lzy/tree/master/docs/arch/key-concepts.md) and [architecture intro](https://github.com/lambdazy/lzy/tree/master/docs/arch/intro_en.md).\n\n## Community\n\nJoin our chat [on telegram](https://t.me/+ad3OA-J96b9jYWJi)!\n\n## Development\n\nDevelopment [guide](https://github.com/lambdazy/lzy/tree/master/docs/development.md).\n\n## Deployment\n\nDeployment guide.\n\n* [YCloud](https://github.com/lambdazy/lzy/tree/master/docs/deployment_ycloud.md)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flambdazy%2Flzy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flambdazy%2Flzy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flambdazy%2Flzy/lists"}