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        "description": "<h1 align=\"center\">asyncio-gcloud-ml-engine</h1>\n\n<div align=\"center\">\n  <strong>Asyncio-based <code>Google Cloud Machine Learning Engine</code> Prediction & Training</strong>\n</div>\n\n<br />\n\n<div align=\"center\">\n  <!-- Build Status -->\n  <a href=\"https://travis-ci.org/brunoalano/asyncio-gcloud-ml-engine\">\n    <img src=\"https://img.shields.io/travis/brunoalano/asyncio-gcloud-ml-engine/master.svg?style=flat-square\"\n      alt=\"Build Status\" />\n  </a>\n  <!-- Test Coverage -->\n  <a href=\"https://codecov.io/github/brunoalano/asyncio-gcloud-ml-engine\">\n    <img src=\"https://img.shields.io/codecov/c/github/brunoalano/asyncio-gcloud-ml-engine/master.svg?style=flat-square\"\n      alt=\"Test Coverage\" />\n</div>\n\n## Features\n- __asyncio:__ better use of your cpu idle time\n- __pep8 compliant:__ following best code standards\n- __tests:__ full tested to keep up-to-date on socketcluster\n- __high-performance prediction:__ we try to use `aio-grpc` when possible\n\n## Example\n```python\nfrom asyncio_ml_engine import MachineLearningClient\nproject = 'my-gcloud-project'\nservice_account = './myserviceaccount.json'\n\nasync def myfunc():\n  async with MachineLearningClient(project, service_account) as client:\n    prediction = await client.predict('mymodel', X)\n```\nYou can find more examples in the `examples/` subdirectory.\n\n\n## Installation\n```sh\n$ pip install asyncio-gcloud-ml-engine\n```\n\n## License\n[MIT](https://tldrlegal.com/license/mit-license)\n\n\n",
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