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align=\"center\"\u003e\n  \u003cimg src=\"https://www.kxy.ai/theme/images/logos/logo.svg\"\u003e\u003cbr\u003e\n\u003c/div\u003e\n\n-----------------\n\n# Boosting The Productivity of Machine Learning Engineers\n[![License](https://img.shields.io/badge/license-GPLv3%2B-blue)](https://github.com/kxytechnologies/kxy-python/blob/master/LICENSE)\n[![PyPI Latest Release](https://img.shields.io/pypi/v/kxy.svg)](https://www.kxy.ai/)\n[![Downloads](https://pepy.tech/badge/kxy)](https://www.kxy.ai/)\n\n\n## Documentation\nhttps://www.kxy.ai/reference/\n\n## Blog\nhttps://blog.kxy.ai\n\n\n## Installation\nFrom PyPi:\n```Bash\npip install kxy -U\n```\nFrom GitHub:\n```Bash\ngit clone https://github.com/kxytechnologies/kxy-python.git \u0026 cd ./kxy-python \u0026 pip install .\n```\n## Authentication\nAll heavy-duty computations are run on our serverless infrastructure and require an API key. To configure the package with your API key, run \n```Bash\nkxy configure\n```\nand follow the instructions. To get your own API key you need an account; you can sign up [here](https://www.kxy.ai/signup/). You'll then be automatically given an API key which you can find [here](https://www.kxy.ai/portal/profile/identity/).\n\n\n## Docker\nThe Docker image [kxytechnologies/kxy](https://hub.docker.com/repository/docker/kxytechnologies/kxy) has been built for your convenience, and comes with anaconda, auto-sklearn, and the kxy package. \n\nTo start a Jupyter Notebook server from a sandboxed Docker environment, run\n```Bash\ndocker run -i -t -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c \"kxy configure \u003cYOUR API KEY\u003e \u0026\u0026 /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''\"\n```\nwhere you should replace `\u003cYOUR API KEY\u003e` with your API key and navigate to [http://localhost:5555](http://localhost:5555) in your browser. This docker environment comes with [all examples available on the documentation website](https://www.kxy.ai/reference/latest/examples/).\n\nTo start a Jupyter Notebook server from an existing directory of notebooks, run\n```Bash\ndocker run -i -t --mount src=\u003c/path/to/your/local/dir\u003e,target=/opt/notebooks,type=bind -p 5555:8888 kxytechnologies/kxy:latest /bin/bash -c \"kxy configure \u003cYOUR API KEY\u003e \u0026\u0026 /opt/conda/bin/jupyter notebook --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root --NotebookApp.token=''\"\n```\nwhere you should replace `\u003c/path/to/your/local/dir\u003e` with the path to your local notebook folder and navigate to [http://localhost:5555](http://localhost:5555) in your browser.\n\nYou can also get the same Docker image from GitHub [here](https://github.com/kxytechnologies/kxy-python/pkgs/container/kxy-python).\n\n## Other Programming Language\nWe plan to release friendly API client in more programming language. \n\nIn the meantime, you can directly issue requests to our [RESTFul API](https://www.kxy.ai/reference/latest/api/index.html) using your favorite programming language. \n\n## Pricing \nAll API keys are given a free quota (a few dozen backend tasks) that should be enough to try out the package and see if you love it. Beyond the free quota you will be billed a small fee per task. \n\nKXY is free for academic use; simply signup with your university email. \n\nKXY is also free for Kaggle competitions; sign up and email kaggle@kxy.ai to get a promotional code.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkxytechnologies%2Fkxy-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkxytechnologies%2Fkxy-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkxytechnologies%2Fkxy-python/lists"}