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Currently it provides a Python and a Lua interface, however\nyou can easily implement a wrapper around the\nprovided [RESTful API](doc/specs.md).\n\n---\n\nThis system is composed of two parts:\n* A server running on a given machine that will be used to display tensorboard\n  and store all the data.\n* A client embedded inside your code that will send the datas to the server.\n\nNote that the server and the client *do not* have to be on the same machine.\n\n\n## Install\n\n### Server machine\n\nThe machine that will host the server needs to\nhave [docker](https://www.docker.com/) installed. The server is completely\npackaged inside a docker container. To get it, run:\n\n```bash\n$ docker pull alband/crayon\n```\n\n### Client machine\n\nThe client machine only need to install the client for the required language.\nDetailed instructions can be read by nagivating to\ntheir [respective directories](client/).\n\nTL;DR:\n\n* Lua / Torch - `$ luarocks install crayon`\n* Python 2 - `$ pip install pycrayon`\n* Python 3 - `$ pip3 install pycrayon`\n\n## Usage\n\n### Server machine\n\nTo start the server, run the following:\n\n```bash\n$ docker run -d -p 8888:8888 -p 8889:8889 --name crayon alband/crayon\n```\n\nTensorboard is now accessible on a browser at `server_machine_address:8888`. The\nclient should send the data at `server_machine_address:8889`.\n\n### Client\n\nSee the documentation for the required language:\n\n* [Lua](client/lua/README.md#usage-example)\n* [Python](client/python/README.md#usage-example)\n\n","funding_links":[],"categories":["tensorflow","Misc"],"sub_categories":["Ranking/Recommender"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftorrvision%2Fcrayon","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftorrvision%2Fcrayon","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftorrvision%2Fcrayon/lists"}