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        "author": "Longbin Chen",
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        "description": "# MLPipe\n\n## Introduction\nMLpipe is a machine learning utility tool to facilitate machine learning tasks. It is a dev tool to manage pipe/jobs and it fetches data and module files and execute the modules on server; and store the output on s3 (or other storage space).\n\nIt modularizes each machine learning algorithms in to 'modules', which are used to build complicate machine learning training process (pipes). In Pipes, the data are either stored in s3, or in git repos or https URIs. Local file will be supported soon.   \n\nWe are working on a Web UI to create/edit/maintain training process and visualize the training process pipes.\n\n## Get started\n\nuse sudo apt-get install to install following packages; please don't change the default admin name as root and remmeber the password for root. \n\n\nFirst setup the environment variable for the home folder\n```\nEXPORT MLPIPE_HOME=~/mlpipe\n```\n\nand run the following pip command to install it. Ideally you can create a virtual environment first and activate it.\n\n```\npip install mlpipe\n```\n\n\n\n#### create mysql user mlusers with passwd mlusers\nrun the following command to initialized the database\n\n```\nml init\n\n```\n\n\n#### Config\nthere are a few import config you need to set up before you can run MLpipe. you can run the following command to view the config views. \n\n```\nml config\n```\n\nThese are the directory to save intermedia data and download/cached datas. For some experiment, the file size might be very large so make sure you config a large enough directory. \n\n\nYou can create a file 'extra_settings.py' if you need to configure your own settings without changing the remote repo. \n\n```\nvi [path_to_mlpipe_library]/mlpipe/extra_settings.py\n```\n\n## Using MLpipe\n### all the commands\n```\nml \n```\n\n### list all available pipe\n```\nml listpipe\n```\n### run a pipe \n\n``` \nml runpipe text_classification/pipe/bow_model.yaml\n``` \n### list all the jobs \n\n``` \nml listjob\n``` \n\n### run a job \n\n``` \nml runjob --job_id [job_id]\n``` \n\n\n## Concepts\n\n### App\n   Each app is a directory contains subdirectories data, module and pipe. It is a collection of training data, training algorithm and pipelines. You can define an app by create a directory and mount to MLpipe. \n\n\n### Data\n\nData in MLpipe has two types, one is external data and one is intermedia data in the training process\n\nExternal Data is stored either at s3, or a file at git repo or a file downloadable from internet, or a local file. Therefore we can represent a data as one of the following three format:\n\n```\n s3://path/to/your/s3/file/test_room_nostopwords     #aws s3 ; you need to config your s3.cfg with your aws credency\n file://text_classification/data/train_data          #app's data file,  the format is file://[app_name]/data/[path_to_data_file] \n http://www.example.com/train.data.tar.gz            #http\n local://home/users/data/train_data.tsv              #local disk\n\n```\n\nMLpipe Daemon will automatically fetch and/or download data from s3, git repo or https, or local disk\n\nIntermedia Data are the output for one jobs and will be used as the input of other jobs in the same pipe. In the pipe defintion, they are represented using a unique data name in the pipe. See the pipe examples for more details. \n\n\n### Module\n\nA module is a predefined code, wrapped with a yaml file interface definition, with specified input/output and command lines to run the module\n\n```\ninput:\n    train_data:\n        type: text\n        optional: false\n        datafile: true\n    test_data:\n        type: text\n        optional: false\n        datafile: true\n    category_label:\n        type: text\n        optional: false\n        datafile: true\n\noutput:\n    model:\n        type: sk_sgd_pickle\n        optional: false\n        datafile: true\n\nparameters:\n    num_epochs:\n        type: int\n        default: 5\n\ncmd: python -m text_classification.module.bow_model --num_epochs num_epochs category_label train_data test_data model\n```\n\n> If you write your command in python with standard argparser library, we created a command tool for you to create the yaml interface definition file for you. \n\nFor example, \n\n```\nml create_yaml $MLpipe/apps/MLpipe/module/upload_to_s3.py\n```\n\n\n\n### Jobs and Pipe\n\nA job is an instance of module, with inputs and parameters set by the yaml configs. \n\nA pipe is a set of jobs defining the data flows, where the inputs and outputs of the jobs are connected together. \n\nA pipe is usually a complete pipeline to finish a task, which usually includes data preprocessing, training, evaluation, and result analysis.\n\nAn example of pipe with only one job is a yaml file looks like:\n\n```\nversion: 1.0\njobs:\n  bow_model:\n      module: text_classification/module/bow_model\n      input:\n        train_data: [input your s3 address here]\n        test_data: [input your s3 file address here]\n        category_label: [input your s3 file address here]\n      output:\n        model: bow_model_model\n```\n\n### Job Caching\n\n\nInternally, we want to avoid duplication of running the example same jobs. In the case that the module's output is deterministic of the input data and parameters (in most case, we can assume that even for random algorithms), we use the hash value of the concatenated string from module_id, md5 of souced_code, input and parameters identify a job. We can create a unique hash for each output data as well using the similar method by adding the output name in computing the hash. \n\n```\njob_hash = md5 (\"\\t\".join[ module_id + md5(source _code of module), input_list, param_list])\ndata_hash = md5 (\"\\t\".join[ module_id + md5(source _code of module), input_list, param_list, outputname])\n```\n\nIn running a pipe, we calculate the data hash and job hash for each job and each data. If we found we already have run the job before and the data are cached, we can skip running the job and fetch the data directly from our storage system. \n\n### External Data Caching\nit is usually time consuming to download external data. In MLpipe, all external data are downloaded and cached. the cached folder is config at MLpipe/django/MLpipe/settings.py file\n\n\n### Job Scheduling\n\n\n### S3 Setup\nYou need to config your s3 access by setting the file ~/.s3cfg\n```\naws --configure\ns3cmd --configure\n```\n\n### More \n\n#### Database (optional)\nBy default, MLpipe uses sqlite for simplicity and  you don't need to install database\n\nHowever if you want to use mysql as the backend database, you also need to install mysql. \n\n```\nsudo apt-get install mysql-server \n```\n\n\n\n### about the authors\nMLpipe was created by @lilia when she was an intern at Houzz's research team in Summer 2017. Since then, @longbin, @xinai @yangli all contribute to MLpipe\n\n\n\n\n\n\n",
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