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    "info": {
        "author": "ParallelM",
        "author_email": "info@parallelm.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: MacOS",
            "Operating System :: POSIX",
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            "Programming Language :: Python :: 2",
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        "description": "# README\n\nThe `mlcomp` module is designed to process and execute complex pipelines, that consist of one or more components chained together such that output of a previous component becomes the input to the next component. Each pipeline has a particular purpose, such as to train a model or generate inferences.\n\nA single pipeline may include components from different languages, such as Python, R and Java.\n\n## Quickstart\n\n#### Steps\n\n- Create a pipeline. Open any text editor and copy the following pipeline description:\n\n        {\n            \"name\": \"Simple MCenter runner test\",\n            \"engineType\": \"Generic\",\n            \"pipe\": [\n                {\n                    \"name\": \"Source String\",\n                    \"id\": 1,\n                    \"type\": \"string-source\",\n                    \"parents\": [],\n                    \"arguments\": {\n                        \"value\": \"Hello World: testing string source and sink\"\n                    }\n                },\n                {\n                    \"name\": \"Sink String\",\n                    \"id\": 2,\n                    \"type\": \"string-sink\",\n                    \"parents\": [{\"parent\": 1, \"output\": 0}],\n                    \"arguments\": {\n                        \"expected-value\": \"Hello World: testing string source and sink\"\n                    }\n                }\n            ]\n        }\n\n- Clone `mlpiper` repo [https://github.com/mlpiper/mlpiper/](https://github.com/mlpiper/mlpiper/)\n- Components `string-source` and `string-sink` can be found in the repo path [https://github.com/mlpiper/mlpiper/tree/master/reflex-algos/components/Python](https://github.com/mlpiper/mlpiper/tree/master/reflex-algos/components/Python)\n- Once the `ml-comp` python package is installed, the `mlpiper` command line tool is available and can be used to execute the above pipeline and the components described in it. Run the example above with:\n\n      mlpiper run -f ~/<pipeline description file> -r <path to mlpiper repo>/reflex-algos/components/Python/ -d <deployment dir>\n\n     Use the **--force** option to overwrite the deployment directory.\n\n## How to construct a component\n\n#### Steps\n\n- Create a directory, the name of which corresponds to the component's name (e.g., source_string)\n\n- Create a `component.json` file (JSON format) inside this directory and make sure to fill in all of the following fields:\n\n        {\n            \"engineType\": \"Generic\",\n            \"language\": \"Python\",\n            \"userStandalone\": false,\n            \"name\": \"<Component name (e.g., string_source)>\",\n            \"label\": \"<A lable that is displayed in the UI>\",\n            \"version\": \"<Component's version (e.g., 1.0.0)>\",\n            \"group\": \"<One of the valid groups (e.g., \"Connectors\")>,\n            \"program\": \"<The Python component main script (e.g., string_source.py)>\",\n            \"componentClass\": \"<The component class name (e.g., StringSource)\n            \"useMLStats\": <true|false - (whether the components uses MLStats)>,\n            \"inputInfo\": [\n                {\n                 \"description\": \"<Description>\",\n                 \"label\": \"<Lable name>\",\n                 \"defaultComponent\": \"\",\n                 \"type\": \"<A type used to verify matching connected legs>,\n                 \"group\": \"<data|model|prediction|statistics|other>\"\n                },\n                {...}\n            ],\n            \"outputInfo\": [\n                <Same as inputInfo above>\n            ],\n            \"arguments\": [\n                {\n                    \"key\": \"<Unique argument key name>\",\n                    \"type\": \"int|long|float|str|bool\",\n                    \"label\": \"<A label that is displayed in the UI>\",\n                    \"description\": \"<Description>\",\n                    \"optional\": <true|false>\n                }\n            ]\n        }\n\n- Create the main component script, which contains the component's class name.\n  This class should inherit from a 'Component' base class, which is taken from\n  `parallelm.components.component`. The class must implement the `materialize`\n  function, with this prototype: `def _materialize(self, parent_data_objs, user_data)`.\n  Here is a complete self contained example:\n\n        from parallelm.components import ConnectableComponent\n        from parallelm.mlops import mlops\n\n\n        class StringSource(ConnectableComponent):\n            def __init__(self, engine):\n                super(self.__class__, self).__init__(engine)\n\n            def _materialize(self, parent_data_objs, user_data):\n                self._logger.info(\"Inside string source component\")\n                str_value = self._params.get('value', \"default-string-value\")\n\n                mlops.set_stat(\"Specific stat title\", 1.0)\n                mlops.set_stat(\"Specific stat title\", 2.0)\n\n                return [str_value]\n\n\n  Notes:\n    - A component can use `self._logger` object to print logs.\n    - A component may access to pipeline parameters via `self._params` dictionary.\n    - The `_materialize` function should return a list of objects or None otherwise.\n      This returned value will be used as an input for the next component\n      in the pipeline chain.\n\n- Place the component's main program (\\*.py) inside a directory along with its JSON\n  description file and any other desired files.\n\n\n## How to construct a pipeline\n\n#### Steps\n\n- Open any text editor and copy the following pipeline description:\n\n        {\n            \"name\": \"Simple MCenter runner test\",\n            \"engineType\": \"Generic\",\n            \"pipe\": [\n                {\n                    \"name\": \"Source String\",\n                    \"id\": 1,\n                    \"type\": \"string-source\",\n                    \"parents\": [],\n                    \"arguments\": {\n                        \"value\": \"Hello World: testing string source and sink\"\n                    }\n                },\n                {\n                    \"name\": \"Sink String\",\n                    \"id\": 2,\n                    \"type\": \"string-sink\",\n                    \"parents\": [{\"parent\": 1, \"output\": 0}],\n                    \"arguments\": {\n                        \"expected-value\": \"Hello World: testing string source and sink\"\n                    }\n                }\n            ]\n        }\n\n  Notes:\n    - It is assumed that you've already constructed two components whose names\n      are: `string-source` and `string-sink`\n    - The output of the `string-source` component (the value returned from\n      `_materialize` function) is supposed to become the input of the `string-sink`\n      component (an input to the `_materialize` function)\n\n- Save it with any desired name\n\n\n## How to test\n\nOnce the `ml-comp` python package is installed, `mlpiper` command line tool is available\nand can be used to execute the above pipeline and the components described in it.\n\nThere are three main commands that can be used as follows:\n\n  - **deploy** - Deploys a pipeline along with provided components into a given\n                 directory. Once deployed, it can be executed directly from \n                 the given directory.\n\n  - **run** - Deploys and then executes the pipeline.\n\n  - **run-deployment** - Executes an already-deployed pipeline.\n\n\n#### Examples:\n\n  - Prepare a deployment. The resulting directory will be copied to a docker container and run\n    there:\n\n        mlpiper deploy -f p1.json -r ~/dev/components -d /tmp/pp\n\n  - Deploy & Run. Useful for development and debugging:\n\n        mlpiper run -f p1.json -r ~/dev/components -d /tmp/pp\n\n       Use **--force** option to overwrite deployment directory\n\n  - Run a deployment. Usually non-interactive and called by another script:\n\n        mlpiper run-deployment --deployment-dir /tmp/pp\n\n\n",
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