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    "info": {
        "author": "Anh Le",
        "author_email": "hi@imanhle.com",
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            "License :: OSI Approved :: MIT License",
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        "description": "# thread6\nSimple parallel processing interface for python\n\n## Why?\nPython's built in parallel processing and threading library is pretty simple to implement but sometimes you just want to chuck data at a function and make it run faster\n\n## Requirements\nPython 3+\n\n## Installation\n\n## Quickstart\nUse the `threaded` decorator to turn a method into a threaded method. That's it!\n```python\n@thread6.threaded()\ndef threaded_print():\n    print(\"\")\n    return 1\n```\n\nAlternatively, use `run_threaded` function\n```python\nthread6.run_threaded(threaded_print)\n```\n\nBoth the `threaded` decorator and `run_threaded` method will return an instance of\n`ResultThread`. This allow you to optionally wait for the function to finish executing \nand get the return value. To get the return value, use `.await_output()`\n```python\nresult = threaded_print()\nresult.await_output()  # this will return 1\n```\n\nIf you have a function that needs to execute on a large list of data, use `run_chunked`\n```python\ndef update_items(items):\n    ...\n\nitems = [...]\nthread6.run_chunked(update_items, items)\n```\n`.await_output()` also work with `run_chunked` but will return a list of return values instead\n\n## Usage\n\n\n## Todo\n- [x] threaded function decorator\n- [x] run something in a separate thread function\n- [x] split data into chunk and run in separate threads\n- [ ] add way for errors to fail loudly\n- [ ] auto spawn to run fx on a set of data\n- [ ] explore multi processing?\n\n\n",
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        "name": "thread6",
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        "summary": "A plug n play multithreading interface",
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