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        "author": "Rohit Singh",
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        "description": "# iterlib\n\nA library for easy iterator-based concurrency and parallelism.\n\n| Build Status |\n|--------------|\n|[![CircleCI](https://circleci.com/gh/rohits2/iterlib.svg?style=svg)](https://circleci.com/gh/rohits2/iterlib)|\n\n## What is this?\n\nHave you ever been working with `map` or a generator and gotten annoyed with how slow lazy evaluation made some tasks?\nHave you ever wondered \"could I run this generator in the background?\"\n\nThis library exists as the answer to that question.\nIt implements asynchronous preloading generators and parallel `map`.\nBoth the preloaders and the parallel `map` implementations support `multiprocessing` and `threading` as backends.\n\n## How do I use it?\n### Preload\nHere's a simple example of preloading a generator:\n\n```python3\nfrom iterlib import thread_preload\n\ngen = (x**2 for x in range(100000))\npreloaded_gen = thread_preload(gen, buffer_size=100)\n```\n\nThat's it! The generator will now preload up to 100 items in the background.  When you call `next(preloaded_gen)`, either directly or indirectly through a `for` statement, it will return values from the preloaded queue.\n\n### Parallel Map\nPreloading generators has a significant limitation: it's impossible to use more than one background executor because access to iterators requires synchronization.  However, most generators tend to be `map`s over other iterators, which opens an opportunity.  We can't parallelize reads from an iterator, but we can parallelize function calls.\n\nUse one of `threaded_map` or `process_map` when you know your generator is a map:\n\n```python3\nfrom iterlib import thread_map\n\ngen = [x for x in range(100000)]\nmapped_gen = thread_map(lambda x: x**2, buffer_size=100, num_workers=4)\n```\n\nThis will create an `ItemizedMap` named `mapped_gen`.  When you call `iter(mapped_gen)`, a generator will be created in the background that will preload up to 100 samples per worker (so 400 total in this example).\n\n> **Careful**: These functions has different semantics than the regular Python `map`!  If you `map` over an indexable collection (like a list or numpy array) the returned `ItemizedMap` will also be an indexable collection that lazily evaluates the `map` for each element you access!  Only when `iter` is called (in a for loop or directly) will it return an asynchronous generator.\n\n\n\n",
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