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        "author_email": "justheuristic@gmail.com",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
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        "description": "# as_a_service\n\nA simple package that transforms a batch function {inputs->results} into a service that\n- groups inputs into batches - you specify max batch size and time waiting\n- processes them - and returns results back to whoever was asking\n\n### Usage:\n__[notebook version](https://github.com/justheuristic/as_a_service/blob/master/example.ipynb)__\n\nHere's how it feels\n```python\n@as_batched_service(batch_size=3, max_delay=0.1)\ndef square(batch_xs):\n    print(\"processing...\", batch_xs)\n    return [x_i ** 2 for x_i in batch_xs]\n\n# submit many queries\nfutures = square.submit_many(range(10))\nprint([f.result() for f in futures])\n```\nThis will print\n```\nprocessing... [0, 1, 2]\nprocessing... [3, 4, 5]\nprocessing... [6, 7, 8]\nprocessing... [9]\n[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n```\n\nYou can also use it as a drop-in replacement for a function that processes one input at a time\n * `square(2.0)` will return 4.0 as if a normal function\n * Under the hood, it submits a request and waits for it to finish\n\nThis package contains three objects\n - BatchedService(batch_process_func, batch_size, max_delay) - main object\n - @as_batched_service(batch_size, max_delay) - same thing as a decorator\n - @as_service(max_delay) - decorator for a function without batches (single input/output)\n\nUse help(BatchedService) and \"Why should I care?\" for more details.\n\n### Install:\n *  ```pip install as_a_service```\n *  No dependencies apart from standard libraries\n *  Works with both python2 and python3 (pip3 install)\n\n\n### Why should I care?\n\nThis primitive is useful for a number of scenarios like:\n1) You are building a web-based demo around your neural network. You want your network to process\n    a stream of user queries, but doing so one query at a time is slow. Batch-parallel processing is way better.\n\n```python\n@as_batched_service(batch_size=32, max_delay=1.0)\ndef service_predict(input_images_list):\n    predictions_list = my_network_predict_batch(input_images_list)\n    return predictions_list\n\n@my_web_framework.run_as_a_thread_for_every_query\ndef handle_user_query(query):\n    input_image = get_image(query)\n    return service_predict(input_image)\n```\n\n2) You are experimenting with a reinforcement learning agent. The agent itself is a neural network\n    that predicts actions. You want to play 100 parallel game sessions to train on.\n    Playing one session at a time is slow. If only we could run multiple sessions on one GPU\n\n```python\nmy_network = make_keras_network_on_gpu()\nservice = BatchedService(my_network.predict, batch_size=32, max_delay=1.0)\nthreads = [\n    GamePlayingThread(predict_action=lambda x: service(x)) for i in range(100)\n]\nfor thread in threads:\n    thread.start()\nfor thread in threads:\n    thread.join()\nservice.stop()\n```\n\nAnd many other scenarios where you want to use a single resource\n(GPU / device /DB) concurrently and utilize batch-parallelism",
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        "keywords": "service,background service,batched executor,parallel executor,background executor,background,deep learning,reinforcement learning",
        "license": "MIT",
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        "name": "as-a-service",
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