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‎dist/auto_function_serving-0.0.1.tar.gz‎

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‎pyproject.toml‎

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build-backend = "setuptools.build_meta"
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[project]
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name = "auto_function_serving"
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version = "0.0.1"
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version = "0.0.3"
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authors = [
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{ name="Arrman Anicket Saha", email="arrmansa99+430@gmail.com" },
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]
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description = "A python package to offload a function call to an http server automatically using a decorator."
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readme = "README.md"
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license = { file="LICENSE" }
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requires-python = ">=3.0"
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classifiers = [
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"Programming Language :: Python :: 3",
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"License :: OSI Approved :: MIT License",
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"License :: OSI Approved :: Apache Software License",
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"Operating System :: OS Independent",
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]
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‎src/auto_function_serving.egg-info/PKG-INFO‎

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Metadata-Version: 2.1
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Name: auto-function-serving
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Version: 0.0.1
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Version: 0.0.3
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Summary: A python package to offload a function call to an http server automatically using a decorator.
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Author-email: Arrman Anicket Saha <arrmansa99+430@gmail.com>
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License: Apache License
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Version 2.0, January 2004
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Classifier: License :: OSI Approved :: Apache Software License
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Description-Content-Type: text/markdown
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License-File: LICENSE
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# auto-function-serving
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A python package to offload a function call to an http server running on localhost automatically using a decorator. Compatible with multiprocessing, pickle and flask.
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A python package to offload a function call to an http server running on localhost automatically using a decorator. Should work with multiprocessing, pickle, flask and others.
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## Why
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Imagine a case of a multi threaded or multiprocessing application where 1 or few functions are heavily resource (cpu or memory) intensive, but the other functions can run in parallel.\
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Example - an api call followed by tokenization and classification using a large DL model followed by further API calls.\
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In such a case, it would make sense to create a server (using torchserve or tfserving) to serve requests.\
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ServerHandler does this automatically during runtime
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## Reccomended Usage
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```python
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from somemodule import someheavyfunction
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from auto_function_serving.ServerHandler import ServerHandler
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someheavyfunction = ServerHandler.decorator(someheavyfunction)
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```
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Any calls to this new **someheavyfunction** will make requests to 1 instance of a process running a [http.server.HTTPServer](https://docs.python.org/3/library/http.server.html) which runs the function within it. Even calls made from different processes, threads, multiprocessing or servers like flask.\
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It can also be used like a traditional decorator for functions with no dependencies specified outside the function.
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```python
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from auto_function_serving.ServerHandler import ServerHandler
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@ServerHandler.decorator
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def someheavyfunctionwithnodependency(args,**kwargs):
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for i in range(big_number)
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someexpensivecomputation
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```
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## Installation
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Use the package manager pip to install [auto_function_serving](https://pypi.org/project/auto-function-serving/)
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```bash
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pip install auto_function_serving
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```
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## More Usage
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In general :
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```
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some independent code with a callable
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```
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can be replaced with
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```python
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from auto_function_serving.ServerHandler import ServerHandler
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callable_name = ServerHandler("""
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some independent code with a callable
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""", "callable_name")
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```
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Example :
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```python
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import module1
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import module2
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def functionname(someinput):
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a = module1.function1(someinput)
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return module2.function2(a)
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```
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can be replaced with
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```python
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from auto_function_serving.ServerHandler import ServerHandler
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functionname = ServerHandler("""
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import module1
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import module2
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def functionname(someinput):
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a = module1.function1(someinput)
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return module2.function2(a)
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""", "functionname")
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```
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# How does this work?
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Code for the server is stored in ServerHandler.base_code, inspect.cleandoc and some string formatting is used to fill in the blanks.\
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A port from 50000 to 60000 is chosen by hashing the input text to make it independent of the source of a function. Collisions are possible, but unlikely. The port can be specified if needed.
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```python
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from somemodule import someheavyfunction
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from auto_function_serving.ServerHandler import ServerHandler
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someheavyfunction = ServerHandler.decorator(someheavyfunction, port = 4321)
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```
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ServerHandler.ip_address is set as "127.0.0.1".\
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The server process is started with Popen (or multiprocessing if specified), and the first thing it does is import socket and bind the port - if it's not available the code stops after an exception. Therefore only 1 instance of the server runs at a time on a machine.\
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We know the function is ready after we can recieve a valid get request from the server.\
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Inputs and outputs are sent as bytes, converted to and from objects using pickle.
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## Performance (On my machine)
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using urllib.request seemed to give the best performance.\
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~2ms overhead for small input and output (few bytes).\
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~10ms overhead for 0.5 mb input and output (1mb total transfer).\
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~60ms overhead for 5 mb input and output (10 mb total transfer).
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## Other things to look into
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Libraries : Celery, Tfserving, Torchserve, Flask\
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Sending globals and locals to exec\
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ast trees
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## Contributing
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Pull requests are welcome.
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## License
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[Apache License 2.0](https://choosealicense.com/licenses/apache-2.0/)

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