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Memory-profiling/Scalene/Readme.md

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### Install
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`pip install scalene`
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### Run
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`scalene <yourapp.py>`
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### Run (on CLI)
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`$ scalene <yourapp.py>`
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### Example output
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If you run the `mlp.py`
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`scalene mlp.py`
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`$ scalene mlp.py`
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You may see something like,
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![scalene-1](https://raw.githubusercontent.com/tirthajyoti/Machine-Learning-with-Python/master/Memory-profiling/Scalene/scalene-1.PNG)
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### Features
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Here are some of the cool features of Scalene. Most of them are self-explanatory and can be gauged from the screenshot above,
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- **Lines or functions**: Reports information both for entire functions and for every independent code line
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- **Threads**: It supports Python threads.
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- **Multiprocessing**: supports use of the multiprocessing library
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- **Python vs. C time**: Scalene breaks out time spent in Python vs. native code (e.g., libraries)
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- **System time**: It distinguishes system time (e.g., sleeping or performing I/O operations)
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- **GPU**: It also can report the time spent on an NVIDIA GPU (if present)
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- **Copy volume**: It reports MBs of data being copied per second 
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- **Detects leaks**: Scalene can automatically pinpoint lines responsible for likely memory leaks!

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