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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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