Metadata-Version: 2.1
Name: scalene
Version: 0.7.5
Summary: Scalene: A high-resolution, low-overhead CPU and memory profiler for Python
Home-page: https://github.com/emeryberger/scalene
Author: Emery Berger
Author-email: emery@cs.umass.edu
License: Apache License 2.0
Description: ![scalene](https://github.com/emeryberger/scalene/raw/master/docs/scalene-image.png)
        
        # scalene: a high-performance CPU and memory profiler for Python
        
        by [Emery Berger](https://emeryberger.com)
        
        ------------
        
        # About Scalene
        
        Scalene is a high-performance CPU *and* memory profiler for Python that does a few things that other Python profilers do not and cannot do.  It runs orders of magnitude faster than other profilers while delivering far more detailed information.
        
        1. Scalene is _fast_. It uses sampling instead of instrumentation or relying on Python's tracing facilities. Its overhead is typically no more than 10-20% (and often less).
        1. Scalene is _precise_. Unlike most other Python profilers, Scalene performs CPU profiling _at the line level_, pointing to the specific lines of code that are responsible for the execution time in your program. This level of detail can be much more useful than the function-level profiles returned by most profilers.
        1. Scalene separates out time spent running in Python from time spent in native code (including libraries). Most Python programmers aren't going to optimize the performance of native code (which is usually either in the Python implementation or external libraries), so this helps developers focus their optimization efforts on the code they can actually improve.
        1. Scalene _profiles memory usage_. In addition to tracking CPU usage, Scalene also points to the specific lines of code responsible for memory growth. It accomplishes this via an included specialized memory allocator.
        
        ## Installation
        
        Scalene is distributed as a `pip` package and works on Linux and Mac OS X platforms. You can install it as follows:
        ```
          % pip install scalene
        ```
        
        or
        ```
          % python -m pip install scalene
        ```
        
        _NOTE_: Currently, installing Scalene in this way does not install its memory profiling library, so you will only be able to use it to perform CPU profiling. To take advantage of its memory profiling capability, you will need to download this repository.
        
        **NEW**: You can now install the memory profiling part on Mac OS X using Homebrew.
        
        ```
          % brew tap emeryberger/scalene
          % brew install --head libscalene
        ```
        
        This will install a `scalene` script you can use (see below).
        
        # Usage
        
        The following command will run Scalene to only perform line-level CPU profiling on a provided example program.
        
        ```
          % python -m scalene test/testme.py
        ```
        
        If you have installed the Scalene library with Homebrew, you can just invoke `scalene` to perform both line-level CPU and memory profiling:
        
        ```
          % scalene test/testme.py
        ```
        
        Otherwise, you first need to build the specialized memory allocator by running `make`:
        
        ```
          % make
        ```
        
        Profiling on a Mac OS X system (without using Homebrew):
        ```
          % DYLD_INSERT_LIBRARIES=$PWD/libscalene.dylib PYTHONMALLOC=malloc python -m scalene test/testme.py
        ``` 
        
        Profiling on a Linux system:
        ```
          % LD_PRELOAD=$PWD/libscalene.so PYTHONMALLOC=malloc python -m scalene test/testme.py
        ``` 
        
        # Comparison to Other Profilers
        
        ## Performance and Features
        
        Below is a table comparing various profilers to scalene, running on an example Python program (`benchmarks/julia1_nopil.py`) from the book _High Performance Python_, by Gorelick and Ozsvald. All of these were run on a 2016 MacBook Pro.
        
        
        |                            | Time (seconds) | Slowdown | Line-level?    | CPU? | Separates Python from native? | Memory? | Unmodified code? |
        | :--- | ---: | ---: | :---: | :---: | :---: | :---: | :---: |
        | _original program_ | 6.71s | 1.0x | | | | | |
        |               |     |        |                    | |
        | `cProfile` | 11.04s | 1.65x | function-level | :heavy_check_mark: |  |  | :heavy_check_mark: |
        | `Profile` | 202.26s | 30.14x | function-level | :heavy_check_mark: |  |  | :heavy_check_mark: |
        | `pyinstrument` | 9.83s | 1.46x | function-level | :heavy_check_mark: |  |  | :heavy_check_mark: |
        | `line_profiler` | 78.0s | 11.62x | :heavy_check_mark: | :heavy_check_mark: |  |  | needs `@profile` decorators |
        | `pprofile` _(deterministic)_ | 403.67s | 60.16x | :heavy_check_mark: | :heavy_check_mark: |  |  | :heavy_check_mark: |
        | `pprofile` _(statistical)_ | 7.47s | 1.11x | :heavy_check_mark: | :heavy_check_mark: |  |  | :heavy_check_mark: |
        | `yappi` _(CPU)_ | 127.53s | 19.01x | function-level | :heavy_check_mark: |  |  | :heavy_check_mark: |
        | `yappi` _(wallclock)_ | 21.45s | 3.2x | function-level | :heavy_check_mark: |  |  | :heavy_check_mark: |
        | `memory_profiler`     | _aborted after 2 hours_ | **>1000x**| line_level |  |  | :heavy_check_mark: | needs `@profile` decorators |
        |               |     |        |                    | |
        | `scalene` _(CPU only)_ | 6.98s | **1.04x** | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |  | :heavy_check_mark: |
        | `scalene` _(CPU + memory)_ | 7.68s | **1.14x** | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
        
        
        ## Output
        
        Scalene prints annotated source code for the program being profiled and any modules it uses in the same directory or subdirectories. Here is a snippet from `pystone.py`, just using CPU profiling:
        
        ```
        benchmarks/pystone.py: % of CPU time =  98.78% out of   3.47s.
                 | CPU %    | CPU %    | 
          Line   | (Python) | (C)      | [benchmarks/pystone.py]
        --------------------------------------------------------------------------------
          [... lines omitted ...]
           137   |   0.87%  |   0.13%  | def Proc1(PtrParIn):
           138   |   1.46%  |   0.36%  |     PtrParIn.PtrComp = NextRecord = PtrGlb.copy()
           139   |          |          |     PtrParIn.IntComp = 5
           140   |   0.87%  |   0.04%  |     NextRecord.IntComp = PtrParIn.IntComp
           141   |   1.46%  |   0.30%  |     NextRecord.PtrComp = PtrParIn.PtrComp
           142   |   2.33%  |   0.26%  |     NextRecord.PtrComp = Proc3(NextRecord.PtrComp)
           143   |   1.46%  |  -0.00%  |     if NextRecord.Discr == Ident1:
           144   |   0.29%  |   0.04%  |         NextRecord.IntComp = 6
           145   |   1.75%  |   0.40%  |         NextRecord.EnumComp = Proc6(PtrParIn.EnumComp)
           146   |   1.75%  |   0.29%  |         NextRecord.PtrComp = PtrGlb.PtrComp
           147   |   0.58%  |   0.12%  |         NextRecord.IntComp = Proc7(NextRecord.IntComp, 10)
           148   |          |          |     else:
           149   |          |          |         PtrParIn = NextRecord.copy()
           150   |   0.87%  |   0.15%  |     NextRecord.PtrComp = None
           151   |          |          |     return PtrParIn
        ```
        
        And here is an example with memory profiling enabled, running the Julia benchmark.
        
        ```
        benchmarks/julia1_nopil.py: % of CPU time =  99.22% out of  12.06s.
                 | CPU %    | CPU %    | Memory (MB) |
          Line   | (Python) | (C)      |             | [benchmarks/julia1_nopil.py]
        --------------------------------------------------------------------------------
             1   |          |          |             | # Pasted from Chapter 2, High Performance Python - O'Reilly Media;
             2   |          |          |             | # minor modifications for Python 3 by Emery Berger
             3   |          |          |             | 
             4   |          |          |             | """Julia set generator without optional PIL-based image drawing"""
             5   |          |          |             | import time
             6   |          |          |             | # area of complex space to investigate
             7   |          |          |             | x1, x2, y1, y2 = -1.8, 1.8, -1.8, 1.8
             8   |          |          |             | c_real, c_imag = -0.62772, -.42193
             9   |          |          |             | 
            10   |          |          |             | #@profile
            11   |          |          |             | def calculate_z_serial_purepython(maxiter, zs, cs):
            12   |          |          |             |     """Calculate output list using Julia update rule"""
            13   |   0.08%  |   0.02%  |      0.06   |     output = [0] * len(zs)
            14   |   0.25%  |   0.01%  |      9.50   |     for i in range(len(zs)):
            15   |          |          |             |         n = 0
            16   |   1.34%  |   0.05%  |     -9.88   |         z = zs[i]
            17   |   0.50%  |   0.01%  |     -8.44   |         c = cs[i]
            18   |   1.25%  |   0.04%  |             |         while abs(z) < 2 and n < maxiter:
            19   |  68.67%  |   2.27%  |     42.50   |             z = z * z + c
            20   |  18.46%  |   0.74%  |    -33.62   |             n += 1
            21   |          |          |             |         output[i] = n
            22   |          |          |             |     return output
        ```
        
        Positive memory numbers indicate total memory allocation in megabytes;
        negative memory numbers indicate memory reclamation. Note that because
        of the way Python's memory management works, frequent allocation and
        de-allocation (as in lines 19-20 above) show up as high positive
        memory on one line followed by an (approximately) corresponding
        negative memory on the following line(s).
        
        # Acknowledgements
        
        Logo created by [Sophia Berger](https://www.linkedin.com/in/sophia-berger/).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
