Microbench

Microbench is a small Python package for benchmarking Python functions, and optionally capturing extra runtime/environment information. It is most useful in clustered/distributed environments, where the same function runs under different environments, and is designed to be extensible with new functionality. In addition to benchmarking, this can help reproducibility by e.g. logging the versions of key Python packages, or even all packages loaded into the global environment.

Requirements

Microbench by default has no dependencies outside of the Python standard library, although pandas is recommended to examine results. However, some mixins (extensions) have specific requirements:

• The line_profiler package needs to be installed for line-by-line code benchmarking.
• MBInstalledPackages requires setuptools, which is not a part of the standard library, but is usually available.
• The CPU cores and total RAM extensions require psutil.
• The NVIDIA GPU plugin requires the nvidia-smi utility, which usually ships with the NVIDIA graphics card drivers. It needs to be on your PATH.

Installation

To install using pip:

pip install microbench

Usage

Microbench is designed for benchmarking Python functions. These examples will assume you have already defined a Python function myfunction that you wish to benchmark:

def myfunction(arg1, arg2, ...):
    ...

Minimal example

First, create a benchmark suite, which specifies the configuration and information to capture.

Here's a minimal, complete example:

from microbench import MicroBench
    
basic_bench = MicroBench()

To attach the benchmark to your function, simply use basic_bench as a decorator, like this:

@basic_bench
def myfunction(arg1, arg2, ...):
    ...

That's it! Benchmark information will be appended to the file specified in outfile. This example captures the fields start_time, finish_time and function_name. See the Examine results section for further information.

Extended examples

Here's a more complete example using mixins (the MB prefixed class names) to extend functionality. Note that keyword arguments can be supplied to the constructor (in this case some_info=123) to specify additional information to capture. This example also specifies the outfile option, which writes conda

from microbench import *
import numpy, pandas

class MyBench(MicroBench, MBFunctionCall, MBPythonVersion, MBHostInfo):
    outfile = '/home/user/my-benchmarks'
    capture_versions = (numpy, pandas)  # Or use MBGlobalPackages/MBInstalledPackages
    env_vars = ('SLURM_ARRAY_TASK_ID', )
    
benchmark = MyBench(some_info=123)

The env_vars option from the example above specifies a list of environment variables to capture as env_<variable name>. In this example, the slurm array task ID will be stored as env_SLURM_ARRAY_TASK_ID. Where the environment variable is not set, the value will be null.

To capture package versions, you can either specify them individually (as above), or you can capture the versions of every package in the global environment. In the following example, we would capture the versions of microbench, numpy, and pandas automatically.

from microbench import *
import numpy, pandas

class Bench2(MicroBench, MBGlobalPackages):
    outfile = '/home/user/bench2'

bench2 = Bench2()

If you want to go even further, and capture the version of every package available for import, there's a mixin for that:

from microbench import *

class Bench3(MicroBench, MBInstalledPackages):
    pass
    
bench3 = Bench3()

Examine results

Each result is a JSON object. When using the outfile option, a JSON object for each @benchmark call is stored on a separate line in the file. The output from the minimal example above for a single run will look similar to the following:

{"start_time": "2018-08-06T10:28:24.806493", "finish_time": "2018-08-06T10:28:24.867456", "function_name": "my_function"}

The simplest way to examine results in detail is to load them into a pandas dataframe:

import pandas
results = pandas.read_json('/home/user/my-benchmarks', lines=True)

Pandas has powerful data manipulation capabilities. For example, to calculate the average runtime by Python version:

# Calculate runtime for each run
results['runtime'] = results['finish_time'] - results['start_time']

# Average runtime by Python version
results.groupby('python_version')['runtime'].mean()

Many more advanced operations are available. The pandas tutorial is recommended.

Line profiler support

Microbench also has support for line_profiler, which shows the execution time of each line of Python code. Note that this will slow down your code, so only use it if needed, but it's useful for discovering bottlenecks within a function. Requires the line_profiler package to be installed (e.g. pip install line_profiler).

from microbench import MicroBench, MBLineProfiler
import pandas

# Create our benchmark suite using the MBLineProfiler mixin
class LineProfilerBench(MicroBench, MBLineProfiler):
    pass

lpbench = LineProfilerBench()

# Decorate our function with the benchmark suite
@lpbench
def my_function():
    """ Inefficient function for line profiler """
    acc = 0
    for i in range(1000000):
        acc += i

    return acc

# Call the function as normal
my_function()

# Read the results into a Pandas DataFrame
results = pandas.read_json(lpbench.outfile.getvalue(), lines=True)

# Get the line profiler report as an object
lp = MBLineProfiler.decode_line_profile(results['line_profiler'][0])

# Print the line profiler report
MBLineProfiler.print_line_profile(results['line_profiler'][0])

The last line of the previous example will print the line profiler report, showing the execution time of each line of code. Example:

Timer unit: 1e-06 s

Total time: 0.476723 s
File: /home/user/my_test.py
Function: my_function at line 12

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
    12                                               @lpbench
    13                                               def my_function():
    14                                                   """ Inefficient function for line profiler """
    15         1          2.0      2.0      0.0          acc = 0
    16   1000001     217874.0      0.2     45.7          for i in range(1000000):
    17   1000000     258846.0      0.3     54.3              acc += i
    18
    19         1          1.0      1.0      0.0          return acc

NVIDIA GPU support

Attributes about NVIDIA GPUs can be captured using the MBNvidiaSmi plugin. This requires the nvidia-smi utility to be available in the current PATH.

By default, the gpu_name (model number) and memory.total attributes are captured. Extra attributes can be specified using the class or object-level variable nvidia_attributes. To see which attributes are available, run nvidia-smi --help-query-gpu.

By default, all installed GPUs will be polled. To limit to a specific GPU, specify the nvidia_gpus attribute as a tuple of GPU IDs, which can be zero-based GPU indexes (can change between reboots, not recommended), GPU UUIDs, or PCI bus IDs. You can find out GPU UUIDs by running nvidia-smi -L.

Here's an example specifying the optional nvidia_attributes and nvidia_gpus fields:

from microbench import MicroBench, MBNvidiaSmi

class GpuBench(MicroBench, MBNvidiaSmi):
    outfile = '/home/user/gpu-benchmarks'
    nvidia_attributes = ('gpu_name', 'memory.total', 'pcie.link.width.max')
    nvidia_gpus = (0, )  # Usually better to specify GPU UUIDs here instead

gpu_bench = GpuBench()

Extending microbench

Microbench includes a few mixins for basic functionality as described in the extended example, above.

You can also add functions to your benchmark suite to capture extra information at runtime. These functions must be prefixed with capture_ for them to run automatically before the function starts. They take a single argument, bm_data, a dictionary to be extended with extra data. Care should be taken to avoid overwriting existing key names.

Here's an example to capture the machine type (i386, x86_64 etc.):

from microbench import MicroBench
import platform

class Bench(MicroBench):
    outfile = '/home/user/my-benchmarks'

    def capture_machine_platform(self, bm_data):
        bm_data['platform'] = platform.machine()
        
benchmark = Bench()

Redis support

By default, microbench appends output to a file, but output can be directed elsewhere, e.g. redis - an in-memory, networked data source. This option is useful when a shared filesystem is not available.

Redis support requires redis-py.

To use this feature, inherit from MicroBenchRedis instead of MicroBench, and specify the redis connection and key name as in the following example:

from microbench import MicroBenchRedis

class RedisBench(MicroBenchRedis):
    # redis_connection contains arguments for redis.StrictClient()
    redis_connection = {'host': 'localhost', 'port': 6379}
    redis_key = 'microbench:mykey'

benchmark = RedisBench()

To retrieve results, the redis package can be used directly:

import redis
import pandas

# Establish the connection to redis
rconn = redis.StrictRedis(host=..., port=...)

# Read the redis data from 'myrediskey' into a list of byte arrays
redis_data = redis.lrange('myrediskey', 0, -1)

# Convert the list into a single string
json_data = '\n'.join(r.decode('utf8') for r in redis_data)

# Read the string into a pandas dataframe
results = pandas.read_json(json_data, lines=True)

Feedback

Please note this is a recently created, experimental package. Please let me know your feedback or feature requests in Github issues.