Hi,

2017-09-12 18:35 GMT+02:00 Wang, Peter Xihong peter.xihong.wang@intel.com:

I am currently using the Python Benchmark Suite https://github.com/python/performance for a customer and running into some issues. Due to specific circumstance, the net speed/bandwidth must be limited. As a result, the real time package checking and pulling step prior to the actual benchmark run was never able to finish (always time out somewhere during the bit pulling).

I guess that you are talking about the creation of the virtual environment.

In the Git repository, I see these dependencies (performance/requirements.txt):

setuptools>=18.5 pip>=6.0 wheel backports-abc==0.5 singledispatch==3.4.0.3 certifi==2017.4.17 lockfile==0.12.2; python_version < '3.0' pydns==2.3.6; python_version < '3.0' MarkupSafe==1.0 webencodings==0.5.1 mpmath==0.19 statistics==1.0.3.5; python_version < '3.4' docutils==0.13.1 six==1.10.0 perf==1.4 Chameleon==3.1 Django==1.11.3 Genshi==0.7 Mako==1.0.6 SQLAlchemy==1.1.11 dulwich==0.17.3 mercurial==4.2.2; python_version < '3.0' html5lib==0.999999999 pathlib2==2.3.0; python_version < '3.4' pyaes==1.6.0 spambayes==1.1b2; python_version < '3.0' sympy==1.0 tornado==4.5.1 psutil==5.2.2

My request: is there a way to get a zipped (or git cloned version) of this benchmark, and stop the package pulling in real time prior to every run? In addition to net speed limit, we have to run the benchmark on multiple machines. Trying to wait for the same source codes being downloaded repeatedly for every run is really painfully, and not very efficient.

You can try to create the virtual environment manually:

$ python3 -m venv myenv $ myenv/bin/python -m pip install -r performance/requirements.txt

And then run benchmarks using:

$ python3 -m performance run --venv myenv/ -b telco --fast -v

Or more directly using:

$ myenv/bin/python -m performance run --inside-venv ...

Instead of getting dependencies from the Internet, you should be able to download them locally to install them.

Example to install Django (but still get dependencies of Django from the Internet) in a new virtual environment:

$ wget https://pypi.python.org/packages/fe/ca/a7b35a0f5088f26b1ef3c7add57161a7d387a... # link found at https://pypi.python.org/pypi/Django/1.11.3 $ python3 -m venv bla $ bla/bin/python -m pip install Django-1.11.3-py2.py3-none-any.whl

There are likely tools to automate these steps.

If you have exactly the same hardware, you *might* copy the full environment environment. But I never tried to do that.

The current implementation does not guarantee the exact same benchmarks are used for all runs. I feel git tag should be used to enforce consistency.

Dependencies are pinned by their exact version: package==x.y.z. Indirect dependencies are also pinned, example: backports-abc==0.5.

I'm not a pip expert, so maybe we can enhance that. But I wouldn't say that "there is no guarantee".

IMHO it's more reliable than the old benchmark suite since the "sys.path" is now under control: benchmarks are run in a "known" environment. PYTHONPATH is ignored. The python_startup benchmark is not impacted by .pth files of your sys.path.

We used The Grand Unified Python Benchmark Suite https://hg.python.org/benchmarks in the past, and found that one was very easy to use, with far less dependency, and can be simply zipped and deployed easily.

Yeah, you are right. But it was more complex to add new dependencies and update dependencies. As a consequence, we tested softwares which were 5 years old... Not really revelant.

Victor