Reproducible library
Project description
The Reproducible Python Library
Keep track of your results.
Ever produced a result for a paper, only to realize a few months later that you could not reproduce it? That you had no idea which version of the code, and which parameter values were used back then?
The reproducible library, developped by the Cognitive Neuro-Robotics Unit at the Okinawa Institute of Science and Technology (OIST), aims to provide an easy way to gather and save important information about the context in which a result was computed. This includes details about the OS, the Python version, the time, the git commit, the command-line arguments, hashes of input and output files, and any user provided data.
Other Python libraries doing just that exists such as Recipy and Sumatra. And they are good. Do try them. They each have their own design philosophy, which proved to be difficult to interface with some of the workflows of the Cognitive Neuro-Robotics Unit lab at OIST.
With reproducible the goal was to have a small non-intrusive library allowing precise control over the data collected and how to output it. In particular, the goal was to have the tracking info sitting next to—or better, directly embedded in—the result files. That makes sending results to collaborators or packaging them for publication straightforward.
The reproducible library is licensed under the LGPL version 3, to allow you to use it along-side code that use other licenses.
The library is in beta; expect some changes. Python 3.5 or later is officially supported, but for the time being, the code runs on 2.7 as well.
Install
pip install reproducible
Instant Tutorial
Say this is your code, which is fully committed using git:
import random
import pickle
def walk(n):
"""A simple random walk generator"""
steps = [0]
for i in range(n):
steps.append(steps[-1] + random.choice([-1, 1]))
return steps
if __name__ == '__main__':
random.seed(1)
results = walk(10)
with open('results.pickle', 'wb') as f:
pickle.dump(results, f)
To add reproducible tracking:
import random
import pickle
import reproducible
def walk(n):
"""A simple random walk generator"""
steps = [0]
for i in range(n):
steps.append(steps[-1] + random.choice([-1, 1]))
return steps
if __name__ == '__main__':
# create a reproducible.Context instance, that will hold all the
# tracked data.
context = reproducible.Context()
# recording git repository state
# here we are okay with running our code with uncommitted changes, but
# we record a diff of the changes in the tracked data.
context.add_repo(path='.', allow_dirty=True, diff=True)
# recording parameters; this is not necessarily needed, as the code state
# is recorded, but it is convenient.
seed = 1
random.seed(seed)
context.add_data('seed', seed)
# add_data return the provided value (here 10), so you can do this:
n = reproducible.add_data('n', 10)
results = walk(n)
with open('results.pickle', 'wb') as f:
pickle.dump(results, f)
# recording the SHA1 hash of the output file
context.add_file('results.pickle', category='output')
# you can examine the tracked data and add or remove from it at any moment
# with `context.data`: it is a simple dictionary. For instance, the
# cpu info is quite detailed. Let's remove it to keep the yaml output short.
context.data.pop('cpuinfo')
# exporting the provenance data to disk
context.export_yaml('results_prov.yaml')
This is the resulting yaml file output containing the tracking data:
argv: [example_after.py]
data: {n: 10, seed: 1}
files:
output:
results.pickle: {mtime: 1531381834.0666547, sha256: 395d8846640c012e3e5c642e7737173a1a74120275b37fa2ded13a211df3264e}
packages: [gitdb2==2.0.3, GitPython==2.1.10, pip==10.0.1, py-cpuinfo==4.0.0, PyYAML==4.2b4,
reproducible==0.1.2, setuptools==39.0.1, smmap2==2.0.3]
platform: Darwin-17.6.0-x86_64-i386-64bit
python:
branch: ''
compiler: Clang 9.1.0 (clang-902.0.39.2)
implementation: CPython
revision: ''
version: ['3', '7', '0']
repositories:
.: {diff: null, dirty: false, hash: 88c1de4ba5fb5cb2564b60245f26d3226ecb20c9, version: git
version 2.18.0}
timestamp: ['2018-07-12T07:50:34.033829Z']
See also the The API Reference.
Roadmap
- Retrieve GPU information.
- More configurability.
- Optionally capture input, output (
sys.stderr
,sys.stdout
). - Easy disabling/reenabling of reproducible
- optional SHA256 in the filename of external files
Changelog
version 0.4.0, 20190703
- new functions
sha256()
,untrack_file()
,find_editable_repos()
,add_editable_repos()
. - fix tests.
version 0.3.0, 20190703 This version introduces API and logic-breaking changes.
add_file()
overwrites by default now, and category is now an optional argument.context.data()
becomescontext.data
.Context(repo_path='.', allow_dirty=False, allow_untracked=False, diff=True, cpuinfo=True)
becomesContext(cpuinfo=True, pip_packages=True)
:add_repo()
needs to be called explicitly now, and pip_packages queries can be made optional.reset()
does not accept any arguments anymore; remembers__init__()
argument values instead.- fixed missing
reproducible.add_pip_packages()
.
version 0.2.4, 20170809
- hotfix for Python 2.7---because I am stupid.
version 0.2.3, 20170809
- add
json()
,yaml()
andrequirements()
function to access the result of export functions programmatically. - YAML output is now generated using
yaml.safe_dump
rather thanyaml.dump
. Leads to safer and simpler output.
version 0.2.2, 20170717
- fix for deprecated
save_yaml()
,save_json()
functions.
version 0.2.1, 20170717
- add readme, license to pypi package.
version 0.2.0, 20170717
- renamed
save_json()
andsave_yaml()
asexport_json()
andexport_yaml()
. The old name remain for now with a deprecation warning. Context
instances for more flexible, non-module level, behavior, much like theRandom
instances of the standardrandom
module.reproducible.function_args()
function to retrieve arguments from inside a function.reproducible.reset()
function for clearing tracked data.reproducible.export_requirements()
to create requirements files from the retrieved list of installed packages.- Fix import of the freeze command from the
pip
package. - Updated readme: yaml output of the example, roadmap, changlog.
version 0.1.2, 20170611
- Various bug fixes.
- The
save_json()
andsave_yaml()
functions now return the SHA256 hash of the file they produce.
version 0.1.1, 20170608
reproducible.data()
function to access and modify the collected data.- more unit tests
version 0.1.0, 20170607
- first version:
add_repo()
,add_file()
,add_data()
,add_random_state()
,git_info()
,git_dirty()
,save_json()
,save_yaml()
functions.
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