Skip to main content

Utilities for multi-modal architectures team

Project description

mmar-utils

Common pure/IO utilities for multi-modal architectures team.

Installation

pip install mmar-utils

parallel_map

Mix of joblib.Parallel and tqdm.

Similar libraries

joblib.Parallel: https://joblib.readthedocs.io/en/latest/parallel.html

  • doesn't support showing progress out of the box

Syntax comparison ( assuming tqdm disabled )

from math import sqrt, pow
from joblib import Parallel as P, delayed as d
from agi_med_utils import parallel_map

# ONE-ARG: THREADING
print(P(n_jobs=2)(map(d(sqrt), range(5))))
print(P(n_jobs=2)(d(sqrt)(i) for i in range(5)))
print(parallel_map(sqrt, range(5)))
# > [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]

# ONE-ARG: MULTIPROCESSING
print(P(n_jobs=2, backend='multiprocessing')(d(sqrt)(i) for i in range(3)))
print(parallel_map(sqrt, range(3), process=True))
# > [0.0, 1.0, 1.4142135623730951]

# MANY-ARGS: THREADING
pow_args = [(i, j) for i in range(1, 4) for j in range(1, 3)]
print(P(n_jobs=2)(d(pow)(i, j) for i, j in pow_args))
print(parallel_map(pow, pow_args, multiple_args=True))
# > [1.0, 1.0, 2.0, 4.0, 3.0, 9.0]

# KWARGS: THREADING
def ipow_kw(*, x, y):
    return 1 / pow(x, y)
ipow_kwargs = [{'x': i, 'y': j} for i, j in pow_args]
print(P(n_jobs=2)(d(ipow_kw)(**kw) for kw in ipow_kwargs))
print(parallel_map(ipow_kw, ipow_kwargs, kwargs_args=True))
# > [1.0, 1.0, 0.5, 0.25, 0.3333333333333333, 0.1111111111111111]

# KWARGS: MULTIPROCESSING
def ipow_kw_inc(*, x, y):
    return 1 / pow(x, y) + 1
print(P(n_jobs=2, backend='multiprocessing')(d(ipow_kw_inc)(**kw) for kw in ipow_kwargs))
print(parallel_map(ipow_kw_inc, ipow_kwargs, kwargs_args=True, process=True))
# > [2.0, 2.0, 1.5, 1.25, 1.3333333333333333, 1.1111111111111112]

pqdm: https://pqdm.readthedocs.io/en/latest/usage.html

trace_with

Decorator for function which executes some callback on each function call.

Similar libraries

functrace: https://github.com/idanhazan/functrace

  • does not remember start datetime of call, which is critical
  • supports selection of parameters to remember
  • functrace.TraceResult object is not serializable out of the box

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mmar_utils-1.1.18.tar.gz (13.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

mmar_utils-1.1.18-py3-none-any.whl (17.8 kB view details)

Uploaded Python 3

File details

Details for the file mmar_utils-1.1.18.tar.gz.

File metadata

  • Download URL: mmar_utils-1.1.18.tar.gz
  • Upload date:
  • Size: 13.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.5.31

File hashes

Hashes for mmar_utils-1.1.18.tar.gz
Algorithm Hash digest
SHA256 f27da79a04e6e2e73c94ee457a9eb7f39ffdd799bd3cec823401fd51d41c5ccc
MD5 99500726ad376e52a8f260d21e18f5fe
BLAKE2b-256 794252c16c9b7a1b9bc87885564e66eb41ab8ed3612b8396e394e352338c0ce3

See more details on using hashes here.

File details

Details for the file mmar_utils-1.1.18-py3-none-any.whl.

File metadata

File hashes

Hashes for mmar_utils-1.1.18-py3-none-any.whl
Algorithm Hash digest
SHA256 1c34681ee69f0175d82f50a477c06bb52fa832ab958840302e2bde610a7f31a4
MD5 c75024fb44788999ce16e2e511467b1f
BLAKE2b-256 985112b1d0d13c09af69cbedb138235b4a986c951bb1d1331aa67d8df5eafd29

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page