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-1.1.6.tar.gz (12.1 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-1.1.6-py3-none-any.whl (16.1 kB view details)

Uploaded Python 3

File details

Details for the file mmar_utils-1.1.6.tar.gz.

File metadata

  • Download URL: mmar_utils-1.1.6.tar.gz
  • Upload date:
  • Size: 12.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.5.31

File hashes

Hashes for mmar_utils-1.1.6.tar.gz
Algorithm Hash digest
SHA256 c1e3a01d4e8050399d5d4ef4fbca89d57570acd14a3a55fdb00cc87138c5c7ce
MD5 81f804774d4ab8bb0f9d47fe8978bd62
BLAKE2b-256 4f068da5f3f74f7f3818914e0708b13ebd6ddfed7baf62707ab4151bcc76edc1

See more details on using hashes here.

File details

Details for the file mmar_utils-1.1.6-py3-none-any.whl.

File metadata

  • Download URL: mmar_utils-1.1.6-py3-none-any.whl
  • Upload date:
  • Size: 16.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.5.31

File hashes

Hashes for mmar_utils-1.1.6-py3-none-any.whl
Algorithm Hash digest
SHA256 453b7a2eaa4c03eaec6032275ef7edb23794da98480bcd251a89e5f766cb7840
MD5 f77195e82b75b3e9e65a259a85c5275b
BLAKE2b-256 7430982737a3bbee08566037ae28b3cca1d8be36d6805719eee10b924caea594

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