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-1.1.1.tar.gz (12.0 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-1.1.1-py3-none-any.whl (16.0 kB view details)

Uploaded Python 3

File details

Details for the file mmar_utils-1.1.1.tar.gz.

File metadata

  • Download URL: mmar_utils-1.1.1.tar.gz
  • Upload date:
  • Size: 12.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.5.31

File hashes

Hashes for mmar_utils-1.1.1.tar.gz
Algorithm Hash digest
SHA256 90f535a87a96510b000830aa71ff7ed8c1e7b1d03cc6c658dba13ea5ae601972
MD5 c11c79696f20c4dbc2a0817dbcc17e81
BLAKE2b-256 f80d85e30a30087137cd5697b720b0eab11e28af7b08a4f0e9031672415402b3

See more details on using hashes here.

File details

Details for the file mmar_utils-1.1.1-py3-none-any.whl.

File metadata

  • Download URL: mmar_utils-1.1.1-py3-none-any.whl
  • Upload date:
  • Size: 16.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.5.31

File hashes

Hashes for mmar_utils-1.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 e6234e3360e9967de1ae40b842f116b0fe14007903e5be56be2c9e174e5067b8
MD5 e2ce0ddffa681baf2878c8b043a20820
BLAKE2b-256 e29ea21779e8f9d2f16f9dce0a736dfac9bb4f7fdacaa04381329dd5e967fdf8

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