No project description provided

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for google_opensource from gravatar.com google_opensource Avatar for Jake.Vanderplas from gravatar.com Jake.Vanderplas Avatar for phawkins from gravatar.com phawkins

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: Apache Software License ( Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR ...)
  • Author: ml_dtypes authors
  • Requires: Python >=3.7
Classifiers

Project description

ml_dtypes

Unittests Wheel Build PyPI version

This is not an officially supported Google product.

ml_dtypes is a stand-alone implementation of several NumPy dtype extensions used in machine learning libraries, including:

  • bfloat16: an alternative to the standard float16 format
  • float8_*: several experimental 8-bit floating point representations including:
    • float8_e4m3b11
    • float8_e4m3fn
    • float8_e5m2

Installation

We will soon release pre-built wheels for this package at http://pypi.org/project/ml-dtypes, so this package can be easily pip installed.

To build locally, clone the repository and run:

$ git submodule init
$ git submodule update
$ pip install .

Example Usage

>>> from ml_dtypes import bfloat16
>>> import numpy as np
>>> np.zeros(4, dtype=bfloat16)
array([0, 0, 0, 0], dtype=bfloat16)

Importing ml_dtypes also registers the data types with numpy, so that they may be referred to by their string name:

>>> np.dtype('bfloat16')
dtype(bfloat16)
>>> np.dtype('float8_e5m2')
dtype(float8_e5m2)

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for google_opensource from gravatar.com google_opensource Avatar for Jake.Vanderplas from gravatar.com Jake.Vanderplas Avatar for phawkins from gravatar.com phawkins

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: Apache Software License ( Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR ...)
  • Author: ml_dtypes authors
  • Requires: Python >=3.7
Classifiers

Release history Release notifications | RSS feed

0.5.0

0.4.1

0.4.0

0.4.0b1 pre-release

0.3.2

0.3.1

0.3.0

0.2.0

0.1.0

0.0.4

0.0.3

0.0.2

0.0.2rc5 pre-release

This version

0.0.2rc4 pre-release

0.0.1

Download files

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

Source Distribution

ml_dtypes-0.0.2rc4.tar.gz (682.9 kB view details)

Uploaded Source

Built Distributions

ml_dtypes-0.0.2rc4-cp311-cp311-win_amd64.whl (135.4 kB view details)

Uploaded CPython 3.11 Windows x86-64

ml_dtypes-0.0.2rc4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (231.0 kB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

ml_dtypes-0.0.2rc4-cp311-cp311-macosx_10_9_universal2.whl (312.0 kB view details)

Uploaded CPython 3.11 macOS 10.9+ universal2 (ARM64, x86-64)

ml_dtypes-0.0.2rc4-cp310-cp310-win_amd64.whl (135.5 kB view details)

Uploaded CPython 3.10 Windows x86-64

ml_dtypes-0.0.2rc4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (231.0 kB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

ml_dtypes-0.0.2rc4-cp310-cp310-macosx_10_9_universal2.whl (312.0 kB view details)

Uploaded CPython 3.10 macOS 10.9+ universal2 (ARM64, x86-64)

ml_dtypes-0.0.2rc4-cp39-cp39-win_amd64.whl (135.4 kB view details)

Uploaded CPython 3.9 Windows x86-64

ml_dtypes-0.0.2rc4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (231.1 kB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

ml_dtypes-0.0.2rc4-cp39-cp39-macosx_10_9_universal2.whl (312.5 kB view details)

Uploaded CPython 3.9 macOS 10.9+ universal2 (ARM64, x86-64)

ml_dtypes-0.0.2rc4-cp38-cp38-win_amd64.whl (134.8 kB view details)

Uploaded CPython 3.8 Windows x86-64

ml_dtypes-0.0.2rc4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (231.0 kB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

ml_dtypes-0.0.2rc4-cp38-cp38-macosx_10_9_universal2.whl (311.9 kB view details)

Uploaded CPython 3.8 macOS 10.9+ universal2 (ARM64, x86-64)

File details

Details for the file ml_dtypes-0.0.2rc4.tar.gz.

File metadata

  • Download URL: ml_dtypes-0.0.2rc4.tar.gz
  • Upload date:
  • Size: 682.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for ml_dtypes-0.0.2rc4.tar.gz
Algorithm Hash digest
SHA256 2cfefe779e06ffaa330ae1d1e2ac1fbf3cef92206d0bd821fe4e651904bd8bed
MD5 f3f02350fdb6fdcdbbd29a552933d4df
BLAKE2b-256 70d62207f173eeeae2adedffddbd46dda2f310f2bae86463f5ca75b6796036ef

See more details on using hashes here.

Provenance

File details

Details for the file ml_dtypes-0.0.2rc4-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for ml_dtypes-0.0.2rc4-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 01529520c5382abb9abfcb5ea0fb57529f9b43bd2b7fc279078de60f5c26f3cf
MD5 6d9fc957d17cac61bff655e1853cd33a
BLAKE2b-256 5337735ea2edf931790c86bfd7d00e7e3d7e5c17727cacef69c093659e9a9aaf

See more details on using hashes here.

Provenance

File details

Details for the file ml_dtypes-0.0.2rc4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ml_dtypes-0.0.2rc4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 212009a3f90e729a6a2852986549482425c3d5e271d9f5892ed5cbe8271bc9df
MD5 2363b9bb56d27c28ef645186caf25394
BLAKE2b-256 625f16ce60b790a3c63215d50222a8b8ebf3772f8e3631076320661f422824d8

See more details on using hashes here.

Provenance

File details

Details for the file ml_dtypes-0.0.2rc4-cp311-cp311-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for ml_dtypes-0.0.2rc4-cp311-cp311-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 b4d1d9863cea3794e7b6c46a9c67c12fef4f357398921e581722b8994f62290b
MD5 2f88771e67bdf6f99fb14a0df6be4ce2
BLAKE2b-256 081cf712baee9c938a4f95ac8ab4f3cbc9aa3c3a4fb00a1d49531764b1d34368

See more details on using hashes here.

Provenance

File details

Details for the file ml_dtypes-0.0.2rc4-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for ml_dtypes-0.0.2rc4-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 9246a5e42dfaa64d015d21d48101f503e63b335ba3f6f51973a3ef9d6bf9361a
MD5 908ba9aba58745ae1c239131a261149a
BLAKE2b-256 23d117dc1bb41836f6e4743e18cd7a4c2ce35ad50edaa8f7fd901bd685669d47

See more details on using hashes here.

Provenance

File details

Details for the file ml_dtypes-0.0.2rc4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ml_dtypes-0.0.2rc4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 517c4496007a200766f625f9a693f67c0e3ad28b79d54ab67f4b6ec5b6b3278c
MD5 ea6734ced0cb178f8cdf46efc361fcd4
BLAKE2b-256 4cb9687ec6525a0616175255a4a43f24c38431aaf0ba3f7c579298dd07ba33dd

See more details on using hashes here.

Provenance

File details

Details for the file ml_dtypes-0.0.2rc4-cp310-cp310-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for ml_dtypes-0.0.2rc4-cp310-cp310-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 d12e05ff0ce313d4141ed49124a09d7a216d21957751adbf08f1054adce13d0a
MD5 2d58f4f49115a59b40b94f7da68cc14f
BLAKE2b-256 910ee2fa1b233205ca51b69154fc95ff381a09872c3653036cf30e607f1f0d2e

See more details on using hashes here.

Provenance

File details

Details for the file ml_dtypes-0.0.2rc4-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for ml_dtypes-0.0.2rc4-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 a7b10caf83c652be1bb6d24f1a87ba69a433d23a98115df9a528d9a117257d7a
MD5 81af23c8bbd180074c8d99c4ff45177b
BLAKE2b-256 449389343aca4e62a4d4c03256d9e167a719e3bfe15f501e88346b95c17b78f9

See more details on using hashes here.

Provenance

File details

Details for the file ml_dtypes-0.0.2rc4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ml_dtypes-0.0.2rc4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c4ef24f64d62a63253a71b9ba848b4a4f15d5147a94db854d7e831fe570d2e64
MD5 4f741f3805d8bc123faa694a1d370722
BLAKE2b-256 7a2df075e12882dd460126ec0318a77f70b1ddcca110b4887a74778b22b0c03c

See more details on using hashes here.

Provenance

File details

Details for the file ml_dtypes-0.0.2rc4-cp39-cp39-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for ml_dtypes-0.0.2rc4-cp39-cp39-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 f99616f3304aaaf02ebe9b6912e4adf16b0e3364f35cb9df592617331744f773
MD5 7a71ebf8bbdc5150639cbb37740b1da1
BLAKE2b-256 861f414656b9a29b094e4cbd1545b8a773e6eee99374188bb3570564d5c2ef05

See more details on using hashes here.

Provenance

File details

Details for the file ml_dtypes-0.0.2rc4-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for ml_dtypes-0.0.2rc4-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 59666a3f94d6831210e8e8ca97fa8948ba099854f3548ffabe52304eddc4c423
MD5 adebfb2008a9feab36bd0ce3353673e9
BLAKE2b-256 574224710448676c0f6861d26bb7dfef273a99e2fc0fe937ea1def0218bb6653

See more details on using hashes here.

Provenance

File details

Details for the file ml_dtypes-0.0.2rc4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ml_dtypes-0.0.2rc4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 299763c225f14e3f0ea9a01d5b40df556406d8be993aa4ea72aa4423347dc6f1
MD5 ace8ced3bfbdb1cdc8f2b46f54e8be9f
BLAKE2b-256 a5f693946b8198bdc8b04b40f4a13f954f2757e4786f7b29b70960e41edba2c6

See more details on using hashes here.

Provenance

File details

Details for the file ml_dtypes-0.0.2rc4-cp38-cp38-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for ml_dtypes-0.0.2rc4-cp38-cp38-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 c2080ad4822d1c139a3bd8385e71f7930c7207b2b56bc69cbc2307efff80045b
MD5 7f6f2a66620673a5221e90a01e12ff50
BLAKE2b-256 6cf291296d0113d393be9496ef025cb1fd283bd2b907abf24619361d36958e59

See more details on using hashes here.

Provenance

Supported by

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