tensorflow-metal

TensorFlow acceleration for Mac GPUs.

These details have been verified by PyPI

Maintainers

dingshuhan jhavukainen kulinseth skotapati

Project description

Accelerate training of machine learning models with TensorFlow right on your Mac. Install the base tensorflow (tensorflow-macos which is using TensorFlow v2.5) and the tensorflow-metal PluggableDevice to accelerate training with Metal on Mac GPUs.

You can learn more about TensorFlow PluggableDevices [here](https://github.com/tensorflow/tensorflow/releases/tag/v2.5.0).

Project details

These details have been verified by PyPI

Maintainers

dingshuhan jhavukainen kulinseth skotapati

Release history Release notifications | RSS feed

1.1.0

Sep 29, 2023

1.0.1

Jun 13, 2023

1.0.0

Jun 7, 2023

0.8.0

Mar 30, 2023

0.7.1

Feb 7, 2023

0.7.0

Dec 9, 2022

0.6.0

Sep 15, 2022

0.5.1

Aug 17, 2022

This version

0.5.0

May 19, 2022

0.4.0

Feb 17, 2022

0.3.0

Dec 2, 2021

0.2.0

Sep 30, 2021

0.1.2

Aug 3, 2021

0.1.1

Jun 7, 2021

0.1.0

Jun 7, 2021

Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

tensorflow_metal-0.5.0-cp310-cp310-macosx_11_0_x86_64.whl (1.5 MB view hashes)

Uploaded May 25, 2022 CPython 3.10 macOS 11.0+ x86-64

tensorflow_metal-0.5.0-cp310-cp310-macosx_11_0_arm64.whl (1.4 MB view hashes)

Uploaded May 23, 2022 CPython 3.10 macOS 11.0+ ARM64

tensorflow_metal-0.5.0-cp39-cp39-macosx_11_0_x86_64.whl (1.5 MB view hashes)

Uploaded May 19, 2022 CPython 3.9 macOS 11.0+ x86-64

tensorflow_metal-0.5.0-cp39-cp39-macosx_11_0_arm64.whl (1.4 MB view hashes)

Uploaded May 19, 2022 CPython 3.9 macOS 11.0+ ARM64

tensorflow_metal-0.5.0-cp38-cp38-macosx_11_0_x86_64.whl (1.5 MB view hashes)

Uploaded May 20, 2022 CPython 3.8 macOS 11.0+ x86-64

tensorflow_metal-0.5.0-cp38-cp38-macosx_11_0_arm64.whl (1.4 MB view hashes)

Uploaded May 19, 2022 CPython 3.8 macOS 11.0+ ARM64

Hashes for tensorflow_metal-0.5.0-cp310-cp310-macosx_11_0_x86_64.whl

Hashes for tensorflow_metal-0.5.0-cp310-cp310-macosx_11_0_x86_64.whl
Algorithm	Hash digest
SHA256	`058da93d9e36f81e0c38137fa0ee53509115f099c40da16603ded3ee429e41fc`
MD5	`1913194c8b0c1e8a0bb69dfb93f7675a`
BLAKE2b-256	`cd7fbbcac864b7ecb164fe8f7e10c29dd2705b165ad82fa182a72f22565e20a2`

Hashes for tensorflow_metal-0.5.0-cp310-cp310-macosx_11_0_arm64.whl

Hashes for tensorflow_metal-0.5.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm	Hash digest
SHA256	`668a452276a9b7536ec67172e4d4812496136c32cc2b13d663bcc9d7f83de26e`
MD5	`772d788268c1e59e9341cd09943532be`
BLAKE2b-256	`eef80635e6f43df015cbd397b504259057bc87a43570d6bcf4e8476eba7bf9a0`

Hashes for tensorflow_metal-0.5.0-cp39-cp39-macosx_11_0_x86_64.whl

Hashes for tensorflow_metal-0.5.0-cp39-cp39-macosx_11_0_x86_64.whl
Algorithm	Hash digest
SHA256	`957e2e03cf5d96c80d8944934a887d15e2f739e3e146cf515afdce207860c94d`
MD5	`ea406d9ae53ca9b91fdf8bb30617ea34`
BLAKE2b-256	`1dd6fd78d61cf5f40d65cf1bff55fcdb96d9a262fa355225a29789ec37221f17`

Hashes for tensorflow_metal-0.5.0-cp39-cp39-macosx_11_0_arm64.whl

Hashes for tensorflow_metal-0.5.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm	Hash digest
SHA256	`757306e1d7298bcf36f927c5f52a3eb490a6b1a474361b7596735ad71de7a422`
MD5	`be3722d4c561297f9fdb99af3e9c3efb`
BLAKE2b-256	`4155ebd1d260c13e7cd7cc3dce746535e9f43cc13f097917f726868759b0c973`

Hashes for tensorflow_metal-0.5.0-cp38-cp38-macosx_11_0_x86_64.whl

Hashes for tensorflow_metal-0.5.0-cp38-cp38-macosx_11_0_x86_64.whl
Algorithm	Hash digest
SHA256	`df24caa771506e4303398075666e2b27cc8eb74759aa624a7f443a9e380fb0b2`
MD5	`24a2b60773fe214d4cac45f82a11a4c9`
BLAKE2b-256	`9da92dbdbfab8f2c11a5c6cddd2f63fc9c33adec09fdb8796c772727db26af39`

Hashes for tensorflow_metal-0.5.0-cp38-cp38-macosx_11_0_arm64.whl

Hashes for tensorflow_metal-0.5.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm	Hash digest
SHA256	`14fc9ccb897e0c403b45c6c438ad4001b652df9e6760f6516677ce36a33fe978`
MD5	`ab32f8b0b2309267b8f0c81eeeeb3998`
BLAKE2b-256	`ede2aaa7e33186e0ed91f988b99d29bde55f159c86fc929630aab7970dabb297`