Skip to main content

Griddly Python Libraries

Project description

Griddly Python

This module contains all you need to run the Griddly AI research environment using python.

Installation

Currently the library supports any python > 3.6. It is recommended to use a conda environment:

conda create --name griddly_env python=3.7
conda activate griddly_env
pip install griddly

Usage

Full documentation can be found here: https://griddly.readthedocs.org

Project details


Release history Release notifications | RSS feed

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

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

griddly-1.3.9-cp39-cp39-win_amd64.whl (6.8 MB view details)

Uploaded CPython 3.9Windows x86-64

griddly-1.3.9-cp39-cp39-manylinux2014_x86_64.whl (7.2 MB view details)

Uploaded CPython 3.9

griddly-1.3.9-cp39-cp39-macosx_10_15_x86_64.whl (7.0 MB view details)

Uploaded CPython 3.9macOS 10.15+ x86-64

griddly-1.3.9-cp38-cp38-win_amd64.whl (6.8 MB view details)

Uploaded CPython 3.8Windows x86-64

griddly-1.3.9-cp38-cp38-manylinux2014_x86_64.whl (7.2 MB view details)

Uploaded CPython 3.8

griddly-1.3.9-cp38-cp38-macosx_10_15_x86_64.whl (7.0 MB view details)

Uploaded CPython 3.8macOS 10.15+ x86-64

griddly-1.3.9-cp37-cp37m-win_amd64.whl (6.8 MB view details)

Uploaded CPython 3.7mWindows x86-64

griddly-1.3.9-cp37-cp37m-manylinux2014_x86_64.whl (7.2 MB view details)

Uploaded CPython 3.7m

griddly-1.3.9-cp37-cp37m-macosx_10_15_x86_64.whl (7.0 MB view details)

Uploaded CPython 3.7mmacOS 10.15+ x86-64

File details

Details for the file griddly-1.3.9-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: griddly-1.3.9-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 6.8 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.13

File hashes

Hashes for griddly-1.3.9-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 c466d64500d76c32ac81584fc84c4494412c2906c13c0a609846f90bb03f0428
MD5 3702fb92e8931fb74d0827a800046959
BLAKE2b-256 7a4969179187bc4c54858acc620fe7184bc8b665ed0c9ce40134c2ae196427ba

See more details on using hashes here.

File details

Details for the file griddly-1.3.9-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for griddly-1.3.9-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a13e7dfc257a1df6e6a00d86893ee81a30eab438c16bfa0563311f63756d8c35
MD5 0753a2f2e00ad6d78456f76889d3f1f4
BLAKE2b-256 53fc12401f64c3355748e1ab40f7331b8131bc8392c4e79ef66636dc32d69d87

See more details on using hashes here.

File details

Details for the file griddly-1.3.9-cp39-cp39-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for griddly-1.3.9-cp39-cp39-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 4ee2fdb7362ed37bbfbf5b82cb516b250f030e1cd94b405a6e40a09a502ade83
MD5 b17c8685d8ed932f76b70bbb08b60e71
BLAKE2b-256 1e568582cbdc0f8cae80c14f6fcf6a9558bf77e0ec59ad5853ae244171ff10e0

See more details on using hashes here.

File details

Details for the file griddly-1.3.9-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: griddly-1.3.9-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 6.8 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.10

File hashes

Hashes for griddly-1.3.9-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 0758a6aaada1d44a38b712e3b0b0bc755f2debc48ce319bf2c914901c5377b85
MD5 8f993168e6014dc3bc990a772b49bacd
BLAKE2b-256 28fbd65a193d4cc3c29963846839d67abf0eda8962df02c30c9f783c1995cba8

See more details on using hashes here.

File details

Details for the file griddly-1.3.9-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for griddly-1.3.9-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3def83b78acc580b211b1370c44d471213c70d74451987a9ddfaf0542235f395
MD5 653ebae6ee446d7654b435b4c6719112
BLAKE2b-256 eb747df0692acec8a6f439a0a641993388694139a3b41d03128d9c6a5a432ee8

See more details on using hashes here.

File details

Details for the file griddly-1.3.9-cp38-cp38-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for griddly-1.3.9-cp38-cp38-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 9b466d4e981eedcfc455c025bb02969d9da0c97525e2dd1e0507c1ab8869f999
MD5 1a20fbc73c7248949ec40afea530f759
BLAKE2b-256 e4065ad4959fa055bdee8ab59a60204ebc2ef596b049f61b3e0571adb6f37f7f

See more details on using hashes here.

File details

Details for the file griddly-1.3.9-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: griddly-1.3.9-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 6.8 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.7.9

File hashes

Hashes for griddly-1.3.9-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 4680f548e36c0ec386cf382652d1a5013c62219de1a25b16c34cd93ef38f59bc
MD5 624daf2e1b01dd12d964e6ef41a66be0
BLAKE2b-256 a5c1230b7941ed797f4468f6929e684135600ae4d10b178bdb5d630b64812198

See more details on using hashes here.

File details

Details for the file griddly-1.3.9-cp37-cp37m-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for griddly-1.3.9-cp37-cp37m-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c9deed520a933283a90f2150fd9399b8a64e7d623911c0d2dd04ac67b72b8e7f
MD5 4fa39f8beae92f92a469867512be04af
BLAKE2b-256 84edefb2260636842a5b5bbdad73c446e02a92230b94a722635f91e19f8377b7

See more details on using hashes here.

File details

Details for the file griddly-1.3.9-cp37-cp37m-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for griddly-1.3.9-cp37-cp37m-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 6b5634f63f1df60ff28bc8594f1be5f3bc782b011feaf86897c10ba25ff2a36e
MD5 3fbdee67536a0ddf6c61d30ab168c9e7
BLAKE2b-256 cb995ad647dd08e4fc50bfe64fde4ad9601de7e181c002ee0f9a67b68198d465

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