v0.0.25

What's Changed

• Make asset requirements for remote storage optional #167
• tf_serving_fixture expects a new parameter: version of the tensorflow/serving image (used to be pinned to v2.4.0) #162

This version contains breaking changes:

• if you are using a remote storage, check this part of the documentation to understand the new optional dependencies.

Full Changelog: v0.0.24...v0.0.25

v0.0.24

Bump version: 0.0.23 → 0.0.24

v0.0.21

What's Changed

• Support python 3.10 by @tbascoul in #130

New Contributors

• @tbascoul made their first contribution in #130

Full Changelog: v0.0.20...v0.0.21

v0.0.20

What's Changed

• Support usage of inheritance for model definitions by @ldeflandre in #128
• Intuitive abstract support by stopping support of no CONFIGURATIONS by @ldeflandre in #129

Full Changelog: v0.0.19...v0.0.20

v0.0.19

What's Changed

• configurations: allow models without CONFIGURATIONS dict by @victorbenichoux in #123
• Raise ModelsNotFound exception when Model is not found by @tgenin in #125
• Enable read-only filesystem when assets do not need to be downloaded by @ldeflandre in #127

New Contributors

• @ldeflandre made their first contribution in #127

Full Changelog: v0.0.18...v0.0.19

v0.0.18

This release includes:

• a fix to be able to install and use modelkit on apple silicon #119
• a new storage driver to support azure blob storage

v0.0.17

Bump version: 0.0.16 → 0.0.17

v0.0.16

Bump version: 0.0.15 → 0.0.16

v0.0.11

• validation of downloads in ASSETS_DIR with .SUCCESS files
• MODELKIT_STORAGE_FORCE_DOWNLOAD to force download of remote assets
• MODELKIT_STORAGE_TIMEOUT to control timeout on storage download lock
• Improved debug level logs when fetching assets

Second release

This is quite a major update, with numerous breaking changes.

• separated sync and async Models for clarity (#10)
• reworked batching predict logic: predict no longer automatically switches between single items and batches. Model.predict takes single items, predict_batch only accepts lists of items, and predict_gen knows how to deal with generators (#15, #16, #19, #20)
• caching with native python caches now available (#22, #23)
• documentation improvements