1.2.3

Release Notes

1. Reverting shape checks for Mask schema to maintain backward compatibility.

1.2.2

Release Notes

1. Hotfix for a bug that resulted in incorrect slicing of TensorView.

1.2.1

Release Notes

1. Dataset copying has been added allowing you to copy your own and other users' datasets easily. Datasets can be copied across gcs, s3, aws, local storage and hub storage. #454 (@AbhinavTuli)
2. Many improvements to the benchmarks #508 #512 #531 #545 #550 (@haiyangdeperci @DebadityaPal)
3. Development Roadmap added #511 (@mynameisvinn)
4. Improved message for Hub transforms by displaying shard size #523 (@DebadityaPal)
5. All windows have now been fixed. #528 (@AbhinavTuli)
6. Hub dataset filtering has been overhauled and a section has been added for the same in the documentation #539 (@AbhinavTuli)
7. to_tensorflow issues with Datasets containing Sequences (such as coco) have been fixed #540 (@AbhinavTuli)
8. Adds get_label parameter to .compute() and .numpy(), to directly retrieve string label from ClassLabel #489 (@DebadityaPal)
9. Tutorial added for using Hub with Hugging Face transformers #536 (@DebadityaPal)
10. Some unit tests have now been parameterized to cover multiple datatypes #527 (@drewpotter)
11. From directory function has been implemented to directly ingest categorical image data #459 (@sparkingdark)
12. Example use case added for creating a Hub dataset for Deep Learning prediction of crop yield #559 (@MargauxMasson)
13. MPL Headers have been added to source files #494 (@KrishnaChaitanya1)

1.2.0

Release Notes

1. Adds support for dataset filtering (#460)(@AbhinavTuli)
2. Greatly improves to_tensorflow performance (#481) (@AbhinavTuli)
3. Benchmarks added for Hub 1.x (#486) (@benchislett)
4. Fixes a bug that caused issues on windows machines (#472)(@FayazRahman)
5. Fixes a bug that caused issues with TF 2.4.0 (#478) (@DebadityaPal)
6. Fixes docker build issue (#463) (@Darkborderman)
7. Added Chinese readme (#458) (@EYH0602)
8. Better automatic determination of Dataset mode depending on permissions (#466)(@edogrigqv2)
9. CoLA dataset uploaded to Hub, upload script added to examples (#487)(@mynameisvinn)
10. Fixes a bug with dataset slicing (#480) (@AbhinavTuli)
11. Adds support for custom s3 endpoints (including MinIO) (#482) (@AbhinavTuli)
12. Adds the ability to set a name to a dataset so it appears better on the visualizer (#468) (@AbhinavTuli)

1.1.3

Fixes an issue in to_pytorch when using a dataset that the user doesn't own.

1.1.0

Release Notes

• Custom s3storage with 5-10x faster than S3FS
• Faster pytorch dataset with current chunk logic
• Fixed caching with in-memory per process without LMDB
• Better Exception handling for loading a dataset, shape and type checks, casting
• Added examples, tutorials, and better GitHub issue handling
• Add the opportunity to fill in additional information about the dataset such as description, license, citation
• Native support with .compute() in the middle for nested tensors

Contributors include. @edogrigqv2 @AbhinavTuli @mynameisvinn @Anselmoo @sparkingdark @sanchitvj @Atom-101

Release v1.0.7

Private dataset support
Improved error handling and exceptions
Test coverage reached 73%->80%
Various bug fixes
Transform speedup ~2x, hence from_x convertors work faster

version 1.0.6

Fixes some issues with segmentation and RAM issues in transform

Version 1.0.6

Fixes some issues with segmentation and RAM issues in transform