Releases: nebuly-ai/optimate
Releases · nebuly-ai/optimate
v0.2.1
nebullvm 0.2.1 Release Notes
The nebullvm 0.2.1 is minor release fixing some bugs and supporting optimization directly on ONNX models.
New Features
- ONNX interface for model optimization
Bug fixed
- Fix bug in tensorRT
Contributors
- Diego Fiori (@morgoth95)
v0.2.0
nebullvm 0.2.0 Release Notes
The nebullvm 0.2.0 is major release implementing new important features and fixing some bugs.
New Features
- Support for dynamic shapes for both the PyTorch and TensorFlow interfaces
- Support for Transformer models built using the HuggingFace framework
- Add ONNXRuntime to the supported backends for optimized models
- New README, updated with benchmarks on SOTA models for both NLP and Computer Vision
Bug fixed
- Fix error in the tensorflow API preventing the usage of the
optimize_tf_model
function
Contributors
- Diego Fiori (@morgoth95)
- Emile Courthoud (@emilecourthoud)
v0.1.2
nebullvm 0.1.2 Release Notes
The nebullvm 0.1.2 is maintenance release fixing few bugs and implementing new features.
New Features
- Support for the TorchScript API when optimising with ApacheTVM compiler.
Bug fixed
- The learners optimised with OpenVino now do not raise
KeyError
s at prediction time anymore. - The learners optimised with ApacheTVM can be saved and loaded multiple times. Previously, trying to save a loaded model ended up in raising an error.
- Fix bug in the auto-installer feature due to incompatibilities between Tensorflow 2.8 and OpenVino
- Modify the behaviour of
MultiCompilerOptimizer
avoiding errors due to the pickling of C-related files.
Contributors
- Diego Fiori (@morgoth95)
v0.1.1
nebullvm 0.1.1 Release Notes
Official Alpha release of the nebullvm
library.
The all-in-one library for deep learning compilers.
Main features
The main release contains:
- wheels for installing with pip
- auto-installation feature for supported compilers
- support for OpenVINO, TensorRT and ApacheTVM
- support for model built in Tensorflow and PyTorch
- Optimised model API identical to the one of the input model
Contributors
A total of 3 people contributed to this release.
- Diego Fiori (@morgoth95)
- Emile Courthoud (@emilecourthoud)
- Francesco Signorato (@FrancescoSignorato)