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Releases: nebuly-ai/optimate

v0.2.1

26 Apr 22:07
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v0.2.1 Pre-release
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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

03 Apr 09:39
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v0.2.0 Pre-release
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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

v0.1.2

01 Mar 17:09
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v0.1.2 Pre-release
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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 KeyErrors 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 MultiCompilerOptimizeravoiding errors due to the pickling of C-related files.

Contributors

  • Diego Fiori (@morgoth95)

v0.1.1

28 Feb 09:32
0d2b215
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v0.1.1 Pre-release
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nebullvm 0.1.1 Release Notes

Official Alpha release of the nebullvmlibrary.
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)