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Changelog
Neo-AI-DLR is a common runtime for machine learning models compiled by AWS SageMaker Neo, TVM, or TreeLite.
Release 1.10.0 can be used to run models compiled using the release-1.10.0 branch of neo-ai/TVM.
This release implements support for TF2 frontend parser, support for TF2 models was the major driving factor behind this release.
It also fixes bugs for TRT memory leak, TRT performance, weights doubled after TRT compilation, and also fix for RelayVM fails on Windows.
This release upgrades Tre...
Release 1.9.0 can be used to run models compiled using the release-1.9.0 branch of neo-ai/TVM.
This Release fixes bug for CUDA NMS implementation affecting MXNet models, and fix for RelayVM for 32 bit platforms.
It also adds support for TF2.x.
Pre-built wheels can be installed via pip install link-to-wheel. If you don't see your platform in the table, see Installing DLR for instructions...
Release 1.8.0 can be used to run models compiled using the release-1.8.0 branch of neo-ai/TVM.
This Release provides better performance on PyTorch object detection and TensorFlow object detection models on GPU devices.
Pre-built wheels can be installed via pip install link-to-wheel. If you don't see your platform in the table, see Installing DLR for instructions to build from source....
Release 1.7.0 can be used to run models compiled using the release-1.7.0 branch of neo-ai/TVM.
This Release supports PyTorch object detection models on GPU.
Loading of Multiple DLR is available.
It also supports YOLO v5 in PyTorch.
Pre-built wheels can be installed via pip install link-to-wheel. If you don't see your platform in the table, see Installing DLR for instructions to build f...
Release 1.6.0 can be used to run models compiled using the release-1.6.0 branch of neo-ai/TVM.
It enables specifying individual TVM artifacts to CreateDLRModel API and add APIs to use DLTensors for SetInput/GetOutput. It provides missing DLR C++ APIs to GraphRuntime & VMRuntime. Version 1.6.0 skips loading tvm artifacts from disk and allows passing in data directly for graph, params and relay_e...