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* Make ORT as Pytorch JIT backend LORT likely doesn't work with aten fallback so we only test LORT in its own CI. * Revert changes to enable external CUDA allocator. Will add it later. Revert "Revert changes to enable external CUDA allocator. Will add it later." This reverts commit d5487f2e193014c805505afae8fb577c53667658. Fix external allocator * Relax tolerance and remove commented code * Print more information in CI * Fix pointer * Address comments. 1. Reuse ORT-eager mode's environment. 2. Remove unused ctor. * Use Pytorch master branch as all PRs are merged Fix * Refine based on cpplint feedbacks * Revert changes to allow custom CUDA allocator in public APIs * Use torch.testing.assert_close * Use unittest framework * Switch docker repo * Rename *.cpp to *.cc * Address comments * Add comment * Use same pipeline file for eager and lort pipelines * Address comments * Add yaml comment * Fix cmake files * Address comments * Rename flags, remove printing code, remove dead comment |
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| .. | ||
| inference | ||
| scripts | ||
| Dockerfile.arm_yocto | ||
| Dockerfile.manylinux2014_aten_cpu | ||
| Dockerfile.manylinux2014_cpu | ||
| Dockerfile.manylinux2014_cuda11 | ||
| Dockerfile.manylinux2014_cuda11_4_tensorrt8_0 | ||
| Dockerfile.manylinux2014_cuda11_4_tensorrt8_2 | ||
| Dockerfile.manylinux2014_cuda11_6_tensorrt8_4 | ||
| Dockerfile.manylinux2014_eager_cpu | ||
| Dockerfile.manylinux2014_lort_cpu | ||
| Dockerfile.manylinux2014_rocm5.1.1 | ||
| Dockerfile.manylinux2014_rocm5.2 | ||
| Dockerfile.manylinux2014_training_cuda11_3 | ||
| Dockerfile.manylinux2014_training_cuda11_5 | ||
| Dockerfile.manylinux_2_27_cpu | ||
| Dockerfile.ubuntu | ||
| Dockerfile.ubuntu_cuda11_4_tensorrt8_0 | ||
| Dockerfile.ubuntu_cuda11_4_tensorrt8_2 | ||
| Dockerfile.ubuntu_cuda11_6_tensorrt8_4 | ||
| Dockerfile.ubuntu_for_arm | ||
| Dockerfile.ubuntu_gpu_training | ||
| Dockerfile.ubuntu_openvino | ||
| Dockerfile.ubuntu_tensorrt | ||
| Dockerfile_manylinux2014_openvino_multipython | ||
| manylinux-entrypoint | ||
| manylinux.patch | ||