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chore: Bumping default dep version on x86 for PyTorch to 1.9.1 #644

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Merged
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Sep 29, 2021
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chore: Bumping default dep version on x86 for PyTorch to 1.9.1 #644

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -78,7 +78,7 @@ torch.jit.save(trt_ts_module, "trt_torchscript_module.ts")
These are the following dependencies used to verify the testcases. TRTorch can work with other versions, but the tests are not guaranteed to pass.

- Bazel 4.0.0
- Libtorch 1.9.0 (built with CUDA 11.1)
- Libtorch 1.9.1 (built with CUDA 11.1)
- CUDA 11.1 (10.2 on Jetson)
- cuDNN 8.2
- TensorRT 8.0.1.6
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Looks like you need to rebase ? I replaced this with TensorRT 8.0.3.4 (TensorRT 8.0.1.6 on Jetson)

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I think it should merge fine

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8 changes: 4 additions & 4 deletions WORKSPACE
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Expand Up @@ -50,17 +50,17 @@ new_local_repository(
http_archive(
name = "libtorch",
build_file = "@//third_party/libtorch:BUILD",
sha256 = "edc12091193ba772db77a6ec14e05cef6da881288fca0dfc89a031f631601f60",
sha256 = "db57b1023fb33768286a98ba22c44cfe03d6ed158bc2dc0ca1d4928ee5f19f60",
strip_prefix = "libtorch",
urls = ["https://download.pytorch.org/libtorch/cu111/libtorch-cxx11-abi-shared-with-deps-1.9.0%2Bcu111.zip"],
urls = ["https://download.pytorch.org/libtorch/cu111/libtorch-cxx11-abi-shared-with-deps-1.9.1%2Bcu111.zip"],
)

http_archive(
name = "libtorch_pre_cxx11_abi",
build_file = "@//third_party/libtorch:BUILD",
sha256 = "af9435fa4b44bb395c1a7645391c00228a72af4305f43a61e9300c0abdbe0819",
sha256 = "5563ca53b2b5342f1ab7eef9baf308f197673663ad5b1458a031c46dd802f413",
strip_prefix = "libtorch",
urls = ["https://download.pytorch.org/libtorch/cu111/libtorch-shared-with-deps-1.9.0%2Bcu111.zip"],
urls = ["https://download.pytorch.org/libtorch/cu111/libtorch-shared-with-deps-1.9.1%2Bcu111.zip"],
)

# Download these tarballs manually from the NVIDIA website
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8 changes: 4 additions & 4 deletions docker/WORKSPACE.docs
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Expand Up @@ -52,17 +52,17 @@ new_local_repository(
http_archive(
name = "libtorch",
build_file = "@//third_party/libtorch:BUILD",
sha256 = "edc12091193ba772db77a6ec14e05cef6da881288fca0dfc89a031f631601f60",
sha256 = "db57b1023fb33768286a98ba22c44cfe03d6ed158bc2dc0ca1d4928ee5f19f60",
strip_prefix = "libtorch",
urls = ["https://download.pytorch.org/libtorch/cu111/libtorch-cxx11-abi-shared-with-deps-1.9.0%2Bcu111.zip"],
urls = ["https://download.pytorch.org/libtorch/cu111/libtorch-cxx11-abi-shared-with-deps-1.9.1%2Bcu111.zip"],
)

http_archive(
name = "libtorch_pre_cxx11_abi",
build_file = "@//third_party/libtorch:BUILD",
sha256 = "af9435fa4b44bb395c1a7645391c00228a72af4305f43a61e9300c0abdbe0819",
sha256 = "5563ca53b2b5342f1ab7eef9baf308f197673663ad5b1458a031c46dd802f413",
strip_prefix = "libtorch",
urls = ["https://download.pytorch.org/libtorch/cu111/libtorch-shared-with-deps-1.9.0%2Bcu111.zip"],
urls = ["https://download.pytorch.org/libtorch/cu111/libtorch-shared-with-deps-1.9.1%2Bcu111.zip"],
)

####################################################################################
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4 changes: 2 additions & 2 deletions docsrc/tutorials/installation.rst
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Expand Up @@ -66,7 +66,7 @@ the CUDA driver installed and the container must have CUDA)

The correct LibTorch version will be pulled down for you by bazel.

NOTE: For best compatability with official PyTorch, use torch==1.9.0+cuda111, TensorRT 7.2 and cuDNN 8.1 for CUDA 11.1 however TRTorch itself supports
NOTE: For best compatability with official PyTorch, use torch==1.9.1+cuda111, TensorRT 8.0 and cuDNN 8.2 for CUDA 11.1 however TRTorch itself supports
TensorRT and cuDNN for CUDA versions other than 11.1 for usecases such as using NVIDIA compiled distributions of PyTorch that use other versions of CUDA
e.g. aarch64 or custom compiled version of PyTorch.

Expand Down Expand Up @@ -327,7 +327,7 @@ use that library, set the paths to the same path but when you compile make sure
Compile C++ Library and Compiler CLI
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

NOTE: Due to shifting dependency locations between Jetpack 4.5 and 4.6 there is a now a flag to inform bazel of the Jetpack version
NOTE: Due to shifting dependency locations between Jetpack 4.5 and 4.6 there is a now a flag to inform bazel of the Jetpack version

.. code-block:: shell

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2 changes: 1 addition & 1 deletion examples/int8/training/vgg16/requirements.txt
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@@ -1,3 +1,3 @@
torch>=1.9.0
torch>=1.9.1
tensorboard>=1.14.0
pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com
2 changes: 1 addition & 1 deletion examples/trtorchrt_example/README.md
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Expand Up @@ -23,7 +23,7 @@ The main goal is to use TRTorch runtime library `libtrtorchrt.so`, a lightweight
cd examples/trtorchrt_example/deps
// Download latest TRTorch release tar file (libtrtorch.tar.gz) from https://github.com/NVIDIA/TRTorch/releases
tar -xvzf libtrtorch.tar.gz
unzip libtorch-cxx11-abi-shared-with-deps-1.9.0+cu111.zip
unzip libtorch-cxx11-abi-shared-with-deps-[PYTORCH_VERSION].zip
```

> If cuDNN and TensorRT are not installed on your system / in your LD_LIBRARY_PATH then do the following as well
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6 changes: 3 additions & 3 deletions py/requirements.txt
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@@ -1,3 +1,3 @@
-f https://download.pytorch.org/whl/torch_stable.html
torch==1.9.0+cu111
pybind11==2.6.2
-f https://download.pytorch.org/whl/torch_stable.html
torch==1.9.1+cu111
pybind11==2.6.2
2 changes: 1 addition & 1 deletion tests/modules/requirements.txt
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@@ -1,3 +1,3 @@
-f https://download.pytorch.org/whl/torch_stable.html
timm==v0.4.12
torch==1.9.0+cu111
torch==1.9.1+cu111
2 changes: 1 addition & 1 deletion tests/py/requirements.txt
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@@ -1,2 +1,2 @@
torchvision==0.10.0+cu111
torchvision==0.10.1+cu111
-f https://download.pytorch.org/whl/torch_stable.html