feat!: Lock bazel version

BREAKING CHANGE: Bazel version is now locked to Bazel 3.2.0 and will be
bumped manually from now on. Builds will fail on all other versions
since now bazel will check the version before it compiles.

Documentation on how to install bazel is added as well to support
aarch64 until bazel releases binaries for the platform (which is soon)

Signed-off-by: Naren Dasan <[email protected]>
Signed-off-by: Naren Dasan <[email protected]>

Author: narendasan

Diff for: .bazelversion

@@ -0,0 +1 @@
+3.2.0

Diff for: README.md

@@ -68,6 +68,7 @@ torch.jit.save(trt_ts_module, "trt_torchscript_module.ts")
 
 ### Dependencies
 
+- Bazel 3.2.0
 - Libtorch 1.5.0
 - CUDA 10.2
 - cuDNN 7.6.5
@@ -81,7 +82,24 @@ Releases: https://github.com/NVIDIA/TRTorch/releases
 
 ### Installing Dependencies
 
-You need to start by having CUDA installed on the system, Libtorch will automatically be pulled for you by bazel,
+#### 0. Install Bazel
+
+If you don't have bazel installed, the easiest way is to install bazelisk using the method of you choosing https://github.com/bazelbuild/bazelisk
+
+Otherwise you can use the following instructions to install binaries https://docs.bazel.build/versions/master/install.html
+
+Finally if you need to compile from source (e.g. aarch64 until bazel distributes binaries for the architecture) you can use these instructions
+
+```sh
+export BAZEL_VERSION=<VERSION>
+mkdir bazel
+cd bazel
+curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-dist.zip
+unzip bazel-$BAZEL_VERSION-dist.zip
+bash ./compile.sh
+```
+
+You need to start by having CUDA installed on the system, LibTorch will automatically be pulled for you by bazel,
 then you have two options.
 
 #### 1. Building using cuDNN & TensorRT tarball distributions

Diff for: docs/_sources/tutorials/installation.rst.txt

@@ -48,7 +48,23 @@ Compiling From Source
 Dependencies for Compilation
 ******************************************
 
-TRTorch is built with Bazel, so begin by installing it. https://docs.bazel.build/versions/master/install.html
+TRTorch is built with Bazel, so begin by installing it.
+
+The easiest way is to install bazelisk using the method of you choosing https://github.com/bazelbuild/bazelisk
+
+Otherwise you can use the following instructions to install binaries https://docs.bazel.build/versions/master/install.html
+
+Finally if you need to compile from source (e.g. aarch64 until bazel distributes binaries for the architecture) you can use these instructions
+
+```sh
+export BAZEL_VERSION=3.2.0
+mkdir bazel
+cd bazel
+curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-dist.zip
+unzip bazel-$BAZEL_VERSION-dist.zip
+bash ./compile.sh
+cp output/bazel /usr/local/bin/
+```
 
 You will also need to have CUDA installed on the system (or if running in a container, the system must have
 the CUDA driver installed and the container must have CUDA)

Diff for: docs/searchindex.js

@@ -1 +1 @@
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+<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/class_view_hierarchy.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/classtrtorch_1_1ExtraInfo_1_1DataType.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/classtrtorch_1_1ExtraInfo_1_1DeviceType.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/classtrtorch_1_1ptq_1_1Int8CacheCalibrator.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/classtrtorch_1_1ptq_1_1Int8Calibrator.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/define_macros_8h_1a20c1fbeb21757871c52299dc52351b5f.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/define_macros_8h_1a25ee153c325dfc7466a33cbd5c1ff055.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/define_macros_8h_1a48d6029a45583a06848891cb0e86f7ba.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/define_macros_8h_1a71b02dddfabe869498ad5a88e11c440f.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/define_macros_8h_1a9d31d0569348d109b1b069b972dd143e.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/define_macros_8h_1ae1c56ab8a40af292a9a4964651524d84.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/dir_cpp.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/dir_cpp_api.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/dir_cpp_api_include.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/dir_cpp_api_include_trtorch.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/enum_logging_8h_1a5f612ff2f783ff4fbe89d168f0d817d4.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/file_cpp_api_include_trtorch_logging.h.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/file_cpp_api_include_trtorch_macros.h.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/file_cpp_api_include_trtorch_ptq.h.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/file_cpp_api_include_trtorch_trtorch.h.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/file_view_hierarchy.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/function_logging_8h_1a118d65b179defff7fff279eb9cd126cb.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/function_logging_8h_1a396a688110397538f8b3fb7dfdaf38bb.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/function_logging_8h_1a9b420280bfacc016d7e36a5704021949.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/function_logging_8h_1aa533955a2b908db9e5df5acdfa24715f.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/function_logging_8h_1abc57d473f3af292551dee8b9c78373ad.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/function_logging_8h_1adf5435f0dbb09c0d931a1b851847236b.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/function_logging_8h_1aef44b69c62af7cf2edc8875a9506641a.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/function_trtorch_8h_1a2cf17d43ba9117b3b4d652744b4f0447.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/function_trtorch_8h_1a4422781719d7befedb364cacd91c6247.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/function_trtorch_8h_1a536bba54b70e44554099d23fa3d7e804.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/function_trtorch_8h_1a5f33b142bc2f3f2aaf462270b3ad7e31.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/function_trtorch_8h_1a726f6e7091b6b7be45b5a4275b2ffb10.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/function_trtorch_8h_1ab01696cfe08b6a5293c55935a9713c25.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/function_trtorch_8h_1ae38897d1ca4438227c970029d0f76fb5.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/namespace_trtorch.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/namespace_trtorch__logging.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/namespace_trtorch__ptq.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/program_listing_file_cpp_api_include_trtorch_logging.h.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/program_listing_file_cpp_api_include_trtorch_macros.h.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/program_listing_file_cpp_api_include_trtorch_ptq.h.html</loc></url><url><loc>https://nvidia.github.io/TRTorch/_cpp_api/program_listing_file_cpp_api_include_trtorch_trtorch.h.html</loc></url><url><loc>https:/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Diff for: docs/sitemap.xml

@@ -455,32 +455,32 @@
            <ul class="md-nav__list">
             <li class="md-nav__item">
              <a class="md-nav__link" href="../contributors/useful_links.html#tensorrt-available-layers-and-expected-dimensions">
-              TensorRT Available Layers and Expected Dimensions:
+              TensorRT Available Layers and Expected Dimensions
              </a>
             </li>
             <li class="md-nav__item">
              <a class="md-nav__link" href="../contributors/useful_links.html#tensorrt-c-documentation">
-              TensorRT C++ Documentation:
+              TensorRT C++ Documentation
              </a>
             </li>
             <li class="md-nav__item">
              <a class="md-nav__link" href="../contributors/useful_links.html#tensorrt-python-documentation-sometimes-easier-to-read">
-              TensorRT Python Documentation (Sometimes easier to read):
+              TensorRT Python Documentation (Sometimes easier to read)
              </a>
             </li>
             <li class="md-nav__item">
              <a class="md-nav__link" href="../contributors/useful_links.html#pytorch-functional-api">
-              PyTorch Functional API:
+              PyTorch Functional API
              </a>
             </li>
             <li class="md-nav__item">
              <a class="md-nav__link" href="../contributors/useful_links.html#pytorch-native-ops">
-              PyTorch native_ops:
+              PyTorch native_ops
              </a>
             </li>
             <li class="md-nav__item">
              <a class="md-nav__link" href="../contributors/useful_links.html#pytorch-ir-documentation">
-              PyTorch IR Documentation:
+              PyTorch IR Documentation
              </a>
             </li>
            </ul>
@@ -751,10 +751,83 @@ <h2 id="dependencies-for-compilation">
        </h2>
        <p>
         TRTorch is built with Bazel, so begin by installing it.
+       </p>
+       <p>
+        The easiest way is to install bazelisk using the method of you choosing
+        <a class="reference external" href="https://github.com/bazelbuild/bazelisk">
+         https://github.com/bazelbuild/bazelisk
+        </a>
+       </p>
+       <p>
+        Otherwise you can use the following instructions to install binaries
         <a class="reference external" href="https://docs.bazel.build/versions/master/install.html">
          https://docs.bazel.build/versions/master/install.html
         </a>
        </p>
+       <p>
+        Finally if you need to compile from source (e.g. aarch64 until bazel distributes binaries for the architecture) you can use these instructions
+       </p>
+       <p>
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          `sh
+         </span>
+         <span class="pre">
+          export
+         </span>
+         <span class="pre">
+          BAZEL_VERSION=3.2.0
+         </span>
+         <span class="pre">
+          mkdir
+         </span>
+         <span class="pre">
+          bazel
+         </span>
+         <span class="pre">
+          cd
+         </span>
+         <span class="pre">
+          bazel
+         </span>
+         <span class="pre">
+          curl
+         </span>
+         <span class="pre">
+          -fSsL
+         </span>
+         <span class="pre">
+          -O
+         </span>
+         <span class="pre">
+          https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-dist.zip
+         </span>
+         <span class="pre">
+          unzip
+         </span>
+         <span class="pre">
+          bazel-$BAZEL_VERSION-dist.zip
+         </span>
+         <span class="pre">
+          bash
+         </span>
+         <span class="pre">
+          ./compile.sh
+         </span>
+         <span class="pre">
+          cp
+         </span>
+         <span class="pre">
+          output/bazel
+         </span>
+         <span class="pre">
+          /usr/local/bin/
+         </span>
+         <span class="pre">
+          `
+         </span>
+        </code>
+       </p>
        <p>
         You will also need to have CUDA installed on the system (or if running in a container, the system must have
 the CUDA driver installed and the container must have CUDA)

Diff for: docs/tutorials/installation.html

@@ -48,7 +48,23 @@ Compiling From Source
 Dependencies for Compilation
 ******************************************
 
-TRTorch is built with Bazel, so begin by installing it. https://docs.bazel.build/versions/master/install.html
+TRTorch is built with Bazel, so begin by installing it.
+
+The easiest way is to install bazelisk using the method of you choosing https://github.com/bazelbuild/bazelisk
+
+Otherwise you can use the following instructions to install binaries https://docs.bazel.build/versions/master/install.html
+
+Finally if you need to compile from source (e.g. aarch64 until bazel distributes binaries for the architecture) you can use these instructions
+
+```sh
+export BAZEL_VERSION=3.2.0
+mkdir bazel
+cd bazel
+curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-dist.zip
+unzip bazel-$BAZEL_VERSION-dist.zip
+bash ./compile.sh
+cp output/bazel /usr/local/bin/
+```
 
 You will also need to have CUDA installed on the system (or if running in a container, the system must have
 the CUDA driver installed and the container must have CUDA)