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‎.github/workflows/blossom-ci.yml

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# Copyright (c) 2022, NVIDIA CORPORATION.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# A workflow to trigger ci on hybrid infra (github + self hosted runner)
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name: Blossom-CI
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on:
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issue_comment:
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types: [created]
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workflow_dispatch:
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inputs:
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platform:
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description: 'runs-on argument'
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required: false
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args:
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description: 'argument'
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required: false
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jobs:
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Get-Authorization:
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runs-on: blossom
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outputs:
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AUTHORIZED: ${{ steps.check-authorization.outputs.defined }}
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steps:
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- id: check-authorization
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env:
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AUTHORIZED_USERS: ${{ secrets.AUTHORIZED_USERS }}
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if: |
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contains( ${{ env.AUTHORIZED_USERS }}, format('{0},', github.actor)) &&
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github.event.comment.body == '/nvidia-ci'
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run: echo "::set-output name=defined::true"
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Authorization:
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needs: [Get-Authorization]
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name: Authorization
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runs-on: blossom
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env:
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AUTHORIZED_USERS: ${{ secrets.AUTHORIZED_USERS }}
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outputs:
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args: ${{ env.args }}
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# This job only runs for pull request comments
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if: needs.Get-Authorization.outputs.check-authorization == 'true'
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steps:
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- name: Check if comment is issued by authorized person
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run: blossom-ci
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env:
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OPERATION: 'AUTH'
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REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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REPO_KEY_DATA: ${{ secrets.BLOSSOM_KEY }}
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Vulnerability-scan:
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name: Vulnerability scan
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needs: [Authorization]
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runs-on: ubuntu-latest
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steps:
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- name: Checkout code
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uses: actions/checkout@v2
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with:
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repository: ${{ fromJson(needs.Authorization.outputs.args).repo }}
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ref: ${{ fromJson(needs.Authorization.outputs.args).ref }}
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lfs: 'true'
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- name: Run blossom action
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uses: NVIDIA/blossom-action@main
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env:
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REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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REPO_KEY_DATA: ${{ secrets.BLOSSOM_KEY }}
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with:
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args1: ${{ fromJson(needs.Authorization.outputs.args).args1 }}
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args2: ${{ fromJson(needs.Authorization.outputs.args).args2 }}
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args3: ${{ fromJson(needs.Authorization.outputs.args).args3 }}
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Job-trigger:
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name: Start ci job
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needs: [Vulnerability-scan]
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runs-on: blossom
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steps:
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- name: Start ci job
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run: blossom-ci
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env:
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OPERATION: 'START-CI-JOB'
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CI_SERVER: ${{ secrets.CI_SERVER }}
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REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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Upload-Log:
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name: Upload log
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runs-on: blossom
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if : github.event_name == 'workflow_dispatch'
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steps:
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- name: Jenkins log for pull request ${{ fromJson(github.event.inputs.args).pr }} (click here)
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run: blossom-ci
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env:
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OPERATION: 'POST-PROCESSING'
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CI_SERVER: ${{ secrets.CI_SERVER }}
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REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}

‎core/conversion/converters/impl/select.cpp

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namespace impl {
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namespace {
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bool add_split(ConversionCtx* ctx, const torch::jit::Node* n, args& args, bool split_list) {
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bool add_split(ConversionCtx* ctx, const torch::jit::Node* n, args& args, bool split_list, bool unbind) {
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auto in = args[0].ITensor();
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auto axis = args[2].unwrapToInt();
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auto inDimSize = in->getDimensions().d[axis];
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auto numOutputs = 1, numRemainder = 0;
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auto numOutputs = 1, numRemainder = 0, axis = 0;
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std::vector<int64_t> sizes;
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if (split_list) {
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sizes = args[1].unwrapToIntList().vec();
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numOutputs = sizes.size();
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if (unbind) {
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axis = args[1].unwrapToInt();
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numOutputs = in->getDimensions().d[axis];
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sizes.insert(sizes.end(), numOutputs, 1);
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} else {
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auto split_size = args[1].unwrapToInt();
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numOutputs = inDimSize / split_size;
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numRemainder = inDimSize % split_size;
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for (int64_t i = 0; i < numOutputs; i++) {
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sizes.push_back(split_size);
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}
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if (numRemainder) {
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numOutputs += 1;
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sizes.push_back(numRemainder);
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axis = args[2].unwrapToInt();
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auto inDimSize = in->getDimensions().d[axis];
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if (split_list) {
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sizes = args[1].unwrapToIntList().vec();
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numOutputs = sizes.size();
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} else {
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auto split_size = args[1].unwrapToInt();
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numOutputs = inDimSize / split_size;
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numRemainder = inDimSize % split_size;
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for (int64_t i = 0; i < numOutputs; i++) {
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sizes.push_back(split_size);
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}
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if (numRemainder) {
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numOutputs += 1;
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sizes.push_back(numRemainder);
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}
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}
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}
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}})
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.pattern({"aten::split(Tensor self, int[] split_sizes, int dim=0) -> (Tensor[])",
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[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
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add_split(ctx, n, args, true);
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add_split(ctx, n, args, true, false);
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LOG_DEBUG("Converted split op into a list of IValues");
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return true;
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}})
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.pattern({"aten::split.Tensor(Tensor(a) self, int split_size, int dim=0) -> (Tensor[])",
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[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
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add_split(ctx, n, args, false);
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add_split(ctx, n, args, false, false);
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LOG_DEBUG("Converted split op into a list of IValues");
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return true;
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}})
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.pattern({"aten::split_with_sizes(Tensor(a) self, int[] split_sizes, int dim=0) -> (Tensor[])",
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[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
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add_split(ctx, n, args, true);
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add_split(ctx, n, args, true, false);
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LOG_DEBUG("Converted split op into a list of IValues");
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return true;
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}})
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.pattern({"aten::unbind.int(Tensor(a -> *) self, int dim=0) -> (Tensor[])",
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[](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
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add_split(ctx, n, args, false, true);
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LOG_DEBUG("Converted split op into a list of IValues");
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return true;
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}})

‎docs/_cpp_api/class_view_hierarchy.html

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Torch-TensorRT Getting Started - CitriNet
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Torch-TensorRT - Using Dynamic Shapes
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Torch-TensorRT Getting Started - EfficientNet-B0
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Masked Language Modeling (MLM) with Hugging Face BERT Transformer
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Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT
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‎docs/_cpp_api/classtorch__tensorrt_1_1DataType.html

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Torch-TensorRT - Using Dynamic Shapes
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Torch-TensorRT Getting Started - EfficientNet-B0
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Masked Language Modeling (MLM) with Hugging Face BERT Transformer
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Torch-TensorRT Getting Started - LeNet
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Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT
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Torch-TensorRT Getting Started - EfficientNet-B0
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Torch-TensorRT - Using Dynamic Shapes
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‎docs/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html

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Torch-TensorRT Getting Started - CitriNet
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Torch-TensorRT - Using Dynamic Shapes
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Torch-TensorRT Getting Started - EfficientNet-B0
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<a class="md-nav__link" href="../_notebooks/Hugging-Face-BERT.html">
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Masked Language Modeling (MLM) with Hugging Face BERT Transformer
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</a>
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Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT
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Torch-TensorRT Getting Started - EfficientNet-B0
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Torch-TensorRT - Using Dynamic Shapes
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‎docs/_cpp_api/classtorch__tensorrt_1_1TensorFormat.html

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Torch-TensorRT Getting Started - CitriNet
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<li class="md-nav__item">
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<a class="md-nav__link" href="../_notebooks/dynamic-shapes.html">
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Torch-TensorRT - Using Dynamic Shapes
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Torch-TensorRT Getting Started - EfficientNet-B0
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<li class="md-nav__item">
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<a class="md-nav__link" href="../_notebooks/Hugging-Face-BERT.html">
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Masked Language Modeling (MLM) with Hugging Face BERT Transformer
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</a>
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<li class="md-nav__item">
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Torch-TensorRT Getting Started - LeNet
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Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT
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Torch-TensorRT Getting Started - EfficientNet-B0
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Torch-TensorRT - Using Dynamic Shapes
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