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narendasannarendasan
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Merge pull request #1098 from tanayvarshney/master
fixed typos
2 parents 6c1d12d + 93cb670 commit b498d8b

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‎notebooks/CitriNet-example.ipynb

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"cell_type": "markdown",
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"<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
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"<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_citrinet/nvidia_logo.png\" width=\"90px\">\n",
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"\n",
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"# Torch-TensorRT Getting Started - CitriNet"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.12"
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"version": "3.8.13"
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"nbformat": 4,

‎notebooks/EfficientNet-example.ipynb

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"cell_type": "markdown",
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"<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
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"<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_efficientnet/nvidia_logo.png\" width=\"90px\">\n",
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"\n",
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"# Torch-TensorRT Getting Started - EfficientNet-B0"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.13"
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‎notebooks/Hugging-Face-BERT.ipynb

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"id": "d0a97ac5",
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"metadata": {},
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"<img src=\"https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png\" style=\"width: 90px; float: right;\">\n",
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"<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_hf_bert/nvidia_logo.png\" width=\"90px\">\n",
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"\n",
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"\n",
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"# Masked Language Modeling (MLM) with Hugging Face BERT Transformer"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.13"
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‎notebooks/dynamic-shapes.ipynb

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"id": "18ee9d62",
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"<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_dynamic_shapes/nvidia_logo.png\" width=\"90px\">\n",
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"\n",
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"# Torch-TensorRT - Using Dynamic Shapes"
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"We highly encourage users to run this notebook using our NVIDIA's [PyTorch container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch). It comes packaged with a host of NVIDIA libraries and optimizations to widely used third-party libraries. In addition, this container is tested and updated on a monthly cadence!\n",
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"\n",
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"This notebook has the following sections:\n",
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"1. TL;DR Explanation\n",
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"1. Setting up the model\n",
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"1. Working with Dynamic shapes in Torch TRT]"
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"1. [TL;DR Explanation](#1)\n",
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"2. [Setting up the model](#2)\n",
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"3. [Working with Dynamic shapes in Torch TRT](#3)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1603028d",
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"metadata": {},
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"<a id=\"1\"></a>\n",
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"---\n",
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"## TL;DR Explanation\n",
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"\n",
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"<a id=\"2\"></a>\n",
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"---\n",
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"## Setting up the model\n",
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"<a id=\"3\"></a>\n",
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"---\n",
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"## Working with Dynamic shapes in Torch TRT\n",
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" \n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.6"
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"version": "3.8.13"
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"nbformat": 4,

‎notebooks/lenet-getting-started.ipynb

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"# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
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"# Copyright 2019-22 NVIDIA Corporation. All Rights Reserved.\n",
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"#\n",
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"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"# you may not use this file except in compliance with the License.\n",
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"cell_type": "markdown",
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"metadata": {},
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"<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
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"<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_lenet/nvidia_logo.png\" width=\"90px\">\n",
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"\n",
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"# Torch-TensorRT Getting Started - LeNet"
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‎notebooks/ssd-object-detection-demo.ipynb

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"<img src=\"https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_ssd_notebook.png\" style=\"width: 90px; float: right;\">\n",
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"<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_ssd/nvidia_logo.png\" width=\"90px\">\n",
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"# Object Detection with Torch-TensorRT (SSD)"
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"name": "python",
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‎notebooks/vgg-qat.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a054a8df",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Copyright 2020-2022 NVIDIA Corporation. All Rights Reserved.\n",
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"#\n",
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"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"# you may not use this file except in compliance with the License.\n",
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"# You may obtain a copy of the License at\n",
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"#\n",
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"# http://www.apache.org/licenses/LICENSE-2.0\n",
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"#\n",
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"# Unless required by applicable law or agreed to in writing, software\n",
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"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
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"# See the License for the specific language governing permissions and\n",
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"# limitations under the License.\n",
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"# =============================================================================="
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_qat_vgg/nvidia_logo.png\" width=\"90px\">\n",
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"\n",
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"# Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT"
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"version": "3.8.12"
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"version": "3.8.13"
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"nbformat": 4,

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