Merge pull request #821 from NVIDIA/tvarshney_docs

Added resources to the readme

Author: narendasan

README.md

@@ -6,10 +6,11 @@
 
 Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA's TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch's Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into an module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extention and compiles modules that integrate into the JIT runtime seamlessly. After compilation using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT's suite of configurations at compile time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.
 
-More Information / System Architecture:
-
-- [GTC 2020 Talk](https://developer.nvidia.com/gtc/2020/video/s21671)
-
+Resources:
+- [Documentation](https://nvidia.github.io/Torch-TensorRT/)
+- [Torch-TensorRT Explained in 2 minutes!](https://www.youtube.com/watch?v=TU5BMU6iYZ0&ab_channel=NVIDIADeveloper)
+- [Comprehensive Discusion (GTC Event)](https://www.nvidia.com/en-us/on-demand/session/gtcfall21-a31107/)
+- [Pre-built Docker Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch). To use this container, make an NGC account and sign in to NVIDIA's registry with an API key. Refer to [this guide](https://docs.nvidia.com/ngc/ngc-catalog-user-guide/index.html#registering-activating-ngc-account) for the same.
 
 
 ## Building a docker container for Torch-TensorRT