Merge branch 'master' into v2.2.1-integration

notebooks/README.md

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+# HugeCTR Jupyter demo notebooks
+This folder contains demo notebooks for HugeCTR.
+
+## 1. Requirements
+
+The most convenient way to run these notebooks is via a docker container, which provides a self-contained, isolated and re-producible environment for all experiments.
+
+First, clone the repository:
+
+```
+git clone https://github.com/NVIDIA/HugeCTR
+```
+
+Next, follow the step in the [README](../README.md) to build the NVIDIA HugeCTR container. Briefly, the steps are as follows.
+
+From repo root:
+
+```
+docker build -t hugectr:devel -f ./tools/dockerfiles/dev.Dockerfile .
+```
+
+Then launch the container in interaction mode (mount the home directory of repo into container for easy development):
+
+```
+docker run --runtime=nvidia --rm -it -u $(id -u):$(id -g) -v $(pwd):/hugectr -w /hugectr hugectr:devel bash
+```
+
+Within the docker interactive bash session, build HugeCTR:
+```
+mkdir -p build
+cd build
+cmake -DCMAKE_BUILD_TYPE=Release -DSM=70 .. # Asuming the target GPU is Volta-generation GPU, such as the Tesla V100
+make -j
+```
+
+Then finally, install and start Jupyter with
+
+```
+pip3 install jupyter
+cd /hugectr
+jupyter notebook --allow-root --ip 0.0.0.0 --port 8888
+```
+
+Navigate a web browser to the IP address or hostname of the host machine
+at port 8888: ```https://[host machine]:8888```
+
+Use the token listed in the output from running the jupyter command to log
+in, for example:
+
+```https://[host machine]:8888/?token=aae96ae9387cd28151868fee318c3b3581a2d794f3b25c6b```
+
+
+Within the container, the notebooks themselves are located at `/hugectr/notebooks`.
+
+## 2. Notebook list
+
+- [movie-lens-example.ipynb](movie-lens-example.ipynb): Training and inference demo on the movie lens dataset.