v1.0.1 release

.gitignore

@@ -1,6 +1,8 @@
 analytics/classical-ml/recsys/training/analytics-with-python
 analytics/classical-ml/synthetic/inference/wafer-insights
 analytics/tensorflow/ssd_resnet34/inference/video-streamer
+big-data/aiok-ray/training/AIOK_Ray/
+big-data/aiok-ray/inference/AIOK_Ray/
 big-data/ppml/chronos
 big-data/friesian/training/BigDL
 big-data/ppml/bigDL-ppml

.gitmodules

@@ -37,3 +37,9 @@
 [submodule "transfer_learning/tensorflow/resnet50/training/transfer-learning-training"]
 	path = transfer_learning/tensorflow/resnet50/training/transfer-learning-training
 	url = https://github.com/intel/vision-based-transfer-learning-and-inference
+[submodule "big-data/aiok-ray/inference/AIOK_Ray"]
+	path = big-data/aiok-ray/inference/AIOK_Ray
+	url = https://github.com/intel/e2eAIOK/
+[submodule "big-data/aiok-ray/training/AIOK_Ray"]
+	path = big-data/aiok-ray/training/AIOK_Ray
+	url = https://github.com/intel/e2eAIOK/

README.md

@@ -46,6 +46,7 @@ Where `KEY` and `VALUE` pairs are environment variables that can be used to cust
 |NLP workflow for Azure ML|PyTorch* and Jupyter|[Training](./language_modeling/pytorch/bert_base/training/) \| [Inference](./language_modeling/pytorch/bert_base/inference/)|
 |Protein Structure Prediction|PyTorch*|[Inference](./protein-folding/pytorch/alphafold2/inference/)
 |Quantization Aware Training and Inference|OpenVINO™|[Quantization Aware Training(QAT)](https://github.com/intel/nlp-training-and-inference-openvino/tree/v1.0/question-answering-bert-qat)|
+|Ray Recommendation System|Ray with PyTorch*|[Training](./big-data/aiok-ray/training/) \| [Inference](./big-data/aiok-ray/inference)|
 |RecSys Challenge Analytics With Python|Hadoop and Spark|[Training](./analytics/classical-ml/recsys/training/)|
 |Video Streamer|TensorFlow|[Inference](./analytics/tensorflow/ssd_resnet34/inference/)|
 |Vision Based Transfer Learning|TensorFlow|[Training](./transfer_learning/tensorflow/resnet50/training/) \| [Inference](./transfer_learning/tensorflow/resnet50/inference/)|

analytics/classical-ml/synthetic/inference/README.md

@@ -59,7 +59,7 @@ End2End AI Workflow utilizing a Flask dashboard to allow users to predict FMAX/I
 ## Quick Start
 * Pull and configure the dependent repo submodule `git submodule update --init --recursive`.
 
-* Install [Pipeline Repository Dependencies]()
+* Install [Pipeline Repository Dependencies](../../../../README.md)
 
 * Get the public IP address of your machine with either `ip a` or https://whatismyipaddress.com/
 

big-data/aiok-ray/inference/DEVCATALOG.md

@@ -0,0 +1,171 @@
+# Distributed training and inference on Ray and Spark with Intel® End-to-End Optimization Kit 
+
+## Overview
+Modern recommendation systems require a complex pipeline to handle both data processing and feature engineering at a tremendous scale, while promising high service level agreements for complex deep learning models. Usually, this leads to two separate clusters for data processing and training: a data process cluster, usually CPU based to process huge dataset (terabytes or petabytes) stored in distributed storage system, and a training cluster, usually GPU based for training. This separate data processing and training cluster results a complex software stack, heavy data movement cost.
+Meanwhile, Deep learning models were commonly used for recommendation systems, quite often, those models are over-parameterized. It takes up to several days or even weeks to training a heavy model on CPU. 
+This workflow trying to address those pain points: unifies both data processing and training on Ray – the open source project that make it simple to scale any compute-intensive python workloads, and then optimize the E2E process, especially the training part, shorten training time weight lighter models, maintaining same accuracy, and delivers high-throughput models for inference with Intel® End-to-End Optimization Kit.
+
+> Important: original source disclose - [Deep Learning Recommendation Model](https://github.com/facebookresearch/dlrm)
+
+## How it works 
+![image](https://user-images.githubusercontent.com/18349036/209234932-12100303-16d7-4352-9d7b-ed23f4cf7028.png)
+
+## Get Started
+NOTE: Before you get started please make sure you have the trained model available from training pipeline.
+
+### Prerequisites
+```bash
+git clone https://github.com/intel/e2eAIOK/ AIOK_Ray
+cd AIOK_Ray
+git checkout tags/aiok-ray-v0.2
+git submodule update --init --recursive
+sh dlrm_all/dlrm/patch_dlrm.sh
+cd ..
+```
+
+### **Docker**
+Below setup and how-to-run sessions are for users who want to use the provided docker image to run the entire pipeline.
+
+##### Pull Docker Image
+```
+docker pull intel/ai-workflows:recommendation-ray-inference
+```
+
+#### How to run 
+
+The code snippet below runs the inference session. The model files will be generated and stored in the `/output/result` folder.
+
+(Optional) Export related proxy into docker environment.
+```bash
+export DOCKER_RUN_ENVS="-e ftp_proxy=${ftp_proxy} \
+  -e FTP_PROXY=${FTP_PROXY} -e http_proxy=${http_proxy} \
+  -e HTTP_PROXY=${HTTP_PROXY} -e https_proxy=${https_proxy} \
+  -e HTTPS_PROXY=${HTTPS_PROXY} -e no_proxy=${no_proxy} \
+  -e NO_PROXY=${NO_PROXY} -e socks_proxy=${socks_proxy} \
+  -e SOCKS_PROXY=${SOCKS_PROXY}"
+```
+
+```bash
+if [ ! "$(docker network ls | grep ray-inference)" ]; then
+    docker network create --driver=bridge ray-inference;
+fi
+export OUTPUT_DIR=/output
+export DATASET_DIR=<Path to Dataset>
+export RUN_MODE=<Pick from kaggle/criteo_small/criteo_full>
+
+docker run \
+  -a stdout $DOCKER_RUN_ENVS \
+  --env RUN_MODE=${RUN_MODE} \
+  --env APP_DIR=/home/vmagent/app/e2eaiok \
+  --env OUTPUT_DIR=/output \
+  --volume ${DATASET_DIR}:/home/vmagent/app/dataset/criteo \
+  --volume $(pwd)/AIOK_Ray:/home/vmagent/app/e2eaiok \
+  --volume ${OUTPUT_DIR}:/output \
+  --workdir /home/vmagent/app/e2eaiok/dlrm_all/dlrm/ \
+  --privileged --init -it \
+  --shm-size=300g --network ray-inference \
+  --device=/dev/dri \
+  --hostname ray \
+  --name ray-inference \
+  intel/ai-workflows:recommendation-ray-inference \
+  bash -c 'bash $APP_DIR/scripts/run_inference_docker.sh $RUN_MODE'
+```
+------
+
+## Useful Resources
+
+## Recommended Hardware and OS
+
+Operating System: Ubuntu 20.04
+Memory Requirement: Minimum 250G
+
+| Dataset Name | Recommended Disk Size |
+| --- | --- |
+| Kaggle | 90G |
+| Criteo Small | 800G |
+| Criteo Full | 4500G |
+
+### Dataset
+> Note: For kaggle run, train.csv and test.csv are required.
+
+kaggle: https://go.criteo.net/criteo-research-kaggle-display-advertising-challenge-dataset.tar.gz
+
+> Note: For criteo small run, day_0, day_1, day_2, day_3, day_23 are required.
+
+> Note: For criteo full test, day_0-day_23 are required
+
+criteo small and criteo full: https://labs.criteo.com/2013/12/download-terabyte-click-logs/
+
+### Step by Step Guide
+
+[option1] Build docker for single or multiple node and enter docker with click-to-run script
+```
+python3 scripts/start_e2eaiok_docker.py
+sshpass -p docker ssh ${local_host_name} -p 12346
+# If you met any network/package not found error, please follow log output to do the fixing and re-run above cmdline.
+
+# If you are behind proxy, use below cmd
+# python3 scripts/start_e2eaiok_docker.py --proxy "http://ip:port"
+# sshpass -p docker ssh ${local_host_name} -p 12346
+
+# If you disk space is limited, you can specify spark_shuffle_dir and dataset_path to different mounted volumn
+# python3 scripts/start_e2eaiok_docker.py --spark_shuffle_dir "" --dataset_path ""
+# sshpass -p docker ssh ${local_host_name} -p 12346
+```
+
+[option2] Build docker manually
+```
+# prepare a folder for dataset
+cd frameworks.bigdata.AIOK
+cur_path=`pwd`
+mkdir -p ../e2eaiok_dataset
+
+# download miniconda
+wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.12.0-Linux-x86_64.sh -P Dockerfile-ubuntu18.04/ -O Dockerfile-ubuntu18.04/miniconda.sh
+
+# build docker from dockerfile
+docker build -t e2eaiok-ray-pytorch Dockerfile-ubuntu18.04 -f Dockerfile-ubuntu18.04/DockerfilePytorch
+# if you are behine proxy
+# docker build -t e2eaiok-ray-pytorch Dockerfile-ubuntu18.04 -f Dockerfile-ubuntu18.04/DockerfilePytorch --build-arg http_proxy={ip}:{port} --build-arg https_proxy=http://{ip}:{port}
+
+# run docker
+docker run -it --shm-size=300g --privileged --network host --device=/dev/dri -v ${cur_path}/../e2eaiok_dataset/:/home/vmagent/app/dataset -v ${cur_path}:/home/vmagent/app/e2eaiok -v ${cur_path}/../spark_local_dir/:/home/vmagent/app/spark_local_dir -w /home/vmagent/app/ --name e2eaiok-ray-pytorch e2eaiok-ray-pytorch /bin/bash
+```
+
+### Test with other dataset (run cmd inside docker)
+```
+# active conda
+conda activate pytorch_mlperf
+
+# if behind proxy, please set proxy firstly
+# export https_proxy=http://{ip}:{port}
+
+# criteo test
+cd /home/vmagent/app/e2eaiok/dlrm_all/dlrm/; bash run_aiokray_dlrm.sh criteo_small ${current_node_ip}
+```
+
+### Test full pipeline manually (run cmd inside docker)
+```
+# active conda
+conda activate pytorch_mlperf
+
+# if behind proxy, please set proxy firstly
+# export https_proxy=http://{ip}:{port}
+
+# prepare env
+bash run_prepare_env.sh ${run_mode} ${current_node_ip}
+
+# data process
+bash run_data_process.sh ${run_mode} ${current_node_ip}
+
+# train
+bash run_train.sh ${run_mode} ${current_node_ip}
+
+# inference
+bash run_inference.sh ${run_mode} ${current_node_ip}
+```
+
+## Support
+
+For questions and support, please contact Jian Shang at [email protected].
+

big-data/aiok-ray/inference/Makefile

@@ -0,0 +1,29 @@
+DATASET_DIR ?= ./data
+FINAL_IMAGE_NAME ?= recommendation-ray
+CHECKPOINT_DIR ?= /output
+RUN_MODE ?= kaggle
+DOCKER_NETWORK_NAME = ray-inference
+
+recommendation-ray:
+	if [ ! -d "AIOK_Ray/dlrm_all/dlrm/dlrm" ]; then \
+		CWD=${PWD}; \
+		cd AIOK_Ray/; \
+		sh dlrm_all/dlrm/patch_dlrm.sh; \
+		cd ${CWD}; \
+	fi
+	@wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.12.0-Linux-x86_64.sh \
+	-P AIOK_Ray/Dockerfile-ubuntu18.04/ \
+	-O AIOK_Ray/Dockerfile-ubuntu18.04/miniconda.sh
+	if [ ! "$(shell docker network ls | grep ${DOCKER_NETWORK_NAME})" ]; then \
+		docker network create --driver=bridge ${DOCKER_NETWORK_NAME} ; \
+	fi
+	@DATASET_DIR=${DATASET_DIR} \
+	 FINAL_IMAGE_NAME=${FINAL_IMAGE_NAME} \
+	 CHECKPOINT_DIR=${CHECKPOINT_DIR} \
+	 RUN_MODE=${RUN_MODE} \
+ 	 docker compose up recommendation-ray --build
+
+clean: 
+	docker network rm ${DOCKER_NETWORK_NAME}
+	DATASET_DIR=${DATASET_DIR} OUTPUT_DIR=${OUTPUT_DIR} docker compose down
+	sudo rm -rf ${OUTPUT_DIR}