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Cloud Training and Cloud Inference on Amazon Sagemaker/Elastic Kubernetes Service

This is a workflow to demonstrate how users utilize the Intel’s hardware (Cascade Lake or above) and related optimized software to perform cloud training and inference on Amazon Sagemaker platform. The main software packages used here are: HuggingFace, Amazon SageMaker, Intel Optimized TensorFlow and Intel Neural Compressor

Table of Contents

• Implementation Details
  • Architecture
  • Model Spec
  • Dataset
• Getting Started
  • Prerequisites
  • Installation
• Usage
  • Training
  • Inference
• License

Implementation Details

This workflow ultilizes the infracsture provided by the Amazon. There are two versions provided by this workflow - EKS and SageMaker.

Architecture

Sagemaker:




EKS:

Model Spec

The uncased BERT base model is used to demonstrate this workflow.

bert-base-uncased-config = {
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 128,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 0,
  "position_embedding_type": "absolute",
  "transformers_version": "4.21.1",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 30522
}

Dataset

Microsoft Research Paraphrase Corpus is used as the dataset for training and testing.

Type	Format	Rows
Training Dataset	HuggingFace Dataset	3668
Testing Dataset	HuggingFace Dataset	1725

Getting Started

Prerequisites

Hardware

Name	Description
CPU	Intel CPU - Cascade Lake or above
Usable RAM	16 GB
Disk Size	256 GB

Software

Docker is required to start this workflow. You will also need AWS credentials and the related AWS CLI installed on the machine to push data/docker image to the Amazon ECR.

For setting up the AWS credential account, you may refer to the following link:
https://aws.amazon.com/account/

For configuring the AWS credentials, you may refer to the following link:
https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html

Installation

1. Clone the repository

git clone https://github.com/intel/NLP-Workflow-with-AWS.git
cd NLP-Workflow-with-AWS

2. Build the docker image to prepare the environment for running the Juypter notebooks.

cd scripts
sh build_main_image.sh

Usage

Use the built docker image (by build_main_image.sh) to run the Juypter notebooks. Execute the following command:

cd scripts
sh start_script.sh

Sagemaker/EKS Training

After starting the container (i.e. executing the step in 'Usage'), execute the following command in the interactive shell.

cd /root/notebooks
jupyter notebook --allow-root

Start the notebook that is named as training and eks/sagemaker.

Sagemaker/EKS Inference

After starting the container (i.e. executing the step in 'Usage'), execute the following command in the interactive shell.

cd /root/notebooks
jupyter notebook --allow-root

Start the notebook that is named as inference and eks/sagemaker.

License

License