supreeth-c / pose-detection Goto Github PK

The repository is to predict human joint location from JPG images that has a pixel size of 256*256. This will be done using the Movnet Singlepose thunder pretrained model, which will be deployed on AWS using CF and SDK to deploy inferencing and monitoring pipeline. The monitoring pipeline will be used to collect and analyze data on average inference time, memory usage, and compute power usage of the deployed model.

Demo

Architecture Overview

Getting Started

Here you will find a short video, highlighting the CD pipeline and its outcomes such as launching a webpage to predict human joints, contents of s3 bucket and its structure and cloudwatch logs on how the API are being invoked

Deployed Endpoint URL For Predictions

Here I describe the setup to build and test deployment pipeline on the platforms of Amazon SageMaker, API Gateway, Lambda, S3 Web-Hosting.

For this example of hosting a pre-trained model have used Movnet SinglePost-Thunder Tflite Float16 pretrained model for an input image of 256 pixels only.

• For the purposes of deploying the stack and endpoints one needs to have an IAM role with trusted relationships for lambda, sagemaker and apigateway, with the following managed permissions

```yaml
ModelMonitoring:
monitoringScheduleName: hp-prediction-monitoring-schdule
threshold: "1.0" 
monitoringImageName: hp-prediction-monitoring-container
baseJobName: hp-prediction-monitoring-job
processingOutput:
    s3DestinationPrefix: endpoint/monitoring_schedule
    outputName: result
    mlDefaultResultSource: /opt/ml/processing/resultdata

EndpointConfig:
    modelName: hp-prediction-model
    endpointConfigName: hp-prediction-config
    endpointName: hp-prediction-endpoint
    roleArn: arn:aws:iam::XXXXXXXXXXXXXX:role/CloudOps

Instance:
    instanceType: ml.m5.xlarge
    instanceCount: 1

InferenceConfig:
    ecrInferenceImageName: hp-inferencing-container

DataCapture:
    initialSamplingPercentage: 100
    enableCapture: True

S3Config:
    bucketName: mlops-pipeline-hp-estimation
    inputDir: inputdata/
    s3Prefix: hp/
    realtimeS3Prefix: realtime/
    outputDir: prediction/
    modelDir: model/
    dataCapture: endpoint/data_capture
    tensors: endpoint/tensors

``` 

Explanation for few of the keys
-- threshold: "1.0" is set for raising violation as a part of monitoring job when avg inference time exceeds 1 second.
-- initialSamplingPercentage: 100% is set to capture all in flowing request via Inference endpoint
-- S3Config: needs to be updated with only unqiue bucket name and rest of the configurations needs to be kept as is.

2. Update the deploy-inference.yaml within .github/workflows folder with the following details within env's in order to ensure the CD pipeline works without any error

   region: 
   AWS_DEFAULT_REGION:

   ap-south-1 has been used as by deafult region to test the endpoint, it can be changed to any available region
   

3. Update the Github Actions secrets with two AWS unqiue account access and secret keys. The below keys are used for deploying the AWS Sagemaker Real Time Endpoints and Monitoring pipeline via SDK-python

       AWS_ACCESS_KEY_ID
       AWS_SECRET_ACCESS_KEY 

   The second set of key are only used to deploy the Static Webpage hosted in s3 bucket

       AWS_ACCESS_KEY_ID_GLOBAL_S3
       AWS_SECRET_ACCESS_KEY_GLOBAL_S3

4. With these modifications pushed into a branch and later upon raising a Pull Request to main branch only, the CD pipleine will get triggered.
   

-- SAGEMAKER REALTIME INFERNECE ENDPOINT URL

-- ATTACHING MONITORING TO ENDPOINT
-- WEB PAGE URL