git init

python3 -m venv .sagemmakerv01 source .sagemmakerv01/bin/activate python3 -m pip install -r requirements.txt

export $(grep -v '^#' .env | xargs) echo $GITHUB_TOKEN git init git add . git commit -m "first commit" git branch -M main

git remote add origin https://$GITHUB_TOKEN@github.com/jesvin1/AWS_Sagemaker_Assignment_V01.git git push -u origin main

export AWS_DEFAULT_REGION=$(aws configure get region) echo $AWS_DEFAULT_REGION export AWS_ACCOUNT_ID=$(aws sts get-caller-identity --query "Account" --output text) echo $AWS_ACCOUNT_ID export DATA_BUCKET="data-${AWS_DEFAULT_REGION}-${AWS_ACCOUNT_ID}" export PIPELINE_BUCKET="mlops-${AWS_DEFAULT_REGION}-${AWS_ACCOUNT_ID}"

echo "export AWS_DEFAULT_REGION=$AWS_DEFAULT_REGION" >> ~/.bashrc echo "export DATA_BUCKET=$DATA_BUCKET" >> ~/.bashrc echo "export PIPELINE_BUCKET=$PIPELINE_BUCKET" >> ~/.bashrc

aws s3 mb "s3://${DATA_BUCKET}" --region $AWS_DEFAULT_REGION

aws s3api put-bucket-versioning --bucket "${DATA_BUCKET}" --versioning-configuration Status=Enabled --region $AWS_DEFAULT_REGION

wget /data https://raw.githubusercontent.com/manifoldailearning/mlops-with-aws-datascientists/main/Section-16-mlops-pipeline/dataset/abalone.csv mv abalone.csv ./data/

aws s3 mb "s3://${PIPELINE_BUCKET}" --region $AWS_DEFAULT_REGION aws s3api put-bucket-versioning --bucket "${PIPELINE_BUCKET}" --versioning-configuration Status=Enabled --region $AWS_DEFAULT_REGION

• Create a Private Repository with repo name as abalone

- Modify the `trainingjob.json` as follows:
    - *Replace Account id with respective Account id*
    - *Replace region with `us-east-1` 

sed -i "s//${AWS_ACCOUNT_ID}/" model/trainingjob.json sed -i "s//${AWS_DEFAULT_REGION}/" model/trainingjob.json

cd model/
aws ecr get-login-password --region $AWS_DEFAULT_REGION | docker login --username AWS --password-stdin 763104351884.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com

docker build --build-arg REGION=$AWS_DEFAULT_REGION -f Dockerfile -t tf_model:1.0 .

cd ..
cd tests/unit_test/
mkdir -p model && \
mkdir -p output
docker run --rm --name 'my_model' \
    -v "$PWD/model:/opt/ml/model" \
    -v "$PWD/output:/opt/ml/output" \
    -v "$PWD/input:/opt/ml/input" tf_model:1.0 train

• Prediction

docker run --rm --name 'my_model' \
-v "$PWD/model:/opt/ml/model" \
-v "$PWD/output:/opt/ml/output" \
-v "$PWD/input:/opt/ml/input" tf_model:1.0 test \
"[[4.400000000000000022e-01,3.449999999999999734e-01,1.000000000000000056e-01,3.659999999999999920e-01,1.219999999999999973e-01,9.049999999999999711e-02,1.199999999999999956e-01,0.000000000000000000e+00,1.000000000000000000e+00,0.000000000000000000e+00]]"

docker run --rm --name 'my_model' \
    -p 8080:8080 \
    -v "$PWD/model:/opt/ml/model" \
    -v "$PWD/output:/opt/ml/output" \
    -v "$PWD/input:/opt/ml/input" tf_model:1.0 serve

change the directory in new terminal to unit_test
python3 app_test.py

- Modify threshold value

cd .. cd system_test/ sed -i "s//3.1/" ./buildspec.yml