Find location of a executable
which pythonFind running networking ports
netstat -lptu
netstat -lpntuFind running processes
ps aux | grep processNameInstall iperf3
sudo yum install iperf3
Run iperf server
iperf3 -s
iperf3 -s -D
Run iperf client
iperf3 -c targetIP
iperf3 -c targetIP -t timeInSeconds
Install AWS CLI 2 (Linux)
curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip
sudo ./aws/install
Edit credentials and config
vi ~/.aws/credentials
vi ~/.aws/config
Switch AWS Profile
export AWS_PROFILE=user1
S3 sync data
aws s3 sync . s3://bucketName/prefix --region ap-east-1
S3 delete data
aw s3 rm s3://bucketName/prefix --recursive --region ap-east-1
Init a sample CDK project
cdk init sample-app --language java
cdk init sample-app --language python
Generate SAM template yaml from cdk
cdk synth --no-staging > lambda/template.yaml
Init a sample SAM project
sam init --runtime nodejs12.x -n hello_world
sam init --runtime python3.7 -n hello_world
sam init --runtime java8 -n hello_world
To build on your workstation, run this command in folder containing SAM template. Built artifacts will be written to .aws-sam/build folder
sam build
sam build --use-container
Invoke lambda locally in a docker, with event json
sam local invoke <functionName> --event testEvent.json
Start local API for debugging
sam local start-api
Package an application
sam build && sam package --s3-bucket <bucketname>
sam package --template-file template.yaml --output-template-file packaged.yaml --s3-bucket samsung-aisage
Publish an application
sam publish --template packaged.yaml --region ap-southeast-1
Guided SAM deployment to AWS
sam deploy -g
Install kubectl (1.16)
curl -o kubectl https://amazon-eks.s3.us-west-2.amazonaws.com/1.16.8/2020-04-16/bin/linux/amd64/kubectl
chmod +x ./kubectl
mkdir -p $HOME/bin && cp ./kubectl $HOME/bin/kubectl && export PATH=$PATH:$HOME/bin
echo 'export PATH=$PATH:$HOME/bin' >> ~/.bashrc
kubectl version --short --client
Use the AWS CLI update-kubeconfig command to create or update your kubeconfig for your cluster
aws eks --region region_code update-kubeconfig --name cluster_name
Test the config
kubectl get svc
Scale a deployment
kubectl scale deployment.apps/nginx-deployment --replicas=10 -n <namespace>
Getting shell into a container
kubectl exec --stdin --tty pod-name -- /bin/bash
Run a python file/module
python app.py
python -c 'from app import lambda_handler; lambda_handler()'
Install package using pip
pip install <package>
Create a virtual environment in python 2, pls install virtualenv via pip first
virtualenv v-env
Create a virtual environment in python 3, the function is built in
python -m venv v-env
Activate the virtual environment
source v-env/bin/activate
Install opencv library for c++/python
sudo apt-get install python-opencv
Install Linux based video camera tool guvcview
sudo apt-get install guvcview
Check if a process using V4L resources
sudo lsof | grep libv4l2
List all virtual video devices
ls -l /dev/video*
Create a Rekognition collection
aws rekognition create-collection --collection-id "test-collection" --region us-west-2
Index face image in S3 into a collection
aws rekognition index-faces --image '{"S3Object":{"Bucket":"test-bucket", "Name":"test.jpg"}}' --collection-id 'test-collection' --max-faces 1 --quality-filter 'AUTO' --detection-attributes 'ALL' --external-image-id 'testperson' --region us-west-2
List faces in a collection
aws rekognition list-faces --collection-id "test-collection" "face1-id" "face2-id" --region us-west-2
Search face by image (face match)
aws rekognition search-faces-by-image --image '{"S3Object":{"Bucket":"test-bucket", "Name":"someone.png"}}' --collection-id "test-collection" --max-faces 1 --quality-filter "AUTO" --region us-west-2
Compare diff between modified/staged files with HEAD
git diff
git diff --cached
Add files to stage
git add .
git add -A
git add -u
Commit to local branch
git commit -m "message"
Push to remote branch
git push
Checkout branch
git checkout master
Git status
git status