This repo provides guide for training YOLOV6 with SageMaker.
Prerequisites
- Docker
- Aws cli
Steps to build and push custom container. For this example we are using yolov6s model.
- Install aws cli tool
# for debian based distros
sudo apt install awscliCreate the api access key from your AWS security credential dashboard.
Configure the aws account with the API access id created
aws configure
# paste the Key ID in the prompt
# paste the Access Key
# choose your region
# chosose json as output format
Login to access the DeepLearning Container image repository before pulling the image. Here are the list of images.
aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 763104351884.dkr.ecr.us-east-1.amazonaws.com
#change the region as requiredPull the Base Pytorch image
docker pull 763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-training:2.0.0-gpu-py310-cu118-ubuntu20.04-ec2Clone the repo
git clone https://github.com/ashwincc/yolov6-sagemaker.git
cd yolov6-sagemaker/containerLogin into your ecr
aws ecr get-login-password --region <region> | docker login --username AWS --password-stdin <account>.dkr.ecr.<region>.amazonaws.comBuild the docker image and tag.
REPO='yolov6s-sagemaker-training'
YOLO_IMAGE=$REPO:$(date '+%Y-%m-%d')
docker build . -t $YOLO_IMAGE
docker tag $YOLO_IMAGE <account>.dkr.ecr.<region>.amazonaws.com/$YOLO_IMAGECreate the repository and push.
aws ecr create-repository --repository-name $REPO
docker push <account>.dkr.ecr.<region>.amazonaws.com/$YOLO_IMAGEFor using the custom container with sagemaker or testing it locally, please go through the main.ipynb notebook.
Todo:
- Add Tensorboard for training visualisation.