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yolov6-sagemaker

This repo provides guide for training YOLOV6 with SageMaker.

Custom training container

Prerequisites

  1. Docker
  2. 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 awscli

Create 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

drawing

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 required

Pull the Base Pytorch image

docker pull 763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-training:2.0.0-gpu-py310-cu118-ubuntu20.04-ec2

Clone the repo

git clone https://github.com/ashwincc/yolov6-sagemaker.git
cd yolov6-sagemaker/container

Login into your ecr

aws ecr get-login-password --region <region> | docker login --username AWS --password-stdin <account>.dkr.ecr.<region>.amazonaws.com

Build 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_IMAGE

Create the repository and push.

aws ecr create-repository --repository-name $REPO

docker push <account>.dkr.ecr.<region>.amazonaws.com/$YOLO_IMAGE

For using the custom container with sagemaker or testing it locally, please go through the main.ipynb notebook.

Todo:

  • Add Tensorboard for training visualisation.

References: