conda env create -f environment.yml
conda activate dog-bark
If you're just looking to play around with the ML model without worrying
about the AWS cloud components I'd strongly suggest looking at
notebook.ipyn after running the conda commands.
It's set up to train the model from scratch and run inference over .wav
files.
QUEUE_NAME=<queue_name> TABLE_NAME=<table_name> python3 main.py
docker build -t dog-bark .
docker run --env QUEUE_NAME=<queue_name> --env TABLE_NAME=<table_name> dog-bark
# Login
aws ecr get-login-password --region ap-southeast-2 | docker login --username AWS --password-stdin 123456789012.dkr.ecr.ap-southeast-2.amazonaws.com/dog-bark-detection
# Build
docker build -t dog-bark-detection .
docker tag dog-bark-detection:latest \
123456789012.dkr.ecr.ap-southeast-2.amazonaws.com/dog-bark-detection:latest
# Push
docker push 123456789012.dkr.ecr.ap-southeast-2.amazonaws.com/dog-bark-detection:latest
gcloud auth login
gcloud config set project <PROJECT>
docker build -t gcr.io/<PROJECT>/bark-detector:latest .
docker push gcr.io/<PROJECT>/bark-detector:latest
# When connected to Kubernetes
kubectl apply -f deploy.yml