Building and publishing Docker image
# Build image.
docker rmi frnkly/learning-magenta
docker build --force-rm --tag frnkly/learning-magenta .
# Publish to Docker hub.
cat ./.docker-hub-token \
| docker login --username $(cat ./.docker-hub-username) --password-stdin \
&& docker push frnkly/learning-magenta
Creating a VM instance on GCP
Paramater |
Value |
Machine type |
n2-standard-16 |
Boot disk |
Container-optimized OS (latest SDD, 50GB) |
Service account |
Compute engine default |
Preemptibility |
On |
Setting up dev environment
# Create Docker container
docker run --interactive --tty --rm frnkly/learning-magenta
# Initialize gcloud
gcloud init --console-only
# Setup magenta
git clone https://github.com/magenta/magenta.git --depth 1 \
&& cd magenta/magenta/models/sketch_rnn \
&& mkdir data logs
# Download a subset of the airplane dataset
gsutil -m cp -r gs://quickdraw_dataset/sketchrnn/airplane.npz data/airplane.npz
# Download the full airplane dataset
gsutil -m cp -r gs://quickdraw_dataset/sketchrnn/airplane.full.npz data/airplane.full.npz
# Download the full dataset
gsutil -m cp -r gs://quickdraw_dataset/sketchrnn ./data
Training the Sketch RNN model
conda activate magenta
python sketch_rnn_train.py --log_root=logs --data_dir=data --hparams="data_set=[airplane.npz]"