pipenv install
pipenv run train-cartpole
pipenv run train-metaltile
# Specify export directory for example:
EXPORT_DIR="sample-models/CartPole-v1/models/episode-4970"
# Specify the name of environment for example:
ENV_NAME="CartPole-v1"
python -m utils.play_game --env ${ENV_NAME} --export_dir ${EXPORT_DIR}
pipenv run train-metaltile-cloud
tensorboard --logdir=gs://${PROJECT_ID}-ml/dqn/${JOB_NAME}
gcloud beta ml predict --model=dqn --json-instances=sampledata/predict_sample.json
predictions:
- key: 0
q:
- 0.711304
- 0.766368
- 0.757209
- 0.75318
- 0.737671
ACCESS_TOKEN=`gcloud auth print-access-token`
curl -X POST -H "Content-Type: application/json" -H "Authorization: Bearer ${ACCESS_TOKEN}" https://ml.googleapis.com/v1beta1/projects/cpb100demo1/models/dqn:predict -d @sampledata/sample_curl.json
{"predictions": [{"q": [0.4523559808731079, 0.38499385118484497, 0.26314204931259155, 0.6228029131889343, 0.5784728527069092], "key": 0}]}
python dqn_server.py --model ${PATH_TO_SAVED_MODEL}
curl -X POST -H "Content-Type: application/json" localhost:5000 -d @sampledata/sample_curl.json