python -m train.1-multiply
Set variables
JOB_NAME=<your job name>
JOB_NAME="task8"
PROJECT_ID=`gcloud config list project --format "value(core.project)"`
STAGING_BUCKET=gs://${PROJECT_ID}-ml
Submit a job
gcloud beta ml jobs submit training ${JOB_NAME} \
--package-path=train \
--staging-bucket="${STAGING_BUCKET}" \
--module-name=train.1-multiply
python -m train.2-input
Set variables
JOB_NAME="task8"
PROJECT_ID=`gcloud config list project --format "value(core.project)"`
STAGING_BUCKET=gs://${PROJECT_ID}-ml
INPUT_PATH=${STAGING_BUCKET}/input
Copy input.csv to Google Storage
gsutil cp input/input.csv $INPUT_PATH/input.csv
Submit a job
gcloud beta ml jobs submit training ${JOB_NAME} \
--package-path=train \
--staging-bucket="${STAGING_BUCKET}" \
--module-name=train.2-input \
-- --input_dir="${INPUT_PATH}"
python -m train.3-output
Set variables
JOB_NAME="task20"
PROJECT_ID=`gcloud config list project --format "value(core.project)"`
STAGING_BUCKET=gs://${PROJECT_ID}-ml
OUTPUT_PATH=${STAGING_BUCKET}/output/
Create the output folder
(Copy an empty file to the GS path with trailing slash, /
)
gsutil cp /dev/null $OUTPUT_PATH
Submit a job
gcloud beta ml jobs submit training ${JOB_NAME} \
--package-path=train \
--staging-bucket="${STAGING_BUCKET}" \
--module-name=train.3-output \
-- --output_dir="${OUTPUT_PATH}"
We always welcome your contributions/comments. Use the Issues.