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bayes gear run

Describe

Run container

bayes gear run [task or workspace or hypertuning] [option]

Available options

-d, --data strings      Bind data
-e, --env string Select Image
-f, --follow [Optional] Tracking the status of running containers
-h, --help see run The Help
-m, --message string Execution description
-o, --open [Optional] After successfully creating the container. Open in browser
-r, --resource string Choose computing power

Use case

# Establish "Python Script execution" task. And open it in the browser. Tracking status at the terminal
bayes gear run task -o -f

# Create a data binding containing "Python script" task
bayes gear run task \
--resource cpu \
--env tensorflow-1.12 \
--data hyperai/eBIQp4yPMtU/1:/input0 \
--data hyperai/sTggKplxyT6/1:/input1 \
--data hyperai/bbNaMvDNqO9/1:/input2 \
--data aisensiy/jobs/3s55ypc33ptl/output:/output --message "task message" -- sleep 60

# Establish Jupyter working space. And open it in the browser. Tracking status at the terminal
bayes gear run workspace -o -f

# Create a data binding containing Jupyter working space
bayes gear run workspace \
--resource cpu \
--env tensorflow-1.12 \
--data hyperai/eBIQp4yPMtU/1:/input0 \
--data hyperai/sTggKplxyT6/1:/input1 \
--data hyperai/bbNaMvDNqO9/1:/input2 \
--data aisensiy/jobs/3s55ypc33ptl/output:/output \
--message "workspace message"

# Create a "Automatic parameter tuning" task, And open it in the browser
bayes gear run hypertuning -o