Environment Variables inside a Run:AI Workload¶
Identifying a Job¶
There may be use cases where your container may need to uniquely identify the Job it is currently running in. A typical use case is for saving Job artifacts under a unique name. Run:AI provides pre-defined environment variables you can use. These variables are guaranteed to be unique even if the Job is preempted or evicted and then runs again.
Run:AI provides the following environment variables:
JOB_NAME- the name of the Job.
JOB_UUID- a unique identifier for the Job.
Note that the Job can be deleted and then recreated with the same name. A Job UUID will be different even if the Job names are the same.
Identifying a Pod¶
With Hyperparameter Optimization, experiments are run as Pods within the Job. Run:AI provides the following environment variables to identify the Pod.
POD_INDEX- An index number (0, 1, 2, 3....) for a specific Pod within the Job. This is useful for Hyperparameter Optimization to allow easy mapping to individual experiments. The Pod index will remain the same if restarted (due to a failure or preemption). Therefore, it can be used by the Researcher to identify experiments.
POD_UUID- a unique identifier for the Pod. if the Pod is restarted, the Pod UUID will change.
Run:AI provides an environment variable, visible inside the container, to help identify the number of GPUs allocated for the container. Use
Usage Example in Python¶
import os jobName = os.environ['JOB_NAME'] jobUUID = os.environ['JOB_UUID']