Terminating AWS Batch jobs broken about raster-vision HOT 6 CLOSED

This could be related to moby/moby#34213.

from raster-vision.

This just happened for a job (job-id=9739ed8b-8125-4e75-9efb-fcf1d89f5c44) I ran today, which was still running after initiating termination via the consolet 1.5h prior. The command was

run_script.sh, lf/train-ships, /opt/src/detection/scripts/train_ec2.sh --config-path /opt/src/detection/configs/ships/ssd_mobilenet_v1.config --train-id ships1 --dataset-id singapore_ships_chips_tiny --model-id ssd_mobilenet_v1_coco_11_06_2017

and the container was 279682201306.dkr.ecr.us-east-1.amazonaws.com/raster-vision-gpu:latest running in the raster-vision-gpu queue.

from raster-vision.

The easiest workaround for now is to kill the container running on the host.

[ec2-user@ip-172-31-53-167 ~]$ sudo docker ps
CONTAINER ID        IMAGE                                                                   COMMAND                  CREATED             STATUS              PORTS               NAMES
c98db2c40493        279682201306.dkr.ecr.us-east-1.amazonaws.com/raster-vision-gpu:latest   "bash run_script.sh l"   18 minutes ago      Up 18 minutes                           ecs-raster-vision-gpu-3-default-90f9cfa59cb5d09bbe01
4d7350e07e59        amazon/amazon-ecs-agent:latest                                          "/agent"                 34 minutes ago      Up 34 minutes                           ecs-agent
[ec2-user@ip-172-31-53-167 ~]$ sudo docker kill c98db2c40493
c98db2c40493

from raster-vision.

If you terminate a job using the above approach, it will get retried if attempts is greater than 1. Since it's already a pain to terminate things, and resuming training is broken as described in #106, we should remember to submit jobs with --attempts 1 so there is no retry attempt.

from raster-vision.

I think the reason that we aren't able to stop Batch Jobs is partly because when the Job receives a SIGINT signal from the Batch console, that signal isn't being propagated to the background processes running in the container. Those processes keep running, so bash can't exit and end the job. I was able to kill Batch jobs from the console using the changes that I made to train_ec2.sh on my branch feature/tnation/batch-job-termination. However, once the Job termination issue was fixed, I noticed that if any of the background processes (i.e. train.py and eval.py) exit without being killed, the Batch job will still hang until it's terminated manually. I think that's because the EXIT signal in the background process isn't being passed back up to the script.

It seems like managing both job control and model training with train_ec2.sh will to require a lot of nonstandard scripting to make it work the way it should; it may be worth it to break this task up into multiple, dependent Batch jobs. One job could run train.py, and the other can run eval.py. If we use either EFS or S3 to store the necessary shared files, we'll be able to create a more robust process that will keep us from having to do process management and error checking with a shell script.

from raster-vision.

Since we've identified the root cause for the job failures (and train_ec2.sh is being rewritten soon), I'm going to close this.

from raster-vision.