Troubleshooting

Lost/unknown job ID

The first step in any investigation is knowing a job ID. If you started your job with apolo run, the job's ID was printed in the output.

However, if you can't find the initial terminal output, you can use one of these commands to find a specific job:

Note: apolo ps is a shortcut for apolo job ls

apolo ps prints only running jobs. apolo ps -a prints all jobs. apolo ps -s failed prints all jobs with the Failed status.

Image build failed

When you run apolo-flow build IMAGE_NAME, apolo-flow uploads the build context to the platform and creates a platform job that uses Kaniko to build a docker image and push it to the platform registry.

If building fails, you can check the job's status and logs to get more information.

Getting a job's status

To check a job's status, run:

$ apolo status <job-ID> 

The Status transitions section in the output can help you learn at which step the job failed.

Getting builder logs

To check builder logs, run:

$ apolo logs <job-ID> 

apolo run / apolo-flow run failed

There are a few main reasons your job may fail. Here are some of the most common:

Incorrect image name

This can happen if you have a typo in the image name or if the specified image was not built before running a job. List of all images can be accessed by running apolo image ls. You can also list tags for a particular image via apolo image tags <IMAGE_URI>.

Incorrect volume mounted

You might have an invalid volume mounted to the job. For example, you've mounted a volume to the /my-project folder, but your code expects /my_project. You can double-check it in the logs.

Cluster scale up failed

If you see a Cluster Scale Up Failed error in the status, it usually means you’ve requested resources that are not available in the cluster at the moment. For example, all GPUs are busy, so your job can’t be scheduled.

Code issues

You may have an error in your python script that prevents the job from running properly.

Can’t access my job via HTTP

There are a few steps to troubleshooting such issues.

Checking for an open HTTP port

The first point of interest is whether you have an open HTTP port for your job. To check this, you can:

Use the --http_port parameter.

Checking the listening IP

Next, make sure that your web app listens on 0.0.0.0, not on 127.0.0.1 or localhost — otherwise it won't be able to accept incoming requests from the outside of the container.

Disabling HTTP authentication

And finally, if you can access your job via browser, but curl and similar tools don’t work, most likely you didn’t disable HTTP authentication. The Apolo platform puts an HTTP authentication layer in front of your app by default for security reasons.

You can disable this behavior manually when running jobs:

Use the --no-http-auth parameter.

Troubleshooting a running job

Just like with Docker, you can get a shell in a running job to check its state. To do this, run:

$ apolo exec JOB_ID -- /bin/sh

Note: In Docker you would typically add the -it parameters to the command, but they’re not necessary for apolo exec.

A job might have one of the following statuses:

Status

Description

pending

The job is being created and scheduled. This includes finding (and possibly waiting for) sufficient amount of resources, pulling an image from a registry, etc.

suspended

The job's execution was temporarily suspended

running

The job is running

succeeded

The job terminated with the exit code 0

cancelled

The running job was manually terminated/deleted

failed

The job terminated with a non-0 exit code.

Each of these statuses have additional sub-statuses that can help you monitor the job and trace errors on an more granular level:

Sub-status

Description

PodInitializing

Initializing a pod for the job

ContainerCreating

Creating a container for the job

ErrImagePull

Failed to pull the specified image

ImagePullBackOff

Stopping image pulling

InvalidImageName

Incorrect image name was specified

OOMKilled

Job terminated due to an Out Of Memory error

Completed

Job is completed

Error

An error occured

ContainerCannotRun

Cannot run the container

Creating

Creating the job

Collected

Job collected

Scheduling

Scheduling the job execution

NotFound

The job could not be scheduled or was preempted

ClusterNotFound

Specified cluster was not found

ClusterScalingUp

Scaling up

ClusterScaleUpFailed

Failed to scale up the cluster

Restarting

Restarting the job

DiskUnavailable

Specified disk is currently unavailable

QuotaExhausted

User quota was exhausted - you will need to renew it to perform more jobs

LifeSpanEnded

The job has reached the end of its lifespan

UserRequested

The job was terminated per user request

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