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Lost/unknown job ID

The first step in any investigation is knowing a job ID. If you started your job with neuro 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:
neuro run jobs
neuro-flow jobs
neuro ps prints only running jobs. neuro ps -a prints all jobs. neuro ps -s failed prints all jobs with the Failed status.
Run neuro-flow ps to get the list of all jobs.

Image build failed

When you run neuro-flow build IMAGE_NAME, neuro-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:
$ neuro 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:
$ neuro logs <job-ID>

neuro run / neuro-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 neuro image ls. You can also list tags for a particular image via neuro 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:
neuro run jobs
neuro-flow jobs
Use the --http_port parameter.
Use the http_port: option.

Checking the listening IP

Next, make sure that your web app listens on, not on 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 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:
neuro run jobs
neuro-flow jobs
Use the --no-http-auth parameter.
Use the http_auth: False option.

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:
$ neuro exec JOB_ID /bin/sh
Note: In Docker you would typically add the -it parameters to the command, but they’re not necessary for neuro exec.
A job might have one of the following statuses:
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.
The job's execution was temporarily suspended
The job is running
The job terminated with the exit code 0
The running job was manually terminated/deleted
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:
Initializing a pod for the job
Creating a container for the job
Failed to pull the specified image
Stopping image pulling
Incorrect image name was specified
Job terminated due to an Out Of Memory error
Job is completed
An error occured
Cannot run the container
Creating the job
Job collected
Scheduling the job execution
The job could not be scheduled or was preempted
Specified cluster was not found
Scaling up
Failed to scale up the cluster
Restarting the job
Specified disk is currently unavailable
User quota was exhausted - you will need to renew it to perform more jobs
The job has reached the end of its lifespan
The job was terminated per user request

Last modified 2yr ago