This page describes problems that can come up when creating Deep Learning VM Images
instances, and tells you how to address the problems.

## Quota exceeded

**Symptom** : `- Quota 'NVIDIA_P4_GPUS' exceeded. Limit: 0.0 in region
us-east1.`

**Problem**: You do not have enough quota.

**Solution** : You must have GPU quota before you can create instances with GPUs.
Check the [quotas page](https://console.cloud.google.com/iam-admin/quotas) to
ensure that you have enough GPUs available in your project. If GPUs are not
listed on the quotas page or you require additional GPU quota, [request a quota
increase](https://docs.cloud.google.com/compute/quotas#requesting_additional_quota). If your project has an
established billing history, it will receive quota automatically after you
submit the request. Free Trial accounts do not receive GPU quota by default.

Keep in mind that preemptible GPUs and normal GPUs require separate quota
requests. You can't use preemptible GPU quota for normal GPUs. Also, quota is
per region, so be sure that you are creating the instance in the region where
you have quota.

## Resource not found

**Symptom** : `- The resource 'projects/deeplearning-platform/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-p4'
was not found`

**Problem** : You are trying to create an instance with one or more GPUs in a
region where GPUs are not available (for example, an instance with a P4 GPU in
`europe-west4-c`).

**Solution** : To determine which region has the required GPU, see
[GPUs on Compute Engine](https://docs.cloud.google.com/compute/docs/gpus).

## Preemptible instances

**Symptom**: I can't create preemptible instance from the UI, even though I
have quota.

**Solution** : At this time, a preemptible instance can't be created from
Google Cloud Marketplace. You must use the CLI. Be sure to add `--preemptible`
when setting up your new instance.

## Unable to use SSH port forwarding to connect to JupyterLab

**Symptom** : When [using SSH port forwarding
to connect to JupyterLab](https://docs.cloud.google.com/deep-learning-vm/docs/jupyter),
you are unable to connect to the instance.

**Problem**: You are trying to connect to the wrong TCP socket.

**Solution**:

- On some Linux clients, the localhost resolves to
  the IPv6 loopback address (`::1`).
  Check this by using `ping -c 1 localhost`. If this command returns
  the IPv6 address, use `-L 8080:127.0.0.1:8080`
  (rather than `-L 8080:localhost:8080`) in the
  `gcloud compute ssh` command.

- Ensure that you connect to `http://localhost:8080`
  (instead of `https://localhost:8080`) on your local client.

## Community support

Ask a question about Deep Learning VM on [Stack
Overflow](https://stackoverflow.com/questions/tagged/google-dl-platform)
or join the
[google-dl-platform](https://groups.google.com/forum/#!forum/google-dl-platform)
Google group to discuss Deep Learning VM.

[Learn more about getting support from the
community](https://docs.cloud.google.com/deep-learning-vm/docs/getting-support#get_support_from_the_community).