Seldon Deploy installs into standard kubernetes clusters and can be accessed by all modern browsers mentioned in the specifications.

Contact the Seldon team with the relevant issue and provide necessary details about your particular install of Seldon Deploy. The version details can be found at the about page of the tool that can be accessed from the user menu on the top-right corner.


This about page displays the Seldon Deploy version details, license details and your browser version. For more details on your specific browser, please visit and share the URL that can provide us with more information about your browser usage like resolution and other support needed.

Browser Specifications

We recommend using the latest browser version available to your operating system. See your browser’s documentation to learn more about checking and updating your version. The project supports all modern browsers based on global usage (> 0.2%). The full list of browser versions supported include stable versions of Chrome, Firefox, Safari, Microsoft Edge and has a global coverage of 91.09%. The full set is listed here

and_chr 86
and_ff 82
and_uc 12.12
chrome 86
chrome 85
chrome 84
chrome 83
chrome 80
chrome 49
edge 86
edge 85
edge 18
firefox 82
firefox 81
ios_saf 14
ios_saf 13.4-13.7
ios_saf 13.3
ios_saf 12.2-12.4
ios_saf 10.3
ios_saf 9.3
opera 71
safari 14
safari 13.1
safari 13
samsung 12.0

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Errors from Seldon Deploy

If Seldon Deploy crashes or returns an error the best first steps to get details are:

  1. Turn on the network tab in the browser (via right-click and ‘Inspect’ in chrome), hit ‘Preserve Log’ and recreate the issue. Then find the failed call to see the full message.

  2. Find the Seldon Deploy pod (usually in the seldon-system namespace) and inspect its logs.

This should help determine if the cause is Seldon Deploy or another component. If another component, see the other debugging sections from here. Either way the information obtained will be useful if an issue needs to be reported to Seldon.

Insufficient ephemeral storage in EKS clusters

When using eksctl, the volume size for each node will be of 20Gb by default. However, with large images this may not be enough. This is discussed at length on this thread in the eksctl repository.

When this happens, pods usually start to get evicted. If you run kubectl describe on any of these pods, you should be able to see errors about not enough ephemeral storage. You should also be able to see some DiskPressure events on the output of kubectl describe nodes.

To fix it, it should be enough to increase the available space. With eksctl, you can do so by tweaking the nodeGroups config and adding a volumeSize and volumeType keys. For instance, to change the volume to 100Gb you could do the following in your ClusterConfig spec:

kind: ClusterConfig


  - volumeSize: 100
    volumeType: gp2

Elastic Queue Capacity

If request logging is used with a high throughput then it’s possible to hit a rejected execution of processing error in the logger. This comes with a queue capacity message. To address this the thread_pool.write.queue_size needs to be increased. For example, with the Elasticsearch Helm chart this could be:

  elasticsearch.yml: |
    thread_pool.write.queue_size: 2000

Request Logs Entries Missing

Sometimes requests fail to appear in the request logs. Often this is a problem with the request logger setup. If so see the request logging docs.

Sometimes we see this error in the request logger logs:

RequestError(400, 'illegal_argument_exception', 'mapper [request.instance] cannot be changed from type [long] to [float]')

What happens here is Elasticsearch has inferred the type of the fields in the request for the model’s index. This is inferred on the first request and if it changes or is inferred incorrectly this has to be addressed manually.

The best thing to do here is to delete the index.

First port-forward Elasticsearch. If the example OpenDistro Elasticsearch is used then this is kubectl port-forward -n seldon-logs svc/opensearch-cluster-master 9200.

To delete the index we need to know its name. These follow a pattern. The pattern is:


Usually endpoint is default unless there’s a canary, and modelid is usually <modelname>-container if created via the Seldon Deploy UI.

You may use the Elasticsearch API to list the available indices in Elasticsearch with curl -k --request GET 'https://localhost:9200/_cat/indices/inference-log-*', which should return something like:

yellow open inference-log-seldon-seldon-income-default-income-container                       xWEBE-xXQA65cEIitp6SHw 1 1 9646    0   3.4mb   3.4mb
yellow open inference-log-seldon-seldon-income-classifier-default-income-classifier-container wUeX7fiISpSIGFCoHI05Cg 1 1   61   60 151.1kb 151.1kb
yellow open inference-log-seldon-seldon-batch-demo-default-batch-demo-container               3oYtAwL1Q-2Gi3og91iGdg 1 1 1848 1093   1.3mb   1.3mb

Optionally, for easier debugging, you may also install Kibana to visualize and inspect indices in Elasticsearch. You may install Kibana either directly or its equivalent OpenSearch Dashboards via the OpenSearch installation. Our trial install docs also provide steps for you to access Kibana at /kibana/, without port-forwarding, via a VirtualService.

Then delete the index with a curl command. If the auth is admin/admin and there’s a cifar10 seldon model in a namespace also called seldon then it’s curl -k -v -XDELETE https://admin:[email protected]:9200/inference-log-seldon-seldon-cifar10-default

Request Logs Entries Incorrect

Model names should not overlap as at the time of writing there is a bug with the request log page. Its searches can get confused between, say, ‘classifier’ and ‘classifier-one’. The logs in the backend are distinct but the searches can get muddled. This issue is being tracked - for now the workaround is to use distinct names. This affects both viewing the prediction logs and also the feature distributions monitoring feature. In the distributions monitoring dashboard, this error may manifest as missing statistics and distribution graphs.


See the auth section for debugging tips.


See the Knative install section for how verify Knative.

Argo and Batch

See the argo section for debugging batch and the minio section for minio.


See the metrics section for debugging prometheus.

Serving Engines

For Seldon Core debugging, it is best to see their respective docs.

In our demos we load models from google storage buckets. In the model logs we sometimes see this:

Compute Engine Metadata server unavailable onattempt

This is a known google storage issue but does not cause failures. Treat as a warning.

Request logging for Tritonserver in Seldon Core 1.11.2

This version of tritonserver supports gzip value in Accept-Encoding header and returns compressed payloads indicated by 'Content-Encoding': 'gzip' header in the response. This value is returned back to the client so the proper content decoding can occur - most api clients do this automatically.

The relevant header unfortunately is not passed to the request logger, see SC3690 issue. This means that gzip compressed payloads will not get logged. To workaroud this issue one can send requests explicitly setting 'Accept-Encoding': 'ident' header. This will instruct Tritonserver to send back uncompressed responses that can be logged.

Another possible workaround is to temporarily downgrade Triton server image tag to previous version, e.g. 20.08-py3 by setting in core-values.yaml a following

        defaultImageVersion: 20.08-py3