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Created how to guide - upgrade deprecated docker images#1352

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326 changes: 326 additions & 0 deletions_docs/kb/articles/upgrade-deprecated-docker-images.md
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---
title:"How to: Upgrade Deprecated Docker Images in Classic Pipelines"
description:"Find and upgrade deprecated Docker v1 and schema 1 images to support docker-27 runner in Classic Pipelines."
group:kb
sub-group:articles
toc:true
kb:false
ht:true
common:false
categories:[Pipelines]
support-reviewed:2025-07-14 AA
---

#How to: Upgrade to the newer classic docker-27 runner
As part of our ongoing platform upgrades, we’re moving to a newer classic runner version based
on docker ver 27. This change deprecates older Docker image formats, and you may need to
identify and upgrade deprecated images in your pipelines.

This guide will help you:

1. Identify deprecated Docker images in your Classic Pipelines. (Step 1)

2. Upgrade those images to a modern, supported format. (Step 2)

###Step 1: Find Deprecated Docker Images
There are two recommended approaches to identify deprecated images in your Classic
Pipelines.

####Analyze Build Logs

This method scans historical build logs for deprecation warnings.

**How it works:**

Latest versions of dind print deprecation warning into build logs each time Docker daemon
tries to pull a deprecated image.

**Example:**
```
[DEPRECATION NOTICE] Docker Image Format v1 and Docker Image manifest
version 2, schema 1 support is disabled by default...
```

Use the following script to scan logs and extract deprecated image names:[Find deprecated images used in previous builds](https://gist.github.com/francisco-cocozza/6046028184cc12b5ee4513bdcb4217c5)

💡 For large-scale environments with thousands of builds, we offer a more scalable,
metrics-based detection method. See Appendix I for details.

###Step 2: Upgrade Deprecated Docker Images
Once you’ve identified deprecated images using engine metrics, the next step is to upgrade
those images to a modern format (OCI or Docker manifest v2, schema 2). This is required to
ensure compatibility with docker-27 and future releases.

####How It Works

Re-pushing an image using a modern Docker client will automatically convert its manifest to a
supported version.

####Sample Codefresh Pipeline to Upgrade an Image

You can automate this process with a Codefresh pipeline like the one below:

```
1. version: "1.0"
2.
3. steps:
4. push:
5. title: "Re-pushing deprecated image"
6. type: push
7. candidate: <source-image-name>
8. registry: <target-registry>
9. tag: <target-tag>
10. image_name: <target-image-name>
11.
12. # Example:
13. # push:
14. # title: "Re-pushing deprecated image"
15. # type: push
16. # candidate: docker/whalesay:latest
17. # registry: docker
18. # tag: new-manifest
19. # image_name: codefresh/whalesay
```

This pulls the deprecated image and re-pushes it under a new name/tag with a compliant
manifest format.

💡 You can repeat this process for each deprecated image discovered via metrics or logs.

##Appendix I:

###Monitor Engine Metrics

This method enables scalable detection of deprecated images by using real-time engine
metrics. Recommended for high-volume or automated environments.

####Monitor engine metrics

Starting from Runtime v7.5.0, engine emits
codefresh_engine_deprecated_images_pulled_total{ account_name,
pipeline_id, workflow, image_name } metric, which should be monitored to detect
deprecated images pulled within DIND.

This metric is a counter increased by 1 each time Docker daemon pulls a deprecated image.

####How to enable

Set the following values for the Hybrid Runtime Chart:

```
1. runtime:
2. engine:
3. env:
4. METRICS_PROMETHEUS_ENABLED: true
5. podMonitor:
6. main:
7. enabled: true
```

Before normal shutdown, the engine pod waits for the scrape of the metrics. This helps ensure
that the latest metric values are not missed.

The timeout is controlled by the METRICS_PROMETHEUS_SCRAPE_TIMEOUT engine variable. If
you want to be sure that the Prometheus operator will scrape the latest metric values before the
engine exits, we recommend setting this variable to 4 times your scrape interval.

To do so, adjust your Hybrid Runtime Chart values:

```
1. runtime:
2. engine:
3. env:
4. # Timeout in ms
5. METRICS_PROMETHEUS_SCRAPE_TIMEOUT: '120000'
```

####How to monitor

Installation of the monitoring stack is up to the customer and not covered by our support.

This section provides very basic instructions suitable for the purpose of this doc, however, they
may not meet customer’s long-term production needs and may require some adjustment by the
end user.

If the monitoring stack is not installed in the target cluster, the easiest way is to install
kube-prometheus-stack Chart:
[kube-prometheus-stack 75.6.2 · prometheus/prometheus-community](https://artifacthub.io/packages/helm/prometheus-community/kube-prometheus-stack)

While installing, please consider enabling persistence for the Prometheus operator so that
collected metrics are not purged on node rotation. Please follow the[official docs](https://artifacthub.io/packages/helm/prometheus-community/kube-prometheus-stack?modal=values&path=prometheus.prometheusSpec.storageSpec) for details
(check prometheus.prometheusSpec.storageSpec value).

####Example installation

**kube-prom-storage-class.yaml**

```
1. # kube-prom-storage-class.yaml
2.
3. apiVersion: storage.k8s.io/v1
4. kind: StorageClass
5. metadata:
6. name: kube-prom
7. provisioner: kubernetes.io/aws-ebs
8. parameters:
9. type: gp3
```

**kube-prom-values.yaml**

```
1. # kube-prom-values.yaml
2.
3. prometheus:
4. prometheusSpec:
5. podMonitorSelectorNilUsesHelmValues: false
6. serviceMonitorSelectorNilUsesHelmValues: false
7. storageSpec:
8. volumeClaimTemplate:
9. spec:
10. storageClassName: kube-prom
11. accessModes: ["ReadWriteOnce"]
12. resources:
13. requests:
14. storage: 20Gi
15. grafana:
16. persistence:
17. storageClassName: kube-prom
18. enabled: true
19. datasources:
20. datasources.yaml:
21. apiVersion: 1
22. datasources:
23. - name: Mimir API
24. type: marcusolsson-json-datasource
25. uid: Mimir-API
26. access: proxy
27. url: http://kube-prom-kube-prometheus-prometheus:9090/api/v1
28. plugins:
29. - marcusolsson-json-datasource
```

###Helm installation

```
1. helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
2. helm repo update
3. helm install kube-prom prometheus-community/kube-prometheus-stack --create-namespace --namespace kube-prom -f ./kube-prom-values.yaml
```

Please follow instructions provided by Helm after successful installation to get access to the
newly installed Grafana instance:

```
1. kubectl --namespace kube-prom get pods -l "release=kube-prom"
2. kubectl --namespace kube-prom get secrets kube-prom-grafana -o jsonpath="{.data.admin-password}" | base64 -d ; echo
3. export POD_NAME=$(kubectl --namespace kube-prom get pod -l "app.kubernetes.io/name=grafana,app.kubernetes.io/instance=kube-prom" -oname)
4. kubectl --namespace kube-prom port-forward $POD_NAME 3000
```

You can now log in to Grafana athttp://localhost:3000 as an admin user. Double-check the
instructions above for the password.

###Verifying metrics

To ensure that engine metrics are collected correctly, run some build on the Runtime
under monitoring. Login to Grafana, open “Explore → Metrics” in the left-hand menu.
Type codefresh_ in “Search” field, ensure that time range covers at least one running
build. You will see a list of available metrics.

{% include
image.html
lightbox="true"
file="/images/troubleshooting/before-verify.png"
url="/images/troubleshooting/before-verify.png"
alt="codefresh_no_such_file_directory.png"
max-width="70%"
%}

##How to use/understand metrics

codefresh_engine_deprecated_images_pulled_total metric is a counter increased
by 1 each time Docker daemon pulls a deprecated image. Labels {account_name,
pipeline_id, workflow, image_name} allow you to group results.

##Grafana dashboard

Easiest way to analyze data — to use the attached Grafana dashboard.

1\. Download[this Grafana dashboard json](../../grafana-dashboard.json) <br>
2\. Login to Grafana <br>
3\. Open “Dashboards → New → Import” <br>

{% include
image.html
lightbox="true"
file="/images/troubleshooting/import-dashboard.png"
url="/images/troubleshooting/import-dashboard.png"
alt="codefresh_no_such_file_directory.png"
max-width="70%"
%}

4\. Upload Dashboard JSON <br>
5\. Select “Select a JSON API data source → Mimir API”. Click “Import”

{% include
image.html
lightbox="true"
file="/images/troubleshooting/select-api-source.png"
url="/images/troubleshooting/select-api-source.png"
alt="codefresh_no_such_file_directory.png"
max-width="70%"
%}

6\. Done! 🎉

Deprecated Images Dashboard overview

{% include
image.html
lightbox="true"
file="/images/troubleshooting/deprecated-images-overview.png"
url="/images/troubleshooting/deprecated-images-overview.png"
alt="codefresh_no_such_file_directory.png"
max-width="70%"
%}

On the screenshot above:

1. Filter by account name.
2. Time range.
3. Account name (build owner). Link leads to the Admin Panel.
4. Deprecated image name.
5. Pipeline ID where pull was detected.

##Query Prometheus metric

You can query codefresh_engine_deprecated_images_pulled_total metric directly.
Consider the following query, that returns per second rate of deprecated images pulls,
aggregated by account ID, pipeline ID, build and image:

```
1. sum(
2. rate(
3. codefresh_engine_deprecated_images_pulled_total
4. [$__rate_interval])
5. ) by (account_name, pipeline_id, workflow, image_name)
```

{% include
image.html
lightbox="true"
file="/images/troubleshooting/deprecated-images.png"
url="/images/troubleshooting/deprecated-images.png"
alt="codefresh_no_such_file_directory.png"
max-width="70%"
%}

Deprecated images docker.io/tutum/dnsutils:latest and docker.io/docker/whalesay:latest
has been pulled in Pipeline #67867cfe8307bd8f9b7b034e

http://docker.io/tutum/dnsutils:latest
http://docker.io/docker/whalesay:latest


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