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<@ULVA73B9P> Can I pass environment variables in my values.yaml when installing an aks worker with h...
# ask-marvin
d
@Marvin Can I pass environment variables in my values.yaml when installing an aks worker with helm?
m
Yes, you can pass environment variables in your 
values.yaml
file when installing an AKS worker with Helm. Here's a general overview on how to do it based on the provided information: 1. Configure Helm chart values: You need to create a 
values.yaml
file to customize the Prefect worker configuration. This file will include settings for connecting to the proper account, workspace, and work pool. 2. Include Environment Variables: To provide environment variables, you can add a section like 
job_variables
to your 
values.yaml
. Here’s an example structure:
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yaml
worker:
  cloudApiConfig:
    accountId: <target account ID>
    workspaceId: <target workspace ID>
  config:
    workPool: <target work pool name>
  job_variables:
    env:
      EXECUTION_ENVIRONMENT: staging
      MY_NOT_SO_SECRET_CONFIG: plumbus
3. Helm Installation: Use Helm to install the Prefect worker with your customized configurations:
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sh
   helm repo add prefect <https://prefecthq.github.io/prefect-helm>
   helm repo update
   kubectl create namespace prefect
   kubectl create secret generic prefect-api-key --namespace=prefect --from-literal=key=your-prefect-cloud-api-key
   helm install prefect-worker prefect/prefect-worker -n prefect -f values.yaml
For more detailed guidance and examples, refer to the relevant documentation page. If you need further customization regarding job variables, check out the Prefect guide on job variables.
d
@Marvin great, thank you. So I should be able to also include configMap for my logging.yml file and set the PREFECT_LOGGING_SETTINGS_PATH, yes?
m
Yes, you can include a 
configMap
for your 
logging.yml
file and set the 
PREFECT_LOGGING_SETTINGS_PATH
in your 
values.yaml
. Here's a step-by-step guide on how to achieve this: 1. Create a ConfigMap for 
logging.yml
: First, you need to create a Kubernetes 
configMap
that contains your custom 
logging.yml
file.
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sh
   kubectl create configmap my-logging-config --from-file=logging.yml --namespace=prefect
2. Update the 
values.yaml
file: In your 
values.yaml
file, you need to set the environment variable 
PREFECT_LOGGING_SETTINGS_PATH
to point to the location of your 
logging.yml
file mounted by the configMap.
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yaml
   worker:
     cloudApiConfig:
       accountId: <target account ID>
       workspaceId: <target workspace ID>
     config:
       workPool: <target work pool name>
     job_variables:
       env:
         EXECUTION_ENVIRONMENT: staging
         MY_NOT_SO_SECRET_CONFIG: plumbus
         PREFECT_LOGGING_SETTINGS_PATH: /path/to/logging.yml
3. Mount the ConfigMap to the Worker Pod: Ensure you mount the 
configMap
containing the 
logging.yml
into the worker pod by adding a volume and volumeMounts section corresponding to the values.yaml.
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yaml
   worker:
     config:
       ...
     volumes:
       - name: logging-config
         configMap:
           name: my-logging-config
     volumeMounts:
       - name: logging-config
         mountPath: /path/to/logging.yml
         subPath: logging.yml
4. Helm Installation: Install the Prefect worker with your customized configurations using Helm.
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sh
   helm repo add prefect <https://prefecthq.github.io/prefect-helm>
   helm repo update
   kubectl create namespace prefect
   kubectl create secret generic prefect-api-key --namespace=prefect --from-literal=key=your-prefect-cloud-api-key
   helm install prefect-worker prefect/prefect-worker -n prefect -f values.yaml
Refer to the relevant Prefect logging documentation for additional customization details.
d
@Marvin No, this does not appear to be correct. When I do my 
helm install...
it states that 
Additional property volumeMounts...job_variables...volumes is not allowed
m
It appears I made a mistake in my previous response. Let's refine the approach to include the correct configuration for setting environment variables and mounting the 
configMap
correctly in the 
values.yaml
file: 1. Create a ConfigMap for 
logging.yml
: Create a 
configMap
which contains your 
logging.yml
.
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sh
   kubectl create configmap my-logging-config --from-file=logging.yml --namespace=prefect
2. Update the 
values.yaml
file: You will need to refer to this 
configMap
in your 
values.yaml
file. However, instead of adding volumes and volumeMounts at the worker level, ensure to follow the Helm Charts syntax. Example `values.yaml`:
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yaml
worker:
  cloudApiConfig:
    accountId: <target-account-ID>
    workspaceId: <target-workspace-ID>
  config:
    workPool: <target-work-pool-name>
  extraEnv:
    - name: PREFECT_LOGGING_SETTINGS_PATH
      value: /etc/prefect/logging.yaml

extraVolumes:
  - name: logging-config
    configMap:
      name: my-logging-config

extraVolumeMounts:
  - name: logging-config
    mountPath: /etc/prefect/logging.yaml
    subPath: logging.yaml
3. Helm Installation: Install the Prefect worker with your customized configuration using Helm:
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sh
helm repo add prefect <https://prefecthq.github.io/prefect-helm>
helm repo update
kubectl create namespace prefect
kubectl create secret generic prefect-api-key --namespace=prefect --from-literal=key=your-prefect-cloud-api-key
helm install prefect-worker prefect/prefect-worker -n prefect -f values.yaml
This configuration correctly applies the 
configMap
and specifies the environment variable while adhering to the correct schema for the Helm Chart.
d
@Marvin I moved 
extraEnv
up a level and that seems to allow the worker to start, but when I enter the interactive shell I have no value in the environment variable and no logging.yml
m
Let's correct the situation by ensuring you appropriately mount the 
logging.yml
using Kubernetes 
configMap
and set the environment variable correctly using the Helm chart. This approach aligns better with Helm's expected structure and practices. 1. Create the ConfigMap for `logging.yml`:
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sh
kubectl create configmap my-logging-config --from-file=logging.yml --namespace=prefect
2. Update your 
values.yaml
file:
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yaml
worker:
  cloudApiConfig:
    accountId: <target-account-ID>
    workspaceId: <target-workspace-ID>
  config:
    workPool: <target-work-pool-name>
  extraEnv:
    - name: PREFECT_LOGGING_SETTINGS_PATH
      value: /etc/prefect/logging.yml

extraVolumeMounts:
  - name: logging-config
    mountPath: /etc/prefect/
3. Add extra Volumes and VolumeMounts to your Helm chart: Ensure that your 
extraVolumes
and 
extraVolumeMounts
are correctly specified within the Helm values file:
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yaml
extraVolumes:
  - name: logging-config
    configMap:
      name: my-logging-config

extraVolumeMounts:
  - name: logging-config
    mountPath: /etc/prefect/
    subPath: logging.yml
4. Helm Installation: Finally, install the Prefect worker using Helm with your customized configuration:
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sh
helm repo add prefect <https://prefecthq.github.io/prefect-helm>
helm repo update
kubectl create namespace prefect
kubectl create secret generic prefect-api-key --namespace=prefect --from-literal=key=your-prefect-cloud-api-key
helm install prefect-worker prefect/prefect-worker -n prefect -f values.yaml
For a detailed guide and verification, you can refer more to Kubernetes Deployment Guide where similar examples are present.
d
Thank you @Marvin, I ended up discovering the values I needed to add in the deployment.yaml of the helm chart.
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worker:
  ...
  extraEnvVars:
    ...
  extraVolumes:
    ...
  extraVolumeMounts:
    ...
m
That makes sense. Customizing the 
deployment.yaml
with the correct sections is vital. For future reference, here is how you can use 
extraEnvVars
, 
extraVolumes
, and 
extraVolumeMounts
correctly in the `values.yaml`:
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yaml
worker:
  ...
  extraEnvVars:
    - name: PREFECT_LOGGING_SETTINGS_PATH
      value: /etc/prefect/logging.yaml
  extraVolumes:
    - name: logging-config
      configMap:
        name: my-logging-config
  extraVolumeMounts:
    - name: logging-config
      mountPath: /etc/prefect/
      subPath: logging.yaml
With these values, the Helm chart should correctly mount the 
logging.yml
and set the 
PREFECT_LOGGING_SETTINGS_PATH
environment variable. Always ensure your configuration aligns with the Helm chart's expected structure. For comprehensive documentation, you can refer to: - Prefect Kubernetes Deployment Guide - The values.yaml file in the 
prefect-helm
repository for further details. Feel free to ask if you have any more questions!
d
@Marvin continuing now with the jobs that the worker starts, I can add the volumes and volumeMounts to the work pool infrastructure setup?
m
To add 
volumes
and 
volumeMounts
to the work pool infrastructure setup for the jobs that the worker starts, you need to adjust the work pool's infrastructure block configuration. Here's how you can achieve that using Prefect. 1. Define the Infrastructure Block: When creating or updating the infrastructure block for your work pool, you can specify the 
volumes
and 
volumeMounts
within the block's configuration. 2. Example with Kubernetes Infrastructure: Assuming you are using Kubernetes, you can define the infrastructure block as shown below:
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python
from prefect.infrastructure.kubernetes import KubernetesJob

k8s_job = KubernetesJob(
    namespace="prefect",
    image="your-custom-image",
    service_account_name="your-service-account",
    env={"PREFECT_API_URL": "<https://api.prefect.io>"},
    volumes=[
        {
            "name": "logging-config",
            "configMap": {
                "name": "my-logging-config",
            },
        },
    ],
    volume_mounts=[
        {
            "name": "logging-config",
            "mountPath": "/etc/prefect",
            "subPath": "logging.yml",
        },
    ],
)

k8s_job.save("my-k8s-job")
3. Deploy with the Work Pool: When deploying your flow, reference the saved infrastructure block in your deployment.
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python
from prefect.deployments import Deployment
from prefect.filesystems import GitHub

storage = GitHub(repository="<https://github.com/your-repo/your-project>")

deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-deployment",
    version=1,
    work_pool_name="my-work-pool",
    parameters={},
    schedule=None,
    storage=storage,
    infrastructure=k8s_job,
)

deployment.apply()
4. Creating and Applying the Work Pool: Ensure you've created a work pool if not already done.
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sh
prefect work-pool create "my-work-pool" --type kubernetes
By saving the infrastructure block with the 
volumes
and 
volumeMounts
defined, Prefect will apply these when running jobs in the specified work pool. For more examples and details, refer to the Kubernetes Worker documentation and the Prefect guide on deploying flows. If you encounter issues or have specific requirements, please check the Prefect GitHub issues for similar cases or raise a new one.
d
@Marvin would this (sniipet of) json work in my base job template?
"spec": { "volumes": [ { "name": "aks-fileshare", "azureFile": { "readOnly": false, "shareName": "aks", "secretName": "persistent-volume-secret" } } ], "containers": [ { "env": "{{ env }}", "args": "{{ command }}", "name": "prefect-job", "image": "{{ image }}", "volumeMounts": [ { "name": "aks-fileshare", "mountPath": "/mnt/azure1" } ],
m
Yes, defining the volumes and volume mounts in a base job template JSON can also work, especially when you want to have a reusable base template for your Kubernetes jobs. Here’s an example of how your 
base_job_template
JSON might look:
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json
{
  "metadata": {
    "name": "base-job-template",
    "namespace": "prefect"
  },
  "spec": {
    "serviceAccountName": "your-service-account",
    "containers": [
      {
        "name": "prefect-job",
        "image": "your-custom-image",
        "env": [
          {
            "name": "PREFECT_API_URL",
            "value": "<https://api.prefect.io>"
          },
          {
            "name": "PREFECT_LOGGING_SETTINGS_PATH",
            "value": "/etc/prefect/logging.yml"
          }
        ],
        "volumeMounts": [
          {
            "name": "logging-config",
            "mountPath": "/etc/prefect/",
            "subPath": "logging.yml"
          }
        ]
      }
    ],
    "volumes": [
      {
        "name": "logging-config",
        "configMap": {
          "name": "my-logging-config"
        }
      }
    ]
  }
}
You can use this JSON in Prefect by referring to it in your work pool configuration. Here is an example of how to use it within your infrastructure setup in Python:
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python
from prefect.infrastructure.kubernetes import KubernetesJob

k8s_job_with_base_template = KubernetesJob(
    namespace="prefect",
    image="your-custom-image",
    service_account_name="your-service-account",
    env={"PREFECT_API_URL": "<https://api.prefect.io>"},
    job_template_path="path-to-your-base-job-template.json"
)

k8s_job_with_base_template.save("my-k8s-job-with-base-template")
And when deploying your flow, you reference this infrastructure block:
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python
from prefect.deployments import Deployment
from prefect.filesystems import GitHub

storage = GitHub(repository="<https://github.com/your-repo/your-project>")

deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-deployment",
    version=1,
    work_pool_name="my-work-pool",
    parameters={},
    schedule=None,
    storage=storage,
    infrastructure=k8s_job_with_base_template,
)

deployment.apply()
Using a base job template can significantly streamline and standardize job specifications across multiple deployments. Be sure to adjust paths, names, and specifics according to your needs. Refer to the Kubernetes Worker documentation for additional details and advanced configuration options.
d
@Marvin can the values for attaching persistent volume be put into the prefect.yaml file, perhaps in "definitions:"
m
Yes, you can include values for attaching a persistent volume in the 
prefect.yaml
file, particularly within the 
definitions
section for work pools and job variables. This allows you to define reusable configurations, such as volumes and volume mounts, which can then be referenced in individual deployments. Here's a step-by-step guide on how to configure this: 1. Define Persistent Volumes and VolumeMounts in `prefect.yaml`: You can set up these configurations in the 
definitions
section of the 
prefect.yaml
file.
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yaml
prefect-version: 2.0

build:
  - prefect_docker.deployments.steps.build_docker_image:
      id: build-image
      image: my-example-image
      tag: latest

push:
  - prefect_docker.deployments.steps.push_docker_image:
      image: "{{ build-image.image }}"
      tag: latest

definitions:
  work_pools:
    my_k8s_work_pool: &my_k8s_work_pool
      type: kubernetes
      job_template:
        metadata:
          namespace: prefect
        spec:
          serviceAccountName: your-service-account
          containers:
            - name: prefect-job
              image: your-custom-image
              env:
                - name: PREFECT_API_URL
                  value: <https://api.prefect.io>
                - name: PREFECT_LOGGING_SETTINGS_PATH
                  value: /etc/prefect/logging.yml
              volumeMounts:
                - name: logging-config
                  mountPath: /etc/prefect
                  subPath: logging.yml
          volumes:
            - name: logging-config
              configMap:
                name: my-logging-config

deployments:
  - name: my-deployment
    flow_name: my_flow
    work_pool:
      <<: *my_k8s_work_pool
    storage: 
      - prefect.filesystems.GitHub:
          repository: <https://github.com/your-repo/your-project>
    schedule:
      interval: 600
2. Apply and Use the Configuration: To use the configuration for your deployments, ensure your deployment command references the 
prefect.yaml
file.
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sh
prefect deployment build -n my-deployment --file prefect.yaml
3. Reference in Deployment Script (if needed): If you need to programmatically deploy flows or use these configurations in your scripts, reference the infrastructure and configurations from the 
prefect.yaml
.
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python
from prefect.deployments import Deployment
from prefect.filesystems import GitHub

storage = GitHub(repository="<https://github.com/your-repo/your-project>")

deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-deployment",
    version=1,
    work_pool_name="my-work-pool",
    parameters={},
    schedule=None,
    storage=storage,
)

deployment.apply()
By defining your volumes and volume mounts in the 
prefect.yaml
file under the 
definitions
section, you create a reusable template that can be referenced across multiple deployments, ensuring consistency and reducing repetition. For further details, refer to the Prefect deployment guide and the Kubernetes Work Pool documentation.
d
@Marvin That didn't work for me, should it be in the build block perhaps?
m
Let’s turn to try the configuration within the 
build
block. Since Prefect's 
prefect.yaml
is fairly flexible, defining volumes and volumeMounts in the 
build
block under definitions and job variables might help. Here's an example configuration:
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yaml
prefect-version: 2.0

build:
  - prefect_docker.deployments.steps.build_docker_image:
      id: build-image
      image: my-example-image
      tag: latest

push:
  - prefect_docker.deployments.steps.push_docker_image:
      image: "{{ build-image.image }}"
      tag: latest

definitions:
  work_pools:
    my_k8s_work_pool: &my_k8s_work_pool
      type: kubernetes
      job_variables:
        metadata:
          namespace: prefect
        spec:
          serviceAccountName: your-service-account
          containers:
            - name: prefect-job
              image: your-custom-image
              env:
                - name: PREFECT_API_URL
                  value: <https://api.prefect.io>
                - name: PREFECT_LOGGING_SETTINGS_PATH
                  value: /etc/prefect/logging.yml
              volumeMounts:
                - name: logging-config
                  mountPath: /etc/prefect
                  subPath: logging.yml
          volumes:
            - name: logging-config
              configMap:
                name: my-logging-config

deployments:
  - name: my-deployment
    flow_name: my_flow
    work_pool:
      <<: *my_k8s_work_pool
    storage: 
      prefect.filesystems.GitHub:
        repository: <https://github.com/your-repo/your-project>
    schedule:
      interval: 600
Steps to Implement: 1. Define the Volumes and Mounts in 
definitions
Section: Here we define the 
work_pools
template and put volume and volume mounts configuration inside 
job_variables
. 2. Apply Configuration: Use the 
prefect.yaml
to build and apply deployments. Build and Apply:
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sh
prefect deployment build -n my-deployment --file prefect.yaml
If you’re still encountering issues, please double-check the alignment with the Prefect documentation or raise a specific issue in the Prefect repository for more tailored guidance: - Prefect GitHub Issues
d
@Marvin, I'm using a devops pipeline to deploy my flows, and am using my own supplied dockerfile which copies my logging.yml in. This is what I have as my definition block in prefect.yaml, but it's not adding the volume for my logs to be saved to, mounted to /mnt/azure1:
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definitions:
  tags: &common_tags
    - "aks"
  work_pool: &common_work_pool
    name: "axym-kubernetes"
    job_variables:
      image: "{{ build-image.image }}"
    job_template:
      spec:
        containers:
          - name: prefect-job
            env: 
              - name: PREFECT_LOGGING_SETTINGS_PATH
                value: /opt/prefect/logging/logging.yml
            volumeMounts:
              - name: aks-fileshare
                mountPath: /mnt/azure1
        volumes:
          - name: aks-fileshare
            azureFile:
              secretName: persistent-volume-secret
              shareName: aks
              readOnly: false
m
It seems like you’re on the right track but there might be some minor adjustments needed. We should ensure that the 
job_template
definition is correctly placed within the Prefect YAML structure, especially concerning the containers and volume mounting. Here is a corrected and complete structure for your Prefect YAML file, focusing on your specific requirements: prefect.yaml
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yaml
prefect-version: 2.0

build:
  - prefect_docker.deployments.steps.build_docker_image:
      id: build-image
      image: my-example-image
      tag: latest

push:
  - prefect_docker.deployments.steps.push_docker_image:
      image: "{{ build-image.image }}"
      tag: latest

definitions:
  tags: &common_tags
    - "aks"
  work_pool: &common_work_pool
    name: "axym-kubernetes"
    job_variables:
      image: "{{ build-image.image }}"
    job_template:
      spec:
        containers:
          - name: prefect-job
            env:
              - name: PREFECT_LOGGING_SETTINGS_PATH
                value: /opt/prefect/logging/logging.yml
            volumeMounts:
              - name: aks-fileshare
                mountPath: /mnt/azure1
        volumes:
          - name: aks-fileshare
            azureFile:
              secretName: persistent-volume-secret
              shareName: aks
              readOnly: false

deployments:
  - name: my-deployment
    flow_name: my_flow
    work_pool:
      <<: *common_work_pool
    tags:
      <<: *common_tags
    storage:
      prefect.filesystems.GitHub:
        repository: <https://github.com/your-repo/your-project>
    schedule:
      interval: 600
Steps: 1. In your 
job_template
, ensure the structure of 
spec
, 
containers
, 
volumes
, and 
volumeMounts
is accurate. 2. Make sure your 
volumeMounts
references match the 
volumes
names correctly. 3. Apply your configurations using the Prefect CLI within your DevOps pipeline. In Your DevOps Pipeline: Make sure to use the Prefect CLI commands to build and apply the deployment once your Prefect YAML file is filled out correctly:
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sh
prefect deployment build -n my-deployment --file prefect.yaml
prefect deployment apply my-deployment.yaml
This should accommodate the persistent volume mounts required for logging, applied through your Kubernetes work pool setup via Prefect YAML definitions. For a detailed guide, you can refer to Prefect Kubernetes Deployment Guide which can shed more light if further fine-tuning is needed. If this still doesn't function as expected, consulting specific examples within Prefect’s documentation or seeking community input on Prefect's Discourse or GitHub Issues might be beneficial.
d
@Marvin I'm going around in circles, since the docs keep taking me to the Deployment.yaml which works for us, but we're moving to the Prefect.yaml so we can employ 
prefect deploy
in our pipeline instead of 
prefect deployment apply
which is deprecated. So, would transposing our infrastructure values work, say like this?
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definitions:
  tags: &common_tags
    - "aks"
  work_pool: &common_work_pool
    name: "axym-kubernetes"
    job_variables:
      image: "{{ build-image.image }}"
  infrastructure:
    job:
      spec:
        template:
          spec:
            containers:
              - name: prefect-job
                env: 
                  - name: PREFECT_LOGGING_SETTINGS_PATH
                    value: /opt/prefect/logging/logging.yml
                volumeMounts:
                  - name: aks-fileshare
                    mountPath: /mnt/azure1
            volumes:
              - name: aks-fileshare
                azureFile:
                  secretName: persistent-volume-secret
                  shareName: aks
                  readOnly: false
m
Yes, aligning your 
prefect.yaml
as shown should allow you to correctly set up your work pool infrastructure values. However, ensure correct YAML indentation and structure for 
infrastructure
within the definitions. Here is your revised configuration: prefect.yaml
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yaml
prefect-version: 2.0

build:
  - prefect_docker.deployments.steps.build_docker_image:
      id: build-image
      image: my-example-image
      tag: latest

push:
  - prefect_docker.deployments.steps.push_docker_image:
      image: "{{ build-image.image }}"
      tag: latest

definitions:
  tags: &common_tags
    - "aks"
  work_pool: &common_work_pool
    name: "axym-kubernetes"
    job_variables:
      image: "{{ build-image.image }}"
  infrastructure:
    job:
      job_template:
        spec:
          template:
            spec:
              containers:
                - name: prefect-job
                  env:
                    - name: PREFECT_LOGGING_SETTINGS_PATH
                      value: /opt/prefect/logging/logging.yml
                  volumeMounts:
                    - name: aks-fileshare
                      mountPath: /mnt/azure1
              volumes:
                - name: aks-fileshare
                  azureFile:
                    secretName: persistent-volume-secret
                    shareName: aks
                    readOnly: false

deployments:
  - name: my-deployment
    flow_name: my_flow
    work_pool:
      <<: *common_work_pool
    tags:
      <<: *common_tags
    infrastructure:
      <<: *infrastructure
    storage:
      prefect.filesystems.GitHub:
        repository: <https://github.com/your-repo/your-project>
    schedule:
      interval: 600
Important Points: 1. Correct Indentation and Structure: Ensure that the YAML indentation is correct since YAML is sensitive to indentation levels. 2. Defining Infrastructure Correctly: Within 
definitions
, align the infrastructure and job templates correctly under 
infrastructure
. 3. References: Ensure that references (like 
<<: *common_work_pool
) are used correctly to merge the common configurations. 4. Using `prefect deploy`: This new 
prefect.yaml
structure is designed to work with the 
prefect deploy
command in your DevOps pipeline. In Your DevOps Pipeline: To deploy using `prefect deploy`:
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sh
prefect deploy --name my-deployment --file prefect.yaml
For further details, you can consult the Prefect 2.0 Deployment guide to ensure you are aligned with the latest practices. If you continue to face difficulty, detailed exploration on community platforms like Prefect Discourse or assistance via GitHub Issues might provide more tailored support.
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