@Marvin I have a k8s cluster where I have installed prefect-server and worker. For the on premise installations I won't have internet connections and so I am trying to use an internal docker registry for storing the flow code in it and pulling it at runtime by the ephimeral pod. I have problems since the internal registry on k8s requires https. What solution can you propose me?

When dealing with internal Docker registries on Kubernetes that require HTTPS, you have a few options to consider: 1. Custom Docker Image: Use Prefect's

DockerImage

class to create a custom Docker image that includes your flow code and dependencies. This image can then be pushed to your internal registry. Make sure to configure the registry with the necessary HTTPS certificates. Prefect documentation provides guidance on using custom Docker images. 2. Registry Configuration: Ensure that your Kubernetes cluster can authenticate and pull images from your internal registry. This might involve setting up a Kubernetes secret with your registry credentials and configuring your pods to use this secret for image pulls. The Kubernetes documentation on private registries can be helpful. 3. Certificate Management: If your internal registry uses self-signed certificates, you might need to configure your cluster nodes to trust these certificates. This can often be done by adding the certificate to the trusted store on each node. 4. Prefect Configuration: Ensure that your Prefect deployment is configured to use the internal registry URLs and that your flow's deployment YAML specifies the correct image pull secrets. For more detailed steps, you might want to look into the Kubernetes deployment guide from Prefect. If you encounter issues, checking the Prefect GitHub issues related to Docker and Kubernetes might also provide insights or workarounds used by others facing similar challenges.

@Marvin is it really not possible to telle the worker to just use http?

It seems that Prefect workers primarily communicate with Prefect Cloud or a Prefect server, and the communication details are generally abstracted away from the user. Workers are responsible for polling work pools and executing flow runs, and they send logs to the Prefect Cloud API if connected. The documentation doesn't explicitly mention configuring workers to use HTTP directly, as the communication protocols are typically handled internally by Prefect. If you are looking to customize the communication protocol for a specific use case, it might require diving into the source code or reaching out to the Prefect community for more detailed guidance. For more information on how workers operate, you can refer to the Prefect documentation on workers. If you have any specific requirements or constraints, feel free to share them!

@Marvin Could I use a remote storage installed on the cluster and with the code loaded on it? such that it could download the code offline from the worker and then being able to create a job with the loaded code?

Yes, you can use remote storage for your flow code in Prefect, which allows workers to download the code when needed. Prefect supports several remote storage options, including: - Git-based storage (e.g., GitHub, GitLab, Bitbucket) - Docker image-based storage - Cloud-provider storage (e.g., AWS S3, Azure Blob Storage, GCP GCS) To set this up, you can create a deployment using either Python code with the

flow.deploy

method or through a YAML specification in a

prefect.yaml

file. If you're using Python, you would typically specify the storage location using the

flow.from_source

method, providing the

source

and

entrypoint

arguments. Here's a brief outline of how you might configure this: 1. Choose a Storage Option: Decide which remote storage option best fits your needs. 2. Configure Storage: Set up your storage to hold the flow code. For example, if using S3, ensure the necessary Python modules are installed. 3. Deployment: Use Prefect's deployment tools to specify the storage location in your deployment configuration. For more detailed guidance, refer to the Prefect documentation on storing flow code. This setup allows workers to download the code as needed, facilitating offline code execution. If you have any specific configuration questions, feel free to ask!

@Marvin How would I do with a GitLab storage, for first installing it on the cluster, then pushing code to it and then reading from it from the worker with python.deploy ?

To use GitLab as a storage option for your Prefect flows, you'll need to set up your cluster to interact with GitLab, push your code to a GitLab repository, and then configure Prefect to pull the code from there. Here's a step-by-step guide: 1. Install Git on the Cluster: Ensure that Git is installed on your cluster nodes so that they can clone repositories from GitLab. 2. Push Code to GitLab: - Create a repository on GitLab. - Add your flow code to the repository. - Commit and push the code to the GitLab repository. 3. Configure Prefect to Use GitLab: - Use the

flow.deploy

method to create a deployment that specifies GitLab as the storage option. - You'll need to provide the repository URL and possibly authentication details (like a personal access token) if your repository is private. Here's a basic example of how you might set this up in Python:

python
from prefect import flow
from prefect.deployments import Deployment
from prefect.filesystems import Git

# Define your flow
@flow
def my_flow():
    print("Hello, Prefect!")

# Create a Git storage block pointing to your GitLab repository
git_storage = Git(
    repository="<https://gitlab.com/your-username/your-repo.git>",
    reference="main",  # or any branch/tag/commit
    access_token="your-access-token"  # if needed for private repos
)

# Deploy your flow with the Git storage
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-flow-deployment",
    storage=git_storage
)

deployment.apply()

4. Worker Configuration: - Ensure your Prefect workers are set up to pull the code using the deployment configuration. Workers will clone the repository as specified in the deployment. For more detailed instructions and options, you might want to consult the Prefect documentation on using Git-based storage. If you encounter any specific issues or need further customization, feel free to ask!

@Marvin Instead why don't push the image directly in the prefect worker? Can't this be a straightforward solution?

Using Docker images to encapsulate your flow code and dependencies can indeed be a straightforward solution. By packaging everything into a Docker image, you ensure that the execution environment is consistent across all workers. Here's how you can approach this: 1. Create a Docker Image: - Write a

Dockerfile

that includes your flow code and any dependencies. - Build the Docker image locally or in your CI/CD pipeline. 2. Push the Docker Image to a Registry: - Push the built Docker image to a container registry like Docker Hub or a private registry. 3. Configure Prefect to Use the Docker Image: - Use Prefect's deployment tools to specify the Docker image as the storage for your flow. - When deploying, use the

docker

worker type to ensure that the flow runs using the Docker image. Here's a basic example of how you might set this up:

python
from prefect import flow
from prefect.deployments import Deployment
from prefect.infrastructure import DockerContainer

# Define your flow
@flow
def my_flow():
    print("Hello, Prefect!")

# Define the Docker infrastructure
docker_infrastructure = DockerContainer(
    image="your-docker-image:latest",  # Replace with your image name
    image_pull_policy="ALWAYS"  # Ensure the latest image is pulled
)

# Deploy your flow with the Docker infrastructure
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-flow-deployment",
    infrastructure=docker_infrastructure
)

deployment.apply()

4. Worker Configuration: - Ensure your workers are configured to use Docker. Workers will pull and run the specified Docker image for each flow run. This approach provides a consistent environment and can simplify dependency management, especially if your flow has complex requirements. For more information, refer to the Prefect documentation on Docker storage. If you have any specific questions or need further assistance, feel free to ask!

@Marvin the problem is I can't pull from a registry with http trough the prefect-woker pod I have installed with Helm in my k8s cluster. Why can't I put the image inside the pod of the worker or the server directly?

The challenge you're facing with pulling images from a registry using HTTP in a Kubernetes environment deployed via Helm is quite common, especially when dealing with restricted network environments or specific security policies. Unfortunately, directly embedding Docker images within the Prefect worker or server pods isn't a typical or recommended practice due to how Kubernetes and container orchestration work. However, there are a few alternative approaches you might consider: 1. Local Docker Registry: - Set up a local Docker registry within your Kubernetes cluster that supports HTTP. You can then push your images to this local registry and configure your Kubernetes nodes to pull images from it. 2. Sidecar Containers: - Use a sidecar container pattern where you include a container in the same pod as the Prefect worker. This sidecar can handle tasks like downloading images or dependencies that the main container can then access. 3. Preloaded Images: - Preload the necessary Docker images onto the nodes of your Kubernetes cluster. This approach involves manually loading images onto each node, which Kubernetes can then use to run your pods. 4. Custom Helm Charts: - Modify your Helm charts to include additional init containers or sidecars that can handle specific tasks like downloading images or dependencies. 5. Use Persistent Volumes: - If the image or the necessary files can be stored on a persistent volume, you could mount this volume to your worker pods. These approaches provide alternative ways to manage and access Docker images without relying solely on pulling from a remote registry. If you're comfortable with Kubernetes and Helm, you can explore these options to find the one that best fits your infrastructure and security requirements. If you need more detailed guidance on any of these methods, feel free to ask!

@Marvin is the prefect-worker being able to pull a docker image trough the persistent volume?

Prefect workers themselves do not directly pull Docker images through a persistent volume. Instead, the concept of persistent volumes in Kubernetes is typically used to persist data across pod restarts or to share data between containers within the same pod. Here's how Docker images are generally handled: 1. Docker Image Pulling: - Kubernetes uses the container runtime (like Docker or containerd) on each node to pull images from a container registry. This process does not involve persistent volumes. 2. Persistent Volumes: - Persistent volumes are used to store data that needs to persist beyond the lifecycle of a single pod. They are not used for storing Docker images themselves. If you're facing issues with pulling Docker images due to network restrictions or HTTP limitations, you might consider setting up a local Docker registry within your Kubernetes cluster or preloading images onto your nodes, as these approaches align better with Kubernetes' architecture. If you need further assistance or have specific constraints, feel free to share more details!

@Marvin ok but if with the remote storage is then prefect able to create a docker image? because otherwise it's the same stuff as before, I have to store the image somewhere

Prefect itself does not create Docker images; it relies on existing Docker images to execute flows when using Docker-based infrastructure. The process of creating Docker images is typically handled outside of Prefect, using Docker tools and workflows. Here's how you can manage the process: 1. Create Docker Image: - Use a

Dockerfile

to define your environment, including your flow code and dependencies. - Build the Docker image using Docker commands (e.g.,

docker build

). 2. Push to a Registry: - Push the built Docker image to a container registry (like Docker Hub, ECR, or a local registry within your Kubernetes cluster). 3. Use in Prefect: - Configure your Prefect deployment to use the Docker image by specifying it in your deployment configuration. While Prefect can use Docker images to run your flows, the creation and management of Docker images are external processes. If you're looking for automation, you might consider integrating this process into a CI/CD pipeline that builds and pushes Docker images whenever your flow code changes. If you need more detailed guidance on setting up such a pipeline or any other aspect, feel free to ask!

@Marvin how is it structured the process of loading ephimeral code from a remotestorage block?

To load ephemeral code from a remote storage block in Prefect, you typically follow these steps: 1. Define Remote Storage: - Use Prefect's storage options to specify where your flow code is stored. This could be a cloud storage service like AWS S3, Google Cloud Storage, or Azure Blob Storage. 2. Configure Storage Block: - Create a storage block in Prefect that points to your remote storage location. This block will handle downloading the code when needed. 3. Deployment Configuration: - When deploying your flow, configure the deployment to use the remote storage block. This tells Prefect where to find the flow code when executing a run. 4. Flow Execution: - When a Prefect worker picks up a flow run, it uses the storage block configuration to download the code from the specified remote storage. - The code is then executed in the environment specified by your deployment (e.g., Docker, Kubernetes, etc.). Here's a conceptual example of setting this up:

python
from prefect import flow
from prefect.deployments import Deployment
from prefect.filesystems import S3

# Define your flow
@flow
def my_flow():
    print("Hello, Prefect!")

# Create an S3 storage block
s3_storage = S3(
    bucket_path="<s3://your-bucket/your-flow-code>",
    aws_access_key_id="your-access-key",
    aws_secret_access_key="your-secret-key"
)

# Deploy your flow with the S3 storage
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-flow-deployment",
    storage=s3_storage
)

deployment.apply()

In this example, the flow code is stored in an S3 bucket. When a worker executes the flow, it downloads the code from S3 using the configuration provided in the storage block. This setup allows for ephemeral execution of code, meaning the code is fetched as needed and doesn't persist on the worker beyond the execution of the flow run. For more detailed information, you can refer to the Prefect documentation on remote storage. If you have further questions or need specific guidance, feel free to ask!

@Marvin and how do I install a remote storage on my k8s cluster?

To set up remote storage for your Prefect flows in a Kubernetes cluster, you typically don't "install" remote storage directly on the cluster. Instead, you configure your cluster to access an external storage service. Here's a general guide on how to set this up: 1. Choose a Remote Storage Service: - Select a cloud storage service such as AWS S3, Google Cloud Storage (GCS), or Azure Blob Storage. 2. Configure Access: - Ensure your Kubernetes cluster can access the chosen storage service. This usually involves setting up service accounts, IAM roles, or credentials that allow your cluster to authenticate with the storage service. 3. Store Credentials Securely: - Use Kubernetes secrets to store any necessary credentials securely. For example, you might create a secret that contains AWS access keys or GCP service account keys.

bash
   kubectl create secret generic s3-credentials --from-literal=aws_access_key_id=YOUR_ACCESS_KEY --from-literal=aws_secret_access_key=YOUR_SECRET_KEY

4. Configure Prefect to Use the Storage: - In your Prefect deployment, configure the storage block to use the credentials stored in your Kubernetes secrets. This can often be done by referencing environment variables or directly accessing the secrets. 5. Access Storage in Your Flows: - When deploying your flows, ensure that they are configured to use the remote storage. Prefect workers running in your Kubernetes cluster will then be able to download the flow code from the remote storage as needed. Here's a brief example of using S3 as remote storage with Prefect:

python
from prefect import flow
from prefect.deployments import Deployment
from prefect.filesystems import S3

# Define your flow
@flow
def my_flow():
    print("Hello, Prefect!")

# Create an S3 storage block
s3_storage = S3(
    bucket_path="<s3://your-bucket/your-flow-code>",
    aws_access_key_id="your-access-key",  # Use env vars or secrets in practice
    aws_secret_access_key="your-secret-key"  # Use env vars or secrets in practice
)

# Deploy your flow with the S3 storage
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-flow-deployment",
    storage=s3_storage
)

deployment.apply()

By setting this up, your Prefect workers running in the Kubernetes cluster will be able to fetch the flow code from the specified remote storage service when executing a flow run. If you need more specific instructions based on your storage service or any other details, feel free to ask!

@Marvin and in this case how is the container for the ephimeral pod created? Or how is the code executed?

When using remote storage to execute flows in a Kubernetes environment with Prefect, here's how the process typically unfolds: 1. Deployment Configuration: - You configure a Prefect deployment that specifies the remote storage block containing your flow code and the infrastructure type (e.g., Kubernetes, Docker). 2. Worker Polling: - Prefect workers, which are already running in your Kubernetes cluster, poll work pools for flow runs to execute. 3. Flow Run Execution: - When a worker picks up a flow run, it provisions the necessary infrastructure to execute the flow. This involves creating an ephemeral pod if you're using Kubernetes. 4. Ephemeral Pod Creation: - The worker uses the Kubernetes API to create a new pod in the cluster. This pod is configured based on the deployment settings, including the Docker image specified in your deployment. - The pod's container pulls the specified Docker image from a registry. This image should contain the necessary runtime environment for your flow. 5. Code Download and Execution: - Once the pod is running, the Prefect worker inside the pod accesses the remote storage block to download the flow code. - The flow code is executed within the pod's container, leveraging the environment and dependencies defined in the Docker image. 6. Completion and Cleanup: - After the flow execution completes, the ephemeral pod is terminated and resources are cleaned up. Here's a simplified view of the process:

yaml
# Example Kubernetes Pod Spec
apiVersion: v1
kind: Pod
metadata:
  name: prefect-flow-run
spec:
  containers:
  - name: flow-container
    image: your-docker-image:latest
    env:
    - name: PREFECT_STORAGE_BLOCK
      value: <s3://your-bucket/your-flow-code>
    # Additional environment variables for credentials
  restartPolicy: Never

The key here is that the Docker image used must contain the necessary environment to run your Prefect flow. The image should be pre-built and pushed to a registry accessible by your Kubernetes cluster. This setup ensures that each flow run is isolated within its own pod, providing a clean and consistent execution environment. If you have any specific questions about configuring this process, feel free to ask!

@Marvin I don't get if with the remote storage I need only some code or I have to specify a base docker image to pull such that I can create the ephimeral pod

When using remote storage with Prefect in a Kubernetes environment, you typically need both a Docker image and the flow code stored remotely. Here's how each component fits into the process: 1. Docker Image: - The Docker image serves as the runtime environment for your flow. It should contain all the necessary dependencies and configurations required to execute your flow code. - This image is specified in your Prefect deployment configuration and is pulled by the Kubernetes pod when a flow run is executed. 2. Remote Storage for Flow Code: - The flow code itself is stored in a remote storage service (like S3, GCS, etc.). This allows the code to be downloaded dynamically by the Prefect worker when the flow run is executed. - The remote storage block in Prefect specifies where this code is located. Process Overview: - Base Docker Image: You specify a base Docker image in your Prefect deployment. This image is used to create the ephemeral pod in Kubernetes. - Code Download: Once the pod is running, the Prefect worker inside the pod downloads the flow code from the remote storage block. - Execution: The flow code is executed within the environment provided by the Docker image. Here's a conceptual example:

python
from prefect import flow
from prefect.deployments import Deployment
from prefect.infrastructure import KubernetesJob
from prefect.filesystems import S3

# Define your flow
@flow
def my_flow():
    print("Hello, Prefect!")

# Create an S3 storage block for the flow code
s3_storage = S3(
    bucket_path="<s3://your-bucket/your-flow-code>"
)

# Define the Kubernetes job infrastructure
k8s_infrastructure = KubernetesJob(
    image="your-docker-image:latest"  # Base Docker image
)

# Deploy your flow with the S3 storage and Kubernetes infrastructure
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-flow-deployment",
    storage=s3_storage,
    infrastructure=k8s_infrastructure
)

deployment.apply()

In this setup: - The Docker image is specified in the

KubernetesJob

configuration. - The flow code is stored in S3 and specified in the

S3

storage block. This approach ensures that your flow runs in a consistent environment while allowing the code to be dynamically loaded from remote storage. If you have further questions or need clarification, feel free to ask!

@Marvin ok so let's say I am offline I still need somewhere from where pulling the image, so If I am offline I still have the same problems as before right?

Yes, if you're offline and unable to access a container registry, you'll encounter similar challenges. The Docker image needs to be available to the Kubernetes nodes for the ephemeral pod creation, and typically, this involves pulling the image from a registry. Here are a few strategies to handle this situation: 1. Preload Docker Images: - Manually preload the necessary Docker images onto each node in your Kubernetes cluster. This way, when a pod is created, it can use the local copy of the image without needing to pull from a registry. 2. Local Docker Registry: - Set up a local Docker registry within your network that is accessible to your Kubernetes nodes. You can push your images to this registry and configure your nodes to pull from it, even when offline. 3. Persistent Storage with Preloaded Images: - Use persistent storage solutions to maintain a cache of Docker images. This involves more complex setup but can be useful for environments with intermittent connectivity. 4. Use a Sidecar Container: - If feasible, use a sidecar container in your pods to handle tasks like downloading dependencies or code from a local or network-accessible location. These strategies require some initial setup but can ensure that your Kubernetes cluster can function independently of an external container registry. If you have specific constraints or need further guidance on implementing one of these solutions, feel free to ask!