@Marvin If we are running flows in Kubernetes ( EKS ) for example and we need S3 access for that flow. Is it possible to somehow pass in an IAM role that it should use, so we can restrict access? Rather than access keys from an IAM user

Yes, you can use an IAM role with Prefect flows running on Kubernetes (EKS) to access AWS services like S3 without using IAM user access keys. You can achieve this by assigning an IAM role to the Kubernetes service account that your Prefect worker pods use. This is a more secure practice as it allows you to restrict access and manage permissions through IAM roles. Here's a general approach: 1. Create an IAM Role: Create an IAM role with the necessary permissions for your S3 access. 2. Trust Policy: Attach a trust policy to this IAM role that allows the EKS service to assume the role. 3. Kubernetes Service Account: Create a Kubernetes service account and annotate it with the IAM role ARN. 4. Use the Service Account in Prefect: Ensure that your Prefect worker pods use this service account. This approach leverages AWS's IAM roles for service accounts (IRSA) feature, which is specifically designed for this type of integration. For more details, you can refer to the Prefect AWS ECS Guide which outlines similar steps for ECS. While it's for ECS, the IAM role setup concept is applicable to EKS as well.

@Marvin So if we have multiple flows/applications within Prefect for example, how do we scalably create these service accounts?

To scalably manage multiple flows or applications within Prefect, especially when dealing with Kubernetes service accounts, you can follow these steps: 1. Create Separate Service Accounts: For each flow or application, create a separate Kubernetes service account. This ensures that each flow has its own IAM role and permissions, which can be managed independently. 2. Automate Service Account Creation: Use infrastructure as code tools like Terraform or AWS CloudFormation to automate the creation of Kubernetes service accounts and their associated IAM roles. This approach allows you to manage and replicate configurations across different environments easily. 3. Role-Based Access Control: Implement role-based access control (RBAC) within Kubernetes to manage permissions at a more granular level. This helps in restricting what each service account can do within the cluster. 4. Use Prefect Cloud Service Accounts: Prefect Cloud provides service accounts that can be used to configure API access for running workers or executing deployment flow runs on remote infrastructure. These service accounts can be managed at the account level and shared across workspaces as needed. For more information on managing service accounts in Prefect Cloud, you can refer to the Prefect Cloud Service Accounts documentation.

@Marvin but do I not need to specify this block within my code?

aws_credentials = AwsCredentials.load("my-creds")

You can configure AWS credentials in Prefect using the

AwsCredentials

block, but it's not strictly necessary to specify them in your code if your compute environment is already set up with the necessary IAM roles. Here's how it works: 1. AwsCredentials Block: You can create an

AwsCredentials

block in Prefect to manage AWS credentials. This block can be created via the Prefect UI or in code. It typically includes the AWS Access Key ID and Secret Access Key. 2. Environment-Based Authentication: If your compute environment, like EKS, is configured with the appropriate IAM roles, Prefect will automatically use these credentials. This means you do not need to explicitly load the

AwsCredentials

block in your code unless you have specific reasons to override the environment-based settings. 3. Order of Authentication: Prefect follows the order of authentication as specified in the Boto3 documentation. It will use the credentials from the environment if available, which is ideal for scenarios where you have set up IAM roles for your Kubernetes service accounts. For more details on configuring AWS credentials in Prefect, you can refer to the Prefect Secrets Documentation.

@Marvin is it possible to deploy a deployment from my local machine? If so, how do I specify for it to use my local code

Yes, you can deploy a Prefect deployment from your local machine and specify it to use your local code. Here's how you can do it: 1. Local Storage Option: Prefect allows you to use local storage for deployments that run locally. This means your flow code can reside on your local machine. 2. Creating a Deployment: You can create a deployment using the

flow.deploy

method in Python or through a YAML specification in a

prefect.yaml

file. 3. Specify Local Code: When using the Python

deploy

method, you need to specify the flow storage location. If you're using local storage, ensure that the

source

and

entrypoint

arguments point to your local code. 4. Run Locally: When you trigger the deployment, it will use the local code specified in the deployment configuration. For more detailed instructions on deploying with local code, you can refer to the Prefect Deployment Documentation.

@Marvin can you show me how I can specify a service account to use if using python .deploy

To specify a service account when deploying using the

.deploy

method in Prefect, you need to ensure that the deployment configuration includes the necessary settings for the service account. However, the

deploy

function signature does not explicitly provide a parameter for specifying a service account directly. Instead, you would typically manage service account settings through your Kubernetes configuration or a YAML configuration file. Here's a general approach using Python and Kubernetes: 1. Kubernetes Configuration: Ensure that your Kubernetes cluster is configured with the service account you want to use. This involves creating a service account in Kubernetes and associating it with the necessary IAM roles. 2. Deployment Configuration: When you configure your deployment (either through Python or YAML), make sure that the Kubernetes pod specification includes the service account name. This is typically done in the Kubernetes manifest files rather than directly in the Prefect deployment code. 3. Prefect Deployment: Use the

deploy

method to create your deployment, ensuring that your Kubernetes environment is set up to use the correct service account. Here's an example of how you might set up your Kubernetes pod specification to use a specific service account:

yaml
apiVersion: v1
kind: Pod
metadata:
  name: my-prefect-pod
spec:
  serviceAccountName: my-service-account
  containers:
  - name: my-container
    image: my-prefect-image

For more details on deploying with Prefect and managing Kubernetes service accounts, you would typically refer to your Kubernetes documentation or consult the Prefect infrastructure setup guide.

@Marvin if name == "__main__": s3_flow.deploy( name="test-deployment", work_pool_name="new-worker-pool", image="xxxxxxxxxx, job_variables={} push=False, build=False ) I meant here

When deploying a flow with Prefect using the

.deploy

method, the service account configuration isn't specified directly in the Python code. Instead, it is managed through the Kubernetes setup. Here's how you can ensure your deployment uses the correct service account: 1. Kubernetes Setup: Before deploying, ensure your Kubernetes cluster has a service account configured with the necessary permissions. This is done outside of Prefect, directly in Kubernetes. 2. Deployment Configuration: In your Prefect deployment, you can specify Kubernetes-specific configurations in a YAML file or through Kubernetes manifests that Prefect will use when running the deployment. 3. Python Deployment Code: The code snippet you provided is primarily for configuring Prefect-specific deployment settings like the work pool and Docker image. The service account is not specified here but is instead part of the Kubernetes environment configuration. Here's an example of how your Kubernetes setup might look:

yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: prefect-deployment
spec:
  template:
    spec:
      serviceAccountName: my-service-account
      containers:
      - name: prefect-container
        image: xxxxxxxxxx

In this setup,

serviceAccountName: my-service-account

is where you specify the service account that the Kubernetes pod should use. This is separate from the Prefect deployment code and needs to be managed in your Kubernetes manifests or configurations.

Can I not specify it as a job variable though @Marvin what you are showing as an example is not how prefect is supposed to be deployed

Yes, you can specify job variables, including a service account, within your Prefect deployment configuration. This is typically done in a

prefect.yaml

file or directly in your deployment code using the

job_variables

parameter. Here's how you can specify a service account as a job variable: 1. prefect.yaml File: You can define job variables, including environment variables or other configurations, in your

prefect.yaml

file. This allows you to pass necessary configurations to your deployment. 2. Python Deployment Code: In your Python code, you can add the service account information to the

job_variables

dictionary when calling the

.deploy

method. Here's an example of how you might specify a service account in your deployment code:

python
if __name__ == "__main__":
    s3_flow.deploy(
        name="test-deployment",
        work_pool_name="new-worker-pool",
        image="xxxxxxxxxx",
        job_variables={
            "KUBERNETES_SERVICE_ACCOUNT": "my-service-account"
        },
        push=False,
        build=False
    )

In this example,

KUBERNETES_SERVICE_ACCOUNT

is a placeholder for whatever key your Kubernetes setup expects for specifying service accounts. You would replace it with the actual key used in your infrastructure. For more details on using job variables, you can refer to the Prefect Deployment Customization Documentation.

@Marvin what is this error? Trying to run a flow via a kubernetes pod ( triggered via a deployment )

Unexpected exception encountered when trying to load flow
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/site-packages/prefect/deployments/steps/core.py", line 163, in run_steps
    step_output = await **kwargs)
  File "/usr/local/lib/python3.10/site-packages/prefect/deployments/steps/pull.py", line 47, in _pull_git_repository_with_retries
    await repo.pull_code()
  File "/usr/local/lib/python3.10/site-"/usr/local/lib/python3.10/asyncio/base_events.py", line 1681, in subprocess_exec
    transport = await self._make_subprocess_transport(
  File "/usr/local/lib/python3.10/asyncio/unix_events.py", line 207, in _make_subprocess_transport
    transp = _UnixSubprocessTransport(self, protocol, args, shell,
  File "/usr/local/lib/python3.10/asyncio/base_subprocess.py", line 36, in __init__
    self._start(args=args, shell=shell, stdin=stdin, stdout=stdout,
  File "/usr/local/lib/python3.10/asyncio/unix_events.py", line 799, in _start
    self._proc = subprocess.Popen(
  File "/usr/local/lib/python3.10/subprocess.py", line 971, in __init__
    self._execute_child(args, executable, preexec_fn, close_fds,
  File "/usr/local/lib/python3.10/subprocess.py", line 1863, in _execute_child
    raise child_exception_type(errno_num, err_msg, err_filename)
FileNotFoundError: [Errno 2] No such file or directory: 'git'

Your message was too long by 1138 tokens...

@Marvin check now

The error you're encountering,

FileNotFoundError: [Errno 2] No such file or directory: 'git'

, indicates that the

git

command is not available in the environment where your Kubernetes pod is running. This is likely because the

git

executable is not installed in the Docker image or environment that your Prefect flow is using. To resolve this issue, you can take the following steps: 1. Install Git in Your Docker Image: Ensure that the Docker image used for your Kubernetes pods includes Git. You can modify your Dockerfile to include the installation of Git. For example, you can add the following line to your Dockerfile:

dockerfile
   RUN apt-get update && apt-get install -y git

2. Verify the Image Used in Deployment: Check that the image specified in your deployment (

image="xxxxxxxxxx"

) is the one that has Git installed. If not, rebuild the image and update your deployment to use the correct image. 3. Test Locally: Before deploying to Kubernetes, test your Docker image locally to ensure that Git is installed and accessible. These steps should help you resolve the issue by ensuring that the necessary tools are available in your execution environment. If you continue to encounter issues, you might want to consult the Prefect GitHub Issues for similar problems and solutions.