@Marvin I’m using

prefect deploy --all

to build the image, which I believe looks inside the directory with the flow code for a requirements.txt file. if that file is actually at the root of the repo, how can I point at that?

I'm sorry, but I couldn't find specific information on how to point to a

requirements.txt

file at the root of the repo when using

prefect deploy --all

. However, Prefect's deployment process does look for a

requirements.txt

file in the same directory as your flow code. If your

requirements.txt

file is in the root directory of your repo, you might need to adjust your project structure or create a symbolic link to the

requirements.txt

file in the directory with your flow code. I recommend checking Prefect's official documentation or reaching out to their community for more specific guidance.

@Marvin how do i add environment variables, like PYTHONPATH to a deployment in the prefect.yaml file?

To add environment variables in the

prefect.yaml

file, you can use the

env

field under the

run_config

section. Here's an example:

yaml
run_config:
  type: "LocalRun"
  env:
    PYTHONPATH: "/path/to/your/python/modules"

This will set the

PYTHONPATH

environment variable for the flow run. Please note that the actual structure of your

prefect.yaml

file might differ based on your specific use case. The above is just an example of how you can set environment variables. For more detailed information, please refer to the Prefect documentation.

@Marvin I don’t have a run_config: section. would it go in the build section to be in the dockerfile?

Yes, you can set environment variables in the

build

section of the

prefect.yaml

file. You can reference environment variables using the special syntax

{{ $MY_ENV_VAR }}

. Here's an example:

yaml
build:
  - prefect_docker.deployments.steps.build_docker_image:
      id: build-image
      requires: prefect-docker>=0.3.0
      image_name: my-repo/my-image
      tag: my-tag
      dockerfile: auto
      push: true
      env:
        MY_ENV_VAR: "my value"

In this example,

MY_ENV_VAR

is the environment variable, and

"my value"

is the value you want to set for that environment variable. Please note that the actual structure of your

prefect.yaml

file might differ based on your specific use case. The above is just an example of how you can set environment variables. For more detailed information, please refer to the Prefect documentation.

@Marvin I’m trying to get permissions set up, so that flows that run in gke kubernetes will have service account permissions that are needed. I followed the gke instructions for workload identity, so that the default service account in k8s will impersonate the service account in gcp IAM that has the correct permissions. The last step of the instructions for that says that I need to add this to pod specs to get it to work. I have 3 questions: 1. does this method make sense for getting permissions working in the gke kubernetes worker pool? 2. If so, how do I use that spec code below, can I add that to the prefect.yaml or something? 3. If not, how should I set my service account permissions up? pod spec addition:

spec:
  serviceAccountName: default
  nodeSelector:
    <http://iam.gke.io/gke-metadata-server-enabled|iam.gke.io/gke-metadata-server-enabled>: "true"

1. Yes, using GKE's Workload Identity feature is a recommended way to manage permissions for your Kubernetes workloads. It allows you to bind a specific Google service account to a Kubernetes service account in your cluster. This way, your applications can authenticate to Google Cloud APIs using the identity of the Google service account, without needing to manage any keys. 2. The pod spec addition you mentioned is not directly added to the

prefect.yaml

file. Instead, it should be included in the Kubernetes job spec that Prefect uses to create new jobs. You can customize this job spec by creating a custom job template and providing it to Prefect. Here's an example of how you can add the

serviceAccountName

and

nodeSelector

to the job spec:

yaml
    apiVersion: batch/v1
    kind: Job
    metadata:
      name: {{flow_run_name}}
      labels:
        flow_run_id: {{flow_run_id}}
        flow_id: {{flow_id}}
    spec:
      template:
        spec:
          serviceAccountName: default
          nodeSelector:
            <http://iam.gke.io/gke-metadata-server-enabled|iam.gke.io/gke-metadata-server-enabled>: "true"
          containers:
          - name: flow
            image: {{image}}
            command: ["prefect", "execute", "flow-run"]
            env:
            - name: PREFECT__CLOUD__API_KEY
              valueFrom:
                secretKeyRef:
                  name: prefect-api-keys
                  key: PREFECT__CLOUD__API_KEY
            - name: PREFECT__CLOUD__AGENT__LABELS
              value: {{labels}}
            - name: PREFECT__CONTEXT__FLOW_RUN_ID
              value: {{flow_run_id}}
            - name: PREFECT__CONTEXT__FLOW_ID
              value: {{flow_id}}
            - name: PREFECT__CONTEXT__IMAGE
              value: {{image}}
            - name: PREFECT__CLOUD__USE_LOCAL_SECRETS
              value: "false"
            - name: PREFECT__ENGINE__FLOW_RUNNER__DEFAULT_CLASS
              value: "prefect.engine.cloud.CloudFlowRunner"
            - name: PREFECT__ENGINE__TASK_RUNNER__DEFAULT_CLASS
              value: "prefect.engine.cloud.CloudTaskRunner"
            - name: PREFECT__LOGGING__LOG_TO_CLOUD
              value: "true"
            - name: PREFECT__LOGGING__LEVEL
              value: "DEBUG"
            - name: GOOGLE_APPLICATION_CREDENTIALS
              value: "/var/secrets/google/key.json"
            volumeMounts:
            - name: google-cloud-key
              mountPath: "/var/secrets/google"
          restartPolicy: Never
          volumes:
          - name: google-cloud-key
            secret:
              secretName: prefect-k8s-secrets

You would then provide this custom job template to Prefect when creating your Kubernetes agent. 3. As mentioned above, using GKE's Workload Identity is a good way to manage your service account permissions. You would create a Google service account with the necessary permissions, and then bind this service account to a Kubernetes service account in your cluster. Any pods that are run using this Kubernetes service account will then have the permissions of the Google service account. Please note that these are general guidelines and the actual implementation might differ based on your specific use case. For more detailed information, please refer to the Prefect and GKE documentation.

@Marvin how do I remove a worker from kubernetes, do i use helm as well? or just delete the pod?

To remove a worker from Kubernetes, you can delete the pod that the worker is running on. You can do this using the

kubectl delete pod

command followed by the name of the pod. Here's an example:

bash
kubectl delete pod <pod-name>

Replace

<pod-name>

with the name of the pod that you want to delete. If you deployed your workers using Helm, you can also use Helm to delete the deployment. You can do this using the

helm uninstall

command followed by the name of the release. Here's an example:

bash
helm uninstall <release-name>

Replace

<release-name>

with the name of the Helm release that you want to delete. Please note that deleting a pod or a Helm release will only remove the worker temporarily. If the worker is managed by a ReplicaSet or a Deployment (which is usually the case if you deployed your workers using Helm), Kubernetes will automatically create a new pod to replace the deleted one. To permanently remove a worker, you need to scale down the ReplicaSet or Deployment, or delete it entirely.

@Marvin If I’m developing and want to test a deployment with my newly updated code, how do I go about deploying (using

prefect deploy --all

) and have it include the updated code. right now I’m getting this during the docker image build

Step 5/6 : WORKDIR /opt/prefect/cybernetics/
 ---> Using cache
 ---> 07afdee1b173

The

Using cache

message during the Docker image build process means that Docker is reusing the layers it previously built because it doesn't detect any changes in the Dockerfile or the context. This can lead to your updated code not being included in the new Docker image. To ensure your updated code is included in the Docker image, you can use the

--no-cache

option when building the Docker image. This will force Docker to rebuild all layers of the Docker image, ensuring your updated code is included. Here's an example:

bash
docker build --no-cache -t my-image .

However, since you're using

prefect deploy --all

to build your Docker image, you might not have direct control over the Docker build process. In this case, you can try one of the following: 1. Make a trivial change to your Dockerfile (like adding a comment) to invalidate the Docker cache for the layers after the change. 2. If your Dockerfile copies your code into the Docker image using a command like

COPY . /app

, make sure you're not copying unnecessary files (like

.git

or

__pycache__

) that might change frequently and invalidate the Docker cache. You can use a

.dockerignore

file to exclude these files. 3. If possible, try to structure your Dockerfile in a way that changes that invalidate the Docker cache (like changing your code) happen as late as possible. This allows Docker to reuse as many cached layers as possible. Please note that these are general guidelines and the actual implementation might differ based on your specific use case. For more detailed information, please refer to the Docker and Prefect documentation.

@Marvin I don’t have a dockerfile, its being created by the deploy command. As context, I have a kubernetes worker pool on gke

If you're using the

prefect deploy --all

command, it's likely that Prefect is automatically generating a Dockerfile for you. In this case, the Docker caching issue might be due to Prefect not detecting changes in your flow code. Here are a few things you can try: 1. Force a rebuild: Prefect CLI doesn't have a built-in option to force a rebuild of the Docker image. However, you can manually delete the Docker image before running the

prefect deploy --all

command. This will force Prefect to build a new Docker image. You can delete the Docker image using the

docker rmi

command:

bash
    docker rmi <image-name>

Replace

<image-name>

with the name of your Docker image. 2. Update your flow version: Prefect uses the flow version to determine whether to rebuild the Docker image. If you increment the version of your flow, Prefect should rebuild the Docker image. You can increment the version of your flow by calling the

flow.version()

method:

flow.version(2)  # replace 2 with your new version number

3. Change your flow's storage location: If you're using Prefect's Docker storage, you can change the

registry_url

or

image_name

properties of your flow's storage. This will force Prefect to build a new Docker image. Here's an example:

flow.storage = Docker(
        registry_url="my-registry-url",  # replace with your registry URL
        image_name="my-image-name",  # replace with your image name
    )

Please note that these are general guidelines and the actual implementation might differ based on your specific use case. For more detailed information, please refer to the Prefect documentation.

@Marvin what are some options in prefect for managing configuration, similar to how Hydra configs work. For example, if you want to manage default values for different workflows, and easily switch between configuration setups (e.g. which cloud project to point to, or which graphql endpoint to use), what options are there for that?

Prefect provides several options for managing configuration: 1. Environment Variables: Prefect allows you to use environment variables for configuration. This can be useful for managing different configuration setups, as you can easily switch between different sets of environment variables. 2. Prefect Config Files: Prefect uses a hierarchical configuration system, with defaults provided by Prefect that can be overridden by user-defined values. These user-defined values can be provided in a

config.toml

file located in the Prefect user configuration directory. This can be useful for managing default values for different workflows. 3. Context: Prefect's context provides a way to pass configuration values to your flows. The context is a dictionary that is available during a flow run, and it can contain any data that you want to make available to your tasks. You can set context values when you create a flow run, and you can also access and modify the context within your tasks. 4. Secrets: Prefect's secrets management system allows you to securely store and access sensitive data, like API keys or database credentials. Secrets can be stored in Prefect Cloud, in environment variables, or in a local secrets file, and they can be accessed from your flows using the

Secret

task. 5. Parameters: Prefect's parameters allow you to provide different inputs to your flows each time you run them. Parameters are a special kind of task that return a value when they are run. You can set the value of a parameter when you create a flow run. Please note that these are general guidelines and the actual implementation might differ based on your specific use case. For more detailed information, please refer to the Prefect documentation.

@Marvin can you point to docs pages for the above options?

Here are the documentation pages for the configuration options in Prefect: 1. Environment Variables 2. Prefect Config Files 3. Context 4. Secrets 5. Parameters Please note that these are general guidelines and the actual implementation might differ based on your specific use case. For more detailed information, please refer to the Prefect documentation.

@Marvin How can I easily see the inputs and outputs of each task in a flow? I saw the artifacts tab, but if I just want to see the input parameters and output values, where can I see that in the UI?

In the Prefect UI, you can view the inputs and outputs of each task in a flow run on the task run page. Here's how you can access it: 1. Navigate to the flow run page by clicking on a flow run from the dashboard or the flow page. 2. On the flow run page, you'll see a list of all the tasks in the flow run. Click on a task to navigate to the task run page. 3. On the task run page, you'll see a "Details" section that includes information about the task run, including its inputs and outputs. Please note that the actual layout and information available might differ based on your specific use case and the version of Prefect you're using. For more detailed information, please refer to the Prefect documentation.

@Marvin I see under

Result

it says

Unpersisted result of type str

, is there something I have to do to be able to see the value itself?

Yes, in order to see the actual value of the result in the Prefect UI, you need to use a result handler that supports reading, such as the

LocalResultHandler

or any of the cloud-based result handlers like

GCSResultHandler

,

S3ResultHandler

, or

AzureResultHandler

. By default, Prefect Flows use a

ResultHandler

called the

Result

class, which doesn't persist or read data anywhere, hence why you're seeing

Unpersisted result of type str

. Here's an example of how you can set a result handler for your flow:

from prefect.engine.results import LocalResult

flow = Flow("my-flow", result=LocalResult(dir="/path/to/dir"))

In this example, results from the flow will be stored in the directory specified by

dir

. Please note that you also need to ensure that your tasks are configured to checkpoint their results. You can do this by setting

checkpoint=True

when defining your task:

@task(checkpoint=True)
def my_task():
    ...

Once you've done this, you should be able to see the actual value of the result in the Prefect UI. Please note that these are general guidelines and the actual implementation might differ based on your specific use case. For more detailed information, please refer to the Prefect documentation.

@Marvin what about for inputs? for the overall flow, I can see input literal values, but for the task, it just has

[]

as the value for each parameter

The Prefect UI displays the inputs to each task in a flow run on the task run page. If you're seeing

[]

as the value for each parameter, it could mean that the task didn't receive any inputs, or that the inputs aren't being correctly logged. Here are a few things you can check: 1. Task Definition: Make sure your tasks are defined to accept inputs. You can do this by adding parameters to your task function:

@task
    def my_task(my_input):
        ...

In this example,

my_task

accepts an input called

my_input

. 2. Task Calls: Make sure you're passing inputs to your tasks when you call them in your flow:

with Flow("my-flow") as flow:
        result = my_task("my input")

In this example,

"my input"

is passed to

my_task

. 3. Flow Runs: Make sure you're passing inputs to your flow when you create a flow run. You can do this by providing a dictionary of inputs when you call `flow.run()`:

flow.run(parameters={"my_input": "my value"})

In this example,

"my value"

is passed as an input to the flow. If you've checked all of these and you're still seeing

[]

as the value for each parameter, it could be a bug or a limitation of the Prefect UI. In this case, I recommend reaching out to the Prefect community for further assistance. Please note that these are general guidelines and the actual implementation might differ based on your specific use case. For more detailed information, please refer to the Prefect documentation.

@Marvin how can I make sure a task runs after the completion of a previous task?

In Prefect, you can ensure that a task runs after the completion of a previous task by setting up task dependencies. This can be done using the

set_dependencies

method or by using the

upstream_tasks

parameter when defining your tasks. Here's an example:

from prefect import task, Flow

@task
def task1():
    print("Task 1 is running")

@task
def task2():
    print("Task 2 is running")

with Flow("My Flow") as flow:
    t1 = task1()
    t2 = task2(upstream_tasks=[t1])

In this example,

task2

will only run after

task1

has completed successfully. You can also use the

set_dependencies

method to set up task dependencies:

from prefect import task, Flow

@task
def task1():
    print("Task 1 is running")

@task
def task2():
    print("Task 2 is running")

with Flow("My Flow") as flow:
    t1 = task1()
    t2 = task2()
    flow.set_dependencies(task=t2, upstream_tasks=[t1])

In this example,

task2

will only run after

task1

has completed successfully. Please note that these are general guidelines and the actual implementation might differ based on your specific use case. For more detailed information, please refer to the Prefect documentation.

@Marvin I got my results to show the path where it was saved, by using persist_result=True. However, how do I get the UI to show the actual value of the result. These are just basic types, for reference, like strings, ints, bools, etc.

To see the actual value of the result in the Prefect UI, you need to use a result handler that supports reading, such as the

LocalResultHandler

or any of the cloud-based result handlers like

GCSResultHandler

,

S3ResultHandler

, or

AzureResultHandler

. By default, Prefect Flows use a

ResultHandler

called the

Result

class, which doesn't persist or read data anywhere. Here's an example of how you can set a result handler for your flow:

from prefect.engine.results import LocalResult

flow = Flow("my-flow", result=LocalResult(dir="/path/to/dir"))

In this example, results from the flow will be stored in the directory specified by

dir

. Please note that you also need to ensure that your tasks are configured to checkpoint their results. You can do this by setting

checkpoint=True

when defining your task:

@task(checkpoint=True)
def my_task():
    ...

Once you've done this, you should be able to see the actual value of the result in the Prefect UI. Please note that these are general guidelines and the actual implementation might differ based on your specific use case. For more detailed information, please refer to the Prefect documentation.

@Marvin can you pass LocalResult into a flow decorator?

Yes, you can pass a

LocalResult

into a flow decorator. Here's an example:

from prefect import Flow
from prefect.engine.results import LocalResult

@Flow(name="my-flow", result=LocalResult(dir="/path/to/dir"))
def my_flow():
    ...

In this example, results from the flow will be stored in the directory specified by

dir

. Please note that you also need to ensure that your tasks are configured to checkpoint their results. You can do this by setting

checkpoint=True

when defining your task:

from prefect import task

@task(checkpoint=True)
def my_task():
    ...

Once you've done this, you should be able to see the actual value of the result in the Prefect UI. Please note that these are general guidelines and the actual implementation might differ based on your specific use case. For more detailed information, please refer to the Prefect documentation.

@Marvin how does it work if I want to call the same task more than once in a flow, but with different parameters? It seems like it only ran the first instance of each task, and then stopped

In Prefect, tasks are uniquely identified by their name and the arguments they are called with. If you call the same task multiple times with the same arguments, Prefect will only run the task once and reuse the result for the other calls. This is known as task caching. If you want to call the same task multiple times with different parameters, you can do so by passing different arguments to each task call. Here's an example:

from prefect import task, Flow

@task
def add(x, y):
    return x + y

with Flow("My Flow") as flow:
    result1 = add(1, 2)  # This will run the task with arguments (1, 2)
    result2 = add(3, 4)  # This will run the task with arguments (3, 4)

In this example, the

add

task is called twice with different arguments, so Prefect will run the task twice. If you're calling the same task multiple times with the same arguments and you want Prefect to run the task each time instead of reusing the result, you can disable task caching by setting

cache_for

to

datetime.timedelta(0)

when defining your task:

from prefect import task
from datetime import timedelta

@task(cache_for=timedelta(0))
def my_task():
    ...

Please note that these are general guidelines and the actual implementation might differ based on your specific use case. For more detailed information, please refer to the Prefect documentation.