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<@ULVA73B9P>, how can I use a parameter in prefect...
# ask-marvin
m
@Marvin, how can I use a parameter in prefect.yml?
m
To use a parameter in 
prefect.yml
, you will need to define the parameter in the appropriate section of the YAML file, often within a templating expression delimited by double curly braces 
{{ }}
. The 
prefect.yml
file is used by the 
prefect deploy
command to deploy Prefect flows, and it can include templated parameters that can be replaced with actual values at runtime. For example, in the 
build
section of the 
prefect.yml
file, you can use parameters to specify image names, tags, or other options that you might want to configure dynamically:
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yaml
build:
  - prefect_docker.deployments.steps.build_docker_image:
      id: build-image
      requires: prefect-docker>=0.4.0
      image_name: "{{ $PREFECT_IMAGE_NAME }}"
      tag: latest
      dockerfile: auto
      platform: "linux/amd64"
In this snippet, 
{{ $PREFECT_IMAGE_NAME }}
is a parameter that can be populated with an environment variable or a value passed to the deployment command. Another example from the 
triggers
section where parameters are used to configure the behavior based on external events:
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yaml
triggers:
  - enabled: true
    match:
      prefect.resource.id: my.external.resource
    expect:
      - external.resource.pinged
    parameters:
      param_1: "{{ event_value }}"
Here, 
param_1
is a dynamic parameter that will be populated with the value of 
event_value
when the trigger is activated. To use these parameters, you would define the actual values in your environment or pass them to the command line when invoking 
prefect deploy
. The templating system will replace the placeholders with the provided values to configure the deployment as required.
m
@Marvin, can, in your example, param_1 be the image_name? Would that work?
m
In my example for using tools, 
param_1
would be a placeholder for an actual parameter name as defined by the tool's specification. Parameters are used to pass specific values or options to the tool in order to execute a function or operation. The name of the parameter such as 
image_name
must match the expected parameter in the tool's definition. If the tool you are referring to expects a parameter named 
image_name
, then you would use 
image_name
as the parameter in the tool's usage. Would you please clarify which tool or scenario you're referring to so I can provide more specific information?
m
@Marvin # Clone Repository pull: - prefect.deployments.steps.git_clone: id: clone-step-prefect repository: https://github.com/xxx/prefect.git branch: '{{$prefect_repo}}' deployments: - name: apis parameters: { prefect_repo: main } would this work?
m
The example you've provided is a YAML configuration for deploying with Prefect, specifying a deployment step to clone a Git repository and a deployment with a parameter. Here's the breakdown: - `prefect.deployments.steps.git_clone`: This is the Prefect deployment step for cloning a Git repository. - `id`: A unique identifier for the deployment step. - `repository`: The URL of the Git repository to clone. - `branch`: The branch of the repository to clone, which in this case uses a dynamic variable 
{{$prefect_repo}}
to be replaced with the value supplied via the deployment's parameters. For the 
deployments
section: - `name`: The name of the deployment. - `parameters`: The key-value pairs where 
prefect_repo
is the parameter's name and 
main
is the value you want to pass. However, the syntax for using dynamic variables like 
{{$prefect_repo}}
is not standard for Prefect deployment YAML. Instead, you should use 
${...}
to reference parameters. Additionally, the repository URL should be provided without angle brackets 
<>
. The corrected version should look like this:
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yaml
# Clone Repository
pull:
  - prefect.deployments.steps.git_clone:
      id: clone-step-prefect
      repository: '<https://github.com/xxx/prefect.git>'
      branch: '${{ parameters.prefect_repo }}'

deployments:
  - name: apis
    parameters: {
      prefect_repo: 'main'
    }
This corrected configuration uses 
${{ parameters.prefect_repo }}
to dynamically inject the 
prefect_repo
parameter into the 
branch
field of the Git clone step. When you run this deployment, it will clone the main branch of the specified repository. Make sure to replace 
<https://github.com/xxx/prefect.git>
with the actual URL of the repository you want to clone. You can initiate a deployment via Prefect CLI, Prefect UI, or Python code, and this configuration will be taken into account, setting the 
prefect_repo
parameter to 
main
. Keep in mind that my ability to validate a Prefect deployment YAML is based on the knowledge available to me as of my last update and the conventions for Prefect 2.x.
m
@Marvin I get the following error: Validation of flow parameters failed with error: Traceback (most recent call last): File "/usr/local/lib/python3.10/site-packages/prefect/engine.py", line 422, in retrieve_flow_then_begin_flow_run parameters = flow.validate_parameters(flow_run.parameters) File "/usr/local/lib/python3.10/site-packages/prefect/flows.py", line 472, in validate_parameters args, kwargs = parameters_to_args_kwargs(self.fn, parameters) File "/usr/local/lib/python3.10/site-packages/prefect/utilities/callables.py", line 168, in parameters_to_args_kwargs raise SignatureMismatchError.from_bad_params( prefect.exceptions.SignatureMismatchError: Function expects parameters ['webhook_url'] but was provided with parameters ['webhook_url', 'prefect_repo'] It seems that the flow itself also wants the parameter, but I only need it for the pull of defining the repository branch. how do I fix this?
m
The issue you're encountering is a 
SignatureMismatchError
, which indicates that the parameters provided to the flow do not match the parameters expected by the flow's function signature. According to the Prefect documentation and community discourse, when defining parameters in the 
deployment.yaml
, you need to ensure that the parameters match those expected by the flow function. If you are providing extra parameters that are not part of the flow function's parameters, you may run into this validation error. To resolve this error, you will need to ensure that only the parameters required by the flow function are passed during the flow run. If you have deployment-specific parameters (such as 
prefect_repo
) that you do not want to pass to the flow function, you should not include them in the flow's parameter signature. Instead, these parameters should be handled separately within the deployment process or through Prefect's configuration management. Here's what you could do: - Review your flow function and determine the required parameters. - Ensure your 
deployment.yaml
only includes parameters that match the flow's signature (e.g., 
webhook_url
). - Find another way to pass deployment-specific configuration that is not part of the flow's parameters. One approach could be using Prefect Secrets, storing configuration in environment variables, or Prefect's Parameter Store, depending on your needs and the capabilities of Prefect in the version you're using. If you would like to search for existing GitHub issues related to this error to see if there are any related discussions or solutions in the Prefect community, I can perform a search on GitHub for you. Would you like me to do that?
m
@Marvin, I will need to use job_variables then. My follow-up question is then. How can I make the job_variable dynamic?
m
To make job variables dynamic in Prefect, you can use placeholders with double curly braces, such as 
{{ variable_name }}
, within the job configuration. You have the flexibility to hard-code values directly if they should not be customizable, or to use placeholders that reference values which can be adjusted on a per-deployment basis or for the entire work pool through the Prefect API and UI. For instance, if using a process work pool, you have the ability to set environment variables that will be applicable for the spawned processes, and you'd specify the placeholders in the job template as needed. These placeholders are then dynamically replaced with the actual values when the job is being executed. For more detailed instructions or specific use cases, reviewing the Prefect documentation on work pools or the Prefect user interface for configuring job templates might be necessary. If you need more specific information on how to implement dynamic job variables in your workflow, please let me know, and I can provide further assistance.
m
@Marvin, provide me with a dockerfile that pulls and runs prefect2 on python3.11 and also installs requirements.txt (and upgrades PIP) and copies my folder 'flows' from my prefect repo in github.
m
Here is an example Dockerfile that accomplishes the described requirements:
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Dockerfile
# Use the official Python 3.11 image as a parent image
FROM python:3.11

# Set the working directory to /app
WORKDIR /app

# Upgrade pip and install prefect
RUN pip install --upgrade pip && pip install prefect

# Copy the requirements.txt file into the container at /app
COPY requirements.txt /app/requirements.txt

# Install any needed packages specified in requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

# Clone the 'flows' folder from your GitHub repository
# Please replace `your_github_username` and `your_repository_name` with your actual GitHub username and repository name
# Also, replace `branch_name` with your repository branch if it is not the main branch
RUN apt-get update && apt-get install --no-install-recommends -y git && \
    git clone --single-branch --branch main --depth 1 <https://github.com/your_github_username/your_repository_name.git> && \
    mv your_repository_name/flows ./flows && \
    rm -rf your_repository_name && \
    apt-get remove -y git && apt-get autoremove -y

# Your "flows" directory is now copied into /app/flows
# Now you can add additional steps to run your Prefect flows if needed
You'll need to replace 
your_github_username
, 
your_repository_name
, and 
branch_name
with your actual GitHub username, the name of your Prefect repository, and the branch you want to copy from, respectively. Note: It's important to note that cloning private repositories requires authentication. If your repository is private, consider using a method such as mounting an SSH key with appropriate access as a secret or token-based access to securely clone the repository within the Dockerfile. Before building the Docker image, make sure your 
requirements.txt
file is located in the same directory as your Dockerfile. You can build and run your Docker container with the following commands:
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bash
docker build -t your-image-name .
docker run -d your-image-name
Replace 
your-image-name
with a name of your choice for the Docker image.
m
@Marvin, it doesn' pull the image for prefect?
m
Apologies for the oversight. The Dockerfile should indeed pull an official Prefect image if you are looking to use the official Prefect Docker images instead of installing Prefect via pip. Since there isn't an official Prefect Docker image specifically for Python 3.11 at the time of my knowledge cutoff in April 2023, you can pull a Prefect image based on the available Python versions and install Python 3.11 manually if needed. Here's a revised example Dockerfile which pulls the latest Prefect 2.x image and then customizes it to install Python 3.11, upgrade pip, install dependencies from 
requirements.txt
, and copy the 'flows' folder from a specified GitHub repository:
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Dockerfile
# Use the official Prefect image as a parent image
# NOTE: Replace "latest" with the specific version tag if needed
FROM prefecthq/prefect:latest

# Upgrade pip
RUN pip install --upgrade pip

# Install any needed packages specified in requirements.txt
# This assumes requirements.txt is in the same directory as the Dockerfile
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Clone the 'flows' folder from your GitHub repository
# Replace `your_github_username` and `your_repository_name` with your actual GitHub username and repository name
# Replace `main` with the branch you want to clone if it's not the main branch
# Ensure you handle private repository cloning securely if applicable
RUN apt-get update && apt-get install -y git && \
    git clone --single-branch --branch main --depth 1 <https://github.com/your_github_username/your_repository_name.git> && \
    mv your_repository_name/flows /home/prefect/flows && \
    rm -rf your_repository_name && \
    apt-get remove -y git && apt-get autoremove -y

# Set the working directory to /home/prefect
WORKDIR /home/prefect

# The flows directory is now copied into /home/prefect/flows

# You can add additional commands to run your Prefect flows if necessary
This Dockerfile uses the official Prefect Docker image, which will be based on a stable Python version compatible with Prefect 2.x. If there's an absolute requirement for Python 3.11, you'll need to follow an alternative approach by starting with a Python 3.11 base image and installing Prefect, but keep in mind this may lead to less tested configurations. Remember to replace 
your_github_username
and 
your_repository_name
with your actual GitHub username and repository name, and adjust the branch as needed. Build your Docker image with:
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bash
docker build -t your-prefect-image .
And run it with:
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bash
docker run -d your-prefect-image
Replace 
your-prefect-image
with the name you choose for your Docker image.
m
@Marvin, I was reading this guide: https://docs.prefect.io/latest/guides/docker/#__tabbed_1_2 on how to deploy Prefect on Docker. I want to run it in GCP's Cloud Run. The tutorial uses only one specific flow to be run on this deployment. Is it best practice to have one container for each flow? I have multiple deployments and flows that I would ideally run on the same Cloud Run. Is that possible? If so, how would I do that?
m
Based on the information from the Prefect documentation: 1. It is common to store your flow code in a Docker image, particularly when using work pools such as Docker, Kubernetes, and serverless cloud-based options like Google Cloud Run. This way, each flow code is directly baked into the image. This suggests that it is not uncommon to have one container per flow, especially for push-based serverless cloud option setups. However, the documentation does not explicitly state this as a best practice, it seems to be more a common approach. 2. It is technically possible to deploy multiple flows or deployments in a single environment. The documentation provides examples of deploying multiple flows in a Kubernetes environment, where individual flows are deployed and run using commands like 
prefect deploy -n hello/default
or 
prefect deployment run hello/arthur
. This indicates that you are not limited to a single flow per deployment or container. While the documentation does not specifically address the question of running multiple deployments or flows within the same instance of Google Cloud Run, the parallel from the Kubernetes example does suggest that multiple deployments can be managed and run within a single orchestrated environment. It comes down to the design of your Docker image and how you wish to manage flows. You could have a single container that contains multiple flows and a mechanism to trigger specific flows within it, or you may opt for separate containers for better isolation and management. For Google Cloud Run, the choice would largely depend on the scalability requirements and how independent your flows are. If the flows have different dependencies or scaling needs, separate containers might be more manageable. However, if they share most dependencies and can be scaled similarly, a single container might be sufficient and more resource-efficient. To implement multiple flows in a single container on Cloud Run, you would: - Include all the required flow files in your Docker image. - Design your Docker image's entrypoint or command to either: - Trigger a specific flow based on a supplied argument or environment variable. - Run a process that can listen to triggers (e.g., via HTTP requests) and start flows accordingly. It's important to ensure that your Cloud Run instance is configured correctly to handle multiple flows, which may involve setting up concurrency controls, memory allocations, and trigger mechanisms properly.
m
@Marvin, what does this error mean in google cloud run: Revision 'prefect-test-00009-8w5' is not ready and cannot serve traffic. The user-provided container failed to start and listen on the port defined provided by the PORT=8080 environment variable. Logs for this revision might contain more information. Logs URL: Open Cloud Logging For more troubleshooting guidance, see https://cloud.google.com/run/docs/troubleshooting#container-failed-to-start
m
The error message you're seeing from Google Cloud Run indicates that the revision of your service (identified by 
prefect-test-00009-8w5
) is failing to become ready to receive traffic. Specifically, the error mentions that the container failed to start and listen on the port provided by the 
PORT
environment variable, which is set to 
8080
. Here's a breakdown of what this means and potential issues that could cause this: 1. Container Failed to Start: This part of the message suggests that there may be an issue with the Docker container itself. Possible causes include: - The container's entrypoint or command is failing. - There might be an issue with the Dockerfile or the application code that prevents it from running properly. 2. Failed to Listen on the Port Defined by PORT=8080: For Google Cloud Run services, the container must listen for HTTP requests on the port specified by the 
PORT
environment variable. This port is set by the Cloud Run environment and is typically 
8080
. - Ensure that your application is configured to listen on 
0.0.0.0
and the port specified by the 
PORT
environment variable. - Check if your Dockerfile exposes the correct port using the 
EXPOSE
instruction. 3. Logs Might Contain More Information: The error advises to check the logs for the specific revision to understand better what's causing the container to fail. It is important that you review these logs, as they will likely provide details, including error messages from your application or the container's startup process. To resolve this issue, you should: - Click on the provided Open Cloud Logging|Logs URL to inspect detailed logs and find specific error messages that occur when the container starts. - Ensure that your web service is starting correctly, without crashing or exiting prematurely, and that it is binding to 
0.0.0.0
and the 
PORT
environment variable appropriately. - Visit the provided troubleshooting guide for more in-depth troubleshooting steps regarding container startup issues. After making necessary changes based on the findings from your logs and configurations, try redeploying the updated container to Cloud Run and observe if the error persists.
m
@Marvin, help me resolve it. This is my dockerfile: FROM prefecthq/prefect:2-python3.11 # Install pip and Python 3.11 RUN apt-get update && apt-get install -y python3-pip RUN python3 -m pip install --upgrade pip # Add our requirements.txt file to the image and install dependencies COPY requirements.txt . RUN pip install -r requirements.txt --trusted-host pypi.python.org # Add our flow code to the image COPY flows /opt/prefect/flows CMD ["python", "flows/docker_tutorial/prefect-docker-guide-flow.py"] Provide me with steps on how to set up the cloud run.
m
Your Dockerfile specifies a 
CMD
to run a Python script directly. However, Google Cloud Run expects a web service that listens for HTTP requests on the 
PORT
environment variable. This discrepancy is likely causing the error message you're receiving about the container not starting and listening on the required port. Here are steps to set up the Cloud Run service with your current setup correctly: 1. Ensure a web server is present in the Prefect flow: Modify your Prefect flow to include a web server component (e.g., using FastAPI, Flask, or another web framework) that responds to health checks and starts the flow based on certain conditions or requests. 2. Modify 
CMD
to start the web server: Update your Dockerfile's 
CMD
instruction to start this web server instead of running the Python script directly. 3. Update the Dockerfile:
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Dockerfile
FROM prefecthq/prefect:2-python3.11

# Install pip and Python 3.11
RUN apt-get update && apt-get install -y python3-pip
RUN python3 -m pip install --upgrade pip

# Add our requirements.txt file to the image and install dependencies
COPY requirements.txt .
RUN pip install -r requirements.txt --trusted-host <http://pypi.python.org|pypi.python.org>

# Add our flow code to the image
COPY flows /opt/prefect/flows

# Replace "my_web_server.py" with the python file that initializes and runs your web server
CMD ["python", "flows/docker_tutorial/my_web_server.py"]
4. Build and push the updated Docker image:
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bash
docker build -t <http://gcr.io/your-gcp-project/your-image-name:your-tag|gcr.io/your-gcp-project/your-image-name:your-tag> .
docker push <http://gcr.io/your-gcp-project/your-image-name:your-tag|gcr.io/your-gcp-project/your-image-name:your-tag>
Replace 
your-gcp-project
, 
your-image-name
, and 
your-tag
with your Google Cloud Project ID, the name you want to give your image, and a tag, respectively. 5. Deploy to Cloud Run: - Go to the Cloud Run page in your Google Cloud Console. - Click "Create Service." - Choose a region where you want your Cloud Run service to be located. - Provide a name for your service. - Click "Next" to specify the container image URL (the one you just pushed). - Set the container port to the one your web server listens on (if different than the default 
8080
). - Under "Advanced settings," add necessary environment variables, memory, CPU, etc. - Click "Create" to deploy the service. 6. Configure Environment Variables and Connections (if necessary): - Add any environment variables needed by your application. - Configure connections (Cloud SQL, VPC connectors, etc.) if your application requires access to other Google Cloud resources. 7. Monitoring the Service: - After deployment, monitor your Cloud Run service by going to the "Logs" tab to ensure your service starts correctly. - You can view the logs to ensure the web server has started and is listening on the correct port. Remember that for your service to run correctly on Cloud Run, the web server component must continuously run and handle requests on the port specified by the 
PORT
environment variable, typically 
8080
. If you are using any other port in your application, make sure to read it from the 
PORT
environment variable instead.
m
@Marvin, thanks! Do I add the command docker run -e PREFECT_API_URL=YOUR_PREFECT_API_URL -e PREFECT_API_KEY=YOUR_API_KEY prefect-docker-guide-image to the 
container command
in the settings? Or do I leave it out? If I put it in how do i make sure I reference the env variable and secret in gcp?
m
You should not use the 
docker run
command directly in the Google Cloud Run "Container command" field. Instead, you will set environment variables and secrets using the Google Cloud Run UI or the 
gcloud
command-line tool. In the Google Cloud Run settings, you can specify both environment variables and secrets. Environment variables are plain text, whereas secrets are encrypted and stored securely. For sensitive information like your Prefect API key, it's recommended to use secrets. Here's how you would configure the environment variables and secrets for your Cloud Run service: 1. Environment Variables: - In the Google Cloud Run UI, when you create or edit a service, you can add environment variables under the "Variables & Secrets" tab. - Click on "Add variable" and enter 
PREFECT_API_URL
as the name and the actual URL as the value. - Do not put your API key directly as an environment variable since it's sensitive information. 2. Secrets: - First, you need to store your API key as a secret in Google Cloud Secret Manager. Go to the Secret Manager page in the console, create a new secret, and paste your API key as its value. - In the Cloud Run service settings, under the "Variables & Secrets" tab, click "Reference a secret" or "Add secret". - Choose the secret that contains your Prefect API key from the dropdown. - As "Environment variable," enter the name 
PREFECT_API_KEY
. - Cloud Run will automatically inject the value of the secret into the environment variable at runtime. 3. Leave the "Container command" field empty: - Since you've already specified the command in the 
CMD
directive in your Dockerfile (
CMD ["python", "flows/docker_tutorial/my_web_server.py"]
), you don't need to enter anything in the "Container command" field in Cloud Run settings. - If you did need to override the 
CMD
directive in your Dockerfile for any reason, you would enter the command without the 
docker run
portion, just the executable and arguments (e.g., 
python flows/docker_tutorial/my_web_server.py
). By setting the environment variables and secrets this way, your application code will be able to access the Prefect API URL and API key through the environment variables 
PREFECT_API_URL
and 
PREFECT_API_KEY
respectively. Make sure your application code is configured to read these environment variables. That should cover configuring the environment variables and secrets for your Cloud Run deployment to connect to Prefect Cloud.
m
hey @Marvin, I replaced PIP with Poetry and my Prefect.yaml looks like this: # build section allows you to manage and build docker images build: # push section allows you to manage if and how this project is uploaded to remote locations push: # pull section allows you to provide instructions for cloning this project in remote locations pull: # Variables - prefect.deployments.steps.run_shell_script: id: install_poetry script: pip install poetry stream_output: false # Clone Repository - prefect.deployments.steps.git_clone: id: clone-step-prefect repository: https://github.com/xyz/prefect.git branch: '{{$prefect_repo}}' include_submodules: True access_token: '{{ prefect.blocks.secret.github-pat}}' - prefect.deployments.steps.run_shell_script: id: install_dependencies script: ls stream_output: true - prefect.deployments.steps.run_shell_script: id: install_dependencies script: poetry install -v / stream_output: true # the deployments section allows you to provide configuration for deploying flows deployments: - name: hello_world version: tags: [dev] description: entrypoint: flows/misc/hello_world.py:hello_world parameters: {} work_pool: name: cloud-run-push-v2-dev work_queue_name: job_variables: env: prefect_repo: add-hello-world schedule: My pyproject.toml file is in the root of my repository which is also cloned. Yet when the poetry install command is executed, the file cannot be found. I am running on Cloud Run Push worker pool.
m
It seems like you're facing an issue where 
poetry install
cannot find the 
pyproject.toml
file after cloning your repository during a deployment on Prefect Cloud. This issue could be caused by the working directory not being set correctly after the repository is cloned, so 
poetry install
is run in the wrong directory. One way to address this issue is to ensure that the working directory is properly set to the directory where the 
pyproject.toml
file is located after cloning the repository and before running 
poetry install
. You can modify the 
script
section of your 
install_dependencies
step to 
cd
into your cloned repository before running 
poetry install
. Here's how you could adjust your 
Prefect.yaml
deployment file:
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yaml
# ... other parts of the file

# Clone Repository
- prefect.deployments.steps.git_clone:
    id: clone-step-prefect
    repository: <https://github.com/xyz/prefect.git>
    branch: '{{$prefect_repo}}'
    include_submodules: True
    access_token: '{{ prefect.blocks.secret.github-pat }}'

# Install Dependencies
- prefect.deployments.steps.run_shell_script:
    id: install_dependencies
    script: |
      cd prefect  # Replace 'prefect' with the actual directory name of your cloned repository
      poetry install -v
    stream_output: true

# the deployments section allows you to provide configuration for deploying flows
deployments:
# ... rest of the file
In the 
script
command, replace 
prefect
with the actual directory name of your cloned repository. If you are using dynamic cloning (e.g., with different branch names), you might need to use an environment variable or argument to switch to the correct directory dynamically. Adjust the script accordingly based on how your repository is structured. Furthermore, I recommend checking the logs of the failed deployment to get more insight into what directory 
poetry install
is being run in, which might help you debug the issue. If you want to confirm whether this is a known issue or check for any troubleshooting advice specific to Prefect Cloud and Poetry, we could look up related issues on GitHub. Would you like me to search for similar issues on the Prefect GitHub repository?
m
@Marvin, it seems that
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- prefect.deployments.steps.git_clone:
    id: clone-step-prefect
    repository: <https://github.com/xyz/prefect.git>
    branch: '{{$prefect_repo}}'
    include_submodules: True
    access_token: '{{ prefect.blocks.secret.github-pat }}'
doesn't clone my whole repository? cause when I run command 'ls' afterwards, I only get the following 2024-01-03 145612.262 CET > Running run_shell_script step... 2024-01-03 145612.262 CET dist 2024-01-03 145612.262 CET entrypoint.sh 2024-01-03 145612.262 CET prefect-add-hello-world 2024-01-03 145612.262 CET requirements-client.txt 2024-01-03 145612.262 CET requirements.txt So I don't see the poetry file that is in my repo.. why?
@Marvin, it seems that
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- prefect.deployments.steps.git_clone:
    id: clone-step-prefect
    repository: <https://github.com/xyz/prefect.git>
    branch: '{{$prefect_repo}}'
    include_submodules: True
    access_token: '{{ prefect.blocks.secret.github-pat }}'
doesn't clone my whole repository? cause when I run command 'ls' afterwards, I only get the following 2024-01-03 145612.262 CET > Running run_shell_script step... 2024-01-03 145612.262 CET dist 2024-01-03 145612.262 CET entrypoint.sh 2024-01-03 145612.262 CET prefect-add-hello-world 2024-01-03 145612.262 CET requirements-client.txt 2024-01-03 145612.262 CET requirements.txt So I don't see the poetry file that is in my repo.. why?
@Marvin, provide me with prefect.yaml steps on how to incorporate a poetry install after the git_clone of the prefect repo.
m
Below is a sample 
prefect.yaml
file that includes steps to clone your Prefect repository and install dependencies with Poetry after the cloning step. I have included a cleanup step and then set the working directory before running the 
poetry install
command.
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yaml
# ... other parts of the file
pull:
  # Cleanup step to ensure the directory is ready for a fresh clone (add this if necessary)
  - prefect.deployments.steps.run_shell_script:
      id: cleanup_old_repo
      script: |
        rm -rf path-to-your-repo  # Adjust the directory name as needed
      stream_output: true

  # Clone the repository
  - prefect.deployments.steps.git_clone:
      id: clone-step-prefect
      repository: <https://github.com/xyz/prefect.git>
      branch: '{{$prefect_repo}}'
      include_submodules: True
      access_token: '{{ prefect.blocks.secret.github-pat }}'

  # Installation of Poetry (if it's not in your Docker image)
  - prefect.deployments.steps.run_shell_script:
      id: install_poetry
      script: pip install poetry
      stream_output: false

  # Set working directory to cloned repo -- adjust to your repo name
  - prefect.deployments.steps.set_working_directory:
      directory: path-to-your-repo
  
  # Install dependencies with Poetry
  - prefect.deployments.steps.run_shell_script:
      id: install_dependencies
      script: poetry install -v
      stream_output: true

# ... rest of the file
In this example, replace 
path-to-your-repo
with the actual directory name where your repository is being cloned. This usually is the repository name unless you're changing it during the cloning process. Please note: - The cleanup step is optional and should be used with caution; ensure that the specified path is correct and that removing it does not delete unintended data. - The 
set_working_directory
step is set just before the 
install_dependencies
to ensure that poetry commands are executed in the directory where your 
pyproject.toml
is located. - 
stream_output: true
will display the output of each command in logs, which is useful for debugging if something goes wrong. Replace the placeholders with your actual repository URL, branches, paths, and tokens as required for your specific setup. This configuration assumes that Poetry is not already installed in your Docker image; if it is, you can remove the step that installs Poetry.
m
@Marvin, the git_clone step returns 
14:11:03.605 | INFO    | prefect.deployment - Cloned repository '<https://github.com/Victoria-CamperHolidays/prefect.git>' into 'prefect-add-hello-world'
. Does that mean a new folder called prefect-add-hello-world was created where the data is now?
m
Yes, based on the log message you provided:
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14:11:03.605 | INFO | prefect.deployment - Cloned repository '<https://github.com/Victoria-CamperHolidays/prefect.git>' into 'prefect-add-hello-world'
This indicates that the 
git_clone
step has successfully cloned the repository from the provided URL into a new directory named 
prefect-add-hello-world
. This directory is created in the current working directory of the environment running the deployment and contains the contents of the cloned repository. So, to run the Poetry commands, you should change the directory to 
prefect-add-hello-world
using the 
set_working_directory
step before running any subsequent steps that operate on files within the repository. Here is the updated 
prefect.yaml
steps that include setting the working directory to 
prefect-add-hello-world
and then running `poetry install`:
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yaml
pull:
  # Clone the repository
  - prefect.deployments.steps.git_clone:
      id: clone-step-prefect
      repository: <https://github.com/Victoria-CamperHolidays/prefect.git>
      branch: '{{$prefect_repo}}'
      include_submodules: True
      access_token: '{{ prefect.blocks.secret.github-pat }}'

  # Set working directory to the cloned repo directory
  - prefect.deployments.steps.set_working_directory:
      directory: prefect-add-hello-world
  
  # Install dependencies with Poetry
  - prefect.deployments.steps.run_shell_script:
      id: install_dependencies
      script: poetry install -v
      stream_output: true

# ... rest of the file
Make sure you use the actual directory name where your repository is cloned. If it's 
prefect-add-hello-world
, then use that in the 
set_working_directory
step. This will ensure that when you run the 
poetry install
command, it is executed within the correct directory where the 
pyproject.toml
file is located.
m
@Marvin, thanks. I have an environment variable that I then want to use to determine the folder (as it is based on the repo name) how do i do that
m
To use an environment variable for determining the folder name dynamically, you can insert an environment variable into your 
Prefect.yaml
configuration file and refer to it in your steps. Prefect supports using Jinja templating to interpolate environment variables into your configuration. Suppose you have an environment variable named 
REPO_NAME
that holds the name of the folder where your repository is cloned. Your 
Prefect.yaml
file would look something like this:
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yaml
pull:
  # Clone the repository
  - prefect.deployments.steps.git_clone:
      id: clone-step-prefect
      repository: <https://github.com/Victoria-CamperHolidays/prefect.git>
      branch: '{{$prefect_repo}}'
      include_submodules: True
      access_token: '{{ prefect.blocks.secret.github-pat }}'

  # Set working directory to the directory specified by the environment variable
  - prefect.deployments.steps.set_working_directory:
      directory: '{{ env.REPO_NAME }}'  # Use the environment variable

  # Install dependencies with Poetry
  - prefect.deployments.steps.run_shell_script:
      id: install_dependencies
      script: poetry install -v
      stream_output: true

# ... rest of the file
In this configuration, 
{{ env.REPO_NAME }}
will be replaced with the value of the 
REPO_NAME
environment variable at runtime. This way, you can set 
REPO_NAME
to match the folder where your repository gets cloned, such as 
prefect-add-hello-world
, and the steps will automatically use this folder. Ensure that the environment variable 
REPO_NAME
is set in the runtime environment where your deployment is executed. If you are using Prefect Cloud or Prefect Server, you will need to ensure that the environment variable is available to your execution environment, which might involve setting it in your Prefect agent configuration or the Docker container environment, depending on your setup. Here is an example of setting an environment variable when starting the Prefect agent, which you would need to adjust to your own runtime environment or container setup:
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sh
export REPO_NAME=prefect-add-hello-world
prefect agent start
Or, if you are using a Docker agent:
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dockerfile
# In your Dockerfile
ENV REPO_NAME=prefect-add-hello-world
# ... rest of your Dockerfile
Remember to replace the placeholder values with your actual repository details and environment variable names.
m
@Marvin, I want to run a shell command in the pull section, something like: poetry install -v "XYZ" the "XYZ" is variable {{github_repo}}. how do I implement that variable?
m
To run a shell command using a variable in the 
pull
section, you can utilize Prefect's built-in support for Jinja templating to interpolate the variable into your 
prefect.yaml
file. Let's say you have a variable 
github_repo
that you want to use as an argument to the 
poetry install
command. You can define it within the same file or pass it as an argument when triggering the flow deployment. Here’s an example of how you can use the 
github_repo
variable in the 
pull
section to dynamically inject the repository name into your shell script command:
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yaml
pull:
  # Clone the repository
  - prefect.deployments.steps.git_clone:
      id: clone-step-prefect
      repository: <https://github.com/Victoria-CamperHolidays/prefect.git>
      branch: '{{$prefect_repo}}'
      include_submodules: True
      access_token: '{{ prefect.blocks.secret.github-pat }}'

  # Set working directory to the cloned repo directory
  # Assume your environment variable or argument name is 'github_repo'
  - prefect.deployments.steps.set_working_directory:
      directory: '{{ env.github_repo }}'  # Use the environment variable or argument

  # Install dependencies with Poetry, including the variable 'github_repo'
  - prefect.deployments.steps.run_shell_script:
      id: install_dependencies
      script: poetry install -v '{{ github_repo }}'  # Use the variable within the script
      stream_output: true

# ... rest of the file
In this configuration, 
{{ github_repo }}
will be interpolated with the value of the 
github_repo
variable during runtime. You will need to ensure that this variable is provided when the deployment is run. This can be done via an environment variable set in the runtime environment or by passing it as an argument, depending on your setup. Remember to replace 
{{ env.github_repo }}
with the appropriate way to access your variable. If it's an environment variable, you would use 
{{ env.YOUR_VARIABLE_NAME }}
. If it's a variable passed at runtime, you'd use 
{{ YOUR_VARIABLE_NAME }}
directly. Please make sure that wherever you are running your Prefect deployment (e.g., Prefect Cloud/Server, local environment), the variable 
github_repo
is set properly so that it can be accessed by the deployment. If you're using Prefect Cloud or Server, consult the documentation on how to manage secrets and environment variables in your particular setup.
m
@Marvin, I am currently running all my flows and deployments using Cloud Run Push workpool and for each flow, all the same dependencies are installed using Poetry. What would be the best way to separate the dependencies for each deployment?
m
To manage separate dependencies for each deployment when running flows using the Cloud Run Push workpool, you can build separate Docker images with the necessary dependencies for each deployment. Here are the steps you can take to achieve this: 1. Create Dockerfiles: Create a different Dockerfile for each set of flow dependencies. These Dockerfiles will serve as templates for building the Docker images used in your deployments. 2. Build Docker Images: Use a build step, such as 
prefect_docker.deployments.steps.build_docker_image
, to build each image based on its Dockerfile. This step will produce 
image_name
and 
tag
fields, which will be used to reference the Docker images in your deployment. 3. Reference Docker Images in Deployments: When you create a deployment, reference the corresponding Docker image and tag that you built in the previous step. This way, your deployment is associated with a specific set of dependencies encapsulated within the Docker image. 4. Use 
DeploymentImage
: In the deployment creation script, specify the Docker image using 
DeploymentImage
with the name and tag of the image you want to use for that deployment. For example:
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python
   DeploymentSpec(
       ...
       work_pool_name="my-work-pool",
       image=DeploymentImage(
           name="my-image:latest",  # Replace with the appropriate image name and tag
           platform="linux/amd64",
       )
   )
5. Create Separate Deployments: Create multiple deployments from one or more Python files using 
.deploy
, or configure multiple deployment configurations within a 
prefect.yaml
file. This allows you to manage deployments independently with different configurations and dependencies. By following this modular approach, you ensure that each flow and deployment have their specific dependencies installed and are isolated from each other, preventing dependency conflicts and making your deployments more maintainable. The documentation I referenced can be further explored for details on deployments and using Cloud Run Push workpools: - Prefect Deployments - Prefect Cloud Run Push Work Pools
m
@Marvin, thanks. How can I use dependabot to manage the dependencies in my github repo if I have multiple deployments with various dependencies? Can you provide an example on how I can best structure this?
m
To use Dependabot for managing dependencies in your GitHub repository with multiple Prefect deployments, you should structure your repository in a way that separates the dependencies for each deployment. Here is an example structure and how to set up Dependabot: ### Repository Structure Suppose you have a repository with the following structure:
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/my-repo
    /deployments
        /deployment_a
            Dockerfile
            pyproject.toml
            poetry.lock
        /deployment_b
            Dockerfile
            pyproject.toml
            poetry.lock
    /flows
        flow_a.py
        flow_b.py
    .github
        /workflows
            ci.yml
        /dependabot.yml
    prefect.yaml
In this example, the 
deployments
directory contains separate directories for 
deployment_a
and 
deployment_b
, each with their own 
Dockerfile
, 
pyproject.toml
, and 
poetry.lock
to manage dependencies independently. ### Configuring Dependabot To configure Dependabot to manage dependencies for each deployment, you create a 
.github/dependabot.yml
file in your repository with the following configuration:
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yaml
version: 2
updates:
  - package-ecosystem: "pip"
    directory: "/deployments/deployment_a"
    schedule:
      interval: "daily"
  - package-ecosystem: "pip"
    directory: "/deployments/deployment_b"
    schedule:
      interval: "daily"
In this 
dependabot.yml
file, you specify two update configurations, one for each deployment's directory. Dependabot will check the 
pyproject.toml
and 
poetry.lock
files in 
/deployments/deployment_a
and 
/deployments/deployment_b
directories daily and create pull requests to update the dependencies as needed. Since each deployment directory is self-contained and has its Dockerfile, these updates won't affect other deployments, and you can test and roll out dependency updates in a controlled manner. This structured approach not only allows for automated dependency updates but also makes it easier to maintain and scale as you add more deployments or change dependencies for existing ones. Note: The provided configuration assumes you use Poetry for Python dependency management, as indicated by the 
pyproject.toml
and 
poetry.lock
files. If you use a different dependency management tool, you might need to adjust the 
package-ecosystem
value accordingly. Dependabot supports multiple ecosystems such as npm, bundler, and others, so ensure the configuration matches your setup.
m
@Marvin, I am trying to build a custom docker image to run in workerpool cloud run v2. I have the container in my registry. The dockerfile is very simple:
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FROM <http://docker.io/prefecthq/prefect:2.13.5-python3.10|docker.io/prefecthq/prefect:2.13.5-python3.10>
Yet it still fails when I change the image in the workerpool settings from docker.io/prefecthq/prefect:2.13.5-python3.10 to gcr.io/xxx/prefect:latest. The error message is: 2024-01-04 161029.368 CET Application exec likely failed 2024-01-04 161029.368 CET terminated: Application failed to start: "/usr/local/bin/python": exec format error Any idea why this is happening?
m
It appears that the error message you've encountered when trying to use a custom Docker image for your workerpool in Cloud Run v2 is not directly related to a known issue in the Prefect GitHub repository, or at least, there is no clear match to this error based on the current issues available there. The error message you received ("Application failed to start: '/usr/local/bin/python': exec format error") suggests that the Python binary inside your Docker container may be incompatible with the runtime environment of Google Cloud Run. This can happen if the base image you're using is built for a different CPU architecture than the one Cloud Run expects, or if there were any issues during the process of building or pushing the Docker image to the registry. Here are a few steps to troubleshoot the problem: 1. Check the CPU architecture: Ensure that the base image you're using (
prefecthq/prefect:2.13.5-python3.10
) is compatible with the CPU architecture of Cloud Run. Make sure that you're not inadvertently building an image for an incompatible architecture. 2. Rebuild and Push the Image: Try rebuilding your Docker image and pushing it to the registry again, making sure that there are no errors during the build and push processes. 3. Review Dockerfile: Although your 
Dockerfile
seems simple, ensure that there are no hidden issues with it. Sometimes, even a simple 
Dockerfile
can cause issues if it's not set up correctly. 4. Test Locally: Before deploying to Cloud Run, test the Docker image locally to confirm that it can start up and execute Python without errors. 5. Permissions and Paths: Verify that the Python binary's permissions and path inside the container are correct and that it is indeed located at 
/usr/local/bin/python
. 6. Review Cloud Run Configuration: Ensure that the configuration settings for your Cloud Run service are correctly specified, including the entry point if necessary. Since the issue seems to be specific to your deployment, you may want to consider reaching out to the Prefect community or support channels for more personalized assistance, providing them with more details about your environment and the steps you've taken to build and deploy your custom image. If it turns out to be a more widespread issue, you can open a new issue in the Prefect GitHub repository to seek help from the developers.
m
@Marvin, can you please generate a dockerfile, prefect.yaml file where specifically is shown what steps are needed for pulling the github repository with flows, and the settings needed for the workerpool in prefect cloud?
m
Certainly! I'll provide you with a template for a 
Dockerfile
that pulls your GitHub repository with flows and a 
prefect.yaml
file to configure a workerpool in Prefect Cloud. Please replace 
YOUR_GITHUB_REPO_URL
with the actual URL of your GitHub repository and ensure you have the necessary credentials or access token to clone it. ### Dockerfile
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Dockerfile
# Use a specific version of the Prefect image as the base
FROM prefecthq/prefect:2.13.5-python3.10

# Set the working directory in the container
WORKDIR /usr/src/app

# Clone your GitHub repository with flows
# Note: You'll need to handle authentication to private repositories
RUN git clone <https://YOUR_GITHUB_REPO_URL.git> .

# Install any additional dependencies if needed
# RUN pip install ...

# Start Prefect agent
CMD ["prefect", "agent", "start"]
### prefect.yaml This 
prefect.yaml
file contains the necessary settings for the workerpool:
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yaml
# prefect.yaml
version: 1

# Settings for the Prefect Cloud service
cloud:
  auth_token: YOUR_PREFECT_CLOUD_AUTH_TOKEN
  api: YOUR_PREFECT_CLOUD_API_URL

# Settings for the work queue
work-queue:
  - name: my-work-queue
    labels: ["my-label"]

# Workerpool configuration
worker-pool:
  - name: my-worker-pool
    type: cloud-run
    spec_options:
      image: <http://gcr.io/xxx/prefect:latest|gcr.io/xxx/prefect:latest>  # Replace with your custom Docker image path
When using these templates, please make sure to replace placeholder strings like 
YOUR_PREFERRED_CREDENTIAL_HERE
and 
YOUR_GITHUB_REPO_URL
with actual values specific to your environment. You also need to properly handle authentication for private GitHub repositories, which typically involves using secret tokens or SSH keys that should be securely managed. These templates serve as starting points and might require additional customization based on your specific use case and requirements. For example, you may need to add environment variables, install additional Python packages, or set up credential blocks within Prefect Cloud for managing secrets. For detailed information and instructions, refer to the official Prefect documentation. The provided 
prefect.yaml
does not cover all possible configurations for a workerpool, so you should review the documentation on configuring work pools in Prefect Cloud to ensure your setup meets your needs.
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