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<@ULVA73B9P> I am struggling to get an image built...
# ask-marvin
a
@Marvin I am struggling to get an image built, stored into an private ECR repository, then pulled down by a Work pool/Worker configured to run within a local Kubernetes cluster. It seems that I have finally got it pulling the image, but when the flow runs the error received is: 
prefect.exceptions.ScriptError: Script at 'flows/sample/pipeline.py' encountered an exception: FileNotFoundError(2, 'No such file or directory')
m
Your message was too long by 241 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
I am struggling to get an image built, stored into an private ECR repository, then pulled down by a Work pool/Worker configured to run within a local Kubernetes cluster. It seems that I have finally got it pulling the image, but when the flow runs the error received is: 
prefect.exceptions.ScriptError: Script at 'flows/sample/pipeline.py' encountered an exception: FileNotFoundError(2, 'No such file or directory')
What should I look at to fix this? Given this `prefect.yaml`: ```name: sample prefect-version: 2.14.13 infrastructure: image_pull_policy: ALWAYS build: - prefect.deployments.steps.run_shell_script: id: get_commit_hash script: git rev-parse --short HEAD stream_output: false - prefect.deployments.steps.run_shell_script: id: ecr_auth_ste_push script: /bin/bash -c 'aws ecr get-login-password --region {{ $AWS_REGION }} --profile ecr | docker login --username AWS --password-stdin {{ $ECR_REPOSITORY }}' stream_output: true - prefect_docker.deployments.steps.build_docker_image: id: build_image requires: prefect-docker>=0.4.0 image_name: "{{ $ECR_REPOSITORY }}" tag: "prefect-disputes-{{ get_commit_hash.stdout }}" dockerfile
a
@Marvin I am struggling to get an image built, stored into an private ECR repository, then pulled down by a Work pool/Worker configured to run within a local Kubernetes cluster. It seems that I have finally got it pulling the image, but when the flow runs the error received is: 
prefect.exceptions.ScriptError: Script at 'flows/sample/pipeline.py' encountered an exception: FileNotFoundError(2, 'No such file or directory')
m
It looks like there are several moving parts to your problem, so let's break it down: 1. Building and Storing a Docker Image in AWS ECR: - Ensure you have the necessary permissions set up in AWS (such as 
ecr:CreateRepository
, 
ecr:DescribeRepositories
, 
ecr:GetAuthorizationToken
, and the full access policies like 
AmazonEC2ContainerRegistryFullAccess
). - Use the 
aws ecr get-login-password
to authenticate your Docker client to your registry. - Tag your image with the ECR repository URI: 
docker tag my-image:latest <ECR-REPOSITORY-URI>
- Push the tagged image to the repository: 
docker push <ECR-REPOSITORY-URI>
2. Pulling a Docker Image from ECR in Kubernetes: - For Kubernetes to pull an image from a private ECR repository, you need to create a secret in Kubernetes to hold your AWS credentials. - You can use 
kubectl create secret docker-registry
to create the secret, ensuring that your credentials are correctly encoded. - Update your Kubernetes pod's YAML to reference the secret (using 
imagePullSecrets
). 3. Resolving 
FileNotFoundError
in Prefect: - This issue generally means that the Prefect flow is trying to access a file that does not exist or is not found at the specified location in the environment where the flow is running. - Check your flow script and verify the path to the required file(s). Ensure that the Kubernetes worker's environment has access to those file(s) or that they are correctly bundled within your Docker image. If the problem persists, you may want to search for issues similar to yours on GitHub, or more specifically, within Prefect's repository. Would you like me to do that for you?
It seems there weren't any directly related open GitHub issues in the PrefectHQ repository at this moment, but let's see what information can be pieced together from the documents and common issues: ### To build and store a Docker image in a private ECR repository: 1. Ensure you have the necessary permissions configured for accessing Amazon ECR, which may include the following AWS permissions: - 
ecr:CreateRepository
- 
ecr:DescribeRepositories
- 
ecr:GetAuthorizationToken
2. Use AWS managed policies such as 
AmazonEC2ContainerRegistryFullAccess
for easier management. 3. Install Docker locally to build and push images if you haven't already. ### To configure a work pool to pull an image from ECR in a Kubernetes cluster: 1. Set up your Kubernetes cluster with appropriate IAM roles or service accounts that have permissions to pull from ECR. 2. Use Kubernetes secrets to securely store AWS credentials (or configure IAM roles for service accounts if using EKS) that have permissions to access ECR. ### Common reasons for a 
FileNotFoundError
when running a flow in Prefect: - The flow script may be looking for a file or directory that doesn't exist at the specified path in the container. - Volume mounts or configurations might be missing or misconfigured, causing files to be inaccessible at runtime. To troubleshoot the `FileNotFoundError`: 1. Confirm all files and directories your flow needs are included in the Docker image. 2. Check your Kubernetes Pod specifications to ensure that any needed volume mounts are properly configured. 3. Double-check the paths in your flow code to ensure they match the expected structure within the container. For more in-depth troubleshooting, a review of the complete error log and flow configuration would be necessary. If you're confident your Kubernetes and Docker configurations are correct, then revisiting the flow script for path-related errors would be the next step. If none of these suggestions resolve your issue, you still have the option to reach out to the Prefect community for support or post a detailed issue in the GitHub repository with relevant configurations and error logs.
Based on your 
prefect.yaml
, it seems you are performing a few steps. Here's a breakdown and some things to check or note for each step: 1. Getting Git Commit Hash: Check if the Git repository is correctly cloned where this command is running. The error about "No such file or directory" may be related to the 
git
command not finding a Git repository. 2. ECR Authentication: Make sure that the 
AWS_REGION
and 
ECR_REPOSITORY
environment variables are set correctly. Also, ensure that your AWS credentials are accessible and the command is correctly logging into the ECR. 3. Building Docker Image: Verify that the file referenced as 
Dockerfile.custom
exists at the location and that all the build context (files and directories needed by Dockerfile) are available. 4. Pushing Docker Image: Ensure that the image push is successful. Any failure in this step would mean that the Kubernetes cluster won't be able to pull the image. 5. Setting Working Directory: This step sets the working directory to 
/opt/prefect
, but ensure that the directory exists in your Docker image. Lastly, remember that Prefect provides a YAML configuration for Docker deployment, and you can specify job variables and other settings as per your deployment needs. The 
prefect.yaml
file is crucial for your deployment, so ensure that it correctly reflects your configurations and environment settings. If you want me to, I can search for similar issues or more resources on configuring the 
prefect.yaml
file for Docker deployments and setting the working directory. Would you like me to do that?
Your message was too long by 69 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
Does any of this look incorrect?
```build: - prefect.deployments.steps.run_shell_script: id: get_commit_hash script: git rev-parse --short HEAD stream_output: false - prefect.deployments.steps.run_shell_script: id: ecr_auth_ste_push script: /bin/bash -c 'aws ecr get-login-password --region {{ $AWS_REGION }} --profile ecr | docker login --username AWS --password-stdin {{ $ECR_REPOSITORY }}' stream_output: true - prefect_docker.deployments.steps.build_docker_image: id: build_image requires: prefect-docker>=0.4.0 image_name: "{{ $ECR_REPOSITORY }}" tag: "prefect-disputes-{{ get_commit_hash.stdout }}" dockerfile: Dockerfile.custom platform: "linux/amd64" push: - prefect_docker.deployments.steps.push_docker_image: requires: prefect-docker>=0.4.0 image_name: "{{ build_image.image_name }}" tag: "{{ build_image.tag }}" pull: - prefect.deployments.steps.set_working_directory: directory: "/opt/prefect" definitions: tags: &common_tags - eks work_pool: &common_work_pool name: local-test job_variables: image: "{{
a
@Marvin could any of this be a problem?
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push:
  - prefect_docker.deployments.steps.push_docker_image:
      requires: prefect-docker>=0.4.0
      image_name: "{{ build_image.image_name }}"
      tag: "{{ build_image.tag }}"

pull:
  - prefect.deployments.steps.set_working_directory:
      directory: "/opt/prefect"

definitions:
  tags: &common_tags
    - eks
  work_pool: &common_work_pool
    name: local-test
    job_variables:
      image: "{{ build_image.image }}"
      environment: "{{ prefect.variables.environment }}"

deployments:
  - name: sample
    tags: *common_tags
    schedule:
    entrypoint: flows/sample/pipeline.py:sample
    work_pool: *common_work_pool
    version: "{{ get_commit_hash.stdout }}"
    description:
    parameters:
m
Indeed, this configuration file could be related to the problem you're experiencing. There are a few points here that may be worth checking: 1. Push Configuration: - You have a push step with 
prefect_docker.deployments.steps.push_docker_image
which requires 
prefect-docker>=0.4.0
. Verify that you've actually installed this version or higher. - Ensure that the 
image_name
and 
tag
are being set properly, and that the image is successfully being pushed to the repository. 2. Pull Configuration: - The 
prefect.deployments.steps.set_working_directory
step sets the working directory to 
/opt/prefect
. Confirm that this directory exists in your Docker image and that it contains your flow file 
pipeline.py
. 3. Work Pool Configuration: - You've defined a work pool named 
local-test
. Verify that this work pool is properly configured in your Prefect installation, and that it has access to pull images from ECR. - Check the 
job_variables
to ensure the 
image
and 
environment
variables are resolving correctly and are valid for the Kubernetes cluster. 4. Deployments Configuration: - The 
deployments
section defines a deployment named 
sample
with certain tags and a job that runs on the 
local-test
work pool. - The 
entrypoint
seems to be pointing to 
flows/sample/pipeline.py:sample
. Ensure that this path is correct and that the 
pipeline.py
file exists at that location inside the Docker image. - The 
version
is being set using 
{{ get_commit_hash.stdout }}
. Verify that the 
get_commit_hash
command successfully sets the correct commit hash. - Ensure that there are no syntax errors, such as incorrect indentation or improper use of YAML anchors and aliases (for example, 
&common_tags
and 
*common_tags
). Moreover, the error message you received (
FileNotFoundError
) suggests that when the flow is run, it is unable to find a necessary file or directory. This could imply that either the Docker image is not correctly including the required files or the Kubernetes configuration is not correctly mounting volumes or passing environment variables. Double-check that the paths you're using in your flow code align with the directory structure inside your Docker image and that any external resources are correctly mounted or accessible within the Kubernetes pod that is executing the flow.
a
@Marvin In a flow run, how do I check the Docker image being pulled and used to run the flow?
m
Based on my research, there are no direct references to how to check which Docker image is being used in a Prefect flow run among active GitHub issues. However, there are some ways you can check which Docker image is being pulled and used during a Prefect flow run: 1. Kubernetes Dashboard or CLI (
kubectl
): If you are using a Kubernetes cluster to run your Prefect flows, you can inspect the specifications of the running pods. By using 
kubectl describe pod <pod-name>
, you can find details about the image being used. 2. Prefect UI: By navigating to the Prefect UI and looking at the information for a specific flow run, you might be able to find details regarding the image used for the execution environment. 3. Flow Configuration: You can check the flow configuration (e.g., prefect.yaml or any other configuration files where the deployment settings are defined) and verify the image name and tag specified under the job_variables or any relevant section. 4. Logging: You can add logging to your flow or tasks that outputs the environment variable(s) associated with the Docker image being used. For instance, Docker typically sets certain environment variables like 
HOSTNAME
which may include the container id, or you can define custom environment variables such as image name or tag when building your Docker image and log them. 5. Prefect Agent Logs: If you're using a Prefect agent, the agent's logs often include information about which Docker image it's pulling and using to start flow runs. Check the agent's logs for messages that might say something like "Pulling image ..." or "Using image ...". Remember that in the end, it will depend on your specific setup and deployment configuration. You might need to combine multiple approaches to ensure you have the complete picture of which image is being used for your flow runs. If you have further issues or require additional information on how to implement these checks, it may be helpful to consult the Prefect documentation or community forums.
n
how are you building the image ?
a
@Nate:
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name: sample
prefect-version: 2.14.13

infrastructure:
  image_pull_policy: ALWAYS

build:
  - prefect.deployments.steps.run_shell_script:
      id: get_commit_hash
      script: git rev-parse --short HEAD
      stream_output: false

  - prefect.deployments.steps.run_shell_script:
      id: ecr_auth_ste_push
      script: /bin/bash -c 'aws ecr get-login-password --region {{ $AWS_REGION }} --profile ecr | docker login --username AWS --password-stdin {{ $ECR_REPOSITORY }}'
      stream_output: true

  - prefect_docker.deployments.steps.build_docker_image:
      id: build_image
      requires: prefect-docker>=0.4.0
      image_name: "{{ $ECR_REPOSITORY }}"
      tag: "prefect-sample-{{ get_commit_hash.stdout }}"
      dockerfile: Dockerfile.custom
      platform: "linux/amd64"

push:
  - prefect_docker.deployments.steps.push_docker_image:
      requires: prefect-docker>=0.4.0
      image_name: "{{ build_image.image_name }}"
      tag: "{{ build_image.tag }}"

pull:
  - prefect.deployments.steps.set_working_directory:
      directory: "/opt/prefect"

definitions:
  tags: &common_tags
    - eks
  work_pool: &common_work_pool
    name: local-test
    job_variables:
      image: "{{ build_image.image }}"
      environment: "{{ prefect.variables.environment }}"

deployments:
  - name: sample
    tags: *common_tags
    schedule:
    entrypoint: flows/sample/pipeline.py:sample
    work_pool: *common_work_pool
    version: "{{ get_commit_hash.stdout }}"
    description:
    parameters:
I figured out (by looking directly at the Kubes pod running) that the image being pulled into the container is the 
prefecthq/prefect:2.14.9-python3.10
) base image, and not the custom-built image. This is the reason for the flow not being found. As to why that image is not being fed in from 
job_variables -> image
... I'm not sure yet.
I figured out why the image was not being set. When I was deploying the flow I had a 
-v
argument to set a particular variable. This overrides whatever is in the 
prefect.yaml
e
@Marvin You I have this command: prefect worker start --pool "gaia-prefect" I want it to run all the time on a ec2 vm: I have this file: [unix_http_server] file=/tmp/supervisor.sock ; the path to the socket file [supervisord] loglevel=debug ; log level; default info; others: debug,warn,trace [rpcinterface:supervisor] supervisor.rpcinterface_factory = supervisor.rpcinterface:make_main_rpcinterface [supervisorctl] serverurl=unix:///tmp/supervisor.sock ; use a unix:// URL for a unix socket [program:prefect-worker] command=prefect worker start --pool "gaia-prefect" I get this response which is incorrect as its a docker worker not a process worker: 2024-01-28 003215,129 INFO daemonizing the supervisord process 2024-01-28 003215,130 INFO supervisord started with pid 626466 2024-01-28 003216,133 INFO spawned: 'prefect-worker' with pid 626467 2024-01-28 003217,135 INFO success: prefect-worker entered RUNNING state, process has stayed up for > than 1 seconds (startsecs) 2024-01-28 003219,459 DEBG 'prefect-worker' stdout output: Discovered worker type 'process' for work pool 'gaia-prefect'. 2024-01-28 003219,511 DEBG 'prefect-worker' stdout output: Worker 'ProcessWorker 1c760ffe-b310-4ed4-ae05-12241624d577' started!
m
Your message was too long by 80 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
You
I have this command: prefect worker start --pool "gaia-prefect" I want it to run all the time on a ec2 vm: I have this file: [unix_http_server] file=/tmp/supervisor.sock ; the path to the socket file [supervisord] loglevel=debug ; log level; default info; others: debug,warn,trace [rpcinterface:supervisor] supervisor.rpcinterface_factory = supervisor.rpcinterface:make_main_rpcinterface [supervisorctl] serverurl=unix:///tmp/supervisor.sock ; use a unix:// URL for a unix socket [program:prefect-worker] command=prefect worker start --pool "gaia-prefect" I get this response which is incorrect as its a docker worker not a process worker: 2024-01-28 003215,129 INFO daemonizing the supervisord process 2024-01-28 003215,130 INFO supervisord started with pid 626466 2024-01-28 003216,133 INFO spawned: 'prefect-worker' with pid 626467 2024-01-28 003217,135 INFO success: prefect-worker entered RUNNING state, process has stayed up for > than 1 seconds (startsecs) 2024-01-28 003219
e
@Marvin I have this command: prefect worker start --pool "gaia-prefect" I want it to run all the time on a ec2 vm: I have this file: [unix_http_server] file=/tmp/supervisor.sock ; the path to the socket file [supervisord] loglevel=debug ; log level; default info; others: debug,warn,trace [rpcinterface:supervisor] supervisor.rpcinterface_factory = supervisor.rpcinterface:make_main_rpcinterface [supervisorctl] serverurl=unix:///tmp/supervisor.sock ; use a unix:// URL for a unix socket [program:prefect-worker] command=prefect worker start --pool "gaia-prefect" I get this response which is incorrect as its a docker worker not a process worker: 2024-01-28 003215,129 INFO daemonizing the supervisord process 2024-01-28 003215,130 INFO supervisord started with pid 626466 2024-01-28 003216,133 INFO spawned: 'prefect-worker' with pid 626467 2024-01-28 003217,135 INFO success: prefect-worker entered RUNNING state, process has stayed up for > than 1 seconds (startsecs) 2024-01-28 003219,459 DEBG 'prefect-worker' stdout output: Discovered worker type 'process' for work pool 'gaia-prefect'. 2024-01-28 003219,511 DEBG 'prefect-worker' stdout output: Worker 'ProcessWorker 1c760ffe-b310-4ed4-ae05-12241624d577' started!
m
Your message was too long by 76 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
I have this command: prefect worker start --pool "gaia-prefect"
I want it to run all the time on a ec2 vm: I have this file: [unix_http_server] file=/tmp/supervisor.sock ; the path to the socket file [supervisord] loglevel=debug ; log level; default info; others: debug,warn,trace [rpcinterface:supervisor] supervisor.rpcinterface_factory = supervisor.rpcinterface:make_main_rpcinterface [supervisorctl] serverurl=unix:///tmp/supervisor.sock ; use a unix:// URL for a unix socket [program:prefect-worker] command=prefect worker start --pool "gaia-prefect" I get this response which is incorrect as its a docker worker not a process worker: 2024-01-28 003215,129 INFO daemonizing the supervisord process 2024-01-28 003215,130 INFO supervisord started with pid 626466 2024-01-28 003216,133 INFO spawned: 'prefect-worker' with pid 626467 2024-01-28 003217,135 INFO success: prefect-worker entered RUNNING state, process has stayed up for > than 1 seconds (startsecs) 2024-01-28 003219,459 DEBG
e
@Marvin What is the best way to run a worker all the time on an ec2
m
To ensure a Prefect worker is always running on an EC2 instance, follow these steps: 1. Provision the EC2 Instance: - Choose an appropriate instance size based on your workload requirements. - Configure security groups and networking as needed for the instance to communicate with the Prefect server or cloud. 2. Install Prefect: - Install Python and any necessary system packages. - Install Prefect using 
pip
, ensuring you have the correct version for your orchestrator (e.g., 
pip install prefect
). 3. Configure the Prefect Agent: - Configure a Prefect agent to run on the EC2 instance. Ensure that it has access to pull your flows and any necessary private repositories. - If you're using Prefect Cloud, authenticate the agent with your Prefect Cloud API token. 4. Set Up the Prefect Worker: - Define a systemd service or use a process manager like supervisord to manage the lifecycle of the Prefect agent process. - This configuration should ensure the Prefect agent starts at boot and restarts in case of failure. 5. Automate Instance Healing: - Configure EC2 Auto Scaling with a desired capacity of 1 to ensure that if the instance goes down, another one will be provisioned to replace it. 6. Monitor and Logging: - Set up CloudWatch or another monitoring tool to keep an eye on the EC2 instance's health and the Prefect agent's logs. Remember that running a Prefect agent on an EC2 instance will incur AWS costs, so it is recommended to properly manage the instance size and running time based on actual usage and optimize for cost where possible. Adjust these steps based on your particular use case and Prefect version (for example, if there are additional configuration steps required by newer Prefect versions).
e
@Marvin give me more details 4. Set Up the Prefect Worker: - Define a systemd service or use a process manager like supervisord to manage the lifecycle of the Prefect agent process. - This configuration should ensure the Prefect agent starts at boot and restarts in case of failure.
m
Certainly, to keep a Prefect agent running at all times on an EC2 instance, using a process manager like 
systemd
is a robust solution. Here are more detailed instructions: 1. Create a Systemd Service File: Create a systemd service file for your Prefect agent. You can create a file named 
prefect-agent.service
in the 
/etc/systemd/system/
directory with the following contents (make sure to adjust the WorkingDirectory, User, and the command to start the Prefect agent as per your environment):
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ini
[Unit]
Description=Prefect Agent
After=network.target

[Service]
Type=simple
User=ubuntu
WorkingDirectory=/home/ubuntu
ExecStart=/usr/bin/env prefect agent start
Restart=on-failure
RestartSec=10

[Install]
WantedBy=multi-user.target
2. Configure the ExecStart Command: - Modify 
ExecStart
to include the appropriate command to start your Prefect agent. This might include specifying the work queue or API key if required. - For example: 
ExecStart=/usr/local/bin/prefect agent start --api-key YOUR_API_KEY --work-queue YOUR_WORK_QUEUE_NAME
3. Reload Systemd Daemon: - Once the service file is in place, reload systemd to read the new service file: 
sudo systemctl daemon-reload
4. Enable and Start the Service: - Enable the Prefect agent service to start on boot: 
sudo systemctl enable prefect-agent.service
- Start the service: 
sudo systemctl start prefect-agent.service
5. Check the Service Status: - You can check the status of your service using: 
sudo systemctl status prefect-agent.service
- You should see an active status indicating that the service is running. 6. View Logs: - To check for logs and make sure your agent is running correctly, you can use: 
sudo journalctl -u prefect-agent.service
- This will show you the logs specific to the Prefect agent systemd service. By setting up a systemd service to manage your Prefect agent, you'll ensure that it starts when the EC2 instance boots up and is restarted automatically if it ever fails. This is a common way to manage long-running services on a Linux-based system such as EC2 instances running Ubuntu or Amazon Linux.
e
@Marvin I want to use work pools not agents
m
Apologies for the confusion earlier. In the context of Prefect 2.x, what was formerly known as "agents" in Prefect 1.0 has been adapted into the concept of "Work Queues" and "Work Pool." Here's a modified step-by-step process to set up a persistent work pool using Prefect 2.x on an EC2 instance: 1. Choose the Appropriate EC2 Instance: - Start by provisioning an EC2 instance that suits your computational needs. 2. Install Required Software: - Ensure Python 3.7+ is installed on the instance. - Install Prefect 2.x using pip (
pip install prefect
). 3. Create and Configure a Work Pool: - You can create a work pool to group work that should be executed under similar conditions. - Use the Prefect 2.x CLI to create a new work pool. 4. Set Up Persistent Work Pool Execution: - Instead of systemd or a process manager, you want the EC2 instance itself to be part of a work pool. - Configure your EC2 instance to join the work pool during the startup by putting the necessary commands in a script that runs on instance boot (e.g., using 
rc.local
, 
cron @reboot
, or other init systems). 5. Auto-Scaling Configuration: - To ensure that the EC2 instance is always running, set up an EC2 Auto Scaling Group with a minimal size of one. 6. Install and Configure Docker (Optional): - If your work requires running flows in Docker containers, you'll also need to install Docker on your EC2 instance. - Configure the Docker daemon to start on boot. 7. Monitoring and Logging: - Use AWS CloudWatch or another monitoring solution to keep an eye on the EC2 instance’s health. - This will alert you to any issues so you can take corrective action promptly. Remember that the exact Prefect CLI commands to create or join work pools may vary depending on the version you're using, so refer to the latest Prefect documentation for accurate instructions.
e
@Marvin how can I do : 4. Set Up Persistent Work Pool Execution: - Instead of systemd or a process manager, you want the EC2 instance itself to be part of a work pool. - Configure your EC2 instance to join the work pool during the startup by putting the necessary commands in a script that runs on instance boot (e.g., using 
rc.local
, 
cron @reboot
, or other init systems).
m
To set up persistent work pool execution on an EC2 instance and have it join a Work Queue (in Prefect 2.x terminology), you can use 
cron
with an 
@reboot
directive or leverage the instance's user data script for execution on startup. Here's how you can achieve this: ### Using User Data Script When you launch an EC2 instance, you can specify user data to configure or run scripts when the instance starts. You can use this feature to have your EC2 instance join a Work Queue whenever it boots up. 1. In the AWS Management Console, start the process of launching a new EC2 instance. 2. In the "Configure Instance Details" step, expand the "Advanced Details" section. 3. Enter your user data script in the "User Data" field. For example:
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bash
#!/bin/bash
# Install Prefect if not already installed
pip install prefect -U

# Join the Prefect Work Queue (replace `YOUR_WORK_QUEUE_NAME` with the name of your Work Queue)
prefect work-queue set-execution YOUR_WORK_QUEUE_NAME --label ec2-worker
Make sure to replace 
YOUR_WORK_QUEUE_NAME
with the actual name of the Work Queue you want this EC2 instance to join. ### Using Cron Alternatively, you can configure 
cron
to run a command at startup. 1. SSH into your EC2 instance after it's running. 2. Open the crontab configuration:
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bash
crontab -e
3. Add the following line (make sure the path to the 
prefect
executable matches your installation):
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bash
@reboot /usr/local/bin/prefect work-queue set-execution YOUR_WORK_QUEUE_NAME --label ec2-worker
Again, replace 
YOUR_WORK_QUEUE_NAME
with your Work Queue name. In both cases, 
--label ec2-worker
is optional but recommended for clarity, especially if you have multiple execution environments or worker types. Make sure the Prefect CLI is installed and configured to communicate with your Prefect server or Prefect Cloud. After you've set this up, every time your EC2 instance boots up, it will automatically join the Work Queue you've specified, ready to pick up and execute flow runs.
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