@Marvin explain to me how this code works. Every aspect of it, where whats running etc import os import time import gc from prefect import flow, task from prefect.task_runners import ConcurrentTaskRunner from prefect.deployments import DeploymentImage from prefect.tasks import task_input_hash from dotenv import load_dotenv from pathlib import Path import random @task(cache_key_fn=task_input_hash) def test(in_arg): print("test task started", in_arg) time.sleep(100) print("test task finished", in_arg) @flow(log_prints=True, task_runner=ConcurrentTaskRunner()) def test_ray(): args = [random.random() for _ in range(10)] args.append(0) # task that shall be always cached print("args", args) test.map(args) if name == "__main__": load_dotenv() # test_ray() test_ray.deploy( name="default", work_pool_name="ecs-pull-workpool-with-ec2", image=DeploymentImage( name=f"{os.getenv('AWS_ECR_REGISTRY')}:latest", platform="linux/amd64", dockerfile="Dockerfile" ), job_variables={ "cpu": 1024, "memory": 2048, "env": { "AWS_ACCESS_KEY_ID": os.getenv("AWS_ACCESS_KEY_ID"), "AWS_SECRET_ACCESS_KEY": os.getenv("AWS_SECRET_ACCESS_KEY"), "AWS_DEFAULT_REGION": os.getenv("AWS_DEFAULT_REGION"), "AWS_DEFAULT_BUCKET": os.getenv("AWS_DEFAULT_BUCKET") } } )

Let's dissect the code step by step, focusing on how each part works: 1. Imports: - Standard Libraries: -

os

,

time

,

gc

, `random`: These are standard Python libraries.

os

is used for environment variables,

time

for delays,

gc

for garbage collection, and

random

for generating random numbers. - `dotenv`: Used to load environment variables from a

.env

file. - `pathlib.Path`: Provides classes to handle filesystem paths. - Prefect Imports: -

flow

, `task`: Decorators from Prefect to define flows and tasks. - `ConcurrentTaskRunner`: A class that allows tasks to run concurrently. - `DeploymentImage`: Represents a Docker image used for deployment. - `task_input_hash`: A function used to generate cache keys for tasks. 2. Task Definition: - `@task(cache_key_fn=task_input_hash)`: This decorator turns the

test

function into a Prefect task. The

cache_key_fn

parameter uses

task_input_hash

to determine if the task can be cached based on its input. - `test(in_arg)`: This task prints a start message, sleeps for 100 seconds, and then prints a finish message. 3. Flow Definition: - `@flow(log_prints=True, task_runner=ConcurrentTaskRunner())`: This decorator defines a Prefect flow. The

log_prints=True

parameter ensures that print statements are logged. The

ConcurrentTaskRunner

allows tasks within the flow to run concurrently. - `test_ray()`: This flow generates a list of random numbers, appends a zero (to ensure one task is always cached), and maps these arguments to the

test

task. 4. Main Execution: - `load_dotenv()`: Loads environment variables from a

.env

file. - `test_ray.deploy(...)`: Deploys the flow using specified parameters. - `name`: Deployment name. - `work_pool_name`: Name of the work pool, presumably an ECS work pool with EC2. - `image`: Specifies the Docker image to use for deployment. - `job_variables`: Sets resource limits and environment variables for the deployment. 5. Environment Variables: - AWS-related environment variables are loaded using

os.getenv

, which are crucial for configuring the deployment. This code utilizes Prefect 3.x features, with a focus on deploying a flow to an AWS ECS environment using Docker images. The flow itself is designed to run tasks concurrently, demonstrating Prefect's capability to handle parallel execution. For more details on the Prefect components used, you can check: - Prefect Task Runners - Prefect Deployments - Prefect Tasks - Prefect Flows

@Marvin Okay so if I run this code, with

python3 filename.py

, what will happen? Explain step by step including if/why something is pulled from docker

Here's a step-by-step explanation of what happens when you run the code with `python3 filename.py`: 1. Environment Setup: - `load_dotenv()`: This function loads environment variables from a

.env

file into the process's environment. These variables include AWS credentials and Docker registry information necessary for deployment. 2. Flow Deployment: - The

test_ray.deploy(...)

method is called. This method is responsible for deploying the

test_ray

flow to a specified environment. 3. Deployment Process: - Docker Image: The deployment specifies a Docker image using

DeploymentImage

. The image name is constructed from the

AWS_ECR_REGISTRY

environment variable, appended with

:latest

. This image should already exist in your AWS Elastic Container Registry (ECR). - Environment Variables: The deployment uses AWS credentials and other environment variables to configure the deployment environment. 4. AWS ECS Configuration: - The deployment is configured to use an ECS work pool named

ecs-pull-workpool-with-ec2

. This means that the flow will be executed in an Amazon ECS cluster, utilizing EC2 instances. - The

job_variables

dictionary specifies the resources (CPU and memory) allocated to the task, as well as additional environment variables. 5. Docker Pull: - If the specified Docker image is not already present on the ECS instances, it will be pulled from the AWS ECR. This ensures that the correct environment is used for running the flow. 6. Flow Execution: - Once deployed, the flow will run on the ECS cluster. The

test_ray

flow generates random numbers, appends a zero, and maps these to the

test

task, which runs concurrently due to the

ConcurrentTaskRunner

. 7. Task Execution: - Each task will print a start message, sleep for 100 seconds, and then print a finish message. If the task input is the same as a previous run, it might use the cached result instead of executing again, thanks to the

task_input_hash

function. 8. Logging: - All print statements are logged due to the

log_prints=True

parameter in the flow decorator. Overall, this code is designed to deploy a Prefect flow to AWS ECS, using a Docker image for the execution environment. The Docker image will be pulled if not already available on the ECS instances, ensuring that the flow runs in a consistent environment.

@Marvin okay so the code in this file will run but in the environment specified by docker DeploymentImage?

Yes, precisely. When you run the code with

python3 filename.py

, the flow is deployed to an environment specified by the

DeploymentImage

within AWS ECS. Here's how it works: 1. Local Execution: The Python script itself runs locally on your machine, but its primary purpose is to set up and deploy the flow to a remote environment. 2. Docker Environment: The

DeploymentImage

specifies the Docker image that contains the environment in which the flow will actually execute. This image is defined by the

AWS_ECR_REGISTRY

environment variable and is expected to be stored in AWS Elastic Container Registry (ECR). 3. Remote Execution: - Once the deployment is initiated, the flow runs on an AWS ECS cluster. The ECS service uses the specified Docker image to create a containerized environment that matches the configuration defined in your deployment. - The tasks within the flow are executed inside this Docker container, ensuring a consistent and isolated environment. 4. Resource and Environment Configuration: - The

job_variables

parameter specifies resource allocations (CPU and memory) and environment variables necessary for the execution within the Docker container. In summary, while the deployment is initiated from your local environment, the actual execution of the flow occurs in the remote Docker environment specified by the

DeploymentImage

. This allows for consistent and scalable execution, leveraging AWS infrastructure.

@Marvin okay, if I wanted to do this with RayTaskRunner, how would I? And how would the infrastructure all work? Be as detailed and precise as possible. I absolutely want to run my code in docker environment but ECS is not necessary.

To use

RayTaskRunner

with Prefect, and ensure your code runs in a Docker environment without relying on ECS, you'll need to follow these steps: 1. Install Prefect-Ray Integration: - First, ensure you have the

prefect-ray

package installed. You can install it via pip:

bash
     pip install prefect-ray

- This package allows Prefect to integrate with Ray, enabling distributed task execution. 2. Modify Your Code: - Replace the

ConcurrentTaskRunner

with

RayTaskRunner

in your flow definition. - Ensure your Docker environment is set up to run Ray. You might need to include Ray installation in your Dockerfile. Here's how your modified code might look:

python
import os
import time
import random
from prefect import flow, task
from prefect_ray import RayTaskRunner
from prefect.deployments import DeploymentImage
from prefect.tasks import task_input_hash
from dotenv import load_dotenv

@task(cache_key_fn=task_input_hash)
def test(in_arg):
    print("test task started", in_arg)
    time.sleep(100)
    print("test task finished", in_arg)

@flow(log_prints=True, task_runner=RayTaskRunner())
def test_ray():
    args = [random.random() for _ in range(10)]
    args.append(0)  # task that shall be always cached
    print("args", args)
    test.map(args)

if __name__ == "__main__":
    load_dotenv()
    test_ray.deploy(
        name="default",
        work_pool_name="docker-pool",
        image=DeploymentImage(
            name=f"{os.getenv('DOCKER_REGISTRY')}:latest",
            platform="linux/amd64",
            dockerfile="Dockerfile"
        ),
        job_variables={
            "cpu": 1024,
            "memory": 2048,
            "env": {
                "AWS_ACCESS_KEY_ID": os.getenv("AWS_ACCESS_KEY_ID"),
                "AWS_SECRET_ACCESS_KEY": os.getenv("AWS_SECRET_ACCESS_KEY"),
                "AWS_DEFAULT_REGION": os.getenv("AWS_DEFAULT_REGION"),
                "AWS_DEFAULT_BUCKET": os.getenv("AWS_DEFAULT_BUCKET")
            }
        }
    )

3. Docker Configuration: - Ensure your Dockerfile includes Ray installation. Here’s a simple example:

dockerfile
     FROM python:3.9-slim

     # Install Ray and Prefect
     RUN pip install prefect-ray

     # Copy your code into the container
     COPY . /app
     WORKDIR /app

     # Set the entrypoint for the container
     ENTRYPOINT ["python", "filename.py"]

- Build and push your Docker image to a registry accessible by your deployment environment. 4. Infrastructure Setup: - You can run this setup on any Docker-compatible infrastructure, such as a local Docker daemon, Docker Swarm, or Kubernetes. The key is ensuring that the Docker environment can pull the image and execute the Ray-based flow. 5. Execution: - When you deploy and run the flow, Prefect will use Ray to distribute tasks across available resources within the Docker environment. By following these steps, you can leverage Ray for parallel task execution within a Docker environment, without relying on ECS. This setup provides flexibility and scalability while keeping your execution environment consistent.

@Marvin Okay, I want to now run these tasks each in docker but on separate EC2 instances.

To run each task in a Docker container on separate EC2 instances, you'll need to configure a setup that combines Docker for containerization and AWS EC2 for distributed execution. Here's a detailed guide on how to achieve this: 1. Set Up AWS EC2 Instances: - Launch EC2 Instances: Start by launching the required number of EC2 instances. Ensure that each instance has Docker installed. You can use an Amazon Machine Image (AMI) that comes with Docker pre-installed or install Docker manually. - Security Groups: Configure security groups to allow necessary traffic, such as SSH for administration and any ports needed for your tasks. 2. Docker Configuration: - Docker Images: Ensure your Docker image is built with all necessary dependencies, including Prefect and any other libraries your tasks require. Push the image to a Docker registry that your EC2 instances can access, such as AWS ECR or Docker Hub. - Docker Compose: Consider using Docker Compose to manage multi-container setups if needed. 3. Prefect Deployment: - RayTaskRunner: Use the

RayTaskRunner

to distribute tasks across the EC2 instances. Ray can orchestrate task execution across nodes, which in this case, are your EC2 instances. - Docker Infrastructure: Prefect's deployment should specify using Docker containers for task execution. You can configure this with Prefect's infrastructure blocks or directly within your flow. 4. Configure Ray Cluster: - Ray Head Node: Designate one EC2 instance as the Ray head node. Start the Ray cluster on this node. - Ray Worker Nodes: Connect the remaining EC2 instances as worker nodes to the Ray cluster. Each worker node will execute tasks in its Docker environment. 5. Flow Execution: - Deploy your flow using Prefect, ensuring that the Ray cluster setup is correctly referenced. Each task will then be executed in its Docker container on separate EC2 instances. 6. Monitoring and Logging: - Use Prefect's logging and monitoring capabilities to track the execution of tasks across the distributed setup. Ensure that logs from Docker containers are accessible for debugging. 7. Networking and Permissions: - Networking: Ensure that all EC2 instances can communicate with each other and with the Docker registry. - AWS Permissions: Use appropriate IAM roles and policies to grant EC2 instances access to necessary AWS resources, like ECR for pulling Docker images. 8. Cost and Scaling Considerations: - Be mindful of AWS costs associated with running multiple EC2 instances. Consider using spot instances for cost savings. - Scale the number of EC2 instances based on the workload and task execution requirements. By following these steps, you can effectively distribute task execution across multiple EC2 instances, each running in its Docker container, leveraging both Docker's containerization and Ray's distributed computing capabilities.

@Marvin deployment script: deployments: - name: my-ray-deployment entrypoint: ./ray-remote.py:count_to parameters: highest_number: 10 work_queue_name: default work_pool: name: my-ray-work-pool job_variables: {} schedules: [] it pulls the code form gitlab interactively and this ray cluster config: cluster_name: aws-example-minimal provider: type: aws region: ap-south-1 idle_timeout_minutes: 5 auth: ssh_user: ec2-user initialization_commands: - sudo yum update -y - sudo yum install -y openssl11 - sudo yum -y groupinstall "Development Tools" - sudo yum -y install openssl11-devel bzip2-devel libffi-devel - wget https://www.python.org/ftp/python/3.10.0/Python-3.10.0.tgz - tar xzf Python-3.10.0.tgz - sudo Python-3.10.0/configure --enable-optimizations - sudo make altinstall - sudo alternatives --install /usr/bin/python3 python3 /usr/local/bin/python3.10 1 - sudo alternatives --set python3 /usr/local/bin/python3.10 - pip3.11 install --upgrade pip - pip3 install -U urllib3 - pip3 install -U botocore - pip3 install -U boto3 - pip3 install -U "ray==2.9.2" - pip3 install "prefect==2.14.20" - pip3 install "prefect-ray==0.3.4" available_node_types: ray.head.default: resources: {} node_config: InstanceType: t3.medium ImageId: ami-06b8001839951865d ray.worker.default: max_workers: 10 resources: {"CPU": 1} node_config: InstanceType: tt3.micro ImageId: ami-06b8001839951865d It works such that the head node ec2 spins up new ec2 instances, runs the tasks and spins them down. I just wanna do this all in docker.

Your message was too long by 628 tokens...

@Marvin how will prefect access my code in this case?

To ensure that Prefect can access your code when running tasks in Docker containers on a Ray-managed EC2 cluster, you need to follow these steps: 1. Include Code in Docker Image: - Dockerfile: Make sure your application code is included in the Docker image. This means copying your code into the image during the build process.

dockerfile
   FROM amazonlinux:2

   # Install system dependencies
   RUN yum update -y && \
       yum install -y openssl11 openssl11-devel bzip2-devel libffi-devel wget && \
       yum groupinstall -y "Development Tools"

   # Install Python
   RUN wget <https://www.python.org/ftp/python/3.10.0/Python-3.10.0.tgz> && \
       tar xzf Python-3.10.0.tgz && \
       cd Python-3.10.0 && \
       ./configure --enable-optimizations && \
       make altinstall && \
       alternatives --install /usr/bin/python3 python3 /usr/local/bin/python3.10 1 && \
       alternatives --set python3 /usr/local/bin/python3.10

   # Install Python packages
   RUN pip3.10 install --upgrade pip && \
       pip3 install -U urllib3 botocore boto3 "ray==2.9.2" "prefect==2.14.20" "prefect-ray==0.3.4"

   # Copy your application code
   COPY . /app

   WORKDIR /app

   # Command to start Ray
   CMD ["ray", "start", "--head"]

2. Use a Git-based Storage Block: - If your code is stored in a Git repository, you can configure Prefect to pull the latest version of your code from the repository. This can be done using Prefect's Git storage block. - Configure the deployment to specify the Git repository URL and branch. 3. Prefect Deployment Configuration: - Ensure your Prefect deployment is set up to use the Docker image and Git storage if applicable. Here’s an example of how you might configure the deployment to pull from Git:

yaml
   deployments:
   - name: my-ray-deployment
     entrypoint: ./ray-remote.py:count_to
     parameters:
       highest_number: 10
     work_queue_name: default
     work_pool:
       name: my-ray-work-pool
       job_variables: {}
     schedules: []
     storage:
       - type: git
         repository: "<https://gitlab.com/your-repo.git>"
         branch: "main"

4. Environment Configuration: - Ensure that any environment variables or secrets needed for accessing the Git repository or other resources are configured in your Docker container or provided through Prefect's environment configuration. By including your code in the Docker image and optionally using a Git-based storage block, you ensure that Prefect can access and execute your code within the Docker containers on the Ray-managed EC2 instances. This setup provides a robust and scalable way to manage and execute your Prefect flows.

@Marvin I dont want to use git repo ideally, wanna package in docker. Does the entrypoint in prefect deployment need to change? entrypoint: ./ray-remote.py:count_to

If you want to package your code entirely within the Docker image and avoid using a Git repository, you should ensure that the entrypoint specified in your Prefect deployment matches the path within the Docker image. Here's how you can manage this: 1. Ensure Code is Packaged in Docker: - When building your Docker image, make sure that your code is copied into the image at the correct path. This path should match where you intend to reference it in your Prefect deployment. 2. Dockerfile: - In your Dockerfile, copy your code to a directory within the container. For example:

dockerfile
   # Copy your application code
   COPY . /app

   WORKDIR /app

3. Prefect Deployment Entry Point: - The entrypoint in your Prefect deployment should reflect the path within the Docker container where your script is located. If your script

ray-remote.py

is located at

/app/ray-remote.py

in the Docker container, your entrypoint should be:

yaml
   entrypoint: /app/ray-remote.py:count_to

4. Verify Docker Build Context: - Ensure that the Docker build context includes all necessary files. When you run

docker build

, the context should be set to the directory containing your code. 5. Build and Push Docker Image: - Build your Docker image and push it to a registry accessible by your EC2 instances:

bash
   docker build -t your-docker-registry/your-ray-image:latest .
   docker push your-docker-registry/your-ray-image:latest

By ensuring that your code is correctly packaged within the Docker image and the entrypoint in the Prefect deployment matches the path within the container, you can effectively run your Prefect flows without relying on an external Git repository. This approach provides a self-contained environment that can be easily deployed and managed.

@Marvin what does .map do here? @flow(log_prints=True, task_runner=RayTaskRunner()) def test_ray(): args = [random.random() for _ in range(10)] args.append(0) # task that shall be always cached print("args", args) test.map(args)

In Prefect, the

.map()

method is used to apply a task across multiple inputs in parallel. Here's how it works in your code: 1. Task Mapping: - The

test.map(args)

line in your

test_ray

flow calls the

map

method on the

test

task. - This method takes an iterable (in this case,

args

, which is a list of random numbers) and applies the

test

task to each element of the iterable concurrently. 2. Parallel Execution: - Each element in the

args

list is passed as an argument to a separate invocation of the

test

task. - This allows for parallel execution of the task across all elements of the list, leveraging Prefect's task concurrency features. 3. Task Runner: - Since your flow uses the

RayTaskRunner

, the execution of these tasks can be distributed across nodes in a Ray cluster, potentially across multiple machines, depending on your cluster configuration. 4. Return Type: - The

map

method returns a

PrefectFutureList

, which contains futures representing the execution state of each task invocation. You can resolve these futures to get the results once the tasks are complete. In summary,

.map()

is a powerful feature in Prefect that allows you to execute tasks concurrently over a list of inputs, making it ideal for parallel processing scenarios. This is particularly useful when combined with a distributed execution framework like Ray.