@Marvin is it possible to run an aws batch job and have prefect wait for the job to complete and get the results from the job?

Yes, Prefect supports running AWS Batch jobs and handling the completion and results. To run an AWS Batch job and wait for it to complete, you can use the

batch_submit

task from

prefect_aws.batch

. You will need to implement logic to poll the job status and retrieve the results once it completes. Here is an example flow that submits a job and waits for its completion:

from prefect import flow, task
from prefect_aws import AwsCredentials
from prefect_aws.batch import batch_submit
import boto3
import time

@task
def wait_for_job_completion(job_id, aws_credentials):
    client = boto3.client(
        "batch",
        aws_access_key_id=aws_credentials.aws_access_key_id,
        aws_secret_access_key=aws_credentials.aws_secret_access_key,
    )
    while True:
        response = client.describe_jobs(jobs=[job_id])
        status = response['jobs'][0]['status']
        if status == 'SUCCEEDED':
            break
        elif status == 'FAILED':
            raise Exception("Batch job failed")
        time.sleep(30) # polling interval

@flow
def example_batch_submit_flow():
    aws_credentials = AwsCredentials(
        aws_access_key_id="your_access_key_id",
        aws_secret_access_key="your_secret_access_key"
    )
    job_id = batch_submit(
        job_name="your_job_name",
        job_queue="your_job_queue",
        job_definition="your_job_definition",
        aws_credentials=aws_credentials
    )
    wait_for_job_completion(job_id, aws_credentials)

    # Retrieve other results here if needed

example_batch_submit_flow()

For more details, refer to the Prefect AWS Batch documentation. If you have other questions, just let me know.

How can I use prefect to have the result output be an s3 file and use that file in the next task?

To handle the result output of an AWS Batch job via an S3 file and use that file in the next task, you can follow these steps: 1. Submit the AWS Batch Job: Use the

batch_submit

task from

prefect_aws.batch

to submit the job. 2. Wait for Job Completion: Poll the job status until it completes. 3. Download the Job Result from S3: Use the

s3_download

task from

prefect_aws.s3

to retrieve the result file. 4. Use the S3 File in Subsequent Task: Process the downloaded file in the next task. Here's an example workflow:

from prefect import flow, task
from prefect_aws import AwsCredentials
from prefect_aws.batch import batch_submit
from prefect_aws.s3 import s3_download
import boto3
import time

@task
def wait_for_job_completion(job_id, aws_credentials):
    client = boto3.client(
        "batch",
        aws_access_key_id=aws_credentials.aws_access_key_id,
        aws_secret_access_key=aws_credentials.aws_secret_access_key,
    )
    while True:
        response = client.describe_jobs(jobs=[job_id])
        status = response['jobs'][0]['status']
        if status == 'SUCCEEDED':
            break
        elif status == 'FAILED':
            raise Exception("Batch job failed")
        time.sleep(30) # polling interval

@task
def process_s3_file(file_contents):
    # Replace with your processing logic
    print(file_contents)

@flow(log_prints=True)
def example_batch_s3_flow():
    aws_credentials = AwsCredentials(
        aws_access_key_id="your_access_key_id",
        aws_secret_access_key="your_secret_access_key"
    )
    job_id = batch_submit(
        job_name="your_job_name",
        job_queue="your_job_queue",
        job_definition="your_job_definition",
        aws_credentials=aws_credentials
    )
    wait_for_job_completion(job_id, aws_credentials)
    
    # Download the S3 object containing the results
    file_contents = s3_download(
        bucket="your_results_bucket",
        key="your_results_key",
        aws_credentials=aws_credentials
    )
    
    # Process the downloaded S3 file
    process_s3_file(file_contents)

example_batch_s3_flow()

Refer to the Prefect AWS S3 documentation for more details on the

s3_download

task. For an example of a basic Prefect flow: Prefect Code Example. If you have further questions, let me know!

@Marvin are there any examples of using prefect for bioinformatics ngs workflows?

There are no specific examples in the Prefect documentation explicitly dedicated to bioinformatics or NGS (Next-Generation Sequencing) workflows. However, you can adapt Prefect to these purposes by integrating it with bioinformatics tools and utilizing parallel and distributed processing. Here's an approach to creating a bioinformatics NGS workflow in Prefect: 1. Define Tasks: Create Prefect tasks to handle different stages of the NGS pipeline, such as data preprocessing, alignment, variant calling, and post-processing. 2. Use Storage (e.g., S3): Store large datasets and intermediate results in cloud storage solutions like AWS S3. 3. Distributed Computing: Utilize cloud services or on-premise clusters for heavy computation tasks. Below is an example of a simplified NGS workflow:

python
from prefect import flow, task
from prefect_aws import AwsCredentials
from prefect_aws.s3 import s3_download, s3_upload

@task
def preprocess_data(input_file, output_file):
    # Code for preprocessing NGS data
    ...

@task
def align_reads(ref_genome, input_file, output_file):
    # Code for aligning reads to reference genome
    ...

@task
def variant_calling(aligned_file, output_file):
    # Code for calling variants from aligned reads
    ...
 
@task
def download_from_s3(bucket, key, aws_credentials):
    return s3_download(bucket=bucket, key=key, aws_credentials=aws_credentials)

@task
def upload_to_s3(bucket, key, data, aws_credentials):
    return s3_upload(bucket=bucket, key=key, data=data, aws_credentials=aws_credentials)

@flow
def ngs_pipeline_flow():
    aws_credentials = AwsCredentials(
        aws_access_key_id="your_access_key_id",
        aws_secret_access_key="your_secret_access_key"
    )
    
    # Download data from S3
    raw_data = download_from_s3(bucket="ngs-data", key="raw_data.fastq", aws_credentials=aws_credentials)
    
    # Preprocess data
    preprocessed_data = preprocess_data(input_file=raw_data, output_file="preprocessed_data.fastq")
    
    # Upload preprocessed data to S3
    upload_to_s3(bucket="ngs-data", key="preprocessed_data.fastq", data=preprocessed_data.read(), aws_credentials=aws_credentials)
    
    # Align reads
    aligned_reads = align_reads(ref_genome="ref_genome.fasta", input_file="preprocessed_data.fastq", output_file="aligned_reads.sam")
    
    # Upload aligned reads to S3
    upload_to_s3(bucket="ngs-data", key="aligned_reads.sam", data=aligned_reads.read(), aws_credentials=aws_credentials)
    
    # Call variants
    variants = variant_calling(aligned_file="aligned_reads.sam", output_file="variants.vcf")
    
    # Upload variants to S3
    upload_to_s3(bucket="ngs-data", key="variants.vcf", data=variants.read(), aws_credentials=aws_credentials)

ngs_pipeline_flow()

This example demonstrates how to construct a basic NGS workflow utilizing Prefect capabilities to manage AWS resources and coordinate tasks in a defined pipeline. You can adjust the tasks according to your specific NGS tools and requirements. For extended examples of Prefect in other domains, you can explore the Prefect Blog and Guides. For AWS integrations, look into the Prefect AWS documentation. If you have more specific requirements or questions, feel free to ask!

@Marvin I have a task that needs to run in a specific docker container. What is the best way to use prefect to run the task in this container?

To run a specific task within a Docker container using Prefect, it is recommended to use the Prefect Docker worker. Here is a step-by-step approach: ### 1. Define Your Dockerized Task First, ensure that your task is defined to execute within the Docker container. ### 2. Create Docker Worker Deployment Your flow should be deployed to run with a Docker-based worker. ### Example Flow Here’s an example of how to structure your flow and deploy it to run in a specific Docker container:

python
from prefect import flow, task
from prefect.deployments import Deployment
from prefect.infrastructure import DockerContainer

@task
def docker_task():
    # Task logic here
    print("Running inside a Docker container!")

@flow
def docker_flow():
    docker_task()

# Define the Docker container infrastructure
docker_infra = DockerContainer(
    image="your_docker_image:latest", # Replace with your Docker image
    image_pull_policy="ALWAYS",
    docker_env={"PREFECT_LOGGING_LEVEL": "INFO"} # Example environment variable
)

# Create a deployment for the flow with the Docker infrastructure
deployment = Deployment.build_from_flow(
    flow=docker_flow,
    name="docker-flow-deployment",
    infrastructure=docker_infra
)

if __name__ == "__main__":
    deployment.apply()
    docker_flow()

### 3. Apply and Run the Deployment Once you have defined your deployment, you can apply and run it to ensure the flow executes within the specified Docker container. This method ensures your task is executed within a Docker container, leveraging Prefect's infrastructure capabilities for Docker. For detailed documentation, refer to Prefect Docker Worker documentation. If you have further questions or need more detailed steps, let me know!

In prefect, how can I pass an s3 file created in one task to the next. task?

To pass an S3 file created in one task to the next task in Prefect, you can use Prefect's built-in state management to handle the flow of data between tasks. Here's an example workflow illustrating how to upload an S3 object in one task and pass its reference to another task for further processing. ### Example Workflow 1. Upload the File to S3: Use

s3_upload

task to upload a file to S3. 2. Pass the S3 Key to the Next Task: Provide the S3 key to the next task as an argument. 3. Download and Process the File: Use

s3_download

to download the file in the next task.

python
from prefect import flow, task
from prefect_aws import AwsCredentials
from prefect_aws.s3 import s3_upload, s3_download

@task
def create_file():
    # Create a file and return its contents
    file_content = "Example content for the file"
    return file_content

@task
def upload_to_s3(file_content, bucket, key, aws_credentials):
    s3_upload(
        bucket=bucket,
        key=key,
        data=file_content,
        aws_credentials=aws_credentials
    )
    return key

@task
def download_from_s3(bucket, key, aws_credentials):
    return s3_download(
        bucket=bucket,
        key=key,
        aws_credentials=aws_credentials
    )

@task
def process_s3_file(file_contents):
    # Replace with your processing logic
    print("Processing file contents:", file_contents)

@flow
def s3_file_flow():
    aws_credentials = AwsCredentials(
        aws_access_key_id="your_access_key_id",
        aws_secret_access_key="your_secret_access_key"
    )
    
    bucket = "your_bucket_name"
    key = "path/to/your/file.txt"
    
    # Create a file
    file_content = create_file()
    
    # Upload the file to S3 and get the S3 key
    s3_key = upload_to_s3(file_content, bucket, key, aws_credentials)
    
    # Download the file from S3
    file_contents = download_from_s3(bucket, s3_key, aws_credentials)
    
    # Process the downloaded file
    process_s3_file(file_contents)

s3_file_flow()

In this workflow: - The

create_file

task generates the file content. - The

upload_to_s3

task uploads this content to an S3 bucket and returns the key. - The

download_from_s3

task downloads the file using the provided key. - The

process_s3_file

task processes the downloaded file. This setup maintains clear data flow management and isolates each task's responsibility, aligning with Prefect's design principles. For more details on Prefect's AWS S3 integration, refer to the Prefect AWS S3 documentation. If you have further questions or need additional examples, feel free to ask!

@Marvin How can I check that an s3 file is actually created when it is run in a task?

To check if an S3 file is actually created when it is run in a task, you can use the

boto3

library to verify the file's existence in the S3 bucket after the upload task. Here’s an enhanced version of the workflow that includes this verification step: ### Example Workflow with Verification

python
from prefect import flow, task
from prefect_aws import AwsCredentials
from prefect_aws.s3 import s3_upload
import boto3

@task
def create_file():
    # Create a file and return its contents
    file_content = "Example content for the file"
    return file_content

@task
def upload_to_s3(file_content, bucket, key, aws_credentials):
    s3_upload(
        bucket=bucket,
        key=key,
        data=file_content,
        aws_credentials=aws_credentials
    )
    return key

@task
def verify_s3_file_exists(bucket, key, aws_credentials):
    client = boto3.client(
        "s3",
        aws_access_key_id=aws_credentials.aws_access_key_id,
        aws_secret_access_key=aws_credentials.aws_secret_access_key,
    )
    response = client.list_objects_v2(Bucket=bucket, Prefix=key)
    files = [obj['Key'] for obj in response.get('Contents', [])]
    if key in files:
        print(f"File '{key}' exists in bucket '{bucket}'.")
        return True
    else:
        print(f"File '{key}' does not exist in bucket '{bucket}'.")
        return False

@task
def process_s3_file(file_exists):
    # Replace with your processing logic
    if file_exists:
        print("Processing the file as it exists.")
    else:
        print("File does not exist. Cannot process.")

@flow
def s3_file_flow():
    aws_credentials = AwsCredentials(
        aws_access_key_id="your_access_key_id",
        aws_secret_access_key="your_secret_access_key"
    )
    
    bucket = "your_bucket_name"
    key = "path/to/your/file.txt"
    
    # Create a file
    file_content = create_file()
    
    # Upload the file to S3 and get the S3 key
    s3_key = upload_to_s3(file_content, bucket, key, aws_credentials)
    
    # Verify if the file exists in S3
    file_exists = verify_s3_file_exists(bucket, s3_key, aws_credentials)
    
    # Process the file based on existence verification
    process_s3_file(file_exists)

s3_file_flow()

### Steps Explained: 1. Create File Content: The

create_file

task generates the content. 2. Upload to S3:

upload_to_s3

uploads the content to S3 and returns the key. 3. Verify Existence:

verify_s3_file_exists

uses

boto3

to check if the file exists in the specified S3 bucket. 4. Conditional Processing:

process_s3_file

performs subsequent actions based on whether the file exists. This setup ensures that your workflow checks for the existence of the S3 file before proceeding with further processing. For a basic example on how to structure a Prefect workflow, refer to this Prefect Code Example. If you need further information, feel free to ask!

@Marvin how can I run a bash script in prefect?

To run a bash script in Prefect, you can use the

ShellOperation

task from the

prefect_shell

module. This allows you to execute shell commands and scripts as part of your Prefect flow. ### Example Workflow Here’s how you can create a Prefect task to run a bash script: #### 1. Define the Flow and Shell Task

python
from prefect import flow
from prefect_shell import ShellOperation

@flow
def example_shell_flow():
    # Define the shell script to run
    shell_task = ShellOperation(
        command="bash /path/to/your/script.sh",  # Replace with your script path
        stream_output=True
    )
    
    # Run the shell task
    result = shell_task()
    
    print("Shell script output:", result)

# Run the flow
example_shell_flow()

#### 2. Run the Flow In this example: - The

ShellOperation

task is used to run the bash script. - The

command

argument specifies the shell command to execute. - The

stream_output

argument, when set to

True

, streams the script output to Prefect logs. ### Key Points: - Shell Script Execution: The

ShellOperation

task executes the bash script specified. - Output Streaming:

stream_output=True

ensures that the script output is captured and logged by Prefect. For more details on Prefect's shell command integration, refer to the Prefect Shell documentation. If you need additional examples or have further questions, feel free to ask!

@Marvin is there an easy way to use docker with prefect when I can't easily customize the docker image to include the prefect libraries?

If you can't easily customize the Docker image to include Prefect libraries, you can still use Docker with Prefect by specifying a base Docker image and utilizing the

prefecthq/prefect

images, which have Prefect pre-installed. This allows you to run your tasks in a Docker container without needing to manually include Prefect in your image. ### Using Prefect with Docker #### 1. Define a Docker Infrastructure Block First, define a Docker infrastructure block using Prefect's official Docker images. This ensures that the required dependencies for Prefect are available.

python
from prefect.deployments import Deployment
from prefect.infrastructure import DockerContainer, KubernetesJob
from prefect import flow, task

@task
def example_task():
    print("Running in Docker container with Prefect!")

@flow
def example_flow():
    example_task()

# Define the Docker container infrastructure
docker_infra = DockerContainer(
    image="prefecthq/prefect:2-latest",  # Use Prefect's official image
    image_pull_policy="ALWAYS",
    auto_remove=True
)

# Create a deployment for the flow with the Docker infrastructure
deployment = Deployment.build_from_flow(
    flow=example_flow,
    name="docker-flow-deployment",
    infrastructure=docker_infra
)

if __name__ == "__main__":
    deployment.apply()
    example_flow()

### How to Use It: 1. Create the Flow: Define your flow and tasks as shown in the code above. 2. Define Docker Infrastructure: Use the

DockerContainer

block pointing to Prefect's official image that already includes Prefect libraries. 3. Build and Apply the Deployment: Build and apply the deployment which sets up Prefect to run your tasks inside the specified Docker container. ### Key Points: - Prefect Docker Images: Utilize Prefect's ready-to-use Docker images to avoid needing to customize your Dockerfile extensively. - Auto-Remove Containers: Setting

auto_remove=True

ensures that containers are cleaned up after execution. - Image Pull Policy: Using

image_pull_policy="ALWAYS"

ensures that the latest image is pulled every time the flow runs. #### Documentation and Guides For further details on running Docker containers with Prefect, refer to: - Prefect Docker Guide - Prefect Deployment Guide If you have further questions or need more detailed examples, feel free to ask!

@Marvin How can I get the s3 path to a block object?

To get the S3 path to a block object in Prefect, you can interact with the

S3Bucket

block or

S3

filesystem block. Here’s an example of how you can create and retrieve the S3 path from an

S3Bucket

block: ### Step-by-Step Example 1. Create an S3Bucket Block:

python
    from prefect_aws import AwsCredentials
    from prefect_aws.s3 import S3Bucket

    credentials = AwsCredentials(
        aws_access_key_id="your_access_key_id",
        aws_secret_access_key="your_secret_access_key"
    )

    s3_bucket = S3Bucket(
        bucket_name="your-bucket-name",
        credentials=credentials
    )

    # Save the block for later use
    s3_bucket.save(name="my-s3-bucket")

2. Retrieve the S3 Path in a Flow:

python
    from prefect import flow, task
    from prefect_aws.s3 import S3Bucket

    @task
    def get_s3_path(block_name: str, key: str) -> str:
        s3_bucket = S3Bucket.load(block_name)
        s3_path = f"s3://{s3_bucket.bucket_name}/{key}"
        return s3_path

    @flow
    def example_flow():
        block_name = "my-s3-bucket"
        key = "path/to/your/file.txt"
        s3_path = get_s3_path(block_name, key)
        print(f"S3 Path: {s3_path}")

    example_flow()

### Explanation: - Step 1: Create an

S3Bucket

block with your AWS credentials and bucket name, then save the block for later use. - Step 2: In the flow, load the saved

S3Bucket

block and construct the S3 path. This approach ensures you can dynamically retrieve the S3 path based on the block's bucket name and a given key. For further details, refer to Prefect's documentation on Blocks and Filesystems. If you have any more questions or need additional examples, feel free to ask!

@Marvin can you show me an example of using

S3Bucket

To demonstrate the usage of the

S3Bucket

block in Prefect, here is a detailed example: ### Step-by-Step Example Using

S3Bucket

1. Setup: Define the S3Bucket block and save it. 2. Usage in Flow: Utilize the S3Bucket block to upload and download data. ### Example Code #### 1. Setup S3Bucket Block First, you create and save the

S3Bucket

block with your AWS credentials and bucket details.

python
from prefect_aws import AwsCredentials
from prefect_aws.s3 import S3Bucket

# Define AWS credentials
aws_credentials = AwsCredentials(
    aws_access_key_id="your_access_key_id",
    aws_secret_access_key="your_secret_access_key"
)

# Create S3 bucket block
s3_bucket = S3Bucket(
    bucket_name="your-bucket-name",
    credentials=aws_credentials
)

# Save the S3 bucket block
s3_bucket.save(name="my-s3-bucket")

#### 2. Use S3Bucket Block in Flow Now, let's create a workflow to demonstrate uploading and downloading a file using the saved

S3Bucket

block.

python
from prefect import flow, task
from prefect_aws.s3 import S3Bucket

@task
def upload_to_s3(data: bytes, key: str):
    s3_bucket = S3Bucket.load("my-s3-bucket")
    s3_bucket.write(key, data)
    print(f"Uploaded data to s3://{s3_bucket.bucket_name}/{key}")

@task
def download_from_s3(key: str) -> bytes:
    s3_bucket = S3Bucket.load("my-s3-bucket")
    data = s3_bucket.read(key)
    print(f"Downloaded data from s3://{s3_bucket.bucket_name}/{key}")
    return data

@flow
def s3_bucket_flow():
    data = b"Example content for the S3 file"
    key = "example_file.txt"
    
    # Upload data to S3
    upload_to_s3(data, key)
    
    # Download data from S3
    downloaded_data = download_from_s3(key)
    print("Data downloaded:", downloaded_data.decode())

# Run the flow
s3_bucket_flow()

### Explanation: 1. Setup AWS Credentials: Use your AWS credentials to define the

AwsCredentials

object used by the

S3Bucket

block. 2. Save S3Bucket Block: Save the

S3Bucket

block named "my-s3-bucket" for later reference. 3. Upload Task: The

upload_to_s3

task uploads data to the specified S3 key. 4. Download Task: The

download_from_s3

task retrieves data from the specified S3 key. 5. Flow: The

s3_bucket_flow

orchestrates the upload and download operations. For a basic example on structuring a Prefect workflow, refer to this Prefect Code Example. If you have further questions or need additional examples, feel free to ask!

Should I access s3 files with

prefect.utilities.filesystem or S3Bucket?

When accessing S3 files in Prefect, it is recommended to use the

S3Bucket

block, as it provides a more integrated and higher-level abstraction specifically configured for AWS S3 interactions. This block is built to manage S3 operations easily within Prefect workflows, handling authentication, file operations, and more. Advantages of Using `S3Bucket`: - Integrated Auth: Simplifies authentication using Prefect's

AwsCredentials

. - Direct Operations: Allows direct read/write operations on S3 with built-in methods. - Consistency: Ensures consistent handling of S3 bucket interactions within Prefect's ecosystem. ### Example of Using `S3Bucket`: This example demonstrates how to use the

S3Bucket

block to interact with S3, performing tasks like uploading and downloading files.

python
from prefect import flow, task
from prefect_aws import AwsCredentials
from prefect_aws.s3 import S3Bucket

@task
def upload_to_s3(data: bytes, key: str):
    s3_bucket = S3Bucket.load("my-s3-bucket")
    s3_bucket.write(key, data)
    print(f"Uploaded data to s3://{s3_bucket.bucket_name}/{key}")

@task
def download_from_s3(key: str) -> bytes:
    s3_bucket = S3Bucket.load("my-s3-bucket")
    data = s3_bucket.read(key)
    print(f"Downloaded data from s3://{s3_bucket.bucket_name}/{key}")
    return data

@flow
def s3_bucket_flow():
    data = b"Example content for the S3 file"
    key = "example_file.txt"
    
    # Upload data to S3
    upload_to_s3(data, key)
    
    # Download data from S3
    downloaded_data = download_from_s3(key)
    print("Data downloaded:", downloaded_data.decode())

# Run the flow
s3_bucket_flow()

### Steps: 1. Upload to S3: The

upload_to_s3

task uploads data to the specified S3 key. 2. Download from S3: The

download_from_s3

task retrieves data from the specified S3 key. ### Using

prefect.utilities.filesystems

Alternatively, you can use

prefect.utilities.filesystems

for more general file operations, but it involves more manual handling, especially for authentication and bucket operations. Recommended Approach: Use the

S3Bucket

block for S3 operations as it simplifies the process and ensures consistency within Prefect workflows. If you have any specific requirements or need further assistance, feel free to ask!

@Marvin How can I set the docker image to use on a per-task basis for an ECS push?

To set the Docker image to use on a per-task basis for an ECS deployment in Prefect, you can use the

ECSRun

infrastructure block. This allows you to specify different Docker images for different tasks. Here’s how you can achieve this: ### Example Code #### 1. Define Tasks with Specific Docker Images First, define your tasks and specify the Docker image to use for each task using the

ECSRun

block. #### 2. Create your ECS deployment Use the ECS work pool configuration for each task.

python
from prefect import flow, task
from prefect.infrastructure.ecs import ECSRun

@task
def task_with_image_1():
    print("Task with Docker Image 1")

@task
def task_with_image_2():
    print("Task with Docker Image 2")

@flow
def example_ecs_flow():
    # Define ECSRun for each task with a specific image
    ecs_run_image_1 = ECSRun(
        image="your_docker_image_1:latest",  # Replace with your Docker image
        cpu=1024,  # Example configuration
        memory=2048
    )
    ecs_run_image_2 = ECSRun(
        image="your_docker_image_2:latest",  # Replace with your Docker image
        cpu=1024,  # Example configuration
        memory=2048
    )
    
    # Run tasks with the specific ECSRun configurations
    task_with_image_1.with_options(infra_overrides={"run_config": ecs_run_image_1})()
    task_with_image_2.with_options(infra_overrides={"run_config": ecs_run_image_2})()

# Run the flow
if __name__ == "__main__":
    example_ecs_flow()

### Explanation: 1. Define Tasks: Define each task you want to run in a specific Docker container. 2. Use ECSRun Block: Create an

ECSRun

block for each Docker image you want to use. 3. Assign ECSRun to Tasks: Use

with_options

to override the task's run configuration to use the specific

ECSRun

block. ### ECSRun Configuration: - image: The Docker image to use for the task. - cpu and memory: Example configurations to specify the compute resources for the ECS task. ### References: For more detailed information, please refer to the Prefect AWS ECS documentation. If you have further questions or need additional examples, feel free to ask!