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how do you specify an aws region to launch in
# ask-ai
b
how do you specify an aws region to launch in
s
To specify an AWS region when launching tasks with Dagster, particularly when using the 
EcsRunLauncher
, you would typically configure the AWS client within your environment to use the desired region. This can be done in several ways: 1. Environment Variables: Set the 
AWS_REGION
environment variable to the desired region. This is a common approach when configuring AWS client tools and SDKs. 2. AWS Configuration File: You can specify the region in your AWS configuration file (
~/.aws/config
) by setting the 
region
value under the appropriate profile. 3. ECSRunLauncher Configuration: If you are using the 
EcsRunLauncher
, you might be able to pass additional arguments through the 
run_launcher
configuration in your Dagster instance YAML file. However, the region is typically determined by the AWS client's configuration and not directly through the 
EcsRunLauncher
settings. Here's an example of setting environment variables in a Dockerfile, which can be used when deploying Dagster on ECS:
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Dockerfile ENV AWS_REGION='us-west-2'
Remember to replace 
'us-west-2'
with the AWS region you intend to use. If you are encountering issues with the AWS region not being recognized, ensure that your AWS client is properly configured and that the region is set in a way that the client can access it. If you're using an ECS agent, make sure that the ECS task execution role has the necessary permissions and that the region is set in the ECS task definition or in the environment from which you are deploying the ECS service. Please note that the documentation does not explicitly provide a method for specifying the AWS region directly in the 
EcsRunLauncher
configuration. If you need further assistance with a specific Dagster configuration, please provide more details about your setup, and I can offer more targeted advice.
b
What if you want tasks to run in different regions? @Scout
s
As of my knowledge cutoff date, Dagster does not natively support configuring tasks to run in different AWS regions within the same instance or pipeline. Typically, the AWS region is set at the environment level, and all tasks within that environment would use the same AWS region configuration. If you need to run tasks in different AWS regions, you would generally have to set up separate Dagster instances for each region, each with its own configuration pointing to the desired AWS region. This could mean having separate deployments of your Dagster environment, each with its own 
workspace.yaml
and instance configuration that specifies the appropriate AWS region. Alternatively, you could consider writing custom logic within your tasks to interact with AWS services in different regions by explicitly setting the region when creating AWS service clients within your code. This approach would require you to manage the region configuration within your business logic rather than relying on the environment configuration. For example, if you are using boto3 (the AWS SDK for Python), you can specify the region when creating a client:
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python import boto3  # Create an S3 client for the us-west-2 region s3_client = boto3.client('s3', region_name='us-west-2')  # Create an EC2 client for the eu-central-1 region ec2_client = boto3.client('ec2', region_name='eu-central-1')
However, this approach only applies to interactions with AWS services within your code and does not affect where the Dagster tasks themselves are executed. If you have a use case that requires running tasks in different AWS regions and you need more integrated support for this within Dagster, you might want to consider raising a feature request on the Dagster GitHub repository. The Dagster team and community can then discuss the feasibility and potential implementation of such a feature. For the most up-to-date information or changes after my knowledge cutoff date, please refer to the latest Dagster documentation or reach out to the Dagster community for assistance.
b
If i wanted to create a custom aws lambda RunLauncher, where would i start? @Scout
s
To create a custom Lambda 
RunLauncher
in Dagster, you would start by implementing a new class that inherits from the 
RunLauncher
base class and overrides the necessary methods to integrate with AWS Lambda. Here's a step-by-step guide based on the documentation: 1. Understand the 
RunLauncher
Interface: Familiarize yourself with the 
RunLauncher
base class, which defines the interface for custom run launchers. You will need to implement methods such as 
launch_run
, which is called to initiate a run, and 
can_terminate
, which indicates whether a run can be terminated. 2. Write the Custom RunLauncher Class: Create a Python class that extends 
RunLauncher
. In this class, you will implement the logic to invoke an AWS Lambda function to execute your Dagster runs. 3. Implement the Launch Method: The 
launch_run
method should contain the logic to trigger an AWS Lambda function. You will likely use the 
boto3
library to interact with AWS Lambda. Here's a simplified example:
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python
import boto3
from dagster import RunLauncher, LaunchRunContext

class LambdaRunLauncher(RunLauncher):
    def __init__(self, lambda_function_name):
        self.lambda_function_name = lambda_function_name
        self.client = boto3.client('lambda')

    def launch_run(self, context: LaunchRunContext) -> None:
        # Serialize the run information to pass to the Lambda function
        run_id = context.pipeline_run.run_id
        payload = {
            'run_id': run_id,
            # Include any other necessary information for the run
        }

        # Invoke the Lambda function asynchronously
        response = self.client.invoke(
            FunctionName=self.lambda_function_name,
            InvocationType='Event',  # Asynchronous invocation
            Payload=json.dumps(payload),
        )

        # Handle the response as needed
        # ...

    # Implement other required methods from the RunLauncher base class
    # ...
4. Handle AWS Lambda Function: On the AWS Lambda side, you will need to create a function that can receive the run information and execute the Dagster run. This function will be responsible for setting up the Dagster environment and invoking the pipeline run. 5. Install the Custom Module: Once you have implemented your custom 
RunLauncher
, you need to install the module where it is defined in the environment where your Dagster instance is running. 6. Configure the Dagster Instance: Update your Dagster instance configuration (
dagster.yaml
) to use your custom 
RunLauncher
. Specify the module and class name of your custom launcher and any additional configuration it requires. ```yaml run_launcher: module: my_custom_module # The module where your custom class is located class: LambdaRunLauncher #
Creating a custom AWS Lambda 
RunLauncher
in Dagster involves writing a Python class that implements the abstract 
RunLauncher
class and handles the specifics of launching runs on AWS Lambda. Here's a step-by-step guide based on the documentation: 1. Understand the 
RunLauncher
Interface: Familiarize yourself with the 
RunLauncher
base class, which is the core abstraction for launching runs. You can find examples of existing run launchers, such as 
EcsRunLauncher
or 
K8sRunLauncher
, in the Dagster codebase to understand how they are implemented. 2. Write a Custom RunLauncher Class: Create a new Python class that inherits from 
RunLauncher
. You will need to implement the required methods, such as 
launch_run
, which should contain the logic to invoke an AWS Lambda function that will execute your Dagster run. 3. Handle AWS Lambda Invocation: Within your custom 
RunLauncher
, use the 
boto3
library to invoke the AWS Lambda function. You will need to serialize the necessary information to start a Dagster run and pass it to the Lambda function as part of the invocation payload. 4. Serialize Run Information: Serialize the Dagster run context or any other necessary information into a format that can be passed to the Lambda function. AWS Lambda expects the payload to be in JSON format. 5. Create a Python Module: Package your custom 
RunLauncher
into a Python module. This module will need to be installed in the environment where your Dagster instance is running. 6. Build and Deploy: If you are using Docker containers for your Dagster deployment, you will need to build a custom Docker image that includes your custom run launcher module. Install the module in the container by adding it to your 
Dockerfile
and running 
pip install
. 7. Configure Your Dagster Instance: Update your 
dagster.yaml
configuration file to use your custom run launcher. Specify the module and class name of your custom run launcher in the configuration. Here's an example snippet of what the 
dagster.yaml
might look like:
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yaml
run_launcher:
  module: my_custom_run_launcher_module
  class: MyCustomLambdaRunLauncher
  config:
    # Any additional configuration for your custom run launcher
8. Test Your Custom RunLauncher: Before deploying to production, thoroughly test your custom run launcher to ensure it correctly invokes AWS Lambda functions and handles run execution as expected. Remember that this is a high-level guide, and the specifics of your implementation will depend on your requirements and the details of how you want to interact with AWS Lambda. Additionally, since Dagster's APIs and capabilities evolve over time, it's important to refer to the latest documentation for any updates or changes that might affect your custom
run launcher development. If you encounter challenges or need further guidance, consider reaching out to the Dagster community for support.
The name of your custom run launcher class config: lambda_function_name: "my_dagster_run_lambda" # The name of your AWS Lambda function ``` 7. Test Your Custom RunLauncher: After configuring your Dagster instance to use the custom 
RunLauncher
, test it to ensure that it correctly triggers runs in AWS Lambda and that runs can be monitored and managed from within Dagster. Remember to handle any necessary permissions and configurations on the AWS side, such as IAM roles and policies that allow your Lambda function to interact with other AWS services as needed for your Dagster runs. For more detailed guidance and best practices on writing custom run launchers, you can refer to the Dagster documentation and discussions in the Dagster community.
b
can a job have tasks that execute in parallel? @Scout can you give an example of how to do this.
s
Yes, a Dagster job can have tasks (referred to as "assets" or "ops" in Dagster terminology) that execute in parallel. To achieve parallel execution, you can use the 
multiprocess_executor
, which allows multiple assets or ops to be executed concurrently across different processes. Here's an example of how to define a job with parallel execution using the `multiprocess_executor`:
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python from dagster import job, op, multiprocess_executor  @op def op1(context):     <http://context.log.info|context.log.info>("Executing op1")     # Your logic here  @op def op2(context):     <http://context.log.info|context.log.info>("Executing op2")     # Your logic here  @op def op3(context):     <http://context.log.info|context.log.info>("Executing op3")     # Your logic here  @job(executor_def=multiprocess_executor) def parallel_job():     op1()     op2()     op3()
In this example, 
op1
, 
op2
, and 
op3
are defined as individual ops that can be executed in parallel. The 
parallel_job
job is defined with the 
multiprocess_executor
, which will execute each op in a separate process, allowing them to run concurrently. To configure the level of parallelism, you can specify the 
max_concurrent
parameter in the executor config when launching the job:
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yaml execution:   multiprocess:     config:       max_concurrent: 3  # Adjust the number of concurrent processes as needed
This configuration can be provided in the 
dagster.yaml
file, or it can be passed as run config when launching the job. Please note that the actual degree of parallelism may be limited by the number of available CPU cores on the machine where the job is running. Additionally, when using the 
multiprocess_executor
, make sure that your ops are designed to be safely executed in parallel, without causing conflicts due to shared resources or data races. For more complex scenarios involving dynamic parallelism, you can use Dagster's dynamic output feature, which allows you to dynamically generate multiple outputs from a single op and then process each output in parallel using downstream ops. The documentation provides more details and examples on how to use dynamic outputs for parallel execution.
b
@Scout what does 
multiprocess_executor
use to execute the task? ECS, or something else?
s
The 
multiprocess_executor
in Dagster uses the Python 
multiprocessing
module to execute tasks (ops or assets) in separate processes on the same machine where the Dagster job is running. It does not use AWS ECS or any other external service for execution; instead, it leverages the local compute resources available on the host machine. When you configure a job with the 
multiprocess_executor
, Dagster will spawn multiple Python processes on the host, allowing for parallel execution of the tasks within the constraints of the host's CPU and memory resources. This is particularly useful for taking advantage of multi-core CPUs to run compute-intensive tasks in parallel. If you want to execute tasks on AWS ECS or another external service, you would need to use a different execution strategy, such as a custom executor or integration with a service like Kubernetes, Celery, or Dask, which can distribute work across a cluster of machines. Dagster provides executors for some of these services, allowing you to run tasks in distributed environments. However, the 
multiprocess_executor
is specifically for local parallel execution using the host's multiprocessing capabilities.
b
how would you do parallel task execution using ecs? @Scout
s
As of my knowledge cutoff date, Dagster does not have a built-in executor for running each task in a separate ECS task directly. However, there are a few approaches you could take to achieve parallel task execution using AWS ECS: 1. Custom Executor: You could write a custom executor that submits each task as a separate ECS task. This would involve using the 
boto3
AWS SDK to interact with ECS, creating task definitions for each Dagster task, and handling the submission and monitoring of ECS tasks. 2. Custom Ops: Within your Dagster job, you could write custom ops that use 
boto3
to launch ECS tasks. Each op would be responsible for submitting an ECS task, polling for its completion, and handling the results. This approach allows you to control the parallelism within your job definition. 3. Dagster ECS Integration: Use the Dagster ECS integration to run your entire job as an ECS task. While this does not run each task within the job as a separate ECS task, it does allow you to run multiple jobs in parallel by launching multiple ECS tasks. You can control the parallelism at the job level rather than the task level. 4. Dagster Kubernetes Executor: If you are open to using Kubernetes instead of ECS, you can use the 
k8s_job_executor
, which allows each task to run as a separate Kubernetes job. This provides task-level parallelism in a Kubernetes environment. 5. External Orchestration: Use an external orchestration tool like AWS Step Functions to manage the execution of ECS tasks. You can define a Step Functions state machine where each state triggers an ECS task, and the state machine manages the parallel execution and coordination of tasks. 6. Dagster with AWS Batch: Another option is to use AWS Batch, which can manage and run batch computing jobs across ECS. You can define your Dagster tasks to submit jobs to AWS Batch, which will then handle the execution on ECS. As of my knowledge cutoff date, if you need to run each task as a separate ECS task with full observability and integration with Dagster's tooling, you would likely need to implement a custom solution, such as a custom executor or custom ops that interact with ECS. For the most up-to-date information or changes after my knowledge cutoff date, please refer to the latest Dagster documentation or reach out to the Dagster community for assistance.
b
Do you have an example on using 
nginx reverse proxy
or some other mechanism to secure the webserver ui @Scout?
s
As of my knowledge cutoff date, the documentation does not provide a specific example of using an 
nginx reverse proxy
to secure the Dagit webserver UI. However, setting up an 
nginx
reverse proxy is a common approach to secure web applications, and you can apply this to Dagit as well. Here's a general example of how you might configure 
nginx
as a reverse proxy for Dagit. This example assumes that Dagit is running on 
localhost
on port 
3000
and that you want to expose it on the standard HTTPS port 
443
.
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nginx server {     listen 443 ssl;     server_name <http://dagit.example.com|dagit.example.com>; # Replace with your domain      ssl_certificate /path/to/your/fullchain.pem; # Replace with your SSL certificate     ssl_certificate_key /path/to/your/privkey.pem; # Replace with your SSL certificate key      location / {         proxy_pass <http://localhost:3000;>         proxy_set_header Host $host;         proxy_set_header X-Real-IP $remote_addr;         proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;         proxy_set_header X-Forwarded-Proto $scheme;                  # WebSocket support         proxy_http_version 1.1;         proxy_set_header Upgrade $http_upgrade;         proxy_set_header Connection "upgrade";     } }
This configuration sets up 
nginx
to listen for HTTPS connections and proxy them to Dagit, which is running on 
localhost:3000
. It also includes the necessary headers to support WebSocket connections, which are used by Dagit. To further secure your Dagit instance, you might consider: - Basic Authentication: Add basic HTTP authentication to your 
nginx
configuration to require a username and password to access Dagit. - IP Whitelisting: Restrict access to certain IP addresses using 
nginx
access control directives. - Rate Limiting: Implement rate limiting in 
nginx
to prevent abuse from excessive requests. Remember to replace the placeholders in the 
nginx
configuration with the actual paths to your SSL certificate files and your domain name. For the most accurate and secure configuration, please consult the 
nginx
documentation and consider best practices for securing web applications, including keeping your 
nginx
server and SSL certificates up to date. If you have specific requirements or need to integrate with other security mechanisms like OAuth or SSO, you may need to extend this basic configuration or use additional 
nginx
modules or third-party services to meet your needs.
b
@Scout do you put a separate 
dagster.yaml
and 
workspace.yaml
in each project folder or at the root?
s
In a typical Dagster project, the 
dagster.yaml
file, which is used for instance-level configuration, and the 
workspace.yaml
file, which defines code locations for local development or deployment, have different considerations regarding their placement. 1. dagster.yaml: This file configures your Dagster instance, including defining storage locations, run launchers, sensors, and schedules. It is typically located in the 
DAGSTER_HOME
directory, which is an environment variable that points to a directory on your filesystem where Dagster stores instance-level information. If you have multiple projects that require different instance configurations, you may need to manage multiple 
dagster.yaml
files or use environment variables to switch between configurations. 2. workspace.yaml: This file is used to define multiple code locations for local development or deploying to your infrastructure. It is often placed at the root of your project to define the repositories and assets that are part of your Dagster workspace. If you have a monorepo with multiple projects, you might have a single 
workspace.yaml
that includes all the code locations, or you might have separate 
workspace.yaml
files if you are managing multiple, separate Dagster workspaces. In summary, you typically have one 
dagster.yaml
file per Dagster instance (located in 
DAGSTER_HOME
), and one 
workspace.yaml
file per workspace, which can be at the root of your project or monorepo. The structure of your project and how you manage your Dagster instances and workspaces will determine the best approach for organizing these configuration files.
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