@Scout – Can you point to any documentation or tutorials that walk through how one can configure an oss workspace.yaml to point to a s3 code location ?

I'm sorry, but the documentation does not provide a direct example or tutorial for configuring an

oss workspace.yaml

to point to an S3 code location. The

workspace.yaml

file is typically used to define code locations for local development or deploying to your infrastructure, and it includes options for loading code from a Python file, Python module, or Python package, among others. If you're looking to configure code locations that involve S3, you might be referring to setting up resources or IO managers that interact with S3 within your Dagster project. However, this would not be directly related to the

workspace.yaml

file but rather to the configuration of your assets or pipelines that use these resources. For more detailed guidance on how to set up your Dagster project and work with S3, you may want to look into the documentation for resources and IO managers that are designed to work with S3, such as the

s3_pickle_io_manager

or

s3_resource

. These components can be configured within your Dagster assets or pipelines to interact with S3 buckets for storing and retrieving data. If you have a specific use case or further questions on how to set up your Dagster project with S3, please provide more details, and I'll do my best to guide you based on the available documentation.

@Scout – Similarly related, as a follow-up, can you share any ci/cd implementation examples with a Kubernetes or AWS ECS OSS deployment?

Certainly! While the documentation does not provide a complete, step-by-step tutorial specifically for CI/CD with Kubernetes or AWS ECS OSS deployments, it does offer guidance that can help you set up a CI/CD pipeline for your Dagster project. For Kubernetes deployments, the documentation suggests the following steps: 1. Push Docker image to registry: After building your Docker image with your Dagster code, you should push it to a container registry that your Kubernetes cluster can access, such as AWS ECR or Docker Hub. 2. Set up Amazon S3: If you're using S3 for IO management, you'll need to configure AWS credentials and ensure that your Kubernetes cluster has access to the necessary S3 buckets. For CI/CD, you would typically automate these steps in your CI/CD pipeline. For example, you could use GitHub Actions to: - Check out your code. - Build a Docker image from your code. - Push the Docker image to a container registry. - Update your Kubernetes deployment to use the new Docker image tag. For AWS ECS deployments, the documentation does not provide a specific CI/CD example, but the process would be similar to Kubernetes in that you would: - Build your Docker image with your Dagster code. - Push the image to a registry like AWS ECR. - Use a CI/CD tool to automate the deployment of your new image to ECS. In both cases, you would need to ensure that your CI/CD tool has the necessary permissions to interact with your Kubernetes cluster or AWS ECS services. This might involve setting up service accounts with the appropriate roles and permissions and configuring your CI/CD tool to use these credentials. For more detailed guidance, you can refer to the documentation for deploying with Helm for Kubernetes and the AWS deployment guide for ECS. These guides provide more context on how to set up your infrastructure, which you can then integrate into your CI/CD pipeline using tools like GitHub Actions, GitLab CI/CD, Jenkins, or any other CI/CD platform you prefer. If you're looking for community-contributed examples or more specific guidance, you might find additional resources or discussions in the Dagster GitHub repository or community channels.

@Scout Can you point me to docs on s3 I/o and resource functionality

Certainly! The documentation provides information on how to use S3 for I/O management in Dagster. Here are some key points and examples from the documentation: ### S3 I/O Managers Dagster includes built-in I/O managers for Amazon S3, which allow you to store and retrieve data from S3 buckets. The

S3PickleIOManager

is one such I/O manager that serializes and deserializes data using Python's pickle format. Here's an example of how you might use the `S3PickleIOManager`:

python from dagster_aws.s3 import S3PickleIOManager, S3Resource from dagster import asset, Definitions  @asset(io_manager_key="s3_io_manager") def upstream_asset():     return [1, 2, 3]  @asset(io_manager_key="s3_io_manager") def downstream_asset(upstream_asset):     return upstream_asset + [4]  defs = Definitions(     assets=[upstream_asset, downstream_asset],     resources={         "s3_io_manager": S3PickleIOManager(             s3_resource=S3Resource(), s3_bucket="my-bucket"         ),     }, )

In this example, the

upstream_asset

and

downstream_asset

are configured to use the

s3_io_manager

for storing their outputs in an S3 bucket named "my-bucket". ### S3 Resource The

S3Resource

is a resource provided by Dagster that allows you to interact with S3. It's used by the S3 I/O managers and can also be used directly in your assets or ops if you need to perform custom S3 operations. Here's an example of how you might define an S3 resource:

python from dagster_aws.s3 import S3Resource  s3_resource = S3Resource(     region_name="us-west-2",     aws_access_key_id="my-access-key-id",     aws_secret_access_key="my-secret-access-key",     # You can also use other authentication methods like IAM roles or environment variables )

This resource can then be passed to your assets or ops that need to interact with S3, or it can be used by an I/O manager like

S3PickleIOManager

. For more detailed information on using S3 with Dagster, you can refer to the following sections of the documentation: - S3 I/O Managers: [I/O Managers](https://docs.dagster.io/concepts/io-management/io-managers) section provides examples and explanations on how to use built-in I/O managers for S3. - S3 Resource: The [dagster-aws library](https://docs.dagster.io/_apidocs/libraries/dagster-aws) documentation provides details on the

S3Resource

and related components. These sections will give you a comprehensive understanding of how to configure and use S3 resources and I/O managers within your Dagster projects.