Hi, I’m trying to host the agent on EC2 but couldn’t get it to work. I’ve already pushed the user code image to ECR. 1. It complained “no basic authentication” even though I’ve logged in docker. I had to manually pull the image 2. Even after the image is available locally, it hangs at the step creating the gRpc server. The error is something like this:

dagster_agent_1  | 2024-02-16 19:06:23 +0000 - dagster_cloud.user_code_launcher - INFO - Created a new server for ('prod', 'painboard-user-code')
dagster_agent_1  | 2024-02-16 19:06:23 +0000 - dagster_cloud.user_code_launcher - INFO - Waiting for new grpc server for ('prod', 'painboard-user-code') for (image=CodeDeploymentMetadata(image='<http://305405436618.dkr.ecr.us-east-1.amazonaws.com/painboard-dagster-user-code:latest|305405436618.dkr.ecr.us-east-1.amazonaws.com/painboard-dagster-user-code:latest>', python_file=None, package_name='painboard', module_name=None, working_directory='/opt/dagster/app', executable_path=None, attribute=None, git_metadata=None, container_context={}, cloud_context_env={'DAGSTER_CLOUD_DEPLOYMENT_NAME': 'prod', 'DAGSTER_CLOUD_IS_BRANCH_DEPLOYMENT': 0, 'DAGSTER_CLOUD_LOCATION_NAME': 'painboard-user-code'}, pex_metadata=None, agent_queue=None)) to be ready...

It seems you are encountering two separate issues with deploying your Dagster agent on EC2 and getting it to work with your user code image hosted on ECR. 1. Authentication Issue: The "no basic authentication" error suggests there might be an issue with the Docker login or the authentication token used to pull images from ECR. The documentation does not provide a specific solution to this problem, but here are some general steps you can take to troubleshoot: - Ensure that you have the AWS CLI installed and configured with the necessary permissions to access ECR. - Use the

aws ecr get-login-password

command to retrieve an authentication token, and then use

docker login

to authenticate to your ECR registry with this token. - Check the IAM policies attached to the EC2 instance or the user/role used for the AWS CLI to ensure they have permissions to pull images from ECR. 2. gRPC Server Creation Hanging: The log message indicates that the agent is attempting to create a new gRPC server for your user code deployment but is waiting for the server to be ready. This could be due to several reasons, such as network configuration issues, problems with the user code image, or misconfiguration of the gRPC server settings. To troubleshoot this issue, consider the following steps: - Verify that the user code image contains the correct entry point to start the gRPC server. The documentation mentions that you can specify arguments for the gRPC server under

dagsterApiGrpcArgs

in the

values.yaml

when deploying with Helm on Kubernetes. Ensure that your Docker image is configured to use these arguments correctly. - Check the logs of the container running the gRPC server to see if there are any errors or exceptions being thrown during startup. - Ensure that the EC2 instance's security group and network ACLs allow inbound and outbound traffic on the port used by the gRPC server (default is 4000). - If you are using a custom network configuration, make sure that the gRPC server is reachable from the agent and that there are no firewall rules blocking the connection. If these steps do not resolve your issues, you may want to provide more detailed information about your setup, including the Dockerfile, the commands used to start the gRPC server, and any relevant configuration files. This will help in diagnosing the problem more effectively.

how to print more debugging information? @Scout

To print more debugging information in a Dagster deployment, you can adjust the log levels and configure loggers to capture more detailed output. Here are some steps you can take to increase the verbosity of logs: 1. Set Log Levels: You can set the log level to

DEBUG

to get more verbose output. This can be done in several ways depending on where you want to set it: - In Code: You can configure the log level programmatically within your Dagster assets or ops by using the

context.log

object. For example:

python      @op      def my_op(context):          context.log.debug("This is a debug message.")

- In Configuration: When running a job, you can specify the log level in the run configuration. For example:

yaml      resources:        console:          config:            log_level: DEBUG

2. Configure Loggers: Dagster allows you to configure loggers in your

dagster.yaml

or when you define a job. You can specify a logger configuration that sets the

log_level

to

DEBUG

. For example:

yaml    loggers:      console:        config:          log_level: DEBUG

3. Use the Dagster CLI: When using the Dagster CLI to execute commands, you can often pass logging-related flags to control the verbosity. For example, you can use the

--verbose

flag or set the

--log-level

flag to

DEBUG

. 4. Check Environment Variables: Some systems and frameworks use environment variables to control logging. For example, setting

DAGSTER_LOG_LEVEL=DEBUG

as an environment variable may increase the verbosity of logs. 5. Monitor gRPC Server Logs: If the gRPC server is hanging, you'll want to ensure that you can access its logs. If you're running the server in a Docker container, you can use

docker logs <container_id>

to see the output. 6. Dagster Debug CLI: The Dagster CLI provides a

debug

command that can be used to export and import debug information. This can be helpful for capturing the state of a run for further analysis. Remember that increasing the verbosity of logs can result in a large amount of output, which can be overwhelming and may include sensitive information. Always review and sanitize logs before sharing them with others. If you continue to face issues, consider reaching out to the Dagster community or support with detailed information about your setup and the steps you've taken to debug the problem.