@Scout How can I limit cpu consumption on a op when using docker

To limit CPU consumption for an op when using Docker with Dagster, you can utilize the

docker_executor

and specify resource limits within the

container_kwargs

configuration. The documentation provides guidance on how to configure the

docker_executor

and mentions that you can pass additional configurations to the Docker containers created for each step. Here's an example from the documentation on how to set up the `docker_executor`:

python from dagster_docker import docker_executor from dagster import job  @job(executor_def=docker_executor) def docker_job():     pass

Then, you can configure the executor with run config as follows, including the

container_kwargs

where you can specify resource limits such as CPU and memory:

yaml execution:   config:     container_kwargs:       resources:         limits:           cpus: '0.5'  # This limits the CPU usage to 0.5 CPUs           memory: '1g'  # This limits the memory usage to 1 GB

Please note that the

container_kwargs

is a dictionary where you can pass any arguments that are accepted by the Docker API for containers. In this case, you would include the

resources

dictionary with

limits

to control the CPU and memory usage. Keep in mind that the exact configuration might vary based on your specific requirements and the version of Dagster and dagster-docker you are using. Always refer to the latest documentation for the most accurate and up-to-date information.

@Scout Can I do the same thing but configure this in my op decorator instead

The documentation does not provide a direct way to specify Docker resource limits such as CPU and memory consumption within the

@op

decorator itself. The

@op

decorator is used to define the logic of an op and its inputs, outputs, and configuration schema, but it does not handle execution environment specifics like Docker resource constraints. To control resource limits for Docker containers, you would typically use an executor configuration, as previously described, which is separate from the

@op

decorator. The executor configuration allows you to specify how the op is executed, including the Docker container's resource limits. If you need to apply specific resource limits to certain ops, you would configure those limits in the executor's run config, ensuring that the Docker executor applies the appropriate resource constraints when running the op within a container. For more advanced scenarios or custom behavior, you might consider creating a custom executor or using hooks to influence the execution environment, but these approaches would be beyond the standard usage of the

@op

decorator and would require a deeper integration with Dagster's execution system. For the most accurate guidance and best practices, always refer to the latest Dagster documentation or seek advice from the Dagster community if you have a use case that isn't covered by the standard documentation.

@Scout I’m using DockerRunLauncher so technically I’m already using docker launcher for all job. Isn;t there another way

When using the

DockerRunLauncher

, the configuration for Docker resource limits is typically specified at the instance level in your

dagster.yaml

file, rather than at the individual op level. The

DockerRunLauncher

is responsible for launching each pipeline run in a new Docker container, and the resource limits for these containers are defined in the

DockerRunLauncher

configuration. As of my knowledge cutoff date, there isn't a built-in way to specify different Docker resource limits for individual ops within a job directly through the

@op

decorator. The resource limits you set in the

DockerRunLauncher

configuration will apply to all runs launched by that instance. If you need to apply different resource limits to specific ops or jobs, you would need to implement a custom solution. One approach could be to create a custom run launcher that extends the

DockerRunLauncher

and adds logic to apply different resource limits based on tags or other criteria specified in the run config. However, this would require a deeper customization of Dagster's run launching mechanism and is not supported out-of-the-box. For the most up-to-date information and potential new features that may have been introduced after my knowledge cutoff date, please refer to the latest Dagster documentation or check for updates in the Dagster GitHub repository. If you have a specific use case or requirement, you may also consider reaching out to the Dagster community for advice or to discuss potential feature requests.

@Scout Can you show me an example of an op that have a DockerRunLauncher configuring limiting is cpu usage

As of my knowledge cutoff date, the

DockerRunLauncher

does not support setting resource limits directly at the op level within a job. Instead, resource limits are specified at the instance level in the

dagster.yaml

configuration file, which applies to all runs launched by the

DockerRunLauncher

. However, I can show you an example of how to configure the

DockerRunLauncher

in the

dagster.yaml

file to limit the CPU usage for the Docker containers that it launches for pipeline runs. This configuration will apply globally to all runs, not to individual ops:

yaml # dagster.yaml  run_launcher:   module: dagster_docker   class: DockerRunLauncher   config:     env_vars:       - AWS_ACCESS_KEY_ID       - AWS_SECRET_ACCESS_KEY     network: "host"     container_kwargs:       resources:         limits:           cpus: '0.5'  # This limits the CPU usage to 0.5 CPUs for each run

In this example, the

container_kwargs

section is used to pass additional arguments to the Docker API, including resource limits. The

cpus

limit is set to

'0.5'

, which means that each container launched for a pipeline run will be limited to using half of a CPU. Please note that this configuration will affect all runs launched by the

DockerRunLauncher

and is not specific to any single op. If you need to apply different resource limits to specific ops, you would need to implement a custom solution, as mentioned earlier. For the most accurate and up-to-date information, please refer to the latest Dagster documentation or seek advice from the Dagster community.