how can I give my asset job more memory to run in k8s?

To give your asset job more memory when running in Kubernetes, you can use job tags to specify the resource requirements for the Kubernetes pods that will execute your job. Here's how you can do it according to the documentation: 1. Define your asset with the appropriate tags that specify the memory requirements. You can use the

op_tags

argument to set the

dagster-k8s/config

tag with the desired resource limits and requests. 2. Use the

k8s_job_executor

to ensure that each asset is materialized in its own Kubernetes pod, which allows the tags to be respected and applied. Here's an example of how you might define an asset with increased memory requirements:

python from dagster import asset from dagster_k8s import k8s_job_executor  @asset(     op_tags={         "dagster-k8s/config": {             "container_config": {                 "resources": {                     "requests": {"memory": "512Mi"},                     "limits": {"memory": "1Gi"},                 },             },         },     } ) def my_asset():     # Your asset code here     pass  # Define the job with the k8s_job_executor my_asset_job = define_asset_job(     "my_asset_job",     selection=AssetSelection.assets(my_asset),     executor_def=k8s_job_executor )

In this example, the

requests

and

limits

within the

resources

dictionary specify the minimum and maximum amount of memory that the Kubernetes pod should have available, respectively. Please note that the exact syntax and structure may vary depending on the version of Dagster you are using, and you should refer to the latest documentation for the most accurate and up-to-date information. Additionally, ensure that your Kubernetes cluster has nodes with sufficient memory to satisfy the resource requests and limits you specify.

how do I define this when defining the asset job? @Scout

When defining an asset job in Dagster, you typically use the

define_asset_job

function to create a job that materializes a selection of assets. However, the

define_asset_job

function itself does not directly accept Kubernetes-specific configuration for memory and other resources. To specify Kubernetes resource requests and limits for an asset job, you would need to configure the executor that the job will use. In Dagster, the Kubernetes executor can be configured to apply resource requests and limits to the pods that run the job's operations. Here's an example of how you might define an asset job with a Kubernetes executor that includes memory resource requests and limits:

python from dagster import asset, define_asset_job, AssetSelection from dagster_k8s.executor import k8s_job_executor  # Define your assets @asset def my_asset():     # Your asset code here     pass  # Define your asset job with a Kubernetes executor my_asset_job = define_asset_job(     name="my_asset_job",     selection=AssetSelection.assets(my_asset),     executor_def=k8s_job_executor.configured(         {             "job_config": {                 "container_config": {                     "resources": {                         "requests": {"memory": "512Mi"},                         "limits": {"memory": "1Gi"},                     }                 }             }         }     ) )

In this example, the

k8s_job_executor

is configured with a

job_config

that includes the desired resource requests and limits for memory. This configuration will apply to the Kubernetes pods that are created to run the operations within the asset job. Please ensure that you have the

dagster-k8s

library installed to use the

k8s_job_executor

, and refer to the latest Dagster documentation for any updates or changes to the API. Additionally, make sure that your Kubernetes cluster has the capacity to fulfill the specified resource requests and limits.