@Scout how to run a job and provide tags for each asset within that job give me an example

As of the last update from the documentation, there isn't a direct way to specify tags for individual assets within a job. Tags are generally applied at the job level, not at the individual asset level. When you define a job using

define_asset_job

, you can provide tags that apply to the entire job run. These tags are then applied to all asset computations within that job. Here's an example of how you can define a job with tags:

python from dagster import define_asset_job, AssetSelection  # Define your assets here @asset def asset1():     # Asset computation logic     ...  @asset def asset2():     # Asset computation logic     ...  # Define a job that includes both assets and apply tags to the job my_asset_job = define_asset_job(     "my_asset_job",     selection=AssetSelection.assets(asset1, asset2),     tags={"key1": "value1", "key2": "value2"},  # Job-level tags )

In this example, the tags

{"key1": "value1", "key2": "value2"}

are applied to the job

my_asset_job

. When this job is run, both

asset1

and

asset2

will be computed, and the tags will be associated with the run of the job, not with the individual assets. If you need to apply different tags based on the asset being selected or materialized, you might need to define separate jobs for each asset with their specific tags or handle the tagging logic externally, such as in a script that triggers the job runs with the appropriate tags based on some condition. Please note that the ability to apply tags at the asset level might be a feature that could be added in the future, and it's always a good idea to check the latest Dagster documentation or release notes for any updates on this functionality.

@Scout so there is no way to provide a k8s container config on runtime based on asset sizes for example

Based on the documentation and discussions, there isn't a built-in way to dynamically apply Kubernetes container configurations at runtime based on asset sizes directly within Dagster's job or asset definitions. Typically, Kubernetes-related configurations are set at the job level using the

tags

parameter, which can include Kubernetes-specific configurations when using a Kubernetes-based run launcher or executor. However, there is a feature request and discussion in the Dagster community about the need for specifying Kubernetes resources at runtime or at the asset level. Users have expressed the need to customize resource requests and limits for specific assets or operations, especially when different assets require different computational resources. As of now, the recommended approach is to define separate jobs with specific Kubernetes configurations for assets that have different resource requirements. You can use the

tags

parameter to include Kubernetes-specific configurations, such as resource requests and limits, when defining a job. Here's an example of how you might define a job with Kubernetes container configuration:

python from dagster import define_asset_job, AssetSelection  # Define your assets here @asset def asset1():     # Asset computation logic     ...  # Define a job for asset1 with specific Kubernetes container configuration asset1_job = define_asset_job(     "asset1_job",     selection=AssetSelection.assets(asset1),     tags={         "dagster-k8s/config": {             "container_config": {                 "resources": {                     "requests": {"memory": "512Mi", "cpu": "250m"},                     "limits": {"memory": "1Gi", "cpu": "500m"},                 }             }         }     } )

In this example, the

asset1_job

is defined with Kubernetes container resource requests and limits specified in the

tags

. When this job is run, the Kubernetes executor or run launcher will use these configurations for the Kubernetes pod that executes the job. For more dynamic scenarios, such as adjusting resources based on asset sizes at runtime, you would currently need to implement custom logic outside of Dagster to determine the appropriate resource requirements and then trigger the job runs with the corresponding tags. It's important to stay updated with the latest Dagster releases and community discussions, as the ability to specify Kubernetes resources at a more granular level, such as per asset or operation, may be introduced in future updates.

@Scout what if I want to define an antiaffinity rule in the previous job definition

As of my knowledge cutoff date, Dagster does not provide a direct way to specify Kubernetes anti-affinity rules within the job definition itself. Kubernetes anti-affinity rules are typically specified in the Kubernetes pod specification to ensure that pods do not get scheduled onto the same node as other pods with certain labels. However, you can specify Kubernetes-specific configurations using the

run_launcher

configuration when setting up your Dagster instance. This is done in the

dagster.yaml

configuration file, which is used to configure the Dagster deployment. Here's an example of how you might specify an anti-affinity rule in the `dagster.yaml`:

yaml run_launcher:   module: dagster_k8s.launcher   class: K8sRunLauncher   config:     k8s_api_retry_attempts: 3     job_namespace: 'dagster'     instance_config_map: 'dagster-instance'     dagster_home: '/opt/dagster/dagster_home'     image_pull_policy: 'Always'     service_account_name: 'dagster'     job_config:       pod_template_spec:         metadata:           labels:             <http://app.kubernetes.io/name|app.kubernetes.io/name>: dagster             <http://app.kubernetes.io/instance|app.kubernetes.io/instance>: dagster         spec:           affinity:             podAntiAffinity:               requiredDuringSchedulingIgnoredDuringExecution:               - labelSelector:                   matchExpressions:                   - key: <http://app.kubernetes.io/name|app.kubernetes.io/name>                     operator: In                     values:                     - dagster                 topologyKey: "<http://kubernetes.io/hostname|kubernetes.io/hostname>"

In this configuration, the

podAntiAffinity

rule is set to prevent pods with the label

<http://app.kubernetes.io/name|app.kubernetes.io/name>: dagster

from being scheduled on the same node. If you need to apply specific anti-affinity rules for a particular job, you would typically handle this by setting up multiple run launchers or executors with different configurations and selecting the appropriate one when launching the job. This would require custom logic outside of the standard Dagster job definition. Please note that Dagster's features and capabilities are continuously evolving, and it's possible that more granular control over Kubernetes configurations at the job or asset level may be introduced in the future. Always refer to the latest Dagster documentation or release notes for the most up-to-date information.