@Scout is it possible for me to increase the shared memory size that

The documentation provides information on memory management in the context of Spark, particularly when using Dagster's integration with Spark. According to the documentation, the absolute amount of memory in bytes for off-heap allocation can be set, and this setting does not impact heap memory usage. Therefore, if you need to fit your executors' total memory consumption within a certain limit, you should adjust your JVM heap size accordingly. If you are referring to increasing the shared memory size for Spark executors when using Dagster, you would typically do this by configuring Spark's memory settings, which can be done through Spark's own configuration options, not directly through Dagster. For example, you might set

spark.executor.memory

to increase the amount of memory allocated to each executor. However, the documentation does not explicitly mention increasing "shared memory size" in a context other than Spark's memory management. If you are asking about a different kind of shared memory, such as operating system shared memory (e.g.,

/dev/shm

in Unix-like systems), then you would need to look into the specific configuration for your operating system or the infrastructure that you are using to run Dagster and Spark. If you need further assistance with a specific scenario or configuration, please provide more details, and I can help guide you to the appropriate resources or offer debugging advice.

@Scout is it possible for me to increase the shared memory size when running an individual op in kubernetes? I need my docker image to be run with more shared memory when the op step is executed

Yes, it is possible to increase the shared memory size for individual ops when running in Kubernetes. This can be done by specifying resource requirements in the Kubernetes configuration for the pod that will execute the op. In the context of Dagster, you can use the

dagster-k8s

library to configure Kubernetes-specific settings for your ops. You can specify resource requests and limits for CPU and memory, including shared memory, by using the

dagster-k8s/config

tag in your op definition. Here's an example of how you might set resource limits, including shared memory (using

emptyDir

with

medium: Memory

), for an op in Dagster:

python from dagster import op from dagster_k8s import k8s_resource_requirements  @op(     tags={         "dagster-k8s/config": {             "pod_spec_config": {                 "volumes": [                     {                         "name": "dshm",                         "emptyDir": {                             "medium": "Memory"                         }                     }                 ],                 "containers": [                     {                         "name": "my-container",                         "volumeMounts": [                             {                                 "mountPath": "/dev/shm",                                 "name": "dshm"                             }                         ],                         "resources": k8s_resource_requirements(                             requests={"memory": "512Mi"},                             limits={"memory": "1Gi"}                         )                     }                 ]             }         }     } ) def my_op(context):     # Your op logic here     pass

In this example, the

emptyDir

volume with

medium: Memory

is used to mount shared memory into the container at

/dev/shm

. The

requests

and

limits

under

resources

specify the memory requirements for the container. Please note that the exact configuration might vary based on your Kubernetes setup and the specifics of your application. You should also ensure that your Kubernetes cluster has the resources available to satisfy the requirements you specify. If you need to apply these settings at runtime rather than at definition time, you would need to look into dynamically configuring these settings when launching the run, which might involve custom tooling or scripts. Remember to consult the Kubernetes documentation for the most accurate and up-to-date information on configuring shared memory and other resources for pods.

@Scout rather than use

emptyDir

, can you help me with making sure that when I run my pod in kubernetes, the op behavior is the same as when I run via docker here?

docker run -it --rm --runtime=nvidia --gpus all --shm-size 100 d2d762bf65f8 /bin/bash

When you run a Docker container with the

--shm-size

option, you're setting the size of

/dev/shm

in the container, which is shared memory. To achieve the same behavior in Kubernetes when running an op, you need to configure the shared memory size for the pod that will execute the op. In Kubernetes, you can set the shared memory size by mounting an

emptyDir

volume to

/dev/shm

. However, Kubernetes does not directly support the

--shm-size

option like Docker does. Instead, you can set the size of the shared memory by using an

emptyDir

volume with a specified size limit. Here's how you can modify the previous example to set the shared memory size to 100Mi (as an approximation of the

--shm-size 100

option in Docker):

python
from dagster import op
from dagster_k8s import k8s_resource_requirements

@op(
    tags={
        "dagster-k8s/config": {
            "pod_spec_config": {
                "volumes": [
                    {
                        "name": "dshm",
                        "emptyDir": {
                            "medium": "Memory",
                            "sizeLimit": "100Mi"
                        }
                    }
                ],
                "containers": [
                    {
                        "name": "my-container",
                        "volumeMounts": [
                            {
                                "mountPath": "/dev/shm",
                                "name": "dshm"
                            }
                        ],
                        "resources": k8s_resource_requirements(
                            requests={"memory": "512Mi"},
                            limits={"memory": "1Gi"}
                        )
                    }
                ]
            }
        }
    }
)
def my_op(context):
    # Your op logic here
    pass

In this configuration, the

sizeLimit

attribute is used to specify the maximum size of the shared memory volume. This is not a direct equivalent to Docker's

--shm-size

because it's a limit, not a reservation, but it's the closest equivalent in Kubernetes terms. Please note that the

medium: Memory

option means that the shared memory is backed by RAM, which is what you typically want for performance reasons, but it does not reserve the memory in the same way that Docker's

--shm-size

does. Also, the

--runtime=nvidia --gpus all

options in your Docker command are for GPU support. To enable GPU support in Kubernetes, you would need to use the appropriate resource limits and potentially node selectors/affinity to ensure that your pod is scheduled on a node with a GPU. This is a more complex setup that involves configuring your Kubernetes cluster to support GPUs, and it's beyond the scope of this example. Remember to adj

@Scout How can I make sure that the shared memory volume that I add to my pod is actually being used during the context of the op? I tried to use this logic

"containers": [
                    {
                        "name": "my-container",
                        "volumeMounts": [{"mountPath": "/dev/shm", "name": "dshm"}],
                    }
                ],

but got an error from kubernetes.

{"name":"dagster-step-d454f4402a2f3f14358549916b21db0c","group":"batch","kind":"Job","causes":[{"reason":"FieldValueRequired","message":"Required value","field":"spec.template.spec.containers[1].image"}]},"code":422}

The error message you're seeing from Kubernetes indicates that the

image

field is required for the container specification but is missing. In Kubernetes, every container in a pod must have an image specified, which is the Docker image that the container will run. To fix this error, you need to include the

image

field in your container configuration. Here's an example of how you might modify your container specification to include the

image

field:

python from dagster import op from dagster_k8s import k8s_resource_requirements  @op(     tags={         "dagster-k8s/config": {             "pod_spec_config": {                 "volumes": [                     {                         "name": "dshm",                         "emptyDir": {                             "medium": "Memory",                             "sizeLimit": "100Mi"                         }                     }                 ],                 "containers": [                     {                         "name": "my-container",                         "image": "my-docker-image:latest",  # Specify the Docker image here                         "volumeMounts": [                             {                                 "mountPath": "/dev/shm",                                 "name": "dshm"                             }                         ],                         "resources": k8s_resource_requirements(                             requests={"memory": "512Mi"},                             limits={"memory": "1Gi"}                         )                     }                 ]             }         }     } ) def my_op(context):     # Your op logic here     pass

In the above example, replace

"my-docker-image:latest"

with the actual Docker image you want to use for your container. This image should contain the necessary software and dependencies to execute your op. As for ensuring that the shared memory volume is being used, if you correctly mount the

emptyDir

volume to

/dev/shm

, the container runtime will use this volume as the shared memory space for the container. This is the standard way to increase the shared memory available to a container in Kubernetes. If you continue to encounter issues, make sure that the rest of your pod specification is correct and that there are no other missing required fields. Additionally, ensure that your Kubernetes cluster has the resources available to satisfy the requests and limits you specify for your containers.

@Scout I don't want to have to specify the docker image because I believe that dagster does this for me as part of the dagster k8s setup. Is that correct that dagster does the container setup for me? If it is correct, how can I make sure dagster uses my shared memory volume

In a Dagster Kubernetes deployment, when you use the

dagster-k8s

library, Dagster does indeed handle much of the container setup for you. The image used for a container running an op is typically specified in the Helm chart configuration for your Dagster deployment, rather than in the op definition itself. To ensure that Dagster uses your shared memory volume, you need to specify the volume and volume mount in the

dagster-k8s/config

tag within your op's or job's configuration. This configuration will instruct Dagster to include the shared memory volume when it creates the Kubernetes job that runs your op. Here's an example of how you might specify the shared memory volume in your op's configuration:

python from dagster import op  @op(     tags={         "dagster-k8s/config": {             "pod_template_spec": {                 "spec": {                     "volumes": [                         {                             "name": "dshm",                             "emptyDir": {                                 "medium": "Memory",                                 "sizeLimit": "100Mi"                             }                         }                     ],                     "containers": [                         {                             "name": "dagster-job",                             "volumeMounts": [                                 {                                     "mountPath": "/dev/shm",                                     "name": "dshm"                                 }                             ]                         }                     ]                 }             }         }     } ) def my_op(context):     # Your op logic here     pass

In this configuration, you're defining an

emptyDir

volume named

dshm

with a size limit of

100Mi

, which is mounted to

/dev/shm

in the container. This setup should increase the shared memory available to your op when it runs in a Kubernetes pod. Please note that the container name

dagster-job

is a placeholder and may need to be adjusted based on your specific deployment configuration. Dagster typically sets the container name for you, so you may not need to include the

name

field under

containers

unless you have a specific reason to override the default name. If you're not sure about the exact configuration or if you encounter any issues, it's a good idea to check the Helm chart values and the Kubernetes job specification that Dagster generates to ensure that your shared memory volume configuration is being applied as expected.