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#ask-ai
Title
# ask-ai
i

Ian Macleod

03/05/2024, 6:09 PM
@Scout is it possible for me to increase the shared memory size that
s

Scout

03/05/2024, 6:09 PM
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.
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Ian Macleod

03/05/2024, 6:09 PM
@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
s

Scout

03/05/2024, 6:09 PM
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:
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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.
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Ian Macleod

03/05/2024, 6:14 PM
@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?
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docker run -it --rm --runtime=nvidia --gpus all --shm-size 100 d2d762bf65f8 /bin/bash
s

Scout

03/05/2024, 6:14 PM
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):
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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
ust the resource requests and limits to match the requirements of your op and the capabilities of your Kubernetes cluster.
i

Ian Macleod

03/05/2024, 6:33 PM
@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
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"containers": [
                    {
                        "name": "my-container",
                        "volumeMounts": [{"mountPath": "/dev/shm", "name": "dshm"}],
                    }
                ],
but got an error from kubernetes.
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{"name":"dagster-step-d454f4402a2f3f14358549916b21db0c","group":"batch","kind":"Job","causes":[{"reason":"FieldValueRequired","message":"Required value","field":"spec.template.spec.containers[1].image"}]},"code":422}
s

Scout

03/05/2024, 6:33 PM
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:
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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.
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Ian Macleod

03/05/2024, 6:35 PM
@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
s

Scout

03/05/2024, 6:35 PM
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:
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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.