@ancient-wolf-19325 How can i add toleration for my flyte task manifest in python

To add tolerations to a Flyte task manifest in Python, you can use the

PodTemplate

class from

flytekit

. This allows you to specify Kubernetes-specific configurations such as tolerations. Here's an example of how to do it:

from flytekit import PodTemplate, Resources, task
from kubernetes.client import V1Container, V1PodSpec, V1Toleration

@task(
    requests=Resources(cpu="0.1", mem="128Mi"),
    limits=Resources(cpu="0.2", mem="256Mi"),
    pod_template=PodTemplate(
        primary_container_name="primary",
        pod_spec=V1PodSpec(
            containers=[
                V1Container(
                    name="primary",
                    image="hello-world",
                ),
            ],
            tolerations=[
                V1Toleration(
                    key="app",
                    operator="Equal",
                    value="flyte",
                    effect="NoSchedule",
                ),
            ],
        ),
    ),
)
def my_task() -> None:
    print("Task with tolerations")

This code sets a toleration on the task's pod, allowing it to be scheduled on nodes with a matching taint. Would you like more detail on this process? Sources: - GitHub Issue: Spark Task tolerations not applied with PodTemplate 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 can i remove a registered workflow version

Currently, Flyte does not support removing a specific registered workflow version. According to Flyte's GitHub issue, the system avoids exposing the ability to remove Flyte entities to prevent a class of errors. However, archiving is supported, which affects all versions of a workflow. Would you like more detail on how to archive workflows or any other specific aspect? Sources: - Flyte's GitHub issue 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 @task( requests=Resources(cpu="8", mem="8Gi"), limits=Resources(cpu="10", mem="12Gi"), _pod_template_=PodTemplate( pod_spec=V1PodSpec( containers=[], tolerations=[ V1Toleration(key="cpu-4xlarge", operator="Exists"), ], ), ), ) when I do this for my task, and also I register the workflow with an image name, what image would be used for my task?

When you register a Flyte task with a specific image name using the

container_image

parameter in the

@task

decorator, that image will be used for the task execution. If you do not specify a

container_image

in the task definition, Flyte will use the default image specified during the registration process or the default image configured in your Flyte deployment. In your example, if you register the workflow with a specific image name using the

--image

flag during registration, that image will be used for the task unless overridden by a

container_image

parameter in the task definition. If

container_image

is not specified in the task, the image provided during registration will be used. Would you like more detail on how to specify images for tasks or workflows? Sources: - Flyte documentation on ImageSpec - Flyte documentation on multiple images in a workflow 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 how can i re-register a workflow with the same version

Re-registering a workflow with the same version in Flyte is not directly supported because Flyte treats each version as immutable. If you attempt to register a workflow with the same version and a different structure, you will encounter an error indicating that a workflow with a different structure already exists for that version. To update a workflow, you typically need to register it with a new version. This ensures that the changes are tracked and do not interfere with existing executions that rely on the previous version. Would you like more detail on how to manage workflow versions or any other specific aspect? Sources: - GitHub Issue: Registering the same workflow twice causes an invalid argument error 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.