New RunLLM response generated: >>>*# Question* Hola I am running a workflow which should use custom image which specify but it using flyte default base image what is the reason can you please help this is my workflow from flytekit import task, workflow #from flytekitplugins.pod import PodTemplate from flytekit import workflow, task, PodTemplate from kubernetes.client import V1PodSpec, V1Container, V1SecurityContext, V1ResourceRequirements, V1VolumeMount, V1SecurityContext import subprocess from flytekitplugins.pod.task import Pod, PodFunctionTask from flytekit import task, workflow, dynamic from flytekit import PodTemplate, Resources import time import os def get_hardcoded_pod_spec() -> V1PodSpec: custom_image = "amorapprendido/flyte-gcld-dock-kube:v2" container = V1Container( name="custom-container", image=custom_image, ) pod_spec = V1PodSpec( containers=[container], restart_policy="OnFailure", security_context=V1SecurityContext(privileged=True) ) return pod_spec @task( task_config=Pod(pod_spec=get_hardcoded_pod_spec(), primary_container_name="custom-container") ) def hardcoded_pod_task() -> str: print("Hello from the task!") time.sleep(190) print("sleep complete") # Running the gsutil command and capturing the output # result = subprocess.run( # ['docker', 'images'], # check=True, # Ensures the command raises an error if it fails # capture_output=True, # Captures stdout and stderr # text=True # Returns output as a string # ) # Print the captured output # print("Subprocess output:", result.stdout.strip()) return "This is a hardcoded task running in a custom container!" @workflow def main_hardcoded_pod_workflow() -> str: return hardcoded_pod_task() # Answer The issue you're encountering is likely due to the way Flyte handles the

container_image

parameter in the task decorator. According to Flyte documentation, specifying the

container_image

directly in the task decorator is necessary to ensure that the custom image is used. Here's how you can modify your task to ensure it uses the custom image: 1. Specify

container_image

in the task decorator: Add the

container_image

parameter to the task decorator to explicitly set the custom image.