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New RunLLM response generated: >>>*# Question* He...
# runllm-flyte-trial
a
New RunLLM response generated: >>>*# Question* Hey all -- currently working on attaching a sampling Python call stack profiler as a sidecar to a Flyte task. This entails: • Creating a new PodSpec within 
flytekit
that asserts shared process namespace for the 
primary
and 
profiler
containers in the pod and adding some e.g. additional capabilities ◦ This ensures any sampling profiler can read the Task Python process' 
proc/$pid/maps
◦ Easy enough, that part is done! • Knowing what 
PID
is the user task being run. This would entail e.g. 
pgrep -f <entrypoint_for_task>
My problem arises in that last bullet point -- what process name should I be attempting to get the 
PID
of? • I first, naively, tried 
pgrep -f pyflyte-execute
given that this is the entrypoint into the lifecycle of a task. This likely spins off separate processes for the user code itself, though, and as such does not work Once 
dispatch_execute
is called somewhere within 
pyflyte-execute
, what would be the appropriate process to grep for? Would it be simpler to attempt a different approach, such as a custom 
PythonFunctionTask
that dumps the task objects PID to a shared volume? # Answer To attach a sampling Python call stack profiler as a sidecar to a Flyte task, you need to identify the correct process ID (PID) of the user task. The 
pyflyte-execute
command is indeed the entry point, but it spawns additional processes for the user code, making it challenging to directly grep for the correct PID. Steps to Identify the Correct PID: 1. Custom PythonFunctionTask: Modify the task to write its PID to a shared volume. This approach ensures you capture the exact PID of the user task. Here's an example of how you can achieve this:
Copy code
from flytekit import task, workflow
import os

@task
def my_task() -> str:
    pid = os.getpid()
    with open("/shared_volume/task_pid.txt", "w") as f:
        f.write(str(pid))
    # Your task logic here
    return "Task completed"

@workflow
def my_workflow() -> str:
    return my_task()
PodSpec Configuration: Ensure your 
PodSpec
includes a shared volume and the necessary capabilities for the profiler container to access the task's process information.
>>```
from flytekit import Resources, task, workflow from flytekitplugins.k8s.pod import Pod, PodTemplate, PodSpec, Container, Volume, VolumeMount, EmptyDirVolumeSource shared_volume = Volume( name="shared-volume", empty_dir=EmptyDirVolumeSource(medium="Memory") ) shared_volume_mount = VolumeMount( name="shared-volume", mount_path="/shared_volume" ) profiler_container = Container( name="profiler", image="profiler_image", command=["profiler_command"], volume_mounts=[shared_volume_mount] ) primary_container = Container( name="primary", image="task_image", command=["pyflyte-execute", "--task-module", "my_module", "--task-name", "my_task"], volume_mounts=[shared_volume_mount] ) pod_spec = PodSpec( containers=[primary_container, profiler_container], volumes=[shared_volume] ) @task( task_config=Pod(pod_spec=pod_spec), requests=Resources(cpu="1", mem="1Gi"), limits=Resources(cpu="2", mem="2Gi") ) def my_task() -> str: pid = os.getpid() with open("/shared_volume/task_pid.txt", "w") as f: f.write(str(pid)) # Your task logic here return "Task completed" @workflow def my_workflow() -> str: return my_task() ``` Summary: - Use a custom 
PythonFunctionTask
to write the task's PID to a shared volume. - Configure the 
PodSpec
to include a shared volume and the necessary capabilities for the profiler container. Would you like more details on any specific part of this process? Sources: - https://docs.flyte.org/en/latest/api/flytekit/design/execution.html - https://docs.flyte.org/en/latest/concepts/workflow_lifecycle.html#understand-the-lifecycle-of-a-flyte-workflow Note: I wasn’t able to find highly relevant data sources, but above are a few potentially relevant links. Slack Link https://flyte-org.slack.com/archives/CP2HDHKE1/p1723841832.206539 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
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