-
Notifications
You must be signed in to change notification settings - Fork 282
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Enable GCP Workload Monitoring #932
base: main
Are you sure you want to change the base?
Conversation
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
@@ -219,6 +221,26 @@ def __init__( | |||
super().__init__(cfg, parent=parent) | |||
cfg = self.config | |||
|
|||
self.gcp_workload_monitor = None |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
The trainer shouldn't have any dependencies on GCP specific logic. BTW, I wonder whether the monitoring events can be inferred from the events recorded by the measurement
utils -- what are the main differences?
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Got it! I will decouple the GCP logic from Trainer
. I am thinking to do the following
- Create a
GCPRecorder
class that inherits fromRecorder
. - Register the
GCPRecorder
with the decorator@register_recorder
. - Finally, in
Trainer
if GCP monitoring is enabled, I will initializemeasurement
with therecorder_type
set togcp:gcp
to use GCPRecorder.
Hopefully this will enable GCP monitoring in a cleaner way!
Let me know if it make sense.
Integrate Workload Observability to AXLearn Framework