Hmm, I'm without, no reason why it will get stuck .
Removing all the auto loggers, this can be done with
Task.init(..., auto_connect_frameworks=False)
which would disconnect all the automatic loggers (Hydra etc) off course this is for debugging purposes
He confirmed that it’s not inside a container. Trying to figure out why it’s running as root but would it make a difference if it was? Is it better to run the agent from a user profile?
Edit: it might be a container! Just checking now...
Hi @<1724960464275771392:profile|DepravedBee82> , can you perhaps add a simple print at the start of your code before any import?
Can you add before the Task.init
import os
print(os.environ)
Hi @<1523701205467926528:profile|AgitatedDove14> , I reordered the imports:
from clearml import Task
print("Before task")
task = Task.init(project_name="ClearML Testing", task_name="FMNIST")
task.set_repo(
repo="git@ssh.dev.azure.com:v3/mclarenracing/Application%20Engineering/ml-queue-test"
)
task.set_packages("requirements.txt")
print("After task")
print("Before import")
from pathlib import Path
import hydra
import lightning as L
import torch
from coolname import generate_slug
from omegaconf import DictConfig
from src.datasets import JobDataModule
from src.models import JobModel
from src.utils import LogSummaryCallback, get_num_steps, prepare_loggers_and_callbacks
for i in range(torch.cuda.device_count()):
print(torch.cuda.get_device_properties(i).name)
And here's the output:
Environment setup completed successfully
Starting Task Execution:
Before task
Still looks like it's getting stuck at Task.init
This is so odd,
could you add prints right after the Task.init?
Also could you verify it still gets stuck with the latest RC
clearml==1.16.3rc2
I managed to set up my (Windows) laptop as a worker and reproduce the issue.
Any insight on how we can reproduce the issue?
confirmed that the change had been added by
Make sure you see them in the Task log in the UI (the agent print it when it starts)
Any insight on how we can reproduce the issue?
Can this be reproducible using a simple script that we can also run?
I just ran with this in my local task, and all the env vars were printed to console, but in ClearML they are not in the console log. Presumably that's because it's printed before ClearML is logging?
It’s a Dell XE9680 rack server with 8xH100s which is located in our office, running AlmaOS. We have successfully run training jobs on it inside Docker (without ClearML) which work fine (will check with my team if we’ve got something to train without Docker). I’ve also tried different Python versions; 3.9 (Alma default) and 3.11 which you can see in the log above. It’s a really bizarre issue and outside of print statements I’m not really sure where to look.
You mentioned sync argparser & reporting, so I’ll try removing Hydra to rule that out, and other loggers in PL and see from there …
Thanks Martin - will try that and see what I can find. Really appreciate your patience with this! 🙂
Thanks for the response @<1523701205467926528:profile|AgitatedDove14> ! The code is a small FMNIST test training job written in PyTorch Lightning. On my local job (laptop GPU, Windows) it completes in ~ 5min. On the server (Linux, H100s) it just hangs at Starting Task Execution: . Neither of these are in Docker.
I would expect to see the standard PL progress bars outputted to the console, but since nothing is outputted, so I'm not sure how to go about debugging this. I've attached the full logs for local and remote
My understanding is that on remote execution Task.init is supposed to be a no-op right?
Our server is deployed on a kube cluster. I'm not too clear on how Helm charts etc.
The only thing that I can think of is that something is not right the the load balancer on the server so maybe some requests coming from an instance on the cluster are blocked ...
Hmm, saying that aloud that actually could be?! Try to add the following line to the end of the clearml.conf on the machine running the agent:
api.http.default_method: "put"