Thanks @<1523701205467926528:profile|AgitatedDove14> . I just got an issue running clearml-task remotely, it has been working fine before today, but now every time I run clearml-task, it shows pending, and I've been waiting for 3 hours the status is still pending. The autoscalers was charging the hourly rate even though the task is still pending for 3 hours. From the console log of Clearml GPU instance, I saw it is listening to the queue, but there is no log even after 3 hours. There is nothing else I am running beside this one task, and seems like the worker never spin up again

2023-08-03 04:41:00,624 - clearml.Auto-Scaler - INFO - Spinning new instance resource='default', prefix='38ae71a80baf4a58893631d23c0c6e72_3090_1', queue='test-gpu'
2023-08-03 04:41:00,625 - clearml.Auto-Scaler - INFO - Creating instance for resource default
2023-08-03 04:41:01,027 - clearml.Auto-Scaler - INFO - New instance b97e702d-e2b3-4f28-adab-be59648601ea listening to test-gpu queue

Thanks for the detials @<1597762318140182528:profile|EnchantingPenguin77>

clearml.Auto-Scaler - INFO - New instance b97e702d-e2b3-4f28-adab-be59648601ea listening to test-gpu queue

This looks like a new agent was spined on your EC2 account, can you see it in the "Workers" page ?

# 

from typing import List, Optional, Tuple
import pyrootutils
import lightning
import hydra
from clearml import Task
from omegaconf import DictConfig, OmegaConf
from lightning import LightningDataModule, LightningModule, Trainer, Callback
from lightning.pytorch.loggers import Logger

pyrootutils.setup_root(__file__, indicator="pyproject.toml", pythonpath=True)
# ------------------------------------------------------------------------------------ #
# the setup_root above is equivalent to:
# - adding project root dir to PYTHONPATH
#       (so you don't need to force user to install project as a package)
#       (necessary before importing any local modules e.g. `from src import utils`)
# - setting up PROJECT_ROOT environment variable
#       (which is used as a base for paths in "configs/paths/default.yaml")
#       (this way all filepaths are the same no matter where you run the code)
# - loading environment variables from ".env" in root dir
#
# you can remove it if you:
# 1. either install project as a package or move entry files to project root dir
# 2. set `root_dir` to "." in "configs/paths/default.yaml"
#
# more info: 

# ------------------------------------------------------------------------------------ #

from src.utils.pylogger import get_pylogger
from src.utils.instantiators import instantiate_callbacks, instantiate_loggers

log = get_pylogger(__name__)


def train(cfg: DictConfig):
    # set seed for random number generators in pytorch, numpy and python.random
    if cfg.get("seed"):
        lightning.seed_everything(cfg.seed, workers=True)

    log.info(f"Instantiating datamodule <{cfg.data._target_}>")
    datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data)

    log.info(f"Instantiating model <{cfg.model._target_}>")
    model: LightningModule = hydra.utils.instantiate(cfg.model)

    log.info("Instantiating callbacks...")
    callbacks: List[Callback] = instantiate_callbacks(cfg.get("callbacks"))

    log.info("Instantiating loggers...")
    logger: List[Logger] = instantiate_loggers(cfg.get("logger"))

    log.info(f"Instantiating trainer <{cfg.trainer._target_}>")
    trainer: Trainer = hydra.utils.instantiate(cfg.trainer, callbacks=callbacks, logger=logger)

    if cfg.get("train"):
        log.info("Starting training!")
        trainer.fit(model=model, datamodule=datamodule, ckpt_path=cfg.get("ckpt_path"))

    if cfg.get("test"):
        log.info("Starting testing!")
        ckpt_path = trainer.checkpoint_callback.best_model_path
        if ckpt_path == "":
            log.warning("Best ckpt not found! Using current weights for testing...")
            ckpt_path = None
        trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path)
        log.info(f"Best ckpt path: {ckpt_path}")


@hydra.main(version_base="1.3", config_path="../../configs", config_name="train.yaml")
def main(cfg: DictConfig):
    OmegaConf.set_struct(cfg, False)  # allow cfg to be mutable

    task = Task.init(project_name="fluoro-motion-detection", task_name="uniformer-test")
    logger = task.get_logger()
    logger.report_text("You can view your full hydra configuration under Configuration tab in the UI")
    print(OmegaConf.to_yaml(cfg))

    train(cfg)


if __name__ == "__main__":
    main()

I've added gpu:True to my hydra config file but the GPU is still not used

is it displaying that it is running anything?

There is nothing on the queue and worker

Actually never mind, it's working now!

I got the same cuda issue after being able to use GPU