well I do not think you set your pytorch lightining to use cuda:

GPU available: True (cuda), used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
/code/.venv/lib/python3.9/site-packages/lightning/pytorch/trainer/setup.py:176: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.

Hi @<1597762318140182528:profile|EnchantingPenguin77>

, but it seems like clearml always create a virtual environmen

Yes that's correct, but the new venv inside the container inherits from the system packages (so if nothing changes it does nothing)

Is there a way that I can have the clearml-task to automatically activated a virtual environment use the activated custom virtual environment in my docker and run the scripts

Yoo can but the "correct" way to work with python and containers is to actually install everything on the system (not venv)
That said, just set this env variable to point top the python binary inside your venv in the container
CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=/root/venv/bin/python
None

@<1523701205467926528:profile|AgitatedDove14> Is there any reason why you mentioned that the "correct" way to work with python and containers is to actually install everything on the system (not venv)?

I actually have aborted it

It seems like CPU is working on something, I saw the usage is spiking periodically but I didn't run any task this morning

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 am using hydra in main.py