I think you can either add the requirement manually through code ( https://clear.ml/docs/latest/docs/references/sdk/task#taskadd_requirements ) or force the agent to use the requirements.txt when running in remote

Here’s the error I get:
https://justpaste.it/7aom5

It’s trying to downgrade pytorch to 1.12.1 for some reason (why?) using a version for an outdated CUDA (I have 11.7, it tries to use pytorch for CUDA 11.6). Finally crashes

The failure is that it does not even run

Well I don’t want that! My local machine is a Mac with no GPU. But I want to execute my code on a server with GPUs. I don’t want my local environment, I want the one configured for the agent!

So I think I'm missing something. What is the point of failure?
ClearML tries to detect the packages you used during the code execution. It will then try to install those packages when running remotely.

Let me get the exact error for you

(agent) adamastor@adamastor:~/clearml_agent$ python -c "import torch; print(torch.__version__)" 1.12.1

What version of python is the agent machine running locally?
Does it support
torch == 1.12.1?

Pytorch is configured on the machine that’s running the agent. It’s also in requirements

Here’s the agent config. It’s basically default
https://justpaste.it/4ozm3

So how do you attach the pytorch requirement?

Yeah, pytorch is a must. This script is a testing one, but after this I need to train stuff on GPUs

Can you add here the agent section of your ~/clearml.conf

Is there a reason it is requiring pytorch? )
The script you provided has only clearml as a requirement

CostlyOstrich36 in installed packages it has:
` # Python 3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:41:22) [Clang 13.0.1 ]

Pillow == 9.2.0
clearml == 1.7.1
minio == 7.1.12
numpy == 1.23.1
pandas == 1.5.0
scikit_learn == 1.1.2
tensorboard == 2.10.1
torch == 1.12.1
torchvision == 0.13.1
tqdm == 4.64.1 `Which is the same as I have locally and on the server that runs clearml-agent

On the agent side it’s trying to install different pytorch versions (even though the env already has it all configured), then fails with torch_<something>.whl is not a valid wheel for this system

When you look at the original task that appears in the UI, what are the requirements shown in the 'execution' tab?

Locally I have a conda env with some packages and a basic requirements file.
I am running this thing:
` from clearml import Task, Dataset
task = Task.init(project_name='Adhoc', task_name='Dataset test')
task.execute_remotely(queue_name="gpu")

from config import DATASET_NAME, CLEARML_PROJECT
print('Getting dataset')

dataset_path = Dataset.get(
dataset_name=DATASET_NAME,
dataset_project=CLEARML_PROJECT,
).get_local_copy()#.get_mutable_local_copy(DATASET_NAME)

print('Dataset path', dataset_path) `Then on the server side I have clear-ml agent running in default (venv) mode, started from a conda env with the same python version. Then it does something to packages and crashes

What I am seeing is that the agent always fails trying to install some packages when I am not asking it at all

I have no idea what it is doing

Hi AdventurousButterfly15 ,

When running code locally, how are the installed packages detected? Does it detect your entire venv or does it detect only the packages that were used?