it is agg

yes you are correct, I would expect the same.
Can you try manually importing pt, and maybe also moving the Task.init before darts?

So when the agent fire up it get's the hostname, which you can then get from the API,

I think it does something like "getlocalhost", a python function that is OS agnostic

Try to upload something to the file server ?
None

Hi @<1603198134261911552:profile|ColossalReindeer77>
I would also check this one: None

check if the fileserver docker is running with docker ps

Any chance you can PR a fix to the docs?

Thank you so much!! 🤩

/opt/clearml/data/fileserver this is ion the host machine and it is mounted Into the container to /mnt/fileserer

For future readers, see discussion here:
https://clearml.slack.com/archives/CTK20V944/p1629840257158900?thread_ts=1629091260.446400&cid=CTK20V944

Hi @<1545216070686609408:profile|EnthusiasticCow4>

Many of the dataset we work with are generated by SQL query.

The main question in these scenarios is, are those DB stable.
By that I mean, generally speaking DB serve applications, and from time to time they undergo migration (i.e. change in schema, more/less data etc).
The most stable way is to create a script that runs the SQL query, and creates a clearml dateset from it (that script becomes part of the Dataset, to have full tracta...

to add an init script or to expand its capacity,

@<1546665634195050496:profile|SolidGoose91> I seem to see it in the wizard here, what am I missing?

btw: what's the OS and python version?

. I'm trying to run to get a task to run using a specific docker image and to source a bash script before execution of the python script.

Are you running an agent in docker mode ? if so you should be able to see the Output of your bash script first thing in the log
(and it will appear in the docker CMD)

Month +- 🙂

Hi PompousParrot44
So do you mean something like:
` task_model_a = Task.get('id_a')
task_model_b = Task.get('id_b')

model_a_file = task_model_a.models['output][-1].get_local_copy()

model_b_file = task_model_b.models['output][-1].get_local_copy() `

okay, now it should work 🙂

You're welcome 🙂

Hmm I see what you mean. It is on the roadmap (ETA the next version 0.17, 0.16 is due in a week or so) to add multiple models per Task so it is easier to see the connections in the UI. I'm assuming this will solve the problem?

Hi WackyRabbit7 ,
Yes we had the same experience with kaggle competitions. We ended up having a flag that skipped the task init :(
Introducing offline mode is on the to do list, but to be honest it is there for a while. The thing is, since the Task object actually interacts with the backend, creating an offline mode means simulation of the backend response. I'm open to hacking suggestions though :)

(since you are using venv mode, if the cuda is not detected at startup time, it will not install the GPU version, as it has no CUDA support)

what do you see in the console when you start the trains-agent , it should detect the cuda version

This is also set in the command line.
--cpu-only or maybe without any --gpus flag at all

You can always force it with environment variable CUDA_VERSION=10.1

This is why it thinks it's CPU 🙂

It is configured as CPU (i.e. no CUDA)

cuda 10.1, I guess this is because no wheel exists for torch==1.3.1 and cuda 11.0

Correct

how can I enforce a specific wheel to be installed?

You mean like specific CUDA wheel ?
you can simple put the http link to the wheel in the "installed packages", it should work

What you actually specified is torch the @ is kind of pip remark, pip will not actually parse it 🙂
use only the link https://download.pytorch.org/whl/cu100/torch-1.3.1%2Bcu100-cp36-cp36m-linux_x86_64.whl

Hi JitteryCoyote63
What do you have in the agent.cuda_version ?
(you can see it printed at the beginning of the log)

That depends on what you have installed 🙂