Then access the 8008 through the tunnel

Here is the screenshot

I have two laptop, one is running ubuntu 20.04 and one is macos, both are running in my local network. I installed the server on ubuntu and ssh from mac to it to bring up the server then build up a tunnel using ssh -L

Appreciated it!

but the solution in the answer doesn’t help cause when i do reverse with -R the server couldn’t be brought up

Yes, when i put the task init into the spawn function, it can run without error, but it seems that each of the child process has their own experiments
ClearML Task: created new task id=54ce0761934c42dbacb02a5c059314da ClearML Task: created new task id=fe66f8ec29a1476c8e6176989a4c67e9 ClearML results page: ClearML results page: `
ClearML Task: overwriting (reusing) task id=de46ccdfb6c047f689db6e50e6fb8291
ClearML Task: created new task id=91f891a272364713a4c3019d0afa058e
ClearML re...

Can get the result now but failed with this

I found server API here https://allegro.ai/clearml/docs/rst/references/clearml_api_ref , but not sure how to use it, for example /debug.ping, should i post request on “ http://localhost:8080/debug/ping ” or “ http://localhost:8080/debug.ping ”?

for example i have this experiments of 99 tasks, i’d like to pull all scaler data. How can i achieve this? Thank you!

is there any document for this?

for now i only have these

Thank you! another question this method seems need to get the result one by one on the fly. Because i have lots of complete experiments, is there a way that i can pull all scalars at once? Or, can i get experiments list and pull the data?

unfortunately, no, when i try to click the link, there is nothing there https://demoapp.demo.clear.ml/projects/0d49bffcdaa441c2aa3224054737d0bd/experiments/26dd46ec11fd4f95ba522955820a8444/output/log

Can you tell me how can i find out where the scalar log is?

Thanks, i’ll give it a try

Yes, i think trains might wrap the torch.load function, but the thing is that i need to load some part of the dataset using torch.load, so this error shows up many time during training, I found i can use this line:
task = Task.init(project_name="Alfred", task_name="trains_plot", auto_connect_frameworks={'pytorch': False})but does it mean i cannot monitor torch.load function any more?

Yeah, i’m done with the test, not i can run as what you said

Ok, i’ll try to do that asap

yes

I’ve been added multi-node support for my code, and i found our lab seems only have shared user file because i installed trains on one node, but it doesn’t appear on the others

never done this before, let me do a quick search

I’ll get back to you after i get this done

Not for now, i think it can only run on multiple GPU at one node

we all use conda, guess not need for docker

yes

but at least it’s working now

you mean two agents on two nodes?

i see, now we are trying to let the agent pop up the experiment separately and see if they can communicate with each other, right?