because step can be constructed with multiple

sub-components

but not all of them might be added to the UI graph

Just to make sure I fully understand when we decorate with @sub_node we want that to also appear in the UI graph (and have it's own Task / metrics etc)
correct?

Sure thing 🙂

Could you run your code not from the git repository.
I have a theory, you never actually added the entry point file to the git repo, so the agent never actually installed it, and it just did nothing (it should have reported an error, I'll look into it)
WDYT?

, the easiest way possible would be if could just some how run task and let the lsf manage the environment

You mean let the LSF set the conda/venv ? or do you also mean to get the code-base, changes etc ?

Thanks!!!

ReassuredTiger98 I'm trying to debug what's going on, because it should have worked.
Regrading Prints ...
` from clearml import Task
from time import sleep

def main():
task = Task.init(project_name="test", task_name="test")
d = {"a": "1"}
print('uploading artifact')
task.upload_artifact("myArtifact", d)
print('done uploading artifact')
# not sure if this helps but it won'r hurt to debug
sleep(3.0)
if name == "main":
main() `

AbruptWorm50 can you send full image (X axis is missing from the graph)

I have to admit, I'm not sure...
Let me talk to backend guys, in theory you are correct the "initial secret" can be injected via the helm env var, but I'm not sure how that would work in this specific case

obviously http://app.trains.domain.com will also work

"what's the trains/trains-agent/trains-server versions ?" how can I check it?

trains/trains-agent are pip packages os,
pip freeze | grep trains
trains-server you can check in the /profile page top left corner

Okay there should not be any difference ... 😞

Hi @<1555362936292118528:profile|AdventurousElephant3>
I think your issue is that Task supports two types of code,

• single script/jupyter notebook
• git repo + git diffIn your example (If I understand correctly) you have a notebook calling another notebook, which means the first notebook will be stored on the Task, but the second notebook (not being part of a repository) will not be stored on the task, and this is why when the agent is running the code it fails to find the second notebook....

Actually this should be a flag

actually the issue is that the packages are not being detected 😞
what happens if you do the following?
Task.add_requirements("tensorflow") task = Task.init(...)

BattyLion34 is this running with an agent ?
What's the comparison with a previously working Task (in terms of python packages) ?

Then as you suggested, I would just use sys.path it is probably the easiest and actually very safe (because the subfolders are Always next to the "main" source code)

,

remote_execute

kills the thread so the multirun stops at the first sub-task.

Hmm

task = Task.init(...)
# config some stuff
task.remote_execute(queue_name_here, exit_process=False)
# this means that the local execution will stop but when running on the remote agent it will be skipped
if Task.running_locally():
  return

I would like to use ClearML together with Hydra multirun sweeps, but I’m having some difficulties with the configuration of tasks.

Hi SoreHorse95
In theory that should work out of the box, why do you need to manually create a Task (as opposed to just have Task.init call inside the code) ?

Hmm @<1523701279472226304:profile|SoreHorse95> this is a good point, I think you are correct we need to fix that,

• Could you open a GitHub issue so this is not forgotten ?
• As a workaround I would use clone=True, then after the call I would call task.close() on the original task, wdyt?

Nice SoreHorse95 !
BTW: you can edit the entire omegaconf yaml externally with set/get configuration object (name = OmegaConf) , do notice you will need to change Hydra/allow_omegaconf_edit to true

Hmm that is odd, let me see if I can reproduce it.
What's the clearml version you are using ?

@<1523701868901961728:profile|ReassuredTiger98> in the UI can you see it in the "installed packages" section under the Execution Tab ?

Hi @<1566596960691949568:profile|UpsetWalrus59>
Could it be the two experiments have the exact name ?
(I sounds like a bug in the UI, but I'm trying to make sure, and also understand how to reproduce)
What's your clearml-server version ?

So clearml server already contains an authentication layer (JWT Token), and you do have a full user management on top:
https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_config#web-login-authentication
Basically what I'm saying if you add httpS on top of the communication, and only open the 3 ports, you should be good to go. Now if you really need SSO (AD included) for user login etc, unfortunately this is not part of the open source, but I know they have it in the scale/ent...

Hi SoreHorse95

I am exploring hiding our clearml server behind

Do you mean add additional reverse proxy to authenticate clearml-server from outside ?

Hmm, it might be sub-sampling on large scalar plots (so that we do not "kill" the ui), but I remember that it only happens above 50k samples. (when you zoom in, do you still get the 0.5 values?)

AWS_ACCESS_KEY_ID AWS_SECRET_ACCESS_KEY AWS_DEFAULT_REGION

Hi MortifiedDove27
I think you can resize the plot area in the UI (try to drag the horizontal separator)

how come the previous gitdiff passed ?

No (this is deprecated and was removed because it was confusing)
https://github.com/allegroai/clearml-agent/blob/cec6420c8f40d92ab1cd6cbe5ca8f24cf351abd8/docs/clearml.conf#L101