Hi RipeGoose2

when I'm using the set_credentials approach does it mean the trains.conf is redundant? if

Yes this means there is no need for trains.conf , all the important stuff (i.e. server + credentials you provide from code).
BTW: When you execute the same code (i.e. code with set_credentials call) the agent's coniguration will override what you have there, so you will be able to run the Task later either on prem/cloud without needing to change the code itself 🙂

Any specific use case for the required "draft" mode?

But essentially Prefect also has agents to run jobs on machines where the processes run (which seems to be exactly the same model as in ClearML),

Yes ait is conceptually very similar

this data is highly regulated data, ...

The main difference that with ClearML the agents are running on Your machines (either local or on Your cloud account) the clearml-server does not actually have access to the data streaming through it.
Does that make sense ?

Hi CleanPigeon16

can I make the steps in the pipeline use the latest commit in the branch?

Yes:
manually clone the stesp's Task (in the UI), and in the UI edit the Execution section and change to "last sommit on branch" and specify the branch name programmatically (as the above, clone+edit)

ValueError: Could not parse reference '${run_experiment.models.output.-1.url}', step run_experiment could not be found

Seems like the "run_experiment" step is not defined. Could that be ...

Yep that will fi it, nice one!!
BTW I think we should addtge ability to continue aborted datasets, wdyt?

yes, so it does exist the local process (at least, the command returns),

What do you mean the command returns ? are running the scipt from bash and it returns to bash ?

Let me check, see what can be learned ...

Whats the conda version you are using ?

If you have the check point (see output_uri for automatically uploading it) then you can always load it. Do you mean if you can change the iteration/ step counter? Or do you mean with trains-agent?

Hi ShortElephant92
No, this is opt-in, so other then checking for updates once in a while, no traffic at all

Actually this is by default for any multi node training framework torch DDP / openmpi etc.

to setup ClearML agent in kubernetes with the SSH keys?

You can add env variable:
CLEARML_AGENT__AGENT__FORCE_GIT_SSH_PROTOCOL="true"https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_config#dynamic-environment-variables

Hi WittyOwl57

I'm guessing clearml is trying to unify the histograms for each iteration, but the result is in this case not useful.

I think you are correct, the TB histograms are actually a 3d histograms (i.e. 2d histograms over time, which would be the default for kernel;/bias etc.)

is there a way to ungroup the result by iteration, and, is it possible to group it by something else (e.g. the tags of the two plots displayed below side by side).

Can you provide a toy example...

Hi JumpyPig73

import data from old experiments into the dashboard.

what do you mean by "old experiments" ?

first try the current setup using 

pip

, and if it fails, use 

poetry

 if 

poetry.lock

 exists

I guess the order here is not clear to me (the agent does the opposite), why would you start with pip if you are using poetry ?

Hmm EmbarrassedPeacock82
Let's try with
--input-size -1 60 1 --aux-config input.format=FORMAT_NCHWBTW: this seems like a triton LSTM configuration issue, we might want to move the discussion to the Triton server issue, wdyt?

Hi @<1668427971179843584:profile|GrumpySeahorse51>
Could you provide the full stack log?
this erros seems to originate from psutil (which is used) but it lacks the clearml-session context

Optional[Sequence[Union[str, Dataset]]]None, list of string or list of Datasets objects
(each one is a parent (supporting multiple parents)

And thanks again, I really appreciate testing it!

No Task.create is for creating an external Task not logging your own process,
That said you can probably override the git repo with env vars:
None

We should probably add (set_task_type :))

https://github.com/allegroai/clearml/blob/master/clearml/automation/trigger.py
Example coming soon, with docs :)

SarcasticSparrow10 how do I reproduce it?
I tried launching from a sub process that is a daemon and it worked. Are you using ProcessPool ?

Okay we have located the issue, thanks guys! We will push a patch release hopefully later today

Here you go:
` @PipelineDecorator.pipeline(name='training', project='kgraph', version='1.2')
def pipeline(...):
return

if name == 'main':
Task.force_requirements_env_freeze(requirements_file="./requirements.txt")
pipeline(...) If you need anything for the pipeline component you can do: @PipelineDecorator.component(packages="./requirements.txt")
def step(data):

some stuff `

Hi RoundMosquito25

however they are not visible either in:

But can you see them in the UI?

I cannot reproduce, tested with the same matplotlib version and python against the community server

Shouldn't this be a real value and not a template

you mean value being pulled to the pod that failed ?

Hi @<1688721797135994880:profile|ThoughtfulPeacock83>

the configuration vault parameters of a pipeline step with the add_function_step method?

The configuration vault are a per set at execution user/project/company .
What would be the value you need to override ? and what is the use case?

QuaintJellyfish58 this is very odd, and the "undefined" is always marked as example?