BTW:
This is very odd "~/.clearml/venvs-builds.3/3.6/bin/python" it thinks it is using "python 3.6" but it is linked with python 2.7 ...
No idea how that could happen

If this doesn't help.
Go to your ~/clearml.conf file, at the bottom of the file you can add agent.python_binary and change it to to the location of python3.6 (you can run which python3.6 to get the full path):
agent.python_binary: /full/path/to/python3.6

Just making sure, the original code was executed on python 3?

Are you running it in venv mode or docker mode?

path: ~/.clearml/venvs-cache

to

# path: ~/.clearml/venvs-cache

In the documentation it warns about

.close()

"Only call Task.close if you are certain the Task is not needed."

Maybe this is not clear enough, this means you do not need to automatically Add/Log/Track things into the Task in the current process.
This does Not mean you cannot access the Task or its artifacts

Mark closed means to externally (i..e not from the process that crated the Task, maybe even from a different machine) close and mark the task as completed (this...

Is

mark_completed

used to complete a task from a different process and

close

from the same process - is that the idea?

Yes

However, when I tried them out,

mark_completed

terminated the process that called

mark_completed

.

Yes if you are changing the state of the Task externally or internally the SDK will kill the process. If you are calling task.close() from the process that created the Task it will gra...

Thanks @<1523704157695905792:profile|VivaciousBadger56> ! great work on the docstring, I also really like the extended example. Let me make sure someone merges it

Hi FrothyShark37
Can you verify with the latest version?
pip install -U clearml

Hi @<1523704157695905792:profile|VivaciousBadger56>
You should replace

task.mark_completed()

with:

task.close()

To your point

parameters = task.connect(parameters)

Will be retrieved with:

task.get_parameters()

fyi:
connect_configuration -> get_configuration_objects

Sure GiddyTurkey39 , Checkout the cleanup service:
https://github.com/allegroai/trains/blob/master/examples/services/cleanup/cleanup_service.py

Scenario 1 & 2 are essentially the same from caching perspective (the face B != B` means they have different caching hashes, but in both cases are cached).
Scenario 3 is the basically removing the cache flag from those components.

Not sure if I'm missing something.

Back to the @<1523701083040387072:profile|UnevenDolphin73>

From decorators - when the pipeline logic is very straightforward ...

Actually I would disagree, the decorators should be used when the pipeline Logic is not a D...

mean? Is it not possible that I call code that is somewhere else on my local computer and/or in my code base? That makes things a bit complicated if my current repository is not somehow available to the agent.

I guess you can ignore this argument for the sake of simple discussion. If you need access to extra files/functions, just make sure you point the repo argument to their repo, and the agent will make sure your code is running from the repo root, with all the repo files under i...

Is the code in this "other" repo downloaded to the agent's machine? Or is the component's code pushed to the machine on which the repository is?

Yes this repo is downloaded into the agent, so your code has access to it

I am writing quite a bit of documentation on the topic of pipelines. I am happy to share the article here, once my questions are answered and we can make a pull request for the official documentation out of it.

Amazing please share once done, I will make sure we merge it into the docs!

Does this mean that within component or add_function_step I cannot use any code of my current directories code base, only code from external packages that are imported - unless I add my code with ...

Hi @<1523704157695905792:profile|VivaciousBadger56>
No these are 3 different ways of building pipelines.
Creating from decorators is recommended when each component can be easily packages into a single function (every function can have an accompanying repository).
Here the idea it is very easy to write complex execution logic, basically the automagic does serialization/deserialization so you can write pipelines like you would code python.

Creating from Tasks is a good match if you need to ...

@<1523704157695905792:profile|VivaciousBadger56>

Is the idea here the following? You want to use inversion-of-control such that I provide a function

f

to a component that takes the above dict an an input. Then I can do whatever I like inside the function

f

and return a different dict as output. If the output dict of

f

changes, the component is rerun; otherwise, the old output of the component is used?

Yes exactly ! this way you...

• Components anyway need to be available when you define the pipeline controller/decorator, i.e. same codebaseNo you an specify a different code base, see here:
  None
• The component code still needs to be self-composed (or, function component can also be quite complex)Well it can address the additional repo (it will be automatically added to the PYTHONPATH), and you c...

Hi @<1573119962950668288:profile|ObliviousSealion5>

Hello, I don't really like the idea of providing my own github credentials to the ClearML agent. We have a local ClearML deployment.

if you own the agent, that should not be an issue,, no?

forward my SSH credentials using

ssh -A

and then starting the clearml agent?

When you are running the agent and you force git clonening with SSH, it will autmatically map the .ssh into the container for the git to use

Ba...

owning the agent helps, but still it's much better if the credentials don't show up in logs,

They are not, they are always filtered out,

• how does force_git_ssh_protocol help please? it doesn't solve the issue of the agent simply not having accessIt automatically maps the host .ssh into the container, so that git can use SSH to clone.
  What exactly is not working?
  and how are you configuring it?

Hmm I think you have a point here, the confusing part is the cp cmd. Can you send the full log? (Regradless , can I assume you are running a rootless container ?)

MotionlessCoral18 I think there is a fix in the latest clearml-agent RC 1.4.0rc0 can you test and update if your are still having this issue?

But this is not copy, this is mount, your log showed cp failing

Agreed, MotionlessCoral18 could you open a feature request on the clearml-agent repo please? (I really do not want this feature to get lost, and I'm with you on the importance, lets' make sure we have it configured from the outside)

LOL yes 🙂
just make sure it won't be part of the uncommitted changes of the AWS autoscaler 😉

Nice!

Ad1. yes, think this is kind of bug. Using _task to get pipeline input values is a little bit ugly

Good point, let;s fix it 🙂

new pipeline is built from scratch (all steps etc), but by clicking "NEW RUN" in GUI it just reuse existing pipeline. Is it correct?

Oh I think I understand what happens, the way the pipeline logic is built, is that the "DAG" is created the first time the code runs, then when you re-run the pipeline step it serializes the DAG from the Task/backend.
Th...

Hi HandsomeGiraffe70

First:
# During pipeline initialisation pipeline_params is empty and we need to use default values. # When pipeline start the run, params are lunched again, and then pipeline_params can be used.Hmm that should probably be fixed, maybe a function on the pipeline to deal with it ?

When I reduce tune_optime value to just 'recall'. Pipeline execution failed with msg:

ValueError: Node 'tune_et_for_Precision', base_task_id is empty

.

I would...

CourageousKoala93 when you call Task.close() it will mark the task as completed, there is no need to do that manually. The idea with mark_completed is that you can forcefully change the state if needed, or externally stop the task and mark it completed. Make sense?

worker nodes are bare metal and they are not in k8s yet

By default the agent will use 10022 as an initial starting port for running the sshd that will be mapped into the container. This has nothing to do with the Host machine's sshd. (I'm assuming agent running in docker mode)