Hmmm, that actually connects with something we were thinking about: introducing sections to the hyper parameters. This way we could easily differentiate between the command line arguments and other types of parameters. DilapidatedDucks58 what do you think?

DilapidatedDucks58 if you have so many parameters, why don't you use the
task.connect_configuration(dict)
It will put it in the artifacts, as an editable json alike string.

BTW copying the cmd line assumes that you are running it in the same machine...

AstonishingSeaturtle47 yes it does. But I have to ask how come you have sub modules that one will have credentials for the master repo and not the sub ones? Also it sounds like a good solution would be for the trains-agent to try and pull the sub-modules and if it cannot, it should just print a warning and continue. What do you think?

Which version? is this reproducible in this example?
None
(can you try with the latest clearml version 1.13.2?)

pip freezeworks ?

This one? https://clear.ml/docs/latest/docs/references/sdk/logger#report_single_value

Is there any way to make that increment from last run?

pipeline_task = Task.clone("pipeline_id_here", name="new execution run here") 
Task.enqueue(pipeline_task, queue_name="services")

wdyt?

Hi @<1692345677285167104:profile|ThoughtfulKitten41>

Is it possible to trigger a pipeline run via API?

Yes! a pipeline is at the end a Task, you can take the pipeline ID and clone and enqueue it

pipeline_task = Task.clone("pipeline_id_here") 
Task.enqueue(pipeline_task, queue_name="services")

You can also monitor the pipeline with the same Task inyerface.
wdyt?

ERROR: Error checking for conflicts. ... AttributeError: _DistInfoDistribution__dep_map

ScaryKoala63
When it fails what's the number of files you have in:
/home/developer/.clearml/cache/storage_manager/global/ ?

Hmm, Notice that it does store sym links to parent data versions (to save on multiple copies of the same file). If you call get_mutable_local_copy() you will get a standalone copy

, i thought there will be some hooks for deploying where the integration with k8s was also taken care automatically.

Hi ObedientToad56
Yes you are correct, basically now you have a docker-compose (spinning everything, even though per example you can also spin a standalone container (mostly for debugging).
We are working on a k8s helm chart so the deployment is easier, it will be based on these docker-compose :
https://github.com/allegroai/clearml-serving/blob/main/docker/docker-comp...

tried it and restarted the agent, but not working properly

What do you mean not working? can you provide logs ?

Hi @<1529271085315395584:profile|AmusedCat74>

ClearML Scheduler where it doesn't reuse the task

What do you mean by doesn't reuse the Task, do you mean you want each time the scheduler is launched to basically overwrite the previous run ?

JitteryCoyote63 any specific reason for the question?

Nice catch AverageBee39 🙂

I lost you SmallBluewhale13 is this the Task init call you used:
task = Task.init( project_name="examples", task_name="load_artifacts", output_uri="s3://company-clearml/artifacts/bethan/sales_journeys/", )

I guess that was never the intention of the function, it just returns the internal representation. Actually my question would be, how do you use it, and why? :)

HealthyStarfish45 this sounds very cool! How can I help?

Hi PompousBeetle71
I remember it was an issue, but it was solved a while ago. Which Trains version are you using?

Hi @<1539780284646428672:profile|PoisedElephant79>

it's giving me no module name clearml

I'm assuming no clearml inside one og the pipeline components ? make sure you have all the imports inside the pipeline component function, like in this example:
None

BTW: trains-agent is leaner, and does not need plotly. And you can use the APIClient to basically query the entire system, would that be a better solution? See https://github.com/allegroai/trains-agent/blob/master/examples/archive_experiments.py

RC3 is out with a few fixes, please feel free to test 🙂

Ho @<1739818374189289472:profile|SourSpider22>
What are you trying to install, just the agent? if so pip install clearml-agent is all you need

I'm with on this one 🙂 it better to make a company wide decision on these things and not allow too much flexibility (just two options to choose from, and it should be enough, I think)

in 

 issues a delete command to the ClearML API server,...

almost, it issues the boto S3 delete commands (directly to the S3 server, not through the cleaml-server)

And that I need to enter an AWS key/secret in the profile page of the web app here? (edited)

correct

Wait who is creating this file? I thought you remove it in the uncommitted changes

Nice, that seems to be the issue. Any chance you can open a GitHub issue, so we do not loose track of it ?

well that depends on you, what did you write there to know it is the best one ? file name ? added some metric ?