AWS_ACCESS_KEY_ID AWS_SECRET_ACCESS_KEY AWS_DEFAULT_REGION

yes, or (because I deployed clearml using helm in kubernetes) from the same machine, but multiple pods (tasks).

Oh now I see, long story short, no 😞 the correct way of doing that is every node/pod creates it's own dataset,
then when you are done, you create a new version with the X datasets that you created as parents, the newly created version is just "meta" it basically tells the system how to combine the previously generated datasets (i.e. no data is actually re-uploa...

EnviousStarfish54 are those scalars reported ?
If they are, you can just do:
task_reporting = Task.init(project_name='project', task_name='report') tasks = Task.get_tasks(project_name='project', task_name='partial_task_name_here') for t in tasks: t.get_last_scalar_metrics() task_reporting.get_logger().report_something

the latter is an ec2 instance

and the agent fails to install on the ec2 machine ?

GiddyTurkey39 do you have an experiment with the jupyter notebook ?

Are you seeing the entire jupyter notebook in the "uncommitted changes" section

I've seen that the file location of a task is saved

What do you mean by that? is it the execution section "entry point" ?

That is exactly that, the trains-agent is replicating the code from the git repo, and trying to apply the git diff (see uncommitted changes section). Obviously it failed 🙂

Hi @<1545216070686609408:profile|EnthusiasticCow4>

will ClearML remove the corresponding folders and files on S3?

Yes and it will ask you for credentials as well. I think there is a way to configure it so that the backend has access to it (somehow) but this breaks the "federated" approach

What's the clearml-server version ?

Then what happens is that 

Task.current_task()

 returns 

None

 for the pipeline's task...

Hmm that sounds like the pipeline Task was closed?! could that be? where (in the code) is the call to Task.current_task ?

SoreDragonfly16 could you reproduce the issue?
What's your OS? trains versions?

Hi ProudMosquito87
My apologies there is still no concrete ETA ...
That said I think a good toy example would really help accelerate this process.
How about opening a PR with a nice hydra example, then we can start discussing implementation details based on the toy example ?

Hi WackyRabbit7
So I'm assuming after the start_locally is called ?
Which clearml version are you using ?
(just making sure, calling Task.current_task() before starting the pipeline returns the correct Task?)

Just making sure, after the pipe object is created, you can call Task.current_task() , is that correct?

is removed from the experiment list?

You mean archived ?

Hurray ?

As we can’t create keys in our AWS due to infosec requirements

Hmmm

So the issue is that you have two reference branches on the local git, one to gitlab one to gitea and it fails to understand which on is the correct remote ...
I wonder if "git ls-remote --get-url" will always work ?!

Btw I sometimes get a gzip error when I am accessing artefacts via the '.get()' part.

Hmm this is odd, is this a download issue? if this is reproducible maybe we should investigate further...

GleamingGrasshopper63 can you ping to your api server ?
!ping api.server.hereAlso what's the api server you configured ? (ip:8008 ?)
Any chance this is a Local machine, i.e. the colab machine cannot get back into the clearml server cunning locally ?

Hi @<1715900788393381888:profile|BitingSpider17>
Notice that you need __ (double underscore) for converting "." in the clearml.conf file,
this means agent.docker_internal_mounts.sdk_cache will be CLEARML_AGENT__AGENT__DOCKER_INTERNAL_MOUNTS__SDK_CACHE
None

@<1546303293918023680:profile|MiniatureRobin9>

, not the pipeline itself. And that's the last part I'm looking for.

Good point, any chance you want to PR this code snippet ?

    def add_tags(self, tags):
        # type: (Union[Sequence[str], str]) -> None
        """
        Add Tags to this pipeline. Old tags are not deleted. 
        When executing a Pipeline remotely (i.e. launching the pipeline from the UI/enqueuing it), this method has no effect.

        :param tags: A li...

Ok, just my ignorance then? 

LOL, no it is just that with a single discrete parameter the strategy makes less sense 🙂

Thank you so much!

JitteryCoyote63 okay... but let me explain a bit so you get a better intuition for next time 🙂
The Task.init call, when running remotely, assumes the Task object already exists in the backend, so it ignores whatever was in the code and uses the data stored on the trains-server, similar to what's happening with Task.connect and the argparser.
This gives you the option of adding/changing the "output_uri" for any Task regardless of the code. In the Execution tab, change the "Output Destina...

Great!

I see what you mean.
an_optimizer = HyperParameterOptimizer( base_task_id='39d2c27baa8145929b2e21f686a17046', hyper_parameters=[], objective_metric_title='epoch_accuracy', objective_metric_series='epoch_accuracy', objective_metric_sign='max', optimizer_class=aSearchStrategy, max_iteration_per_job=0, total_max_jobs=0, auto_connect_task=False, ) print(an_optimizer.get_top_experiments(top_k=5))

PricklyRaven28 did you set the iam role support in the conf?
https://github.com/allegroai/clearml/blob/0397f2b41e41325db2a191070e01b218251bc8b2/docs/clearml.conf#L86

Hi @<1601023807399661568:profile|PompousSpider11>
Yes "activating" a conda/python environment in a docker is more complicated then it should be ...
To debug, what are you getting when you do:

docker run -it <docker name here> bash -c "set"