I created my own docker image with a newer python and the error disappeared

I'm not sure I understand how that solved it?!

Depending on your security restrictions, but generally yes.

HandsomeCrow5 OMG the guys already added it to the debug samples as well, checkout the demo app (drop down "test html sample"):
https://demoapp.trains.allegro.ai/projects/4e7fef090aa849b1acc37d92b59b3360/experiments/83c9ed509f0e421eaadc1ef56b3af5b4/info-output/debugImages

No, an old experiment changed, nothing was rerun

ohh, that is odd. I think the max iteration value is stored on the DB, which is odd if it changed after an update.
BTW: just making sure, could it be these Tasks were imported ? (i.e. offline execution + import)

Ok i did a pip install -r requirements.txt and NOW it picks them up correctly
So packages have to be installed and not just be mentioned in requirements / imported?

Yes, it looks for them locally so it has all the specific versions you need.
If the "installed packages" is totally empty the agent will revert to looking for requirements.txt inside the repository.

@<1545216077846286336:profile|DistraughtSquirrel81> shoot an email to "support@clear.ml" and provide all the information you can on the "lost account" (i.e. the one you had the data on), this means email account that created it (or your colleagues emails), and any other information that might help to locate it.

it handles 2FA if my repo lies in Github and my account needs 2FA to sign in

It does not 😞

poetry

 stores git related data in ... you get an internal package we have with its version, but no git reference, i.e. 

internal_module==1.2.3

 instead of 

internal_module @H4dr1en

This seems like a bug with poetry (and I think I have run into this one), worth reporting it, no?

Hmmm maybe 

 I thought that was expected behavior from poetry side actually

I think this is the expected behavior, hence bug?!

task.set_script(working_dir=dir, entry_point="my_script.py")Why do you have this part? isn't it the same code, the script entry point is auto detected ?

... or when I run my_script.py locally (in order to create and enqueue the task)?

the latter, When the script is running locally

So something like

os.path.join(os.path.dirname(file), "requirements.txt")

is the right way?

Sure this will work 🙂

My question is what should be the path to the requirements.txt file?
Is it relative to the repo base?

This is actually in runtime (i.e. when running the code), so relative to the working directory. Make sense ? (you can specify absolute path, probably something I would avoid in the code base though...)

Hi OddAlligator72

it 

 that they do not support PBT.

The optimization algorithm themselves are usually external (although the trivial stuff are in within Trains)
Do you have a specific PBT implementation you are considering ?

MysteriousBee56 when you execute your code once it will appear in the server (with all fields pre-populated based on your setup/git etc.) once it is there you can "clone" them and move them around.
Is this what you mean?
A bit of background, the idea behind Trains is that the environment definition (i.e,. git repo packages etc, code entry arguments etc.) is collected when executing the code. This avoids the tedious task of generating and maintaining YAML/Json configuration files.
What is exa...

clearml-agent  repo please 🙂

Nice workaround!
RoughTiger69 how do I reproduce this behavior? (I'm still unsure on why exactly the clearml binding broke it, and would like to fix that)
(can you also provide the crash trace, maybe that could help as well)

Hi @<1523707131994312704:profile|CrabbyKoala94>

I wanted to use method Task.reset() or Task.delete() however none of that seems to be able to delete

only

the logs in the "console" section in the UI.

So Task.reset will reset the entire outputs of the Task (and the status), as you noticed. Why would you want to just remove the logs?
You can disable the auto logs altogether if you really want to, see Task.init [auto_connect_streams](https://github.com/allegroai/cl...

BTW, 

 has this at the bottom:

Yes, it is the company legal entity name. But I think that for refrencing it makes more sense to mention the product name ClearML

I think this looks good 🙂

Hi @<1535069219354316800:profile|PerplexedRaccoon19>
What do you mean by simulate?
You can manually setup and run a Task if you need,
'clearml-agent execute --id task_id' add --docker for docker mode.
This will setup the env and run the task

For example, the

Task

object is heavily overloaded and its documentation would benefit from being separated into logical units of work. It would also make it easier for the ClearML team to spot any formatting issues.

This is a very good point (the current documentation is basically docstring, but we should create a structured one)

... but some visualization/inline code with explanation is also very much welcome.

I'm assuming this connected with the previous po...

Yes that makes total sense to me. How about a GitHub issue on the clearml-docs ?

If possible, can we have a "only one experiment can be given a single tag"

You mean "moving a tag" automatically (i.e. if someone else had the same tag it is removed from it)?

Very lacking wrt to how things interact with one another

If I'm reading it correctly, what you are saying is that some of the "big picture" / holistic approach on how different parts interact with one another is missing, is that correct?

I think ClearML would benefit itself a lot if it adopted a documentation structure similar to numpy ecosystem

Interesting thought, what exactly would you suggest we "borrow" in terms of approach?

Example Task.get_task(..., task_filter={'tags': ['best'], 'order_by': ["-last_update"]})

still it is a chatgpt interface correct ?

Actually, no. And we will change the wording on the website so it is more intuitive to understand.
The idea is you actually train your own model (not chatgpt/openai) and use that model internally, which means everything is done inside your organisation, from data through training and ending with deployment. Does that make sense ?

That is correct. Unfortunately though this is not part of the open source, this means that for the open source it might be a bit more hands-on to deploy an llm model

GorgeousSeagull44 I think this should have worked (basically replacing all the links on the mongo DB with the new IP)

What's the error you are getting ?

JitteryCoyote63 next week is the Trains next release with upgrade to ES 7, do you want to wait or sort a solution for this one ?
(BTW: I think that you can mount a license file or delete one, and it should be okay, I'll ask the backend guys regradless)

JitteryCoyote63 This seems like exactly what you are saying, elastic license issue...

My bad, I worded my question wrong I see,

LOL no worries 🙂

Any chance you have some "debug" leftover in the Pipeline code:
https://github.com/allegroai/clearml/blob/7016138c849a4f8d0b4d296b319e0b23a1b7bd9e/examples/pipeline/pipeline_from_decorator.py#L113

Maybe we should show a warning when we it is being called, or ignore it when running via an agent ...