Exporter would be nice I agree, not sure it is on the roadmap at the moment 😞
Should not be very complicated to implement if you want to take a stab at it.

PleasantGiraffe85

it took the repo from the cache. When I delete the cache, it can't get the repo any longer.

what error are you getting ? (are we talking about the internal repo)

can you tell me what the serving example is in terms of the explanation above and what the triton serving engine is,

Great idea!

This line actually creates the control Task (2)
clearml-serving triton --project "serving" --name "serving example"
This line configures the control Task (the idea is that you can do that even when the control Task is already running, but in this case it is still in draft mode).
Notice the actual model serving configuration is already stored on the crea...

Meanwhile check CreateFromFunction(object).create_task_from_function(...)
It might be better suited than execute remotely for your specific workflow 🙂

TenseOstrich47 / PleasantGiraffe85
The next version (I think releasing today) will already contain scheduling, and the next one (probably RC right after) will include triggering. That said currently the UI wizard for both (i.e. creating the triggers), is only available in the community hosted service. That said I think that creating it from code (triggers/schedule) actually makes a lot of sense,

pipeline presented in a clear UI,

This is actually actively worked on, I think Anxious...

If Task.init() is called in an already running task, don’t reset auto_connect_frameworks? (if i am understanding the behaviour right)

Hmm we might need to somehow store the state of it ...

Option to disable these in the clearml.conf

I think this will be to general, as this is code specific , no?

Ohh sorry. task_log_buffer_capacity is actually internal buffer for the console output, on how many lines it will store before flushing it to the server.
To be honest, I can't think of a reason to expose / modify it...

Apologies on the typo ;)
There is also a global "running_remotely" but it's not on the task

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 ?

Now I need to figure out how to export that task id

You can always look it up 🙂
How come you do not have it?

Hi SubstantialElk6
You are uploading an artifact, a good use case for numpy artifact would be a feature table.
If you want to upload an image use either report_media or report_image or upload PIL image as artifact.
What do you think?

Hi CleanPigeon16
You need to be able access the machine running the agent, usually the default port will be 10022.
If you need further debug message, add --debug at the beginning of the clearml-session.
clearml-session --debug ...To get all the debug print, please upgrade to clearml-session==0.3.3

GreasyPenguin14

Is it possible in ClearML to have a main task (the complete cross validation) and subtasks (one for each fold)?

You mean to see it as nested in the UI? or Auto logged by the code ?

try to break it into parts and understand what produces the error
for example:
increase(test12_model_custom:Glucose_bucket[1m])
increase(test12_model_custom:Glucose_sum[1m])
increase(test12_model_custom:Glucose_bucket[1m])/increase(test12_model_custom:Glucose_sum[1m])
and so on

Are you running the agent in docker mode? or venv mode ?
Can you manually ssh on port 10022 to the remote agent's machine ?
ssh -p 10022 root@agent_ip_here

JitteryCoyote63

are the calls from the agents made asynchronously/in a non blocking separate thread?

You mean like request processing on the apiserver are multi-threaded / multi-processed ?

yup, it's there in draft mode so I can get the latest git commit when it's used as a base task

Yes that seems to be the problem, if it is in draft mode, you have no outputs...

I think you are onto a good flow, quick iterations / discussions here, then if we need more support or an action-item then we can switch to GitHub. For example with feature requests we usually wait to see if different people find them useful, then we bump their priority internally, this is best done using GitHub Issues 🙂

Hi @<1571308003204796416:profile|HollowPeacock58>

parameters = task.connect(config, name='config_params')

It seems that your DotDict does not support the python copy operator?
i.e.

from copy import copy
copy(DotDict())

fails ?

setting max_workers to 1 prevents the error (but, I assume, it may come the cost of slower sequential uploads).

This seems like a question to GS storage, maybe we should open an issue there, their backend does the rate limit

My main concern now is that this may happen within a pipeline leading to unreliable data handling.

I'm assuming the pipeline code will have max_workers, but maybe we could have a configuration value so that we can set it across all workers, wdyt?

If
...

Are the key/secret the one you see in the UI?

diff line by line is probably not useful for my data config

You could request a better configuration diff feature 🙂 Feel free to add to GitHub

But this also mean I have to first load all the configuration to a dictionary first.

Yes 😞

if it ain't broke, don't fix it

😄

Up to you, just a few features & nicer UI.
BTW: everything is backwards compatible, there is no need to change anything all the previous trains/trains-agent packages will work without changing anything 🙂
(This even includes the configuration file, so you can keep the current ~/trains.conf and work with whatever combination you like of trains/clearml on the same machine)

BTW: I think it was fixed in the latest trains package as well as the cleaml package

Hi JuicyFox94
you pointed to exactly the issue 🙂
In your trains.conf
https://github.com/allegroai/trains/blob/f27aed767cb3aa3ea83d8f273e48460dd79a90df/docs/trains.conf#L94

GrievingTurkey78
Both are now supported, they basically act the same way 🙂
and log overrides + the final omegaconf

😞 CooperativeFox72 please see if you can send a code snippet to reproduce the issue. I'd be happy to solve the it ...

simply record the type of each argument when you store it, and keep it in the database, unbeknownst to the user, what do you say?

This is now supported, but then you still need to flatten the dict.
Maybe we can just support "empty_dict/new_value = 42" if the original was "empty_dict = {}"
WDYT?

CooperativeFox72 you can you start by checking the latest RC :)
pip install trains==0.15.2rc0

Okay let me check if we can reproduce, definitely not the way it is supposed to work 😞