Can you send the console output of this entire session please ?

Yes this is a misleading title

What is the difference to 

file_history_size

Number of unique files per titles/series combination (aka how many images to store in the history, when the iteration is constantly increasing)

Thanks ScantChimpanzee51 !
Let me see what I can find, should be easy enough to fix now 🙂

Hi ExuberantParrot61
Is the pipeline logic code running from inside the repo?

Sorry if it's something trivial. I recently started working with ClearML.

No worries, this has actually more to do with how you work with Dask
The Task ID is the unique id of the any Task in the system (task.id will return the UID str)
Can you post a toy Dash code here, I'll explain how to make it compatible with clearml 🙂

Ohh yes, if the execution script is not on git and git exists, it will not add it (it will add it if it is in a tracked file via the uncommitted changes section)
ZanyPig66 in order to expand the support to your case. Can you explain exactly which files are on git and which are not?

Hi @<1603198134261911552:profile|ColossalReindeer77>
When you select poetry as package manager the agent passes control to poetry, this means poetry needs to decide on hte correct torch wheel based on your cuda. I do not think poetry can do that, but I do think you can specify the extra index url to take the torch wheel from:
None

Okay found it 🙂 it returns 11020 instead of 112

WickedGoat98 the mechanism of cloning and parameter overriding is working only when the trains-agent is launching the experiment. Think of it this way:
Manual execution: trains sends data to server
Automatic (trains-agent) execution: trains pulls data from the server
This applies for both the argparse and connect and connect configuration.
The trains code itself is acting differently when it is executed from the 'trains-agent' context.
Does that help clear things ?

FiercePenguin76 in the Tasks execution tab, under "script path", change to "-m filprofiler run catboost_train.py".
It should work (assuming the "catboost_train.py" is in the working directory).

Copy paste it here 🙂

Hi PanickyMoth78
Hmm it I think it might be that it overrides it with the environment variables it sets ...
optional one, add:
sdk.development.default_output_uri: " "https://github.com/allegroai/clearml-agent/blob/d96b8ff9068233103053bfe8305fb88274c2c9bf/docs/clearml.conf#L404
Option two (which should work as well):
environment { CLEARML_FILES_HOST: " " }https://github.com/allegroai/clearml-agent/blob/d96b8ff9068233103053bfe8305fb88274c2c9bf/docs/clearml.conf#L421

🤞

task.models["outputs"][-1].tags (plural, a list of strings) and yes I mean the UI 🙂

I get the n_saved what's missing for me is how would you tell the TrainsLogger/Trains the current one is the best? Or are we assuming the last saved model is always the best ? (in that case there is no need for tag, you just take the last in the list)

If we are going with: "I'm only saving the model if it is better than the previous checkpoint" then just always use the same name i.e. " http:/...

We are planning an RC later this week, I'll make sure this fix is part of it

Basically what I want is a 

clearml-session

 but with a docker container running JupyterHub instead of JupyterLab.

I missed that 🙂

The idea of clearml-session is to launch a container with jupyterlab (or vscode) on a remote machine, and connect the users machines (i.e. the machine executed the clearml-session CLI) directly into the container.

Pleacing the jupyterlab with JupyterHub will be meaningless here, becuase the idea it spins an instance (contai...

I just called exit(0) in a notebooke and it closed it (the kernel) no exception

Wait so how come the "clearml" package is not installed ? can you provide the full log?

WickedGoat98 if this is the case, you can check this example. Same idea only "manual":
https://github.com/allegroai/trains/blob/master/examples/automation/task_piping_example.py

Hi FantasticPig28

or does every individual user have to configure their own minio credentials?

You can configure the clients files entry in the clearml.conf (or use an OS environment)
files_server: " "https://github.com/allegroai/clearml/blob/12fa7c92aaf8770d770c8ed05094e924b9099c16/docs/clearml.conf#L10
Notice to make sure you also provide credentials here:
https://github.com/allegroai/clearml/blob/12fa7c92aaf8770d770c8ed05094e924b9099c16/docs/clearml.conf#L97

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

Thanks for the ping ConvolutedChicken69 , I missed it 😞

from what i see in the docs it's only for Jupyter / VS Code, i didn't see anything about pycharm

PyCharm is basically SSH, which is supported 🙂
(Maybe we should mention it in the docs?)

StorageManager 🙂

the optimizer such that the study object of the optimizer keeps track of the results and the next sample will be aware of all previous studies

This is done from the optimizer side, by sampling the scalars reported by any experiment the optimizer created.

I am looking for a way to manually sample and report from and to the optimizer...
.. I can avoid running unnecessary common heavy setup, for a light weight experiment

Maybe it makes sense to inherit from the Optimizer and add ...

will my datasets be stored on the same machine that hosts the clearml server?

By default yes, they will be stored to the files-server (but you can change it, this is an argument for both the CLI and the python interface)

Hi RoughTiger69
Interesting question, maybe something like:

` @PipelineDecorator.component(...)
def process_sub_list(things_to_do=[0,1,2]):
r = []
for i in things_to_do:
print("doing", i)
r.append("done{}".format(i))
return r

@PipelineDecorator.pipeline(...)
def pipeline():

create some stuff to do:

results = []
for step in range(10):
r = process_sub_list(list(range(step*10, (step+1)*10)))
results.append(r)

push into one list with all result, this will ac...

Hi RipeGoose2
Could you expand on "inconsistency in the iteration reporting" ? Also "calling trainer.fit multiple" would you expect it to show as a single experiment or is it kind of param search ?

Thanks!!

ShaggyHare67 could you send the console log trains-agent outputs when you run it?

Now the 

trains-agent

 is running my code but it is unable to import 

trains

Do you have the package "trains" listed under "installed packages" in your experiment?