Hi SmugOx94
Hmm are you creating the environment manually, or is it done by Task.init ?
(Basically Task.init will store the entire environment of conda, and if the agent is working with conda package manager it will use it to restore it)
https://github.com/allegroai/clearml-agent/blob/77d6ff6630e97ec9a322e6d265cd874d0ab00c87/docs/clearml.conf#L50

DistressedGoat23
you can now access the weights model object
pip install 1.8.1rc0
then:
` def callback(_, model_info):
model_info.weights_object # this is your xgboost object
model_info.name = "my new name"
return model_info

WeightsFileHandler.add_pre_callback(callback) `

Hmm let me check something

LudicrousParrot69
I "think" I have a better handle on what you wish to do.
Is it kind of generic "serving" solution?
FYI:
Model artifact is, usually, a weights/model file. The idea that later you will be able to access it and serve it. Now the problem is (and I think this is what you are referring to) there is usually a specific piece of code tied to that model that can use it (a.k.a pyfunc)
A few ideas:
These days everyone is trying to build their models with generic interface, so that scik...

And no scalars ?

YEY!

Can't see any reason not to 🙂

JitteryCoyote63
Could you copy paste the "installed packages" section? the answer might be there

StorageManager is what you need, if you want to download/upload files to any server (this is a utility class the takes care of the DL/uL + adds caching) storage helper is used internally

What should have happened is the experiments should have been pending (i.e. in a queue)
(Not sure why they are not).
You can manually send them for execution , right click on an experiment in the able, select enqueue and select the default queue (This will be the one the trains-agent will pull from , by default)

A more detailed instructions:
https://github.com/allegroai/trains-agent#installing-the-trains-agent

MagnificentSeaurchin79 making sure the basics work.
Can you see the 3D plots under the Plot section ?
Regrading the Tensors, could you provide a toy example for us to test ?

Hi PanickyMoth78

My local

clearml.conf

file has agent's

git_user

and

git_pass

defined as in my

in order for the autoscaler to access your git , in the wizard you have to provide the git user/token

The component agent's log has:

Executing task id [90de043e354b4b28a84d5cc0788fe63c]: repository = branch = version_num =Hmm, how does the decorator of the component looks like ? meaning did you specify a repo/branch/commi...

okay, wait I'll see if I can come up with something .

Yep, and this is the root cause of the issue (But easily fixable) 🙂

Thanks!

We do upload the final model manually.

If this is the case just name it based on the parameters, no? am I missing soemthing?
https://github.com/allegroai/clearml/blob/cf7361e134554f4effd939ca67e8ecb2345bebff/clearml/model.py#L1229

I was just wondering if i can make the autologging usable.

It kind of assumes these are different "checkpoints" on the same experiment, and then stores them based on the file name
You can however change the model names later:
` Task.current_task().mo...

So it should cache the venvs right?

Correct,

path: /clearml-cache/venvs-cache

Just making sure, this is the path to the host cache folder

ClumsyElephant70 I think I lost track of the current issue 😞 what's exactly not being cached (or working)?

I can't seem to find a difference between the two, why would matplotlib get listed and pandas does not... Any other package that is missing?
BTW: as an immediate "hack" , before your Task.init call add the following:
Task.add_requirements("pandas")

BTW MagnificentSeaurchin79 just making sure here:

but I don't see the loss plot in scalars

This is only with Detect API ?

actually no

hmm, are those packages correct ?

Hi @<1540142641931358208:profile|FancyBaldeagle86>
You mean in the UI? i.e. clone an experiment hover over the Configuration / Hyperparameter section and clicking edit ?

Hi DepressedChimpanzee34 , took me a while but I think there is a solution:
In your docker file, replace:
https://github.com/allegroai/clearml-server/blob/a64c4d264d00eadd2d11818b37151d3cc6266d99/docker/docker-compose.yml#L5
with
entrypoint: /bin/bash command: -c "mkdir -p /var/log/clearml && cd /opt/clearml/ && python3 -m apiserver.apierrors_generator && gunicorn -w 4 -t 600 --bind=0.0.0.0:8008 apiserver.server:app"

Number of entries in the dataset cache can be controlled via cleaml.conf : sdk.storage.cache.default_cache_manager_size

CharmingBeetle38 try adding "General/" before the arguments. This means batch_size becomes General/batch_size. This is only because we are accessing the parameters externally, when the task is executed it is resolved automatically

for visibility, we continue here:
https://clearml.slack.com/archives/CTK20V944/p1671729558008189

JitteryCoyote63 could you send the log maybe ?

With pleasure 🙂

Hi @<1572395184505753600:profile|GleamingSeagull15>

Is there an official place to report bugs and add feature requests for the app.clear.ml website?

GitHub issues is usually the place, or the

Assuming GitHub, but just making sure you don't have another PM tool you'd rather use.

Really appreciate asking! it is always hard to keep track 🙏

GiganticTurtle0 your timing is great, the plan is to wrap-up efforts and release early next week (I'm assuming GitHub fixes will be pushed tomorrow I'll post here once they are there)