Tried context provider for Task?

I guess that would only make sense inside notebooks ?!

Hi StaleKangaroo85 which trains version are you using ? Also which trains-server are you using?

Thanks @<1569496075083976704:profile|SweetShells3> ! let me see if I can reproduce the issue

Hi CooperativeFox72
I think the upload reporting (files over 5mb) was added post 0.17 version, hence the log.
The default is upload chunk reporting is 5MB, but it is not configurable, maybe we should add it to the clearml.conf ? wdyt?

there was a problem with index order when converting from pytorch tensor to numpy array

HealthyStarfish45 I'm assuming you are sending numpy to report_image (which makes sense) if you want to debug it, you can also test tensorboard add_image or matplotlib imshow. both will send debug images

(torchvision vs. cuda compatibility, will work on that),

The agent will pull the correct torch based on the cuda version that is available at runtime (or configured via the clearml.conf)

a task of queue B if the next task is of type A it will have to wait,

It seems you imply there are two types of Tasks and they need to be executed one after the other ?

ShallowCat10 Of course it is 🙂

PungentLouse55 hmmm
Do you have an idea on how we could quickly reproduce it?

conda list | grep matplotlib ?

BTW: if you need you can do the following:
` from clearml import Task
from clearml.automation import PipelineController

task = Task.init(project_name='pipelines', task_name='pipeline test')
task.set_base_docker(...)

the pipeline object is using the Current Task, hence docker image is set

pipe = PipelineController(...)

pipe.start() `

From creating the event to actually sending it ... 30 min sounds like enough "time"...

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)?