🙂

MagnificentPig49 that's a good question, I'll ask the guys 🙂
BTW, I think the main issues is actually making sure there is enough documentation on how to compile it...
Anyhow I'll update here

Hi, is there a way to force the requirements.txt?

You mean to ignore the "Installed Packages" ?

I guess that has nothing to do with the diff version, right ?

I found "scheduler" on allegroai github, is it something related to the case I want to make?

MoodyCentipede68 it is exactly what you are looking for 🙂
Do notice that you need to make sure you have your services queue configured and running for that to work 🙂

Thank you AttractiveWoodpecker16 !
Removing the uncommitted changes so that you can launch it from an agent? Or is it visual only?

I can raise this as an issue on the repo if that is useful?

I think this is a good idea, at least increased visibility 🙂
Please do 🙏

Hmm SuccessfulKoala55 any chance the nginx http was pushed to v1.1 on the latest cloud helm chart?

Hi JitteryCoyote63
could you check if the problem exists in the latest RC?
pip install clearml==1.0.4rc1

Is the agent itself registered on the clearml-server (a.k.a can you see it in the UI?)

Hi @<1523702786867335168:profile|AdventurousButterfly15>
I do not think they log more than that ?!
(what happens if you use TB?)

EnviousStarfish54 Sure, see scatter2d
https://allegro.ai/docs/examples/reporting/scatter_hist_confusion_mat_reporting/#2d-scatter-plots

how can I for example convert it back to a pandas dataframe?

You can always report csv file with report_media as well, or if this is not for debugging maybe an artifact ?

Your code should have worked, i.e. you should see the 'model.h5' in the artifacts tab. What do you have there?
It should look something like this one:
https://demoapp.trains.allegro.ai/projects/531785e122644ca5b85b2e19b0321def/experiments/e185cf31b2634e95abc7f9fbdef60e0f/artifacts/output-model

BTW:
To manually register any model:

from trains import Task, OutputModel task = Task.init('examples', 'my model') OutputModel().update_weights('my_best_model.h5')

Correct 🙂

When are those keys used?

They are the default keys for internal access, basically just make up something, otherwise someoune could access the server with the default keys

JitteryCoyote63 it should just "freeze" after a while as it will constantly try to resend logs. Basically you should be fine 🙂
(If for some reason something crashed, please let me know so we can fix it)

Sorry @<1657918706052763648:profile|SillyRobin38> I missed this reply

Is ClearML-Serving using either System or CUCA shared memory? O

This needs to be set on the docker-compose:
and I think this line actually includes ipc: host which means there is no need to set the shm_size, but you can play around with it and let me know if you see a difference
[None](https://github.com/allegroai/clearml-serving/blob/7ba356efc97a6ae2159283d198d981b3c1ab85e6/docker/docker-compose-triton-gpu.yml#L1...

Hi StraightDog31

I am having trouble using the 

StorageManager

  to upload files to GCP bucket

Are you using the storagemanager directly ? or are you using task.upload_artifact ?
Did you provide the GS credentials in the clearml.conf file, see example here:
https://github.com/allegroai/clearml/blob/c9121debc2998ec6245fe858781eae11c62abd84/docs/clearml.conf#L110

Nice! TrickySheep9 any chance you can share them ?

Hmm I think the easiest is using the helm chart:
https://github.com/allegroai/clearml-server-helm-cloud-ready
I know there is work on a teraform template, not sure about instio.
Is helm okay for you ?

Hmm is this similar to this one https://allegroai-trains.slack.com/archives/CTK20V944/p1597845996171600?thread_ts=1597845996.171600&cid=CTK20V944

Hmm what do you mean? Isn't it under installed packages?

Assuming this is a followup on:
https://clearml.slack.com/archives/CTK20V944/p1626184974199700?thread_ts=1625407069.458400&cid=CTK20V944

This depends on how you set it with the clearml-serving --endpoint my_model_entry
curl <serving-engine-ip>:8000/v2/models/my_model_entry/versions/1

should work as well 🙂

Hi ReassuredTiger98
It's clearml that needs to support subparser, and it does support it.
What are you seeing in the Args section ?
(Notice that at the end all the args parsing are stored on the global "args" variable after you call the pasre_args(), clearml will basically take those variables and put them into Args section)

if executed remotely...

You mean cloning the local execution, sending to the agent, then when running on the agent the Args/command is updated to a list ?

actually no

hmm, are those packages correct ?

(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)

BTW: how is it missing listing torch ? Do you have "import torch" in the code ?