Hi @<1523701205467926528:profile|AgitatedDove14> , thanks for coming back to my issue. Unfortunately I have a lot of other stuff on my desk right now so I have to postpone finishing this issue. I will reach out to you again as soon as possible (especially if I was able to find a solution).

Hi @<1523701205467926528:profile|AgitatedDove14> , now there are some interesting things happening: Like I wrote before I got the error message but one minute later the model was added successfully nonetheless. The log says

E0603 09:43:01.652550 41 model_repository_manager.cc:996] Poll failed for model directory 'test_model_pytorch': Invalid model name: Could not determine backend for model 'test_model_pytorch' with no backend in model configuration. Expected model name of the form 'mo...

Hello CostlyOstrich36 , thanks for your question. At the moment I am training a MLP for a regression problem and in one case I want to store the number of neurons per layer. Note that in my case it is not a hyperparameter because I calculate the number of neurons based on the number of layers and the number of model parameters. Another case is that I want to store some local paths where the models are stored, since I currently don't have any remote storage set up for my models.

Full log:
` Current configuration (clearml_agent v1.5.1, location: C:/Users/USER~1/AppData/Local/Temp/.clearml_agent.g6ysfs_g.cfg):

agent.worker_id = HPZBook:0
agent.worker_name = HPZBook
agent.force_git_ssh_protocol = false
agent.python_binary =
agent.package_manager.type = pip
agent.package_manager.pip_version.0 = <20.2 ; python_version < '3.10'
agent.package_manager.pip_version.1 = <22.3 ; python_version >= '3.10'
agent.package_manager.system_site_packages = false
...

Hi @<1523701087100473344:profile|SuccessfulKoala55> , thanks for your message! 🙂 I am aware that the console is also logged on the server, but I somehow find it not optimal to look for relevant information in the console log and would like to place the information in a more structured way.

I think you are correct with your guess that the services were not shut down properly. I noticed that some services were still shown as running on the clear ml dashboard. I aborted all and at least got rid of the error ValueError: triton-server process ended with error code 1 . But the two errors you named are still there and I also got these two warnings:
` clearml-serving-triton | Warning: more than one valid Controller Tasks found, using Task ID=4709b0b383a04bb1a033e99fd325dc...

Thank you, I did not think about that. It helped a lot! I found out that the problem causing the unicode error was, that only the 'python' command was set up on my windows machine, but not the 'python3' command. This was the exact error for documentation:

DEBUG:clearml_agent.commands.worker:Searching for python3
Traceback (most recent call last):
  File "C:\Users\User\venvs\clearml\lib\site-packages\clearml_agent\helper\process.py", line 204, in normalize_exception
    yield
  File "C:\...

Hi @<1523701323046850560:profile|OutrageousSheep60> , thanks for your message as well. So far I have actually been using these exact functions until I noticed the following: when I run a task with these calls, everything works as expected. However, if I do a hyperparameter tuning and change some of the hyperparameters so that the additional information that is not a hyperparameter also changes, they are not adjusted. For better understanding again my concrete example: I have 3 parameters/inf...

Hi @<1523701205467926528:profile|AgitatedDove14> , that is an interesting idea! But wouldn't it be better to load the model in the load() function, so that the model doesn't have to be loaded again with every request? Or is there kind of internal link that when the load() method is implemented it is expected that there was a custom model loaded and applied in the process() function?

Hi @<1523701205467926528:profile|AgitatedDove14> thanks for your answer! 🙂 I think my case is a bit different. I do not want to load a custom model but I want to load a custom object used for preprocessing. So I think the load method would not fit, as the local_file_name parameter I get in the load function would lead to the model file. And as far as I can see there is no mechanism installed to load other objects than the model file inside the Preprocess class, right?

Yes I am running the agent by calling clearml-agent daemon --queue default in my virtual environment on my local computer.

Hi @<1523701118159294464:profile|ExasperatedCrab78> , thanks for your answer. 🙂 Yes sure! I will create the issue right away.

Hi @<1523701205467926528:profile|AgitatedDove14> , thanks for your answer!
I reached over 1M API calls in about one week using clearml-serving on one machine with only calling the deployed model a few hundred times for testing purpose. So I wanted to dig a little bit deeper on that. Thanks for the channel suggestion, I will repost my question there. :)

Ok, I have found the issue. 🙌 When I try to serve a model which is saved on azure (generated by Task.init(..., output_uri='azure://...') ) I get the poll failed for model directory 'test_model_pytorch': failed to open text file for read /models/test_model_pytorch/config.pbtxt: No such file or directory error. A model which was saved on the clearML server (generated by Task.init(..., output_uri=True) ) can be served without any problems.
For now I am not sure why th...

Yes it is.

Hi @<1523701205467926528:profile|AgitatedDove14> thanks for your hint! I already convert it to torch script using tracing. Everything around the model should be fine, since it already worked with the docker clearml-serving setup.
I think the real issue is that I am not able to specify a platform for the model, as the error above tells me that no platform is given no matter how I try to pass it.

Hi @<1523701205467926528:profile|AgitatedDove14> , thanks for your answer I will check if I get that working!

Hi @<1523701205467926528:profile|AgitatedDove14> , I serialized a sklearn MinMaxScaler object which I created on the training data using pickle. So when serving the model I would like to load that pickle file in the preprocess script such that I can perform the same normalization as done during training. Unless there is a better practice applying the same normalization during training and serving time.

Hi @<1523701435869433856:profile|SmugDolphin23> , thanks for your question. For now I just deleted the requirements.txt and let ClearML track the requirements automatically and it works. For long term I would still like to use a requirements.txt, so I will come back to this topic a little later.

Hi @<1523701205467926528:profile|AgitatedDove14> you are right for the docker setup. But with the k8s setup I get the error Poll failed for model directory 'advanced_basic_classifier.pytorch': unexpected 'platform' and 'backend' pair, got:, pytorch when I do not specify the platform, which sounds like I should specify the platform.

Btw if I do not name the model after the 'model.<backend_name>' convention then I get this error
`Poll failed for model directory 'advanced_basic_classifi...

What do you mean by "How are you creating the model?"? I executed a pytorch model training saved a traced version of the model so that saved with the executed task. This was also no problem with the docker container setup.

My pre- and postprocessing code should be correct, because it already worked when I used the docker container clearml-serving setup. But in case you want to have a look, here it is:

Hi ExasperatedCrab78 , thanks for your answer! In fact I used your recommended format for passing input and output size before and changed it in my debugging process. I have just tried again but got the same error message.
Also thanks for the hint to check the log for warnings I wil do this in a moment.

Hi @<1523701118159294464:profile|ExasperatedCrab78> , I have a sad update on this issue. It does not seem to be completely solved yet. 😕 But I think I can at least describe it a bit better now:

• Models which are located on the clearML servers (created by Task.init(..., output_uri=True) ) still run perfectly.
• Models which are located on azure blob storage make different problems in different scenarios (which made me think we resolved this issue):- When I start the docker con...

You're very welcome, thank you again for the great support. :)) I followed the instructions of the clearml-serving README on github None . There is one section called 'Optional: advanced setup - S3/GS/Azure access'. Maybe the syntax could be added there? I also saw the additonal link to configure the storage access, but this site focuses on setting up the clearml.conf and I was not sure how and if I could transfer it to the docker .env-file.
A...

Ok, I have some weird update... I shut down and restarted the docker container just to get fresh logs and now I am getting the following error message by clearml-serving-triton
` clearml-serving-triton | clearml-serving - Nvidia Triton Engine Controller
clearml-serving-triton | Warning: more than one valid Controller Tasks found, using Task ID=433aa14db3f545ad852ddf846e25dcf0
clearml-serving-triton | ClearML Task: overwriting (reusing) task id=350a5a919ff648148a3de4483878...

I got the last bit of my issue solved. I thought for a start it would be easier to provide the AZURE_STORAGE_ACCOUNT and AZURE_STORAGE_KEY in my 'example.env' in plain text and not access my environment variables because I was not sure about the syntax. Turns out the syntax is not AZURE_STORAGE_KEY="mystoragekey123" but AZURE_STORAGE_KEY=mystoragekey123 . Same for AZURE_STORAGE_ACCOUNT . Also the syntax for accessing my environment variables is just the same as in the clear...

Hi @<1523701205467926528:profile|AgitatedDove14> , thanks for your answer! Can you tell me, how specifically I map my clearml.conf to the containers? By the way, the credentials are already set (and working) in the clearml.conf.

Hi @<1523701205467926528:profile|AgitatedDove14> , exactly!
I just tried the pytorch example from the clearml-serving repo and got the error about the wrong model name Poll failed for model directory 'test_model_pytorch': Invalid model name: Could not determine backend for model 'test_model_pytorch' with no backend in model configuration. Expected model name of the form 'model.<backend_name>'.

Ok, so I killed all docker containers (the proposal by chatgpt did not work for me, but your commands did). The result is that we have one less warning. The warning clearml-serving-triton | Warning: more than one valid Controller Tasks found, using Task ID=4709b0b383a04bb1a033e99fd325dcbf seems to be solved. All remaining errors come up in the clearml-serving-triton service and this is the log I get

CLEARML_SERVING_TASK_ID=9309c20af9244d919b0f063642198c57
CLEARML_TRITON_POLL...