The built in HPO uses tags to group experiment runs together and actually use the original optimizer task ID as tag to be able to quickly go back and see where they came from. You can find an example in the ClearML Examples project.

Yes, with docker auto-starting containers is def a thing 🙂 We set the containers to restart automatically (a reboot will do that too) for when the container crashes it will immediately restarts, let's say in a production environment.

So the best thing to do there is to use docker ps to get all running containers and then kill them using docker kill <container_id> . Chatgpt tells me this command should kill all currently running containers:
docker rm -f $(docker ps -aq)And I...

Wow! Awesome to hear :D

This update was just to modernize the example itself 🙂

Hi ExasperatedCrocodile76

In terms of the Alias, yes, you don't need to specify one. Recently we changed things that if you do add an alias, the dataset alias and ID will automatically be added in the experiment manager. So it's more a print that says: "hey, you might want to try this!"

Second, this is my own version of the example that's no longer maintained. The official version is here: https://github.com/allegroai/clearml-blogs/tree/master/urbansounds8k
We changed the link in that v...

Can you elaborate a bit more, I don't quite understand yet. So it works when you update an existing task by adding a tag to it, but it doesn't work when adding a tag for the first time?

Hey @<1523701949617147904:profile|PricklyRaven28> I'm checking! Have you updated anything else and on which exact commit of transformers are you now?

Hey @<1523701949617147904:profile|PricklyRaven28> , So as discussed above there were 2 issues. The first one is still waiting on the second, it's on the backlog of our devs and should be done soon(tm).

That said, in the meantime I also wanted to do fun stuff with transformers, so I've written a quick hack that deals with the bug. It's bascially 2 functions that keep track of which types of keys are in the dict.

def cast_keys_to_string(d, changed_keys=dict()):
    nd = dict()
    for k...

Hi! You should add extra packages in your docker-compse through your env file, they'll get installed when building the serving container. In this case you're missing the transformers package.

You'll also get the same explanation here .

Allright, a bit of searching later and I've found 2 things:

• You were right about the task! I've staged a fix here . It basically detects whether a task is already running (e.g. from the pipelinedecorator component) and if so, uses that task instead. We should probably do this for all of our integrations.
• But then I found another bug. Basically the pipeline decorator task wou...

It should, but please check first. This is some code I quickly made for myself. It did make tests for it, but it would be nice to hear from someone else that it worked (as evidenced by the error above 😅 )

Hey! Sorry, didn't fully read your question and missed that you already did it. It should not be done inside the clearm-serving-triton service but instead inside the clearml-serving-inference service. This is where the preprocessing script is ran and it seems to be where the error is coming from.

If they don't want to use ClearML, why not just run docker run ... from the command line? Does he want to use the queueing system without using a clearml task?

Don't paste your API keys! 🙈

VivaciousBadger56 Thanks for your patience, I was away for a week 🙂 Can you check that you properly changed the project name in the line above the one you posted?

In the example, by default, the project name is "ClearML Examples/Urbansounds". But it should give you an error when first running the get_data.py script that you can't actually modify that project (by design). You need to change it to one of you own choice. You might have done that in get_data.py but forgot to do s...

Thanks! I've asked this to the autoscaler devs and it might be a possible bug, you are the second one. He's checking and we'll come back to you!

Yeah, I do the same thing all the time. You can limit the amount of tasks that are kept in HPO with the save_top_k_tasks_only parameter and you can create subprojects by simply using a slash in the name 🙂 https://clear.ml/docs/latest/docs/fundamentals/projects#creating-subprojects

It seems to be up for me

When I run the example this way, everything seems to be working.

That makes sense! Maybe something like dataset querying as is used in the clearml hyperdatasets might be useful here? Basically you'd query your dataset to only include sample you want and have the query itself be a hyperparameter in your experiment?

Hi GrittyHawk31 ! ClearML is integrated with a bunch of frameworks from which it tries to automatically gather information. You can find a list here: https://clear.ml/docs/latest/docs/integrations/libraries

For example, if you're already reporting scalars to tensorboard, you won't have to add any clearml code, it will automatically be captured. The same will happen with e.g. LightGBM. Take a look at the example codes in the link to find what is automatically supported for your framework.
...

That might make things harder indeed 🙂 It does explain some things

Hey ExasperatedCrocodile76 ! Thanks for checking back in and letting me know 😄 Glad I could help!

VivaciousBadger56 Thank you for the screenshots! I appreciate the effort. You indeed clicked on the right link, I was on mobile so had to instruct from memory 🙂

First of all: every 'object' in the ClearML ecosystem is a task. Experiments are tasks, so are dataset versions and even pipelines! Each task can be viewed using the experiment manager UI, that's just how the backend is structured. Of course we keep experiments and data separate by giving them a separate tab and different UI, but...

Could you try and create a new task with the tag already added? Instead of adding a tag on an existing task. It should work then. If it does, this might be a bug? Or if not, a good feature to exist 🙂

AdventurousButterfly15 The fact that it tries to ping localhost means you are running the ClearML server locally right? In that case, it is a docker thing: it cannot access localhost because localhost inside a docker image is not the same one as your machine itself. They're isolated.

That said, adding --network=host to the docker command usually fixes this by connecting the container to the local network instead of the internal docker one.

You can add a custom argument either i...

Like Nathan said, custom engines are a TODO, but for your second question, you can add that API request in the model preprocessing, which is a function you can define yourself! It will be ran every time a request comes in and you can do whatever you want in it and change the incoming data however you wish 🙂

example: https://github.com/allegroai/clearml-serving/blob/main/examples/keras/preprocess.py

Also, please note that since the video has been uploaded, the dataset UI has changed. So now a dataset will be found under the dataset tab on the left instead of in the experiment manager 🙂

am I getting it right that alias = dataset id which can be found in the clearml dashboard?

Not really. It's Alias + dataset_id that will be found in the clearml dashboard 🙂 I'm attaching a screenshot of what that looks like in both the code and the dashboard

The scheduler just downloads a dataset using the ID right? So if you don't upload a new dataset, the scheduler is just downloading the dataset from the last known ID then. I don't really see how that could lead to a new dataset with it's own ID as the parent. Would you mind explaining your setup in a little more detail? 🙂