Just for reference, the main issue is that ClearML does not allow non-string types as dict keys for its configuration. Usually the labeling mapping does have ints as keys. Which is why we need to cast them to strings first, then pass them to ClearML then cast them back.

The point of the alias is for better visibility in the Experiment Manager. Check the screenshots above for what it looks like in the UI. Essentially, setting an Alias makes sure the task that is getting the dataset automatically logs the ID that it gets using Dataset.get() . The reason being that if you later on look back to your experiment, you can also see what dataset was .get() 't back then.

ExuberantBat52 When you still get the log messages, where did you specify the alias?...

In the meantime, it might help to limit the amount of jobs using the advanced settings. If you know the exact amount and want to do every one for sure, just set it that way 🙂

Hi @<1540142651142049792:profile|BurlyHorse22> I think I know what is happening. So, ClearML does not support having dict keys by any other type than string. This is why I made these functions to cast the dict keys to string and back after we connect them to clearml.

What happens I think is that [id2label](https://github.com/thepycoder/sarcasm_detector/blob/dbbddec35...

Wow! Awesome to hear :D

Hi @<1529633462108033024:profile|SucculentCrab55> ! In which step do you get this error? I assume get_data? Does it work locally?

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 .

And maybe also what kind of docker install do you have given that you changed off of snap?

Hi VictoriousPenguin97 ! I think you should be able to change it in the docker-compose file here: https://github.com/allegroai/clearml-server/blob/master/docker/docker-compose.yml

You can map the internal 8008 port to another port on your local machine. But beware to provide the different port number to any client that tries to connect (using clearml-init )

effectively making us lose 24 hours of GPU compute

Oof, sorry about that, man 😞

It shouldn't quite take 4 minutes. We did just have a server update so maybe there are some delays from there?

That said, it will take a little time, even if the code itself is very simple. This is a fixed cost overhead of ClearML analysing your runtime environment, packages etc.
This runs async though, so when the code itself takes more time, you won't notice this fixed cost as much, of course with just printing text it will be the majority of the runtime 🙂

Hi EmbarrassedSpider34 , would you mind showing us a screenshot of your machine configuration? Can you check for any output logs that ClearML might have given you? Depending on the region, maybe there were no GPUs available, so could you maybe also check if you can manually spin up a GPU vm?

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!

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.

Ok, no problem! Take your time, I think I can help you, but I don't understand yet 🙂

As you can see in the issue comments, for now you can report it using iteration=0 and then adding the value as a field in the experiment overview (as leaderboard). This will give you a quick overview of your metrics per experiment in the main experiment list 🙂

Now worries! Just so I understand fully though: you were already using the patch with success from my branch. Now that it has been merged into transformers main branch you installed it from there and that's when you started having issues with not saving models? Then installing transformers 4.21.3 fixes it (which should have the old clearml integration even before the patch?)

No inputs and outputs are ever set automatically 🙂 For e.g. Keras you'll have to specify it using the CLI when making the endpoint, so Triton knows how to optimise as well as set it correctly in your preprocessing so Triton receives the format it expects.

I think that would defeat the purpose of lineage no? The point is to keep track of where data came from in the real world. Rewriting that record is just kind of... metadata?
As for the (*) line, could it be that "0385db..." itself does not have parents itself? So "0385db..." is the base dataset, without parents, and it has 1 child, which has "0385db..." as its parent

Great to know you found it 😄

Hi ExuberantParrot61 ! Can you try using a wildcard? E.g. ds.remove_files(dataset_path='folder_to_delete/*')

You can apply git diffs by copying the diff to a file and then running git apply <file_containing_diff>

But check this thread to make sure to dry-run first, to check what it will do, before you overwrite anything
https://stackoverflow.com/questions/2249852/how-to-apply-a-patch-generated-with-git-format-patch

Does it help to also run docker login in the init bash script?
You should be able to access your AWS credentials from the environment (the agent will inject them based on your config)

Are you running a self-hosted/enterprise server or on app.clear.ml? Can you confirm that the field in the screenshot is empty for you?

Or are you using the SDK to create an autoscaler script?

Ok I check 3: The command
clearml-serving --id <your_id> model add --engine triton --endpoint "test_model_keras" --preprocess "examples/keras/preprocess.py" --name "train keras model" --project "serving examples" --input-size 1 784 --input-name "dense_input" --input-type float32 --output-size -1 10 --output-name "activation_2" --output-type float32should be
` clearml-serving --id <your_id> model add --engine triton --endpoint "test_model_keras" --preprocess "examples/keras/preprocess.py" ...

Just to be sure I understand you correctly: you're saving/dumping an sklearn model in the clearml experiment manager, then want to serve it using clearml serving, but you do not wish to specify the model input and ouput shapes in the CLI?

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

Don't paste your API keys! 🙈

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?