if a provide a PR, there I don’t see any CI processes in place that will verify the correctness of my code.

well, I first run clearml-session to start everything on the remote machine, then I close the local process (while Interactive is still running on the remote machine)

also, I don’t see an edit button near input models

it certainly does not use tensorboard python lib

log:
[2021-09-09 11:22:09,339] [8] [WARNING] [clearml.service_repo] Returned 400 for tasks.dequeue in 2ms, msg=Invalid task id: id=28d2cf5233fe41399c255950aa8b 8c9d,company=d1bd92a3b039400cbafc60a7a5b1e52b

Thanks for the answer! Registering some metadata as a model doesn’t feel correct to me. But anyway this is certainly not a show-stopper. Just wanted to clarify.

docker: nvidia/cuda:11.2.1-cudnn8-runtime-ubuntu20.04
jupyterlab 3.0.11
clearml lib 0.17.5
no warnings
2021-03-24 17:55:44,672 - clearml.Task - INFO - No repository found, storing script code instead

like replace a model in staging seldon with this model from clearml; push this model to prod seldon, but in shadow mode

it is missing in CLI, but I was able to set external_ssh_port and external_address in GUI. It was certainly a step forward, but still failed

“assuming the “catboost_train.py” is in the working directory” - maybe I get this part wrong?

Congrats on the release! Are you planning to release a roadmap so that the community would know what to expect next from ClearML?

Although it is only for model tracking, autologging is yet to be implemented there

I think they appeared when I had a lot of HPO tasks enqueued and not started yet, and then I decided to either Abort or Archive them - I don’t remember already

I tried this, but didn’t help:
input_models = current_task.models["input"] if len(input_models) == 1: input_model_as_input = {"name": input_models[0].name, "type": ModelTypeEnum.input} response = current_task.send(DeleteModelsRequest( task=current_task.task_id, models=[input_model_as_input] ))

AgitatedDove14 I did exactly that.

I see that scheduler task UI has the capabilities to edit user properties. But I don’t see how I can read and/or write them through code

when I go into Dataset.list_datasets with the debugger and remove system_tags=[“dataset”] from api call params - I get the correct response back

so probably, my question can be transformed into: “Can I have control over what command is used to start my script on clearml-agent”

“To have the Full pip freeze as “installed packages” - that’s exactly what I’m trying to prevent. Locally my virtualenv has all the dependencies for all the clearml tasks, which is fine because I don’t need to download and install them every time I launch a task. But remotely I want to keep the bare minimum needed for the concrete task. Which clearml successfully does, as long as I don’t import any local modules.

I had a bunch of training tasks each of which outputted a model. I want to apply each one of them to a specific dataset. I have a clearml task ( apply_model ) for that, which takes dataset_id and model-producing task_id as input. First time I initiate apply model by hardcoding ids and starting the run from my machine (it is then goes into cloud, when it reaches execute_remotely )

I can try, but difficult to verify correctness without a publicly available test suite

mostly the transformation of the pandas Dataframe - how the columns are added/removed/change types, NAs removed, rows removed etc

"system_tags": [ "development" ],

not a full log yet (will have to inspect it to not have any non-public info), but something potentially interesting

if the task is of wrong type (not data_processing) - then it’ll get both correct type and correct system tag

system tags cannot be seen from UI. right?

I guess so, lemme double-check

“VSCode running locally connected to the remote machine over the SSH” - exactly

here: https://github.com/allegroai/clearml/blob/master/clearml/datasets/dataset.py#L75