Verified, and already fixed with 1.0.6rc2

LethalDolphin75 Yes you are correct, we should add here:
https://github.com/allegroai/clearml/blob/400c6ec103d9f2193694c54d7491bb1a74bbe8e8/clearml/automation/optuna/optuna.py#L210
elif isinstance(p, UniformLogarithmicParameterRange): hp_type = 'suggest_float' hp_params = dict(low=p.min_value, high=p.max_value if p.include_max else p.max_value - p.step_size, log=True, step=p.step_size)btw: I'm not sure if the ...

MagnificentSeaurchin79
"requirements.txt" is ignored if the Task has an "installed packges" section (i.e. not completely empty) Task.add_requirements('pandas') needs to be called before Task.init() (I'll make sure there is a warning if called after)

Is there a way to connect to the task without initiating a new one without overriding the execution?

You can, but not with automagic, you can manually send metrics/logs...
Does that help? or do we need the automagic?

Why does ClearML hide the dataset task from the main WebUI?

Basically you have the details from the Dataset page, why should it be mixed with the others ?

If I specified a project for the dataset, I specifically want it there, in that project, not hidden away in some

.datasets

hidden sub-project.

This maybe a request for "Dataset" tab under project, why would you need the Dataset Task itself is the main question?

Not all dataset objects are equal, and perhap...

Hi UnevenDolphin73

Is there an easy way to add a link to one of the tasks panels? (as an artifact, configuration, info, etc)?

You can add a link as an artifact, that is probably the easiest:
tasl.upload_artifact(name="just link", artifact_object=" ")

EDIT: And follow up regarding the dataset. As discussed somewhere previously, the datasets are now automatically moved to a hidden "sub-project" prefixed with

.datasets

. This creates several annoyances that I...

What if I have multiple files that are not in the same folder? (That is the current use-case)

I think you can do weights_filenames= ['a_folder/firstfile.bin', 'b_folder/secondfile.bin']
(it will look for a common file path for both so it retains the folder structure)

Our workaround now for using a

Dataset

as we do, is to store the dataset ID as a configuration parameter, so it's always included too

Exactly, so with Input Model it's the same only kind of ...

We are working hard on release 1.7 once that is out we will push an RC for review (I hope) 🙂

For now we've monkey-patched it to our usecase:

LOL, that's a cool hack

That gives us the benefit of creating "local datasets" (confined to the scope of the project, do not appear in

Datasets

tabs, but appear as normal tasks within the project)

So what would be a "perfect" solution here?
I think I'm missing the point on why it became an issue in the first place.
Notice that in new versions Dataset will be registered on the Tasks that use them (they are already...

The current implementation (since 1.6.3 I think) creates the issues in the linked comment (with images to visualize).

Understood, basically the moment we add nested project view to the dataset (and pipelines for that matter, and both are already being worked on), it should solve everything. Is that correct?

Yes. Because my old

has never been resolved (though closed), we use the dataset object to upload e.g. local files needed for remote execution.

Ohh No I remember... following this line, can I assume these files are reused, i.e. this is not a "per instance" . I have to admit that I have a feeling this is a very unique usecase. and Maybe the "old" way Dataset were shown is better suited ?

No, I mean why does it show up in the task view (see attached image), forcing me to clic...

I'll give it a shot. Honestly, the SDK documentation for both InputModel and OutputModel is (sorry)

horrible

...

I have to agree, we are changing this interface, I do not think it is good 😞

LOL love that approach.
Basically here is what I'm thinking,
` from clearml import Task, InputModel, OutputModel

task = Task.init(...)

run this part once

if task.running_locally():
my_auxiliary_stuff = OutputModel()
my_auxiliary_stuff.system_tags = ["DATA"]
my_auxiliary_stuff.update_weights_package(weights_path="/path/to/additional/files")
input_my_auxiliary = InputModel(model_id=my_auxiliary_stuff.id)
task.connect(input_my_auxiliary, "my_auxiliary")

task.execute_remotely()
my_a...

A definite maybe, they may or may not be used, but we'd like to keep that option

The precursor to the question is the idea of storing local files as "input artifacts" on the Task, which means that if the Task is cloned the links go with it. Let's assume for a second this is the case, how would you upload these artifacts in the first place?

Hmm, maybe the right way to do so is to abuse "models" which have entity, you can specify a system_tag on them, they can store a folder (and extract it if you need), they are on projects and they are cloned and can be changed.
wdyt?

Correct 🙂
I'm assuming the Task object is not your Current task, but a different one?

now i cant download neither of them

would be nice if address of the artifacts (state and zips) was assembled on the fly and not hardcoded into db.

The idea is this is fully federated, the server is not actually aware of it, so users can manage multiple storage locations in a transparent way.

if you have any tips how to fix it in the mongo db that would be great ....

Yes that should be similar, but the links would be in artifact property on the Tasks object
not exactly...

I was expecting the remote experiment to behave similarly, why do I need to import pandas there?

The only problem os that the remote code did not install pandas , once the package is there we can read the artifacts
(this is in contrast to the local machine where pandas is installed and so we can create/read the object)
Does that make sense ?

DrabOwl94 how many 1M files did you end up having ?

I reached over 1M API calls in about one week using clearml-serving

Oh that makes sense now 🙂
If I remember correctly, adding an additional model to a signal clearml-serving instance should not actually change the number of API calls, they are mostly affected by the number of clearml-serving / containers and not in the number of models.

RobustGoldfish9
I think you need to set the trains-agent docker to be aware of the host, so it knows how to mount data/cache/configurations into the sibling docker

It should look something like:
TRAINS_AGENT_DOCKER_HOST_MOUNT="/mnt/host/data:/root/.trains"So if running a docker:
docker run -e TRAINS_AGENT_DOCKER_HOST_MOUNT="/mnt/host/data:/root/.trains" ...

SmallBluewhale13 the final path is automatically generated, you only need to specify the bucket itself. By default it will be your "files_server"
https://github.com/allegroai/clearml/blob/c58e8a4c6a1294f8acec6ed9cba81c3b91aa2abd/docs/clearml.conf#L10
You can either change the configuration (which will make sure All uploaded artificats will always be there, including debug images etc.)
You can specify where you want the artifacts and debug images to be uploaded by setting:
https://allegro....

Hi NonchalantGiraffe17
You mean this documentation?
https://clear.ml/docs/latest/docs/references/api/tasks#post-tasksclone

How are you getting:

beautifulsoup4 @ file:///croot/beautifulsoup4-split_1681493039619/work

is this what you had on the Original manual execution ? (i.e. not the one executed by the agent) - you can also look under "org _pip" dropdown in the "installed packages" of the failed Task

Hi ShinyWhale52
Every execution of the pipeline (by definition) will create a new job based on the pipeline steps
This is the reason you see all the steps twice (the default assumption is you wish to re-run the step, as this is part of the processing workflow (e.g. training a model)

the model has been overwritten. I guess this is due to this instruction:

This is because you are storing it locally to the same path, it just reflects the fact you just overwrote your model.
To create a...

Great to hear it got solved. BTW network drives are supported but you have to make sure the mount file system supports locks (NFS does)

Ohh that's why you don't have it 🙂

Yes, it should be fixed

understood trains does not have auto versioning

What do you mean auto versioning ?

task name is not unique, task ID is unique, you can have multiple tasks with the same name and you can edit the name post execution

Hi FiercePenguin76
It seems it fails detecting the notebook server and thinks this is a "script running".
What is exactly your setup?
docker image ?
jupyter-lab version ?
clearml version?
Also are you getting any warning when calling Task.init ?