Hi IrritableJellyfish76
https://clear.ml/docs/latest/docs/references/sdk/task#taskget_tasks

task_name

(

str

) – The full name or partial name of the Tasks to match within the specified

project_name

(or all projects if

project_name

is

None

). This method supports regular expressions for name matching. (Optional)

You are right, this is a bit confusing, I will make sure that we add in the docstring an examp...

Hmm, I think you should use --template-yaml

Hi LudicrousDeer3
It should not be a problem see iteration argument in Logger.report_scalar
https://github.com/allegroai/clearml/blob/22d795f68f0175ba9511cabd444ea4dba464f3cd/examples/reporting/scalar_reporting.py#L19
https://allegro.ai/clearml/docs/rst/references/clearml_python_ref/logger_module/logger_logger.html?highlight=report_scalar#clearml.logger.Logger.report_scalar

GrotesqueDog77 this should just work, decorate the functions with @PipelineDecorator.component and call the functions one after the other
paths = step_one() step_two(paths)ClearML will make sure it serializes the strings and pass them to step two (of course step two should actually run on a machine with access to the same folder, but this is another issue 🙂 )

JealousParrot68 yes this seems like a correct description.
The main diff between 1 & 2 is what is the actual data, if this is training/testing data, then Dataset would make sense, if this is a part of a preprocessing pipeline, then artifacts make more sense (notice we added pipeline step caching in the artifacts, so that you can reuse steps if they have the same parameters/code, which means you are able to clone a pipeline and rerun without repeating unnecessary data processing.

there is a bug wherein both

Task.current_task()

and

Logger.current_logger()

return

None

.

This is not a bug this means something broke, the environment variable CLEARML_TASK_ID Has to be set inside the agent's process
How are you running it? (also log 🙂 , you can DM so it is not public here)

My only point is, if we have no force_git_ssh_port or force_git_ssh_user we should not touch the SSH link (i.e. less chance of us messing with the original URL if no one asked us to)

Hi @<1523701083040387072:profile|UnevenDolphin73>

How can I ensure tasks in a pipeline have the same environment as the pipeline itself?
...
but the tasks (executed remotely) do not use that same environment?

Just verifying, we are talking about pipeline decorators?

We also wanted this, we preferred to create a docker image with all we need, and let the pipeline steps use that docker image

You can specify the docker on the decorator itself:
[None](https://github.com/allegroai...

using caching where specified but the pipeline page doesn't show anything at all.

What do you mean by " the pipeline page doesn't show anything at all."? are you running the pipeline ? how ?
Notice PipelineDecorator.component needs to be Top level not nested inside the pipeline logic, like in the original example

@PipelineDecorator.component(
        cache=True,
        name=f'append_string_{x}',
    )

Hi ReassuredTiger98
Basically assuming Linux, init.d will do the trick
https://unix.stackexchange.com/questions/20357/how-can-i-make-a-script-in-etc-init-d-start-at-boot

Hmm yes this is exactly what should not happen 🙂
Let me check it

s there any way to see datasets uploaded to ClearML Data without downloading them using ClearML Data?

Hi VexedCat68
Currently when you create datasets with clearml-data it has to repackage your files, i.e. upload them. That said we have received numerous requests on "registering data", and we are looking into it.
Here is the main technical hurdles we are facing, and I would love to get your perspective:
If the data is not available locally, we cannot calculate the hash of the conten...

You mean the entire organization already has Kubeflow, or to better organize something (if this is the second, what are we organizing, pipelines?)

Woo, what a doozy.

yeah those "broken" pip versions are making our life hard ...

On my to do list, but will have to wait for later this week (feel free to ping on this thread to remind me).
Regrading the issue at hand, let me check the requirements it is using.

ClumsyElephant70 the odd thing is the error here:
docker: Error response from daemon: manifest for nvidia/cuda:latest not found: manifest unknown: manifest unknown.I would imagine it will be with "nvidia/cuda:11.3.0-cudnn8-runtime-ubuntu18.04" but the error is saying "nvidia/cuda:latest"
How could that be ?
Also can you manually run the same command (i.e. docker run --gpus device=0 --rm -it nvidia/cuda:11.3.0-cudnn8-runtime-ubuntu18.04 bash )?

🙂

Hi ScantChimpanzee51

having the ClearML auto scaler at all is super great and an impressive tool!

Thank you! 😍

As all data resides within the container, it is lost afterwards.

Nothing to fear there, if you are using the StorageManager, the destination is always the cache folder, which the agent automatically mounts to the host machine.
That said if the EC2 instance is taken down (i.e. idle) then the cache is lost with it.

Make sense?

Sorry I missed the additional "." in the _update_requirements
Let me check ....

link with "localhost" in it Oo

Hmm I think this is the main issue, for some reason the dataset default upload destination is "localhost", what do you have configured in your clearml.conf under files server?

DefeatedOstrich93 many thanks I was able to reproduce it (basically newly added files caused git apply to fail)
Fix will be part of the next clearml-agent RC

Yes, I was referring to logging the "clearlm-data" Dataset ID on the Task itself, not an external database.
Make sense?

Is this a logging issue, or clearml issue ?

ReassuredTiger98 in theory it should work, do you know what is actually stored ? (I mean reencoding it means you have to have opencv / ffmpeg which might be too much to ask)

WackyRabbit7
regular trains-agent modus operandi is one job at a time (i.e. until the Task is done, no other Tasks will be pulled from the queue).

When adding --services-mode, it is Not 1-1 but 1-N, meaning a single trains-agent will launch as many Tasks as it can.
The trains-agent pulls a job from the queue and spins a docker (only dockers are supported for the time being) and lets the job run in the background (the job itself will be registered as another "worker" in the system). Then the...

agent.cuda_driver_version = ...
agent.cuda_runtime_version = ...

Interesting idea! (I assume for reporting only, not configuration)

... The agent mentionned used output from nvcc (2) ...

The dependencies I shared are not how the agent works, but how Nvidia CUDA works 🙂
regrading the cuda check with nvcc , I'm not saying this is a perfect solution, I just mentioned that this is how this is currently done.
I'm actually not sure if there is an easy way to get it from nvid...

Hi GreasyPenguin14

Could you tell me what the differences are and why we should use ClearML data?

The first difference is in the approach itself, DVC ties the data with the code (i.e. git repo), where we (ClearML - but not just us) actually think data should be abstracted from the Code-Base and become a standalone argument, allowing users to build/execute against different dataset/versions. ClearML Data becomes part of the workflow as it is visible from the UI including the abili...

If you could provide the specific task ID then it could fetch the training data and study from the previous task and continue with the specified number of trainings.

Yes exactly, and also all the definitions for the HPO process (variables space, study etc.)

The reason that being able to continue from a past study would be useful is that the study provides a base for pruning and optimization of the task. The task would be stopped by aborting when the gpu-rig that it is using is neede...

CrookedWalrus33 can you send the entire log? (you can DM it to me)

ComfortableShark77 are you saying you need "transformers" in the serving container?
CLEARML_EXTRA_PYTHON_PACKAGES: "transformers==x.y"https://github.com/allegroai/clearml-serving/blob/6005e238cac6f7fa7406d7276a5662791ccc6c55/docker/docker-compose.yml#L97