Why? The task should have completed successfully, how is this aborting?

Early stopping by the HPO process, like hyper-band, e.g. this training model is going nowhere let's stop it.

I will create a minimal example.

Many thanks ReassuredTiger98 !

Basically lock the Task (so you cannot reset it or change it). Usually it also marks "ready to use" etc. It also will publish the models the Task created.

Hi @<1571308003204796416:profile|HollowPeacock58>

parameters = task.connect(config, name='config_params')

It seems that your DotDict does not support the python copy operator?
i.e.

from copy import copy
copy(DotDict())

fails ?

Decorators are good 🙂
Something along the lines of
` @PipelineDecorator.pipeline(...)
def pipeline(skip_a=False):
if not skip_a:
a = step_a()
else:
# somehow get a previous A?
# let's call it cached A
a = "replace with real'

step_b(a)
... `Is this the gist?
If it is, this looks like, "how can I control whether A is cached or not", is that correct?

It looks like the tag being used is hardcoded to 1.24-18. Was this issue identified and fixed in later versions?

BoredHedgehog47 what do you mean by "hardcoded 1.24-18" ? tag to what I think I lost context here

Hi ReassuredOwl55

How would I find Tasks that have the same code with different inputs/parameters?

Assuming you have the git repo
you can do:
Task.query_tasks(..., task_filter={'_all_'=dict(fields=['script.repository'], pattern='github.com/user/repo'))wdyt?

but the debug samples and monitored performance metric show a different count

Hmm could you expand on what you are getting, and what you are expecting to get

Thanks SmallDeer34 , I think you are correct, the 'output' model is returned properly, but "input" are returned as model name not model object.
Let me check something

Most likely yes, but I don't see how clearml would have an impact here, I am more inclined to think it would be a pytorch dataloader issue, although I don't see why

These are most certainly dataloader process. But clearml-agent when killing the process should also kill all subprocesses, and it might be there is something going on that prenets it from killing the subprocesses ...

Is this easily reproducible ? Can you verify it is still the case with the latest RC of clearml-agent ?

If you use this one for example, will the component have pandas as part of the requirement
None

def step_two(...):
    import pandas as pd
    # do stuff

If so (and it should), what's the difference, where is "internal.repo " different from pandas ?

. I'm thinking it's generically a kernel gateway issue, but I'm not sure if other platforms are using that yet

The odd thing is that you can access the notebook, but it returns zero kernels ..

And is this repo installed on the pipeline creating machine ?
Basically I'm asking how come it did not automatically detect it?

I always have my notebooks in git repo but suddenly it's not running them correctly.

What do you mean?

Can I switch off git diff (change detection?)

Yes, Task.init(..., auto_connect_frameworks={"detect_repository": False})

Any chance you can open a GitHub issue so we do not forget this feature ?

🤞

WDYT?

👍

Yes, I mean use the helm chart to deploy the server, but manually deploy the agent glue.
wdyt?

@<1523701868901961728:profile|ReassuredTiger98> what do you have in the clearml.conf under "conda_channels" ?
Is this it ?
None

Hi FiercePenguin76
By default clearml will list only the packages you import, and not derivative packages.
This means that if you import package X and it imports package Y , only package X will be listed.
The way it should work is by statically analyzing the entire repository, but if you import a local package from a different local folder, and that folder is Not in the same repo, it will not get listed (obviously if you install the external local package, it will be...

quick question:
CLEAR_DATA="./data/dataset_for_modeling"Should I pass the folder of the extracted zip file (assuming train.txt is the training dataset) ?

Using agent v1.01r1 in k8s glue.

I think a fix was recently committed, let me check it

and then in Preprocess:

self.model = get_model(task_id=os.environ['TASK_ID'], model_name=os.environ['MODEL_NAME'])That's the part I do not get, Models have their own entity (with UID), this is in contrast to artifacts that are only stored on Tasks.
The idea when you are registering a model with clearml-serving, you can specify the model ID, this should replace the need for the TASK_ID+model_name in your code, and the clearml-serving will basically bring it to you
Basically this fun...

Hi DilapidatedDucks58
apologies, this thread slipped way.
I double checked, there server will not allow you to overwrite it (meaning to have it fixed will need to release a server version which usually takes longer)
That said maybe we can pass an argument to the "Task.init" so it ignores it? wdyt?

The agent is using Bash (but when you add command line to the docker run, .bashrc is not executed, hence no conda in PATH)
Maybe add the full path to the conda executable:
ocker_setup_bash_script= [ "export PATH=""/workspace/miniconda/bin:$PATH", "export LOCAL_PYTHON=/workspace/miniconda/bin/python3","/workspace/miniconda/bin/conda activate /PATH_GOES_HERE"])

Maybe there is setting in docker to move the space used in a different location?

No that I know of...

I can simply increase the storage of the first disk, no problem with that

probably the easiest 🙂

But as you described 

 it looks like an edge case, so I don’t mind 

🙂

Oh I see the pipeline controller itself (not the components) is the one with the repo
To fix that add at the top of the script the following:
` from clearml import Task

Task.force_store_standalone_script()

@PipelineDecorator.pipeline(...) `That should do the trick

Yes that makes sense, if the overhead of the additional packages is not huge, I do not think it is worth the maintenance 🙂
BTW clearml-agent has full venv caching that you can turn on, so when running remotely you are not "paying" for the additional packages being installed:
Un-comment this line 🙂
https://github.com/allegroai/clearml-agent/blob/51eb0a713cc78bd35ca15ed9440ddc92ffe7f37c/docs/clearml.conf#L116

Basically just change the helm yaml
queue: my_second_queue_name_here