However, SNPE performs quantization with precompiled CLI binary instead of python library (which also needs to be installed). What would be the pipeline in this case?

I would imagine a container with preinstalled SNPE compiler / quantizer, and a python script triggering the process ?

one more question: in case of triggering the quantization process, will it be considered as separate task?

I think this makes sense, since you probably want a container with the SNE environment, m...

Hi GreasyLeopard35

I try to resume a stopped or aborted parameter optimization experiment,

How are you continuing the HPO? are you runing everything locally? is this with an agent? are you seeing the '[0, 0]' value on the configuration when launching the HPO or when continuing it ?

We do upload the final model manually.

wait you said upload manually, and now you are saying "saved automatically", I'm confused.

No the "original one"

Ohh I see.
In your web app, look for the "?" icon (bottom left corner), click on it, it should open the full platform documentation

Since the error says network error, is it possible because I'm in Taiwan? Like downloading from Asia leads to this kind of issue

Can you download it from the browser ? (I mean the file size after download , is it 400mb?)

It runs into the above error when I clone the task or reset it.

from here:

AssertionError: ERROR: --resume checkpoint does not exist

I assume the "internal" code state changed, and now it is looking for a file that does not exist, how would your code state change, in other words why would it be looking for the file only when cloning? could it be you put the state on the Task, then you clone it (i.e. clone the exact same dict, and now the newly cloned Task "thinks" it resuming ?!)

How did you define the decorator of "train_image_classifier_component" ?
Did you define:
@PipelineDecorator.component(return_values=['run_model_path', 'run_tb_path'], ...Notice two return values

Hi MortifiedCrow63
I have to admit this is very strange, I think the fact it works for the artifacts and not for the model is kind of a fluke ...
If you use "wait_on_upload" argument in the upload_artifact you end up with the same behavior. Even if uploaded in the background, the issue is still there, for me it was revealed the minute I limited the upload bandwidth to under 300kbps.It seems the internal GS timeout assumes every chunk should be uploaded in under 60 seconds.
The default chunk...

Hi WickedStarfish97

As a result, I don’t want the Agent to parse what imports are being used / install dependencies whatsoever

Nothing to worry about here, even if the agent detects the python packages, they are installed on top of the preexisting packages inside the docker. That said if you want to over ride it, you can also pass packages=[]

Adding to the next version to do list! 🙂

So maybe the path is related to the fact I have venv caching on?

hmmm could be...
Can you quickly disable the caching and try ?

yep, that's the reason it is failing, how did you train the model itself ?

Hi CleanPigeon16

I was wondering how (or if) you handle interruptions.

Good question, basically (and I might be missing a few details but I think that's the general gist).
A new instance will be spinned (spot/regular based on your "compute budget") as long as there is a job in the "monitored" queue. that mean that if a worker was kicked by amazon (i.e. is spot) another one will be spinned instead as long as there is a job in the queue. That means that what is probably missing in you...

When I start the serving containers it can't retrieve the model:

Hi BrightRabbit75
I think you need to pass the credentials for your S3 account to the clearml-serving containers
Basically just add AWS_ACCESS_KEY_ID , AWS_SECRET_ACCESS_KEY to your docker compose:

https://github.com/allegroai/clearml-serving/blob/4b52103636bc7430d4a6666ee85fd126fcb49e2e/docker/docker-compose-triton-gpu.yml#L110
https://github.com/allegroai/clearml-serving/blob/4b52103636bc7430d4a6666e...

PanickyMoth78 RC is out
pip install clearml==1.6.3rc1🤞

ZanyPig66 what do you mean with "git integration " ? So what would be two ways of calling the function, where one works and the other does not?

CooperativeFox72
Could you try to run the docker and then inside the docker try to do:
su root whoami

Apparently it ignores it and replaces everything...

, the easiest way possible would be if could just some how run task and let the lsf manage the environment

You mean let the LSF set the conda/venv ? or do you also mean to get the code-base, changes etc ?

so it would be better just to use the original code files and the same conda env. if possible…

Hmm you can actually run your code in "agent mode" assuming you have everything else setup.
This basically means you set a few environment variables prior to launching the code:
Basically:
export CLEARML_TASK_ID=<The_task_id_to_run> export CLEARML_LOG_TASK_TO_BACKEND=1 export CLEARML_SIMULATE_REMOTE_TASK=1 python my_script_here.py

I guess this is doable:
You can get the entire set of scalars like as pandas DF: https://www.tensorflow.org/tensorboard/dataframe_api
(another example: https://stackoverflow.com/a/45899735 )
Then iterate over the different runs and create + report scalars)
` from clearml import Task

for run in runs:
task = Task.create_task(...)
logger = task.get_logger()

not real code, just example:

w_times, step_nums, vals = zip(*event_acc.Scalars('Accuracy'))
for step, val in zip(step_nums...

I assume this is missing:
https://github.com/allegroai/clearml/blob/b21e93272682af99fffc861224f38d65b42c2354/clearml/backend_config/bucket_config.py#L102

Okay that means it is running in virtual environment mode.
On the original Task (the one you enqueued) what were the installed packages (specifically the torch/torchvision) ?

DeterminedToad86 I suspect that since it was executed on sagemaker it registered a specific package that is unique for Sagemaker (no to worry installed packages can be edited after you clone/reset the Task)

DeterminedToad86
Yes I think this is the issue, on SageMaker a specific compiled version of torchvision was installed (probably part of the image)
Edit the Task (before enqueuing) and change the torchvision URL to:
torchvision==0.7.0Let me know if it worked

Good question 🙂
from clearml import Task Task.init('examples', 'test')

Hi ReassuredOwl55
The easiest is to configure it as default output_uri in the clearml.conf of file the agent, wdyt?
https://github.com/allegroai/clearml-agent/blob/ebb955187dea384f574a52d059c02e16a49aeead/docs/clearml.conf#L430

Hi HappyLion37
It seems that you are "reusing" the Tasks. Which means the second time you open them you are essentially resetting the old run and starting all over.
Try to do:
task1 = Task.init('examples', 'step one', reuse_last_task_id=False) print('do stuff') task1.close() task2 = Task.init('examples', 'step two', reuse_last_task_id=False) print('do some more stuff') task2.close()

Hmm that is odd, it seemed to missed the fact this is a jupyter notbook.
What's the clearml version you are using ?