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

😊

@<1564422644407734272:profile|DistressedCoyote60> could you open a GitHub issue on it in clearml-agent, just so we know of the problem and fix it for next version ?

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 PanickyMoth78
So the current implantation of the pipeline parallelization is exactly like python async function calls:
for dataset_conf in dataset_configs: dataset = make_dataset_component(dataset_conf) for training_conf in training_configs: model_path = train_image_classifier_component(training_conf) eval_result_path = eval_model_component(model_path)Specifically here since you are passing the output of one function to another, image what happens is a wait operation, hence it ...

BoredHedgehog47 you need to make sure "<path here>/train.py" also calls Task.init (again no need to worry about calling it twice with different project/name)
The Task.init call will make sure the auto-connect works.
BTW: if you do os.fork , then there is no need for the Task.init, the main difference is that POpen starts a whole new process, and we need to make sure the newly created process is auto-connected as well (i.e. calling Task.init)

I still don't get resource logging when I run in an agent.

@<1533620191232004096:profile|NuttyLobster9> there should be no difference ... are we still talking about <30 sec? or a sleep test? (no resource logging at all?)

have a separate task that is logging metrics with tensorboard. When running locally, I see the metrics appear in the "scalars" tab in ClearML, but when running in an agent, nothing. Any suggestions on where to look?

This is odd and somewhat consistent with actu...

Yes, that sounds like a good start, DilapidatedDucks58 can you open a github issue with the feature request ?
I want to make sure we do not forget

I was thinking mainly about AWS.

Meaning S3?

Thank you!

@<1671689437261598720:profile|FranticWhale40> I might have found something, let me see if I can reproduce it

Besides that, what are your impressions on these serving engines? Are they much better than just creating my own API + ONNX or even my own API + normal Pytorch inference?

I would separate ML frameworks from DL frameworks.
With ML frameworks, the main advantage is multi-model serving on a single container, which is more cost effective when it comes to multiple model serving. As well as the ability to quickly update models from the clearml model repository (just tag + publish and the end...

"Fix TF 2.4 keras load/save model"

any chance StorageManager could re-download files only if their size is different from file in cache (as an option)?

I think there is force argument, to force download.
I think the main issue is getting the size from different backends (i.e. s3 /https / etc.)
Maybe we should add it as a GitHub feature request issue?
The main limitation is that the driver "list()" does not return file size.
For example it might be an issue with the default http files-server.
wdyt?

But adding a simple 

force_download

 flag to the 

get_local_copy

That's sounds like a good idea

😞 anything that can be done?

Do I set the 

CLEARML_FILES_HOST

 to the end point instead of an s3 bucket?

Yes you are right this is not straight forward:
CLEARML_FILES_HOST=" s3://minio_ip:9001 "
Notice you must specify "port" , this is how it knows this is not AWS. I would avoid using an IP and register the minio as a host on your local DNS / firewall. This way if you change the IP the links will not get broken 🙂

I am using importlib and this is probably why everythings weird.

Yes that will explain a lot 🙂
No worries, glad to hear it worked out

Hmm, conda_freeze in the clearml.conf on the development machine ?

ReassuredTiger98 could you provide more information ? (versions, scenario. etc.)

PompousParrot44 the fundamental difference is that artifacts are uploaded manually (i.e. a user will specifically "ask" to upload an artifact), models are logged automatically and a user might not want them uploaded (imagine debugging sessions, or testing).
By adding the 'upload_uri' arguments, you can specify to trains that you want all models to be automatically uploaded (not just logged).
Now here is the nice thing, when running using the trains-agent, you can have:
Always upload the mod...

ohh right, my bad:
docker run -t --rm nvidia/cuda:10.1-base-ubuntu18.04 bash -c "echo 'Binary::apt::APT::Keep-Downloaded-Packages \"true\";' > /etc/apt/apt.conf.d/docker-clean && apt-get update && apt-get install -y git python3-pip && pip install trains-agent && echo done"

making me realize that this may have been optional

I think it is optional, and this is why it was not entered in the first place.
Could you double check and just remove it from your manual pbtxt ?

Hi SubstantialElk6

noted that clearml-serving does not support Spacy models out of the box and

So this is a good point.

To add any pissing package to the preprocessing docker you can just add them in the following environment variable here: https://github.com/allegroai/clearml-serving/blob/d15bfcade54c7bdd8f3765408adc480d5ceb4b45/docker/docker-compose.yml#L83
EXTRA_PYTHON_PACKAGES="spacy>1"
Regrading a custom engine, basically this is supported with --engine custom
you c...

These are maybe good features to include in ClearML:

or

.

Sure, we should probably add a section into the doc explaining how to do that

Other approach is creating my own API on the top of clearml-serving endpoints and there I control each tenant authentication.

I have to admit that to me this is a much better solution (then my/bento integrated JWT option). Generally speaking I think this is the best approach, it separates authentication layer from execution ...

WackyRabbit7
Long story short, yes, only by name (hashing might be too slow on large files)
The easiest solution, if the hash is incorrect, delete the local copy it returns, and ask again, it will download it.
I'm not sure if the hashing is exposed, but if it is not, we can add it.
What do you think?

files_server:

://genuin-ai/

should be:

files_server: 

2 and 3 - I want to manage access control over the RestAPI

Long story short, put a load-balancer in front of the entire thing (see the k8s setup), and have the load-balancer verify JWT token as authentication (this is usually the easiest)

1- Exactly, custom code

Yes, we need to add a custom example there (somehow forgotten)
Could you open an Issue for that?
in the meantime:
` #

Preprocess class Must be named "Preprocess"

No need to inherit or to implement all methods

lass P...

Hi PerplexedCow66

I would like to know how to serve a model, even if I do not use any serving engine

What do you mean no serving engine, i.e. custom code?

Besides that, how can I manage authorization between multiple endpoints?

Are you referring to limiting access to all the endpoints?

How can I manage API keys to control who can access my endpoints?

Just to be clear, accessing the endpoints has nothing to do with the clearml-server credentials, so are you asking how to...

SubstantialElk6
The ~<package name with first name dropped> == a.b.c is a known conda/pip temporary install issue. (Some left over from previous package install)
The easiest way is to find the site-packages folder and delete the package, or create a new virtual environment
BTW:
pip freeze will also list these broken packages