trained model class...

You mean the pytorch model object?

We do upload the final model manually.

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

Yes this is a misleading title

I think this is the issue, it was search and replaced . The thing is I'm not sure the helm chart is updated to clearml. Let me check

ElegantCoyote26 what is the model input layer definition? This implies the data format to pass to the serve endpoint

I suspect it failed to create one on the host and then mount into the docker

Anyway, in the docs, there is a function called task.register_artifact()

Yes, this is rather deprecated... The idea is that it will monitor an obejct and auto sync it (i.e. serialize and upload).
That said, it is just so much easier to do task.upload_artifact and you can always update/overrwrite if you are passing the same name, that I cannot see the actual use case. Does that make sense? What are you using it for ?

Hi WhimsicalLion91
You can always explicitly send a value:
from trains import Logger Logger.current_logger().report_scalar("title", "series", iteration=0, value=1337)A full example can be found here:
https://github.com/allegroai/trains/blob/master/examples/reporting/scalar_reporting.py

This is cleaml python client, no need to change the server

Hi SmallDeer34
Did you call Task.init ?

@<1541954607595393024:profile|BattyCrocodile47>

Is that instance only able to handle one task at a time?

You could have multiple agents on the same machine, each one with its own dedicated GPU, but you will not be able to change the allocation (i.e. now I want 2 GPUs on one agent) without restarting the agents on the instance. In either case, this is for a "bare-metal" machine, and in the AWS autoscaler case, this goes under "dynamic" GPUs (see above)

Ohh, sorry 🙂
:param run_pipeline_steps_locally: (default False) If True, run the pipeline steps themselves locally as a subprocess (use for debugging the pipeline locally, notice the pipeline code is expected to be available on the local machine)

That was the idea behind the feature (and BTW any feedback on usability and debugging will be appreciated here, pipelines are notorious to debug 🙂 )

the ability to exexute without an agent i was just talking about thia functionality the other day in the community channel

What would be the use case ? (actually the infrastructure now supports it)

Is this a common case? maybe we should change the run_pipeline_steps_locally argument to False?
(The idea of run_pipeline_steps_locally=True is that it will be easier to debug the entire pipeline on the same machine)

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 ?

DilapidatedDucks58 I think they are used here:
https://github.com/allegroai/clearml/blob/3d3a835435cc2f01ff19fe0a58a8d7db10fd2de2/clearml/storage/helper.py#L1407

https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session.resource

Hi FiercePenguin76

should return all datasets from all projects?

Correct 🙂

JitteryCoyote63 instead of _update_requirements, call the following before Task.init:
Task.add_requirements('torch', '1.3.1') Task.add_requirements('git+ ')

WorriedParrot51 trains should support subparsers etc.
Even if your code calls the parsing before trains.
The only thing you need is to import the package when argparser is called (not to initialize it, that can happen later)
It should (hopefully) solve the issue.

ImmensePenguin78
I think the latest RC adds it, should be released later today 🙂

the Task scheduler itself is a Task. What we did is we added a new parameter section on the Task (the task.connect call), so that we can later clone and modify it and use the new value in runtime
(Task.connect will put the data from the Task/UI back into the dict when the agent is running the Scheduler)
Does that make sense?

Hi CourageousWhale20
Most documentation is here https://allegro.ai/docs

So only the before resolving? How would you think to configure it?

I think so, when you are saying "clearml (bash script..." you basically mean, "put my code + packages + and run it" , correct ?

Nice!!!

Hi all! Does anyone know a solution to my issue with deploying models saved on azure on the clearml-serving docker container?

Hi NuttyCamel41
The easiest is to map the clearml.conf to both the serving and triton containers in your docker-compose.yaml (or k8s secrets) and make sure the conf file has the credentials to access the azure blob. wdyt ?

Where did you add the Task.init call ?

@<1587253076522176512:profile|HollowPeacock33>
Is this a commercial ad? this seems like out of scope for this channel
Can you expand?

So are you saying why do we need to install a specific pip version ?
You can "disable it" by selecting a very high version
pip_version: "<40"https://github.com/allegroai/clearml-agent/blob/077148be00ead21084d63a14bf89d13d049cf7db/docs/clearml.conf#L67

What do you mean cache files ? Cache is machine specific and is set in the clearml.conf file.
Artifacts / models are uploaded to the files server (or any other object storage solution)