I think you are correct, it seems like it is missing requirements to boto/azure/google (I will make sure this is added). In the meantime, you can stop the "triton serving engine" Task, reset it, add boto3 to the installed packages and relaunch.
That said your main issue might be packaging the python model. Basically you need to create a model from the entire folder (with whatever there is inside the folder), then Triton should be able to run it (if the config.pbtxt is correct).
` m = OutputMo...

What does spin mean in this context?

This line:
docker-compose --env-file example.env -f docker-compose-triton-gpu.yml up

But these have: different task ids, same endpoints (from looking through the tabs)
So I am not sure why they are here and why not somewhere else

You can safely ignore them for the time being 🙂

but is it true that I can have multiple models on the same docker instance with different endpoints?

Yes! this is exactly the idea (and again I'm not sure ...

Hi CleanWhale17 let me see if I can address them all

Email Alert for finished Job(I'm not sure if it's already there).

Slack integration will be public by the end of the weekend 🙂
It is fully customization / extendable, I'll be happy to help .

DVC

Full dataset tracking is supported using the artifacts and the ability to integrate to any central storage (shared folders/ S3 / GS / Azure etc.)
From my experience, it is easier to work with artifacts from Data-Processing Tasks...

Hi EnviousStarfish54
Verified with the frontend / backend guys.
Backend allows to search for "all" tags, and frontend will add a toggle button for the UI to select or/all for the selected Tags.
Should be part of the next release

Hi @<1541229818828296192:profile|HurtHedgehog47>

plots we create in the notebook are not saved as it was made.

I'm assuming these are matplotlib plots ?
Notice that ClearML tries to convert the plot into interactive plots, in that process sometimes, colors and legend is being lost (becomes generic).
You can however manually report the plot, and force it to store it as non-interactive:

task.logger.report_matplotlib_figure(
    title="Manual Reporting", series="Just a plot", ite...

PunySquid88 RC1 is out with a fix:
pip install trains-agent==0.14.2rc1

Hi StickyMonkey98

I'm (again) having trouble with the lack of documentation regarding Task.get_tasks(task_filter={STUFF}).

Yes we really have to add documentation there... Let me add that to the todo list

How do I filter tasks by time started? It seems there's a "started" property, and the web ui uses "started" as a key-word in the url query, but task_filter results in an error when I try that...Is there some other filter keyword for filtering by start-time??

last 10 started ...

this sounds like docker build issue on macos M1
https://pythonspeed.com/articles/docker-build-problems-mac/

It's in my local conda environment though.

Meaning this is a wheel installed manually in conda? or is it a folder inside the conda environment ?

If the manual execution (i.e. pycharm) was working it should have stored it on the Pipeline Task.

. Looking at this example here, it looks like it only works with tasks:

Aha! Pipeline is a Task 🙂 (a specific type of Task, nonetheless a Task)
Just use the pipeline ID, and make sure you push it into the services queue, voila

Hi @<1600661423610925056:profile|StrongMouse81>

using serving base url and also other endpoint of model we add using:

clearml-serving model add

we get the attached respond:

And other model endpoints are working for you?

Great to hear!

DrabSwan66
Did you set "docker_install_opencv_libs: true" in your clearml.conf on the host machine ?
https://github.com/allegroai/clearml-agent/blob/e416ab526ba9fe05daa977b34c9e46b50fb214a0/docs/clearml.conf#L150
Just making sure, you are running clearml-agent in docker mode, correct?
What's the container you are using ?

I am symlinking the .clearml directory to a NAS server and this is perhaps part of the problem.

Yep, that sounds about right, it uses Posix file system for internal lock mechanisms (multi process locks), and my guess is that the NAS for some reason does not support it...

basically @<1554638166823014400:profile|ExuberantBat24> you can think of hyper-datasets as a "feature-store for unstructured data"

Okay here is a standalone code that should be close enough? (if I missed anything let me know)

` import tempfile
from datetime import datetime
from pathlib import Path

import tensorflow as tf
import tensorflow_datasets as tfds
from clearml import Task

task = Task.init(project_name="debug", task_name="test")
(ds_train, ds_test), ds_info = tfds.load(
'mnist',
split=['train', 'test'],
shuffle_files=True,
as_supervised=True,
with_info=True,
)

def normalize_img(image, labe...

I basically moved the Task.init() call below the imports

Okay that is odd, can you copy pate the before/after of the import, so we can fix that?!

Thank you!!!

BoredHedgehog47 can you provide some logs, this is odd..

Hi MammothGoat53
Basically what you are missing are the headers with the Token you have:
https://blog.logrocket.com/secure-rest-api-jwt-authentication/

BoredHedgehog47 could it be "python" python points to python 2.7 inside your container, as opposed to python3 on your machine
(this error is python2 trying to run python 3 code)
https://stackoverflow.com/questions/20555517/using-multiple-versions-of-python
"Training classifier with command:\n python -m sfi.imagery.models.bbox_predictorv2.train

This one?
https://stackoverflow.com/questions/6870824/what-is-the-maximum-length-of-a-filename-in-s3

Hi EnviousStarfish54
docker on windows , with nvidia runtime support is only with WSL (I think)
https://docs.nvidia.com/cuda/wsl-user-guide/index.html#installing-wip
https://medium.com/@dalgibbard/docker-with-gpu-support-in-wsl2-ebbc94251cf5

Ohh I see, so basically the ASG should check if the agent is Idle, rather than the Task is running ?

Notice that if you are using TB, everything you report to the TB will appear as well 🙂

The thing I don't understand is how come this DOES work on our linux setups

I do not think it actually works... I could not have find a code that will convert the ENV in the config string ...

I'll be happy to test it out if there's any commit available?

Please do, and feel free to PR it 😍
https://github.com/allegroai/clearml/blob/d3e986393ac8d1a1ea48302224962570ab8e6f9e/clearml/backend_api/session/session.py#L576
https://github.com/allegroai/clearml/blob/d3e98639...

RestAPI refrence:
https://allegro.ai/docs/references/trains_api_ref/trains_api_ref.html
https://allegro.ai/docs/references/trains_api_ref/trains_api_ref.html

notice that even inside docker the venv is cached on the host machine 🙂

Maybe something similar to dockers

I like this approach maybe we could add --name as well, so it is easier to name them.
trains-agent daemon stop --gpus all
trains-agent daemon stop --cpu-only
trains-agent daemon stop --gpus 0
What do you think?

Also being able to separate their configurations files would be good (maybe there is and I don't know?)

This is already supported --config-file , see trains-agent --help for details 🙂