Hi @<1560798754280312832:profile|AntsyPenguin90>
The image itself is uploaded in a blackground process, flush just triggers the starting of the process.
Could it be that it is showing a few seconds after?

How does this work in the context of a pipeline?

Is your pipeline from functions / decorators ? or is it from Tasks ?
(if this is Tasks then just changing the entry point in the overides)
In case of functions or decorators, you have to do that manually (i.e. your function needs to do "accelerate launch"

from accelerate.commands.launch import launch_command, launch_command_parser
parser = launch_command_parser()
args = parser.parse_args("-command -here".split())
launch_command(arg...

Ohh ignore the YAML

, but it seems like I can only trigger a task using a Task scheduler but not a pipeline.

@<1523701132025663488:profile|SlimyElephant79> Maybe we should better state it, but Pipeline is "just" another type of Task. so triggering a Task with the Pipeline ID is essentially triggering the pipeline (do notice you need to select the "services" queue to be used so that the pipeline runs on the correct resource). Make sense ?

Hi SarcasticSparrow10

which database services are used to...

Mongo & Elastic
You can query everything using ClearML interface, or talk directly with the databases.
Full RestAPI is here:
https://clear.ml/docs/latest/docs/references/api/endpoints
You can use the APIClient for easier pythonic interface:
See example here
https://github.com/allegroai/clearml/blob/master/examples/services/cleanup/cleanup_service.py

What is the exact use case you have in mind?

Maybe this is part of the paid version, but would be cool if each user (in the web UI) could define their own secrets,

Very cool (and actually how it works), but at the end someone needs to pay for salaries 😉

The S3 bucket credentials are defined on the agent, as the bucket is also running locally on the same machine - but I would love for the code to download and apply the file automatically!

I have an idea here, why not use the "docker bash script" argument for that ?...

Thanks for checking NastyFox63
I double checked with both front/backend , there should not be any limit...
Could you maybe provide a toy demo to reproduce the issue ?

There is no way to create an artifact/model/dataset without a task, right?

Models are a an entity of it's own, and you can actually create one without a Task.

(just for my own interest: how much does the enterprise version divert from the open source version? It it just extended or are there core changes to the enterprise version)

It adds a few security layers on top, and adds a few features that are just not part of the open source (RBAC, hyper-datasets, advanced scheduling, cu...

YEY!

Hi @<1603198134261911552:profile|ColossalReindeer77>
When you select poetry as package manager the agent passes control to poetry, this means poetry needs to decide on hte correct torch wheel based on your cuda. I do not think poetry can do that, but I do think you can specify the extra index url to take the torch wheel from:
None

Yep the automagic only kick in with Task.init... The main difference and the advantage of using a Dataset object is the underlying Task resides in a specific structure that is used when searching based on project/name/version, but other than that, it should just work

(once you verify PR the fix, I'll make sure it is merged)

Hi @<1631102016807768064:profile|ZanySealion18>

I'm using SSH for authentication, however, known_hosts doesn't seem to be passed to the docker so it prompts for authentification/fingerprint. Any ideas?

Hmm it is supposed to automatically mount your ~/.ssh folder into the docker to solve for that.
First try to set force_git_ssh_protocol: true
None
If that does not he...

Hmm not sure, try the latest anyhow 🙂

instead of the one that I want or the one of the env which it is started from.

The default is the python that is used to run the agent.
agent.ignore_requested_python_version = true agent.python_binary = /my/selected/python3.8

Oh that is odd. Is this reproducible? @<1533620191232004096:profile|NuttyLobster9> what was the flow that required another task.init?

Ooops 😞
task.get_tags()
task.set_tags()

No sure what O'm seeing here

I can't seem to figure out what the names should be from the pytorch example - where did INPUT__0 come from

This is actually the latyer name in the model:
https://github.com/allegroai/clearml-serving/blob/4b52103636bc7430d4a6666ee85fd126fcb49e2e/examples/pytorch/train_pytorch_mnist.py#L24
Which is just the default name Pytorch gives the layer
https://discuss.pytorch.org/t/how-to-get-layer-names-in-a-network/134238

it appears I need to converted into TorchScript?

Yes, this ...

I was using clearml == 0.17.5 and I also had this issue

I think it was introduced when we moved to subprocess reporting, with 0.17.5
You can disable it with the following in clearml.conf:
sdk.development.report_use_subprocess = false

For setting trains-server I would recommend the docker-compose, it is very easy to setup, and you just need a single fixed compute instance, details https://github.com/allegroai/trains-server/blob/master/docs/install_linux_mac.md With regards to the "low prio clusters", are you asking how they could be connected with the trains-agent or if running code that uses trains will work on them?

Hi SkinnyPanda43
Are you trying to access the same Task or an external one ?

Ohh "~/trains.conf" is root probably

So does that mean "origin" solves the issue ?

great 🙂
two things:
I'm not sure argparse supports dict as a type (I mean it will take anything but I'm not sure it will parse your arguments as dict) I know there was an issue with argparsing, but I think it was solvedbtw: Basically the way clearml-agent works, it does not actually pass the arguments in commandline but directly to the argparser at runtime
What happens if you clone the Task (the one with Args showing and without the explicit task.connect(_args) and send it to the age...

Hi FancyWhale93 you can disable the auto model uploading with
@PipelineDecorator.component(..., auto_connect_frameworks={'pytorch': False}) def step(): pass

clearml-task

 seems does not allow me passing the 

run

 argument without value

EnviousStarfish54 did you try --args run=True
I'm assuming run is a boolean of a sort ?

a. The submitted job would automatically download data from internal data repository, but it will be time consuming if data is re-downloaded every time. Does ClearML caching the data somewhere?

What do you mean by the agent will download the data ? are you referring to Dataset ?

MysteriousBee56 I would do Task.create()
you can get the full Task internal representation with task.data
Then call task._edit(script={'repo': ...}) to edit/update all the Task entries.
You can check the dull details of the task object here: https://github.com/allegroai/trains/blob/master/trains/backend_api/services/v2_8/tasks.py#L954
BTW: when you have a sample script working, consider PR-ing it, I'm sure it will be useful for others 🙂 (also a great way to get us involved with debuggin...

How does ClearML select reference branch? Could it be that ClearML only checks "origin" branch?

Yes 😞 I think we can quickly fix that, I'm just trying to realize if there are down sides to running "git ls-remote --get-url" without origin

Oh that makes sense, This depends on how you setup the clearml k8s glue, (becuase the resource allocation is done by k8s) a good hack to limit the number of containers per GPU is to set a RAM limitation per pod, then k8s will know to limit the number of pods on the same GPU machine,
wdty?