And anyway once a model is published can’t update it right?

Correct, and also the creating Task is published (i.e. locked)

NastySeahorse61 I would try to open in incognito mode (i.e. no cookies etc.), did you also change the address of the server?

Hi BroadSeaturtle49
torchvision!=0.13.0,>=0.8.1 is this what you have in the requirements ?
The clearml-agent is parsing the requested version and tries to match it to the version found/supported by the installed cuda
There is the possibility the combinarion wither does not exist or fore some reason the parsing (i.e. clearml-agent's parsing) fails
can you maybe provide the Task's full log?

I think it's supposed to be out early Nov 🙂

Hi CheekyElephant36
First you need to run it once on your machine, once this is done (only a few steps is enough), you can one it and enqueue it. Then to actually connect the aws autoscaler (the part that spins machines and runs tasks) go to applications and select the aqs autoscaler.
Btw i think the next video will be about YOLO + autoscaler

(just using local server not connected to Internet), am I right?

You can if you host your own git server, Or if your code is a single file / jupyter notebook, then the entire code is stored on the Task.
btw: what is the exact setup, how come there is no git repo?

yes exactly my thinking 👍

Yes, or at least credentials and API...
Maybe inside your code you can later copy the model into fixed location ?
This way you have the model in the model repository and a copy in a fixed location (StorageManager can upload to a specific bucket/folder with the same credentials you already have)
Would that work?

Hi RotundHedgehog76

we have issues with

clearml-agent

when using standalone mode. ...

What is the use case for standalone mode? is this venv or docker mode?

RoughTiger69
move the files locally (i.e. based on the example move folder b into folder a ) Create a new version with two parents ('a' and 'b') then sync the local root folder ('a' in your case). Only the meta-data should change (because the referenced files are already in one of the datasets)wdyt?

and those env variables are credentials for ClearML. Since they are taken from k8s secrets, they are the same for every user.

Oh ...

I can create secrets for every new user and set env variables accordingly, but perhaps you see a better way out?

So the thing is, if a User spins the k8s job, the user needs to pass their credentials (so the system knows who it is)... You could just pass the user's key/secret (not nice, but probably not a big issue, as everyone is an Admin anyhow,...

Hi @<1577106212921544704:profile|WickedSquirrel54>

We are self hosting it using Docker Swarm

Nice!

and were wondering if this is something that the community would be interested in.

Always!

what did you have in mind? I have to admit I'm not familiar with the latest in Docker swarm but we all lover Docker the product and the company

Hi VexedCat68

One of my steps just finds the latest model to use. I want the task to output the id, and the next step to use it. How would I go about doing this?

When you say "I want the task to output the id" do you mean to pass t to the next step:
Something like this one:
https://github.com/allegroai/clearml/blob/c226a748066daa3c62eddc6e378fa6f5bae879a1/clearml/automation/controller.py#L224

Sure, to get what exactly ?

Okay let me check

Is there a quicker way to abort all running experiments in a project? I have over a thousand running anonymous data tasks in a specific project and I want to abort them before debugging them.

We are adding "select" all in the next UI version to do that as quickly as possible 🙂

but I can't seem to figure out a way to do something similar using a task in add_step

VexedCat68 With "add_step" it assumes the Task you are adding is self contained (i.e. there is no "return object" to serialize), this means you can only add arguments, or use the artifacts the Task (i.e. step) will recreate, obviously you knowing in advance what the step creates. Make sense ?

https://github.com/allegroai/trains-web

Hi RipeGoose2
Yes the slide feature is definelty on the do do list (a lot of users asked for it).
Unfortunately other than actually PR-ing to the UI repo, there is no easy way to add customization (If you have an idea on how we could have an easy interface, that would be great.)
I'll check what's the status with the slider, maybe we will be lucky enough to see it in he next update 🙂

Hi RipeGoose2
So the http://app.community.clear.ml already contains it.
Next release of the standalone server (a.k.a clearml-server) will include it as well.
I think the ETA is end of the year (i.e. 2 weeks), but I'm not sure on the exact timeframe.
Sounds good ?

RipeGoose2 good news 🙂

CleanWhale17 what is " Online-Training  Support(for Dataset Shifts" ?

SillyPuppy19 yes you are correct, actually I can promise you the callback will be called from a different thread (basically the monitoring thread) so it's on the user to make sure the callback can handle it .
How about we move this discussion to GitHub?

Hi JitteryCoyote63

experiments logs ...

You mean the console outputs ?

Hi @<1546303293918023680:profile|MiniatureRobin9> could it be the pipeline logic is created via the clrarml-task CLI? If this is the case, I think this is an edge case we should fix. Basically it creates a Task instead of pipeline, which in.essence only effects the UI. To solve it, just run the pipeline locally, notice that by default when you start it, it will actually stop the local run and relaunch itself on an agent.
Also, could you open a GitHub issue so we add a flag for it?

JitteryCoyote63 next week is the Trains next release with upgrade to ES 7, do you want to wait or sort a solution for this one ?
(BTW: I think that you can mount a license file or delete one, and it should be okay, I'll ask the backend guys regradless)

AdventurousButterfly15

Despite having manually installed this torch version, during task execution agent still tries to install it somehow and fails:

Are you running the agent in venv mode? or docker mode?
Notice that in docker mode it inherits the python packages from the container, and adds/reinstalls missing packages. In venv mode it creates a New clean venv (there is no way to inherit a venv, venv can only inherit from system wide installed packages)

The idea is that you cannot e...

Hi ConvolutedSealion94
Yes 🙂
Task.set_random_seed(my_seed=123) # disable setting random number generators by passing None task = Task.init(...)

This really makes little sense to me...

Can you send the full clearml-session --verbose console output ?

Something is not working as it should obviously, console output will be a good starting point

Because it lives behind a VPN and github workers don’t have access to it

makes sense
If this is the case, I have to admit that combining offline-mode and remote execution makes sense, no?