So I have a task that just loads a model, but I don't see it as an artifact in the UI

You should see it under Artifacts, Input model if you are calling Keras load function (or similar)

And generated a new toek on the web UI?
If cleaml-init finished it means that everyhting should be fine.
You can test it by starting python and testing:
from clearml import Task Task.init('examples', 'test')

Does this mean the model weights are stored on the clearml-server file system?

By default they are just logged (i.e. the local path is stored, but the file is not uploaded). If you want to automatically store the model, pass output_uri=True to the Task.init , or any object store / shared folder (e.g. output_uri=' s3://bucket/folder ' ). ClearML will automatically create a subfolder for the Task, and upload all models/artifacts to it.
` task = Task.init(project_name='ex...

A single query will return if the agent is running anything, and for how long, but I do not think you can get the idle time ...

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?

And how is the endpoint registered ?

Hi @<1526371965655322624:profile|NuttyCamel41>
I think that the only way to actually get huge number of api calls is with a lot of machines.
For example, regardless of the amount of console-logs you print, it will only be a single call, as these are packages every 2-10 seconds. The same with metric reporting etc.
On the free tier you cal already test the amount of API calls, I think the mechanism is exactly the same
fyi: I would put this question in the channel

Let me check

But I do not know how it can help me:(

In your code itself after the Task.init call add:
task.set_initial_iteration(0)See reply here:
https://github.com/allegroai/clearml/issues/496#issuecomment-980037382

So like a UI for creating pipelines doing different things on the different solutions ?

In our case, we have a custom YAML instruction

!include

, i.e.

Hmm interesting, in theory this might work since configuration encoding (when passing dicts), is handled with HOCON which does support referencing.
That said currently it is not aware of "remote configurations" only ENV variables and local files.
It will be cool to add, do we have a github issue on that? (would you like to see if you can PR such a thing?)

Correct indeed 👌

They all "inherit" the same user / environment from one another

I think we were able to fix it, let me check if it was pushed 🙂

Hi UnsightlySeagull42

But now I need the hyperparameters in every python file.

You can always get the Task from anywhere?
main_task = Task.current_task()

(some packages that are not inside the cache seem to have be missing and then everything fails)

How did that happen?

Also, I just wanted to say thanks for the tool! I'm managing a small data science practice and it's going to be really nice to have a view of all of the experiments we've got and know our GPU utilization, all without having to give every data scientist access to each box where the workflows are run. Incredibly stoked.

♥ ❤ ♥

seems to run properly now

Are you saying the problem disappeared ?

Hi, I was expecting to see the container rather then the actual physical machine.

It is the container, it should tunnels directly into it. (or that's how it should be).
SSH port 10022

Oh I see, this seems like Triton configuration issue, usually dim -1 means flexible. I can also mention that serving 1.1 should be released later this week with better multiple input support for triton. Does that make sense?

Hmm and how would you imagine a transparent integration here (the example looks like a lot of boilerplate code...)

Thanks!

A few more details on the New RC  (1.1.2rc0) change set:

Upload dataset now supports chunksize, for multi-part upload/download (useful with large datasets)
backwards compatibility, i.e. parent datasets do not have to support multi-part datasets
Notice multi-part datasets should be accessed with latest RC
cleaml-data upload --chunk-size Dataset().upload(..., chunk_size=None)
Get Dataset support partial download (i.e. for debugging, or for more efficient multi-node support)
Notice total n...

Although it's still really weird how it was failing silently

totally agree, I think the main issue was the agent had the correct configuration, but the container / env the agent was spinning was missing it,
I'll double check how come it did not print anything

Hi LooseClams37
From the docker compose, I see the agent is running in venv mode, is that correct?
Also notice that when configuring the minio credentials you can specify if this is an https connection (secure: true) which by default it is not.
See here: https://github.com/allegroai/clearml-agent/blob/5a6caf6399a0128ad81e8723d0a847e2ded5b75e/docs/clearml.conf#L287

@<1523701868901961728:profile|ReassuredTiger98> what are you getting with:

nvidia-smi

And here:

ls -la /usr/local/

Hi @<1529271085315395584:profile|AmusedCat74>

ClearML Scheduler where it doesn't reuse the task

What do you mean by doesn't reuse the Task, do you mean you want each time the scheduler is launched to basically overwrite the previous run ?

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 🙂

This is odd because the screen grab point to CUDA 10.2 ...

Also this message suggests that I can change the configuration, but as said I can't find it anywhere and wouldn't know hot to change the configuration.

This means that you can launch a new one (i.e. abort, clone, edit, enqueue) directly from the web UI and in the UI edit the configuration. Unfortunately it does not support changing the configuration "live"