Yes in the UI, clone or reset the Task, then youcan edit the installed packages section under the Execution tab

Thanks SmallDeer34 , I think you are correct, the 'output' model is returned properly, but "input" are returned as model name not model object.
Let me check something

Yep 🙂

yes ...
What's your use case for passing an empty dict ? (meaning how would one use it later)

Hi @<1730033904972206080:profile|FantasticSeaurchin8>
Is this only relates to this
https://github.com/coqui-ai/Trainer/issues/7
Or is it a clearml sdk issue?

what's the docker version?

1. Could we add a comparison feature directly from the search results (Dashboard view -> search -> highlight some experiments for comparison)?

Totally forgot about the global search feature, hmm I'm not sure the webapp is in the correct "state" for that, i.e. I think that the selection only works in "table view", which is the "all experiments" flat table

2. Could we add a filter on the project name in the "All Experiments" project?

You mean "filter by project" ?

Could we ad...

GreasyPenguin14 makes total sense.
In that case I would say variants to the accuracy make sense to me, I would suggest:
title='trains', series='accuracy/day' and title='trains', series='accuracy/night'
Regrading hierarchy, from the implementation perspective a unique identifier is always the combination of title/series (or in other words metric/variant), introducing another level is a system wide change.
This means it might be more challenging than expected ...

This only talks about bugs reporting and enhancement suggestions

I'll make sure this is fixed 🙂

By default the pl Trainer will output everything to TB, which we automatically store. But verify that TB is installed

So I might be a bit out of sync, but I think there should be Triton serving and OpenVino serving built into it (or at least in progress).

Yep I think you are correct, you should have had the same output as a local jupyter notebook, and it seems that in sagemaker studio it is not working 😞
Let me check something

When a remote task runs

Dataset.get()

it is not using the correct URL

BoredHedgehog47 it will get the link the data was Registered with, when creating the Dataset.
This has Nothing to do with the local configuration, it can point to any arbitrary file location on the internet.
It was created there, because at the time of the dataset creation someone (manually or via the config) set a specific host as the file location, and to that host the files were uploaded (again ...

The notebook path goes through a symlink a few levels up the file system (before hitting the repo root, though)

Hmm sounds interesting, how can I reproduce it?

The notebook kernel is also not the default kernel,

What do you mean?

We workaround the issue by downloading the file with a request and unzipping only when needed.

We have located the issue, it seems the file-server is changing the header when sending back the file (basically saying CSV with gzip compression, which in turn will cause any http download client to automatically unzip the content). Working on a hot fix for it 🙂

Hi JitteryCoyote63 you can bus obviously you should be careful they might both try to allocate more GPU memory than they the HW actually has.
TRAINS_WORKER_NAME=machine_gpu0A trains-agent daemon --gpus 0 --queue default --detached TRAINS_WORKER_NAME=machine_gpu0B trains-agent daemon --gpus 0 --queue default --detached

Was I right to put the credentials in

clearml.conf

on the machine I am starting the agent on?

AdventurousButterfly15 Yes exactly!
you should be able to see that in the log of the Task (at the top of the log there will be the entire configuration), can you see the git user there?

, 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 ?

Actually that is less interesting, as it is quite straight forward

it works if I run the same command manually.

What do you mean?
Can you do:
docker run -it <my container here> bashThen immediately get an interactive bash ?

Where are you seeing this message?

MelancholyBeetle72 I think we collect them in Issue 81 on GitHub, feel free to add it if it is missing 🙂
https://github.com/allegroai/clearml/issues/81

Hi DrabCockroach54
This seems like a pip issue trying to install from source, try upgrading the pip version and before installing numpy, it should solve it 🤞

Is it possible to do something so that the change of the server address is supported and the pictures are pulled up on the new server from the new server?

The link itself (full link) is stored inside the server. Can I assume the access is IP based not host based (i.e. dns) ?

How do I best utilize clearml in this scenario such that any coworker of mine is able to reproduce my work with the same pipeline?

Basically this sounds to me like proper software developemnt design (i.e. the class vs stages).
In order to make sure Anyone can reproduce it, you mean anyone can rerun the "pipeline" ? If this is the case just add Task.init (maybe use a specific Task type) and the agents will make sure this is Fully reproducible.
If you mean the data itself is stored, the...

and this link on it's own works?
if it does, open your browser dev tools (ctrl+shift+I on chrome, I think), I'm assuming you will see a few errors on CORS or the alike, paste them here

Yes, I do have my files in the git repo. Although I have not quite understood which part it takes from the remote git repo, and which part it takes from my local system.

it will do "git pull" on the remote machine and then apply any uncommitted changes it has stored in the Task

It seems that one also needs to explicitly hand in the git repo in the pipeline and task definitions via PipelineController,

Correct, unless the pipeline logic and the steps are the same git repo, you can...

this is not the case as all the scalars report the same iterations

MassiveHippopotamus56 could it be the the machine statistics? (i.e. cpu/gpu etc. these are considered scalars as well...)

Nicely done DeterminedToad86 🙂
Wasn't this issue resolved by torch?

DeterminedToad86 were you running a jupyter notebook or a jupyter console ?