should i only do mongodb

No, you should do all 3 DBs ELK , Mongo, Redis

Hi @<1523706645840924672:profile|VirtuousFish83>
could it be you have some permission issues ?

: Forbidden: updates to statefulset spec for fields other than 'replicas', 

It might be that you will need to take it down and restart it. not while it is running.
(do make sure you backup your server 🙂 )

I wonder if I just need to join 2 docker-compose files to run everything in one session

Actually that could also work

But for reference, when I said IP i meant the actual host network IP not the 127.0.0.1 (which is the same as localhost)

Interesting, i wasn't aware of this python module for executing accelerate. I'll try to use that.

It's essentially the cmd line:
None
None

GiganticTurtle0 you mean the repo for the function itself ?
the default assumes the function is "standalone", you can specify a repo with:
@PipelineDecorator.component(..., repo='.')
will take the current folder's repo (i.e. the local one)
you can also specify repo url/commit etc (repo=' https://github/user/repo/repo.git ' ....)
See here:
https://github.com/allegroai/clearml/blob/dd3d4cec948c9f6583a0b69b05043fd60d8c103a/clearml/automation/controller.py#L1931

I created my own docker image with a newer python and the error disappeared

I'm not sure I understand how that solved it?!

Sounds good to me 🙂

Hi SarcasticSparrow10
I think the default search is any partial match, let me check if there is a way to do some regexp / wildcard

Thanks!

Here, I

know

the pattern is incomplete and invalid. A less advanced user might not understand what's up.

Basically like your suggestion that if the request fails while typing instead of the error popup the search bar will turn "dark red", and on the next key stroke will be "cleaned" ?

CooperativeFox72 I would think the easiest would be to configure it globally in the clearml.conf (rather than add more arguments to the already packed Task.init) 🙂
I'm with on 60 messages being way too much..
Could you open a Github Issue on it, so we do not forget ?

Well, in that case, just change the order it should solve it (I'll make sure we have that as the default:

conda_channels: ["pytorch", "conda-forge", "defaults", ]

It should solve the issue 🙂

It's always preferred to use conda_freeze: false
That said, if you do use conda_freeze: true it should also freeze the cudatoolkit, so it should have worked.
BTW when you say it worked, is it 0.17.2 version or the hacked RC I sent ?

Let me check something

Would you have an example of this in your code blogs to demonstrate this utilisation?

Yes! I definitely think this is important, and hopefully we will see something there 🙂 (or at least in the docs)

@<1523701868901961728:profile|ReassuredTiger98> thank you so much for testing it!

not really 😞
Why would you want to set it up manually ? makes sense to have it in the cache folder, no?

Hi AbruptHedgehog21
can you send the two models info page (i.e. the original and the updated one) ?
do you see the two endpoints ?
BTW: --version would add a version to the model (i.e. create a new endpoint with version "endpoint/{version}"

'

' error [Errno 13] Permission denied:

Seems like a permission issue ?
Try to remove your entire clearml cache folder None

PanickyMoth78

LockException: [Errno 11] Resource temporarily unavailable

I'm not sure I understand how you got to this error (obviously creating datasets and getting them back works), what is unique in the setup/flow itself ?

Hi RipeGoose2
You can also report_table them? what do you think?
https://github.com/allegroai/clearml/blob/master/examples/reporting/pandas_reporting.py
https://github.com/allegroai/clearml/blob/9ff52a8699266fec1cca486b239efa5ff1f681bc/clearml/logger.py#L277

Hi @<1619867971730018304:profile|WhimsicalGorilla67>
No 😞 only the "admin" (owner) of the workspace has access to it

Hi @<1555362936292118528:profile|AdventurousElephant3>

hard time connecting to non-aws s3 bucket

if this is a non-s3 the output_uri should look something like:
output_uri = " None :port/backet"
Then make sure you have the correct credentials in your clearml.conf:
None

My pleasure 😊

Do you know how I can make sure I do not have CUDA or a broken installation installed?

I don't think this is the case, it is quite specifically installing the CPU version.
BTW: after the agent fails it will not remove the venv, so you can get into it and check, from the log it will be in: /home/tim/.clearml/venvs-builds/3.7

Sorry just saw it,
https://github.com/allegroai/clearml-agent/commit/918dd39b87501dc873354b7cc5c9efa933650897

Hi ExasperatedCrocodile76
This is quite the hack, but doable 🙂
`
file_path = task.connect_configuration(name = 'augmentations', configuration = 'augmentations.py')

import importlib

module_name = 'augmentations'

spec = importlib.util.spec_from_file_location(module_name, file_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module) `
https://stackoverflow.com/a/54956419

Yes the clearml-server AMI - we want to be able to back it up and encrypt it on our account

I think the easiest and safest way for you is to actually have full control over the AMI, and recreate once from scratch.
Basically any ubuntu/centos + docker and docker-compose should do the trick, wdyt ?

send the agent's logs to log management and monitoring service,

These are stored into ELK, it was built to store large amounts of logs, I cannot see any reason why one would want to remove it?

Maybe if there would be a way to change their format, it could also help filtering them from my side.

You mean in the UI?

Ohh okay something seems to half work in terms of configuration, the agent has enough configuration to register itself, but fails to pass it to the task.
Can you test with the latest agent RC:
0.17.2rc4