should i only do mongodb

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

The main reason to add the timeout is because the warning was annoying to users 🙂
The secondary was that clearml will start reporting based on seconds from start, then when iterations start it will revert back to iterations. But if the iterations are "epochs" the numbers are lower so you end up with a graph that does not match the expected "iterations" x-axis. Make sense ?

woot woot, glad to hear that!

Any chance your code needs more than the main script, but it is Not in a git repo? Because the agent supports either single script file, or a git repo with multiple files

https://allegro.ai/clearml/docs/rst/references/clearml_python_ref/logger_module/logger_logger.html#clearml.logger.Logger.report_image

Oh I see, what you need is to pass '--script script.py' as entry-point and ' --cwd folder' as working dir

Hi @<1523701868901961728:profile|ReassuredTiger98> when you get to it...
please download the wheel, then install it with

pip3 install -U clearml_agent-0.17.3rc0-py3-none-any.whl

Then run the daemon with the additional --debug argument, basically:

clearml-agent --debug daemon --foreground ...

Once the agent is running please send the Task's log from your console 🙂

It seems the code is trying to access an s3 bucket, could that be the case? PanickyMoth78 any chance you can post the full execution log? (Feel free to DM so it won't end up being public)

I did nothing to generate a command-line. Just cloned the experiment and enqueued it. Used the server GUI.

Who/What created the initial experiment ?

I noticed that if I run the initial experiment by "python -m folder_name.script_name"

"-m module" as script entry is used to launch entry points like python modules (which is translated to "python -m script")
Why isn't the entry point just the python script?
The command line arguments are passed as arguments on the Args section of t...

Interesting use case, do you already have the connect_configuration in the code? or do we need to somehow create it ?

I think that clearml should be able to do parameter sweeps using pipelines in a manner that makes use of parallelisation.

Use the HPO, it is basically doing the same thing with some more sophisticated algorithm (HBOB):
https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py

For example - how would this task-based example be done with pipelines?

Sure, you could do something like:
` from clearml import Pi...

pip install clearml==0.17.5rc4

Hi ReassuredTiger98
I think it used to be the default and then it was removed, it has no real affect on performance but it remove all asserts ... what is your use case ? do you see any performance gains ?

Yey!

Hi WickedGoat98
This sounds like a great design (obviously you have scale in mind 😉 ) Feel free to ask "stupid" questions, based on what you already wrote I doubt they will be
A few questions that come to mind (probably a few others after):
You mentioned FS synchronization, from where? i.e. what is the single source of truth ? K8s (Rancher 2.0 is basically k8s manager) can take care of mounting volumes, so no need to sync, is this a valid solution ?

BTW : (you can drag and drop an i...

Hi @<1691620877822595072:profile|FlutteringMouse14>

In the latest project I created, Hydra conf is not logged automatically.

Any chance the Task.init call is not on the main script (where the Hydra is) ?

YummyFish22 can you point to the huggingface example you are using?

The experiment finished completely this time again

With the RC version or the latest ?

Are you saying you had that odd script entry-point created by calling Task.init? (To clarify this is the problem)
Btw after you clone the experiment you can always manually edit both entry point and working dir, which based on what you said should be "script.py" and "folder"

So this is why 🙂
an agent can only run one Task at a time.
The HPO (being a Task on its own) should run on the "services" queue, where the agent can run multiple "cpu controller" Tasks like the HPO.
Make sense ?

ResponsiveCamel97
could you attach the full log?

My driver says "CUDA Version: 11.2" (I am not even sure this is correct, since I do not remember installing code in this machine, but idk) and there is no pytorch for 11.2, so maybe it fallbacks to cpu?

For some reason it detect CUDA 11.1 (I assume this is what you have installed, the driver CUDA version is the highest it will support not necessary what you have installed)

You can get a mutable copy of the entire dataset (original version), with get_mutable_copy() Then change the files on the returned directory, then create a new Dataset with the parent dataset as the original verison, then sync the folder.
You can also just update the specific file (without needing to download the entire original version)

Hi CurvedHedgehog15

User aborted: stopping task (3)

?

This means "someone" externally aborted the Task, in your case the HPO aborted it (the sophisticated HyperBand Bayesian optimization algorithms we use, both Optuna and HpBandster) will early stop experiments based on their performance and continue if they need later

Hmm I see what you mean. It is on the roadmap (ETA the next version 0.17, 0.16 is due in a week or so) to add multiple models per Task so it is easier to see the connections in the UI. I'm assuming this will solve the problem?

WackyRabbit7 my apologies for the lack of background in my answer 🙂
Let me start from the top, one of the goal of the trains-agent is to reproduce the "original" execution environment. Once that is done, it will launch the code and monitor it. In order to reproduce the original execution environment, trains-agent will install all the needed python packages, pull the code, and apply the uncommitted changes.
If your entire environment is python based, then virtual-environment mode is proba...

MuddySquid7 you mean you are creating them with TB ? or are you uploading them as debug images ?
Specifically in the ClearML UI, do you have it under "plots" tab or "debug samples" tab ?

ngrok to connect to the remote server at the office?
That makes sense, I guess this is the equivalent of using a VPN, from that point onward clearml-session can directly access the remote machine, right?

Hi MinuteWalrus85
This is great question, and super important when training models. This is why we designed a whole system to manage datasets (including storage querying, balancing data, and caching). Unfortunately this is only available in the paid tier of Allegro... You are welcome to https://allegro.ai/enterprise/ the sales guys.
🙂

you can run md5 on the file as stored in the remote storage (nfs or s3)

s3 is implementation specific (i.e. minio weka wassaby etc, might not support it) and I'm actually not sure regrading nfs (I mean you can run it, but it actually means you are reading the data, that said, nfs by definition I'm assuming is relatively fast access)
wdyt?