Hi GreasyRaven35
You should set the output_uri, in Task init, it will auto upload the model, and register the remote location URL
task = Task.init(..., output_uri=True)You can also specify a target bucket, if you configured credentials (e.g. output_uri=" s3://bucket ")

It's the safest way to run multiple processes and make sure they are cleaned afterwards ...

Yes clearml is much better 🙂
(joking aside, mlops & orchestration in clearml is miles better)
CheerfulGorilla72 What are you looking for?

I think you are correct, we should move the definition so you can control it from the clearml.conf, make sense to you?

You described getting a secret key pair from the UI and feeding it back into the compose file. Does this mean it's not possible to seed the secrets in the compose file, starting from clean state? If so, that would explain why I can't get it to work.

Long story short, no. This would basically mean you have a pre-build credentials in the docker, this sounds dangerous 🙂
I'm not sure I'm following the use case here, what exactly are we trying to do?
(or maybe I missed something here?)

IntriguedRat44 could I ask you to open a GitHub issue on it?
I really do not want it to slip through our fingers...
(BTW: meanwhile I was not able to reproduce it, what's the OS / nvidia drivers you are using )?

Why does ClearML hide the dataset task from the main WebUI?

Basically you have the details from the Dataset page, why should it be mixed with the others ?

If I specified a project for the dataset, I specifically want it there, in that project, not hidden away in some

.datasets

hidden sub-project.

This maybe a request for "Dataset" tab under project, why would you need the Dataset Task itself is the main question?

Not all dataset objects are equal, and perhap...

Please let me know what you find 🤞

MysteriousBee56 that is so weird ... last one, I promise 🙂
docker run -t --rm nvidia/cuda:10.1-base-ubuntu18.04 bash -c "echo 'Binary::apt::APT::Keep-Downloaded-Packages \"true\";' > /etc/apt/apt.conf.d/docker-clean && apt-get update && apt-get install -y git python3-pip && python3 -m pip install trains-agent && echo \$(which python3) && echo \$(which trains-agent)"

Should work out of the box, maybe the only thing to notice is that you will get a Task for every local_rank 0 process
does that make sense ?

Hi @<1547028116780617728:profile|TimelyRabbit96>
Start with the simple scikit learn example
https://github.com/allegroai/clearml-serving/tree/main/examples/sklearn
The pipeline example is more complicated, it needs the base endpoints, start simple 😃

but I cannot compare between them

I think we noticed it, and this will be fixed in the next server update (again, some plotly.js issue there)

Thanks TroubledHedgehog16 for the context.

sdk.development.worker.report_period_sec

Yes please update to the latest version 1.8.0 for full support (to be released today, I think)
https://github.com/allegroai/clearml/blob/f6238b8a0fb662540bca9095cc0c22bd7af483c1/docs/clearml.conf#L196
https://github.com/allegroai/clearml/blob/f6238b8a0fb662540bca9095cc0c22bd7af483c1/docs/clearml.conf#L199

we have have been running agents on 3 on-premise systems.

Do notice that by default an...

@<1523712386849050624:profile|NastyFox63>

is there a limit to the search depth for this?

Yes, the Task.init auto package listing is Only the first depth (i.e. directly imported),
the reason is that the derivative packages should be resolved by pip, when the agent remotely executes that Task.
Now when the Agent is installing the task the Entire python environment is stored, so that it is always fully reprpoducible,
Make sense ?

Hi MysteriousBee56 , do you have Trains installed from the git?
Another question, you mentioned "it breaks my execution", I'm assuming you mean trains-agent?!
If that is the case, there is a fix for trains-agent install 0.15.2rc0

Okay let me check if we can reproduce, definitely not the way it is supposed to work 😞

Hi GiganticTurtle0

I have found that 

clearml

 does not automatically detect the imports specified within the function decorated

The pipeline decorator will automatically detect the imports Inside the funciton, but not outside (i.e. global), to allow better control of packages (think for example one step needs the huge torch package, and the other does not.
Make sense ?

How can I tell 

clearml

 I will use the same virtual environment in all steps...

What I'd really want is the same behaviour in the console (one smooth progress bar) and one line per epoch in the logs; high hopes, right?

I think they send some "odd" character instead of CR, otherwise I cannot explain the difference.
Can you point to a toy example demonstrating the same issue ?

Also I just tried the pytorch-lightning 

RichProgressBar

 (not yet released) instead of the default (which is unfortunately based on tqdm) and it works great.

Yey!

Weird that this code is also uploading to the 'Plots'. I replicated the same thing as my main script, but main script is still uploading to Debug Samples.

SmarmyDolphin68 are you saying the same code behaves differently ?

/opt/clearml/data/fileserver this is ion the host machine and it is mounted Into the container to /mnt/fileserer

You added it to the deprecated function 🙂
https://github.com/allegroai/clearml/blob/8496b99888deda45955bdd95f22dc929a88ec67b/clearml/backend_config/config.py#L400
See this line
https://github.com/allegroai/clearml/blob/8496b99888deda45955bdd95f22dc929a88ec67b/clearml/backend_config/config.py#L338

JitteryCoyote63 maybe this is an old example of the pytrorch ddp code? it is basically copy pasted from the pytorch website:
https://pytorch.org/tutorials/intermediate/dist_tuto.html

ReassuredTiger98 the environment is currently only set in runtime of the process (not before), this will change in the next RC of trains-agent (due is a few days)

And is "requirements-dev.txt" in your git root folder?
What is your clearml-agent version?

HighOtter69 , let me check something

Could you test with the latest "cleaml"
pip install git+Task.add_requirement(".") should be supported now 🙂

The way ClearML thinks about it is the execution graph would be something like:
script_1 -> script_2 -> script_3 ->

Where each script would have in/out, so that you can trace the usage.

Trying to combine the two into a single "execution" graph might not represent the orchestration process.

That said visualizing them could be done.
I mean in theory there is no reason why we could add those "datasets" as other types of building blocks, for visualization purposes only

(Of course this would o...

what just happened next time and what is happening underneath.

Not sure I follow, is there still an issue ?

Hi OddAlligator72

it 

 that they do not support PBT.

The optimization algorithm themselves are usually external (although the trivial stuff are in within Trains)
Do you have a specific PBT implementation you are considering ?

@<1532532498972545024:profile|LittleReindeer37> nice!!! 😍
Do you want to PR? it will be relatively easy to merge and test, and I think that they might even push it to the next version (or worst case quick RC)