So actually while we’re at it, we also need to return back a string from the model, which would be where the results are uploaded to (S3).

Is this being returned from your Triton Model? or the pre/post processing code?

Hi NaughtyFish36

c++ module fails to import, anyone have any insight? required c++ compilers seem to be installed on the docker container.

Can you provide log for the failed Task?
BTW: if you need build-essentials you can add it as the Task startup script
apt-get install build-essentials

understood, can you try
Task.add_requirements("-e path/to/folder/")

YEYYYYYYyyyyyyyyyyyyyyyyyy

So I see this in the build, which means it works , and compiles, what is missing ?
` Building wheels for collected packages: leap
Building wheel for leap (setup.py) ... [?25l- \ |

1667848450770 UH-LPT371:0 DEBUG / - \ | / - done
[?25h Created wheel for leap: filename=leap-0.4.1-cp38-cp38-linux_x86_64.whl size=1052746 sha256=1dcffa8da97522b2611f7b3e18ef4847f8938610180132a75fd9369f7cbcf0b6
Stored in directory: /root/.cache/pip/wheels/b4/0c/2c/37102da47f10c22620075914c8bb4a9a2b1f858263021...

I lost you SmallBluewhale13 is this the Task init call you used:
task = Task.init( project_name="examples", task_name="load_artifacts", output_uri="s3://company-clearml/artifacts/bethan/sales_journeys/", )

The point is, " leap" is proeperly installed, this is the main issue. And although installed it is missing the ".so" ? what am I missing? what are you doing manually that does Not show in the log?
In other words how did you install it "menually" inside the docker when you mentioned it worked for you when running without the agent ?

Nice!!!

function and just seem to be getting an "isadirectory" error?

Can you post here what you are getting ? which clearml version are you using ?!

also tried manually adding

leap==0.4.1

in the task UI which didn't work.

That has to work, if it did not, can you send the log for the failed Task (or the Task that did not install it)?

The environment in the logs does show that leap is being installed potentially from a cache?

• leap @ file:///opt/keras-hannd...

trains[azure] give you the possibility to do the following:
from trains import StorageManager my_local_cached_file = StorageManager.get_local_copy('azure://bucket/folder/file.bin')This means you do not have to manually download stuff/ and maintain the cache local cache, the StorageManager will do that for you.
If you do no need that ability, no need to install the trains[azure] you can just install trains
Unfortunately, we haven't had the time to upgrade to the Azure storage v...

Manually I was installing the

leap

package through

python -m pip install .

when building the docker container.

NaughtyFish36 what happnes if you add to your "installed packages" /opt/keras-hannd ? This should translate to "pip install /opt/keras-hannd" which seems like exactly what you want, no ?

Sure thing, my pleasure 🙂

SmarmySeaurchin8 yes, you should avoid that (we are saving it for a future feature 😉 )

Hi TrickyFox41

Hey since Hydra does not work with

clearml-task

I should shouldn't it? what does not work ?

check the latest RC, it solved an issue with dataset uploading,
Let me check if it also solved this issue

I think it would be nicer if the CLI had a subcommand to show the content of 

~/.clearml_data.json

 .

Actually, it only stores the last dataset id at the moment, no not much 🙂
But maybe we should have a cmd line that just outputs the current datasetid, this means it will be easier to grab and pipe
WDYT?

(currently I think the implementation expects that if the download completed, it was successful)

Hi AdorableFrog70
I assume so, there's API for everything so you can always get the data. wdty?

task=Task.current_task()
Will get me the task object. (right?)

PanickyMoth78 yes, always, from anywhere, this is a singleton object 🙂

Hi SubstantialElk6

We can't seem to find a way for the end user to pass in their git credentials when they run their codes in both agent and non-agent scenarios. Any advice here?

The bottom line is the agent needs to have read-only access to all the repositories so it can launch any Task. I would recommend to create an agent git user with read-only credentials and configure the agent to use it. wdyt?

BitterStarfish58 I would suspect the upload was corrupted (I think this is the discrepancy between the files size logged, to the actual file size uploaded)

Hi ColossalDeer61 ,
the next trains-agent RC (solving the #196 issue) will also solve the double install issue 🙂

Another (minor) issue is that all the packages that are installed using git+https are cloned and installed twice, immediately one after the other

Yes this is so that we can better log the installed package name, not a major issue, but we just fixed a bug with derivative packages from git packages.
https://github.com/allegroai/trains/issues/196

Hi FierceHamster54
Are you saying the pipeline component is a standalone script?
If this is the case then you are correct, it should not need to, I think you can specify it in the decorator.
I think this might work 🤞
@PipelineDecorator.component(..., repo=False)

LOL can't wait to see that

Give me a minute

My pleasure 🙂

HandsomeCrow5 I see, my bad.
BTW: Did you see this one?
https://github.com/allegroai/trains/blob/master/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py
And the helper classes here: https://github.com/allegroai/trains/tree/master/trains/automation

Is it possible to make a connection to a S3 bucket via this authentication method with the open source version on EKS?

Hi BoredBluewhale23
In your setup, are we talking about agents running inside the Kubernetes cluster, or clients connecting from their own machine ?

GreasyPenguin14 whats the clearml version you are using, OS & Python ?
Notice this happens on the "connect_configuration" that seems to be called after the Task was closed, could that be the case ?