Cloud Access section is in the 

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 page.

Any storage credentials (S3 for example) are only stored on the client side (never the trains-server), this is the reason we need to configure them in the trains.conf. When the browser needs to access those URL's (downloading an artifact) it also needs the secret/key, it automatically display a popup requesting them, and will store them in this section. Notice they are stored on the browser session (as a cookie).

Thanks ShallowCat10 !
I'll make sure we fix it 🙂

You put it there 🙂 so the assumption you know what you are looking for, or use glob? wdyt?

LazyLeopard18 could you explain some more on the specific use case you have in mind?

SmarmySeaurchin8 what's the mount command you are using?

No worries, condatoolkit is not part of it. "trains-agent" will create a new clean venv for every experiment, and by default it will not inherit the system packages.
So basically I think you are "stuck" with the cuda drivers you have on the system

TroubledHedgehog16 generally speaking you can expect about 10 api calls per minute if you have many reports, and about 3 per minute on low report. We just optimized the sdk so in cases there are lots of consequential reports they are better batched, I would recommend the latest RC

DefeatedOstrich93 what do you mean by "I am wondering why do I need to create files before applying diff ?"
git diff will not list files unless their are added (they are marked as "untracked") think temp files logs etc. until you add a file to git it will basically ignore that file. Make sense ?

No 😞

Yep 😞

(with older clearml versions though…).

Yes, we added content type header for the files when uploading to S3 (so it is easier for users to serve them back). But it seems the python 3.5 casting from Path to str breaks it mimetype call....

it handles 2FA if my repo lies in Github and my account needs 2FA to sign in

It does not 😞

Hi EnviousStarfish54
docker on windows , with nvidia runtime support is only with WSL (I think)
https://docs.nvidia.com/cuda/wsl-user-guide/index.html#installing-wip
https://medium.com/@dalgibbard/docker-with-gpu-support-in-wsl2-ebbc94251cf5

basically @<1554638166823014400:profile|ExuberantBat24> you can think of hyper-datasets as a "feature-store for unstructured data"

My apologies, let me rephrase:
if you are using pip ans package manager and not running in docker-mode, trains-agent cannot touch the cuda/cuddn drivers (actually .so) library.
If you want to verify you can check echo $LD_LIBRARY_PATH

Hmm, I think I need more to try and reproduce, what exactly did you do, what was the expected behavior vs reality ?

UnevenDolphin73 something like this one?
https://github.com/allegroai/clearml/pull/225

correct (i.e. not frontend)

HealthyStarfish45 the pycharm plugin is mainly for remote debugging, you can of course use it for local debugging but the value is just to be able to configure your user credentials and trains-server.
In remote debbugging, it will make sure the correct git repo/diff are stored alongside the experiment (this is due to the fact that pycharm will no sync the .git folder to the remote machine, so without the plugin Trains will not know the git repo etc.)

Is that helpful ?

Unfortunately not, the queues tab shows only the number of tasks, but not resources used

in the queue

Oh, yes, that makes sense to add, I like that 🙂
(the main question is what data is there in the backend DBs, let me know what I can get)

Let me check ....

VirtuousFish83 I can confirm clearml-server 1.3 solves the issue.

TrickyRaccoon92 I'm not sure I follow, TB do show? and you want to add additional plotly plot ?

PreciousParrot26 I think this is really a matter of the CI process having very limited resources. just to be clear, you are correct and the steps them selves are Not executed inside the CI environment, but it seems that even running the pipeline logic is somehow "too much" for the limited resources... Make sense ?

It also seems that

PipelineDecorator.upload_artifact

is not compatible with caching, sadly,

Both use the exact same mechanism of uploading artifacts (i.e. including caching for downloaded artifacts), in terms of caching pipeline components, this is on a component level (i.e. same code/task same arguments, equals cache hit)
What exactly are you getting ? how is it that the "PipelineDecorator.upload_artifact" uploads to a different storage ? is that reproducible ?

SourLion48 you mean the wraparound ?
https://github.com/allegroai/clearml/blob/168074acd97589df58436a3ec122a95a077620c2/docs/clearml.conf#L33

SoggyFrog26 you'll have it in the next RC 🙂
Not sure what's the plan I know one should be out today/tomorrow, worst case on the next one 🙂

Hi JumpyDragonfly13

1. is "10.19.20.15" accessible from your machine (i.e. can you ping to it)?
2. Can you manually SSH to 10.19.20.15 on port 10022 ?

SoggyBeetle95 you can configure the credentials in the clearml.conf running on the agent machines:
https://github.com/allegroai/clearml-agent/blob/a5a797ec5e5e3e90b115213c0411a516cab60e83/docs/clearml.conf#L320
(I'm assuming these are storage credentials)
If you need general purpose env variables, you can ad them here:
https://github.com/allegroai/clearml-agent/blob/a5a797ec5e5e3e90b115213c0411a516cab60e83/docs/clearml.conf#L149
with ["-e", "MY_VAR=MY_VALUE"]

How did you define the decorator of "train_image_classifier_component" ?
Did you define:
@PipelineDecorator.component(return_values=['run_model_path', 'run_tb_path'], ...Notice two return values