Correct 🙂

TrickyRaccoon92 actually Click is on the to do list as well ...

this is very odd, can you post the log?

Hi PerplexedCow66
I'm assuming an extension for this:
https://github.com/allegroai/clearml-serving/issues/32

Basically JWT can be used as a general access/block all endpoints, which is most efficnely used if handled by k8s loadbalancer (nginx/envoy),
but if you want a per-endpoint check (or maybe do something based on the JWT values)
See adding JWT to FastAPI here:
https://fastapi.tiangolo.com/tutorial/security/oauth2-jwt/?h=jwt#oauth2-with-password-and-hashing-bearer-with-jwt-tokens
T...

MagnificentPig49 that's a good question, I'll ask the guys 🙂
BTW, I think the main issues is actually making sure there is enough documentation on how to compile it...
Anyhow I'll update here

Hi @<1576381444509405184:profile|ManiacalLizard2>
Yeah that should work, assuming credentials are set in your clearml.conf

Hi ReassuredTiger98
So let's assume we call:
logger.report_image(title='training', series='sample_1', iteration=1, ...)And we report every iteration (keeping the same title.series names). Then in the UI we could iterate back on the last 100 images (back in time) for this title / series.
We could also report a second image with:
logger.report_image(title='training', series='sample_2', iteration=1, ...)which means that for each one we will have 100 past images to review ( i.e. same ti...

No should be fine... Let me see if I can get a windows box 🙂

The bug was fixed 🙂

That makes total sense. The question was about the Mac users and OS environment in the configuration file and having that os environment set in code (this is my assumption as it seems that at import time it does not exist). What am I missing here?

Hmm I think this was the fix (only with TF2.4), let me check a sec

In the Task log itself it will say the version of all the packages, basically I wonder maybe it is using an older clearml version, and this is why I cannot reproduce it..

Yes, though the main caveat is the data is not really immutable 😞

ERROR: Error checking for conflicts. ... AttributeError: _DistInfoDistribution__dep_map

Thanks a lot. I meant running a bash script after cloning the repository and setting the environment

Hmm that is currently not supported 😞
The main issue in adding support is where to store this bash script...

Perhaps somewhere inside clear ml there is an order of actions for starting that can be changed?

Not that I can think of,
but let's assume you could have such a thing, what would you have put in the bash script (basically I want to see maybe there is a worka...

Oh, fork the repository (this will create a copy on your GitHub account), this is done from GitHub's web page
Then commit to your repository (on the master branch)
Then in the GitHub page of the repository on your account, you will have a green button suggesting you to PR it 🙂

Hmmm, are you running inside pycharm, or similar ?

Okay, so you want to take the jupyter notebook (aka colab) and have that experiment show on Trains, then use the Trains UI to launch it remotely on one of the machines running the trains-agent. Is that correct?

Is this consistent on the same file? can you provide a code snippet to reproduce (or understand the flow) ?
Could it be two machines are accessing the same cache folder ?

Makes total sense!
Interesting, you are defining the sub-component inside the function, I like that, this makes the code closer to how this is executed!

The .ssh is mounted, but the owner is my local user,

sudo -H clearml-agent ...to allow sudo to access home

I think your use case is the original idea behind "use_current_task" option, it was basically designed to connect code that creates the Dataset together with the dataset itself.
I think the only caveat in the current implementation is that it should "move" the current Task into the dataset project / set the name. wdyt?

Could it be in a python at_exit event ?

It’s only on this specific local machine that we’re facing this truncated download.

Yes that what the log says, make sense

Seems like this still doesn’t solve the problem, how can we verify this setting has been applied correctly?

hmm exec into the container? what did you put in clearml.conf?

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 ?

TroubledHedgehog16

but doesn't run when I deploy it using clearml. Here's the log of the error:

...

My guess is that clearml is reimporting keras somewhere, leading to circular dependencies.

It might not be circular, but I would guess it does have something to do with order of imports. I'm trying to figure out what would be the difference between local run and using an agent
Is it the exact same TF version?

EnviousStarfish54 Notice that you can configure it on the agent machine only, so in development you are not "wasting" storage when uploading debug checkpoints/models 🙂

How can the first process corrupt the second

I think that something went wrong and both Agents are using the same "temp" folder to setup the experiment.

why doesn't this occur if I run pipeline from command line?

The services queue is creating new dockers with everything in them so they cannot step on each others toes (so to speak)

I run all the processes as administrator. However, I've tested running the pipeline from command line in non-administrator mode, it works fine....

Hmm that is odd.
Can you verify with the latest from GitHub?
Is this reproducible with the pipeline example code?

Hi NastyOtter17

"Project" is so ambiguous

LOL yes, this is something GCP/GS is using:
https://googleapis.dev/python/storage/latest/client.html#module-google.cloud.storage.client