I took their and add my project

I did it just because FAIR did it in detectron2 Dockerfile

my docker has my project on it all ready so I know where to mount. Maybe the agent moves/create copy of my project somewhere else?

like that I won't need to change metadata

Not a very good one, they just installed everything under the user and used --user for the pip.
It really does not matter inside a docker, the only reason one might want to do that is if you are mounting other drives and you want to make sure they are not accessed with "root" user, but with 1000 user id.

This sounds a good reason haha 😄

Let me check if we can hack something...

Thanks 🙏

SuccessfulKoala55 Thanks 🙏 I will give it a try tomorrow 🙂

Thanks, I will make sure that all the python packages install as root..
And will let you know if it works

Thanks!! you are the best..
I will give it a try when the runs will finish

Hi AppetizingMouse58 , I had around 200GB when I started the migration now I have 169GB/
And yes, It looks it is growing was 9.4GB and now 9.5G

because my project use it

Hey SuccessfulKoala55 again, how long does the migration script should take?

I was on 0.17.2 for long time and it did not happen there

AgitatedDove14 Maybe I need to change something here: apiserver.conf
for increasing workers number?

yes it looks like this.. I just wanted to understand if it is should be so slow.. or I did something wrong

I tried you solution but since my path is to a YAML file,
and task.set_configuration_object(name=name, config_taxt=my_params) upload this not in the same format task.connect_configuration(path, name=name) it not working for me 😞
(even when I am using config_type='yaml' )

From the UI it will since it getting the temp file from there.
I mean from the code (let say remotely)

If I will mount the S3 bucket to the trains-server and link the mount to /opt/trains/data/fileserver does it will work?

it stuck on Creating the backup file: /opt/trains/data/elastic_5.tar.gz

Thanks I will upgrade the server for now and will let you know

Thanks for the quick replay.
This will set more time before the timeout right?

Maybe there is a way to do something like:
task.freeze_monitor() download() task.defrost_monitor()

Ok thanks for the really fast answer 🙂

Hi AgitatedDove14 ,
Sorry for the late response It was late at my country 🙂 .

This what I am getting
appuser@219886f802f0:~$ sudo su root root@219886f802f0:/home/appuser# whoami root

how long? 😅
I am now stuck in
Copying index events-training_stats_scalar-d1bd92a3b039400cbafc60a7a5b1e52b
for more then 40 min 😥

I have an other question.
Now that I using the root user it looks better,
But my docker image has all my code and all the packages it needed I don't understand why the agent need to install all of those again?

I update to the new version 0.16.1 few weeks away and it works using the elastic_upgrade.py

Hi SuccessfulKoala55 ,
Dose running_remotely() will return True even if the task was enqueued from UI and not by execute_remotely ?

Thanks I will upgrade my instance type and the add more workers. where I need to configure it?

OHH nice, I thought that it just some kind of job queue on up and running machines

So I ask my boss and DevOps and they say for now we can use the root user inside the docker image...

Regarding of moving the fileserver to S3, what is the best way to move the old data to S3 ?

I think if I will move all the /opt/trains/data/fileserver to s3,
the trains-server will not know that right?