SteadyFox10 could you try replacing the slash in the image name?

BTW: seems like conda doesn't support git+git:// packages
How about switching to pip ? you can still run the entire thing from conda env, it will just use pip & venv to install everything, other than that it should work as expected.

Please send the full log, I just tested it here, and it seems to be working

Hi HappyLion37
It seems that you are "reusing" the Tasks. Which means the second time you open them you are essentially resetting the old run and starting all over.
Try to do:
task1 = Task.init('examples', 'step one', reuse_last_task_id=False) print('do stuff') task1.close() task2 = Task.init('examples', 'step two', reuse_last_task_id=False) print('do some more stuff') task2.close()

HappyLion37 did you check the https://github.com/allegroai/trains/tree/master/examples/services/hyper-parameter-optimization ?
You can very quickly get it distributed as well

Anyhow if the StorageManager.upload was fast, the upload_artifact is calling that exact function. So I don't think we actually have an issue here. What do you think?

HighOtter69 I was able to change the color individually without an issue. What's your clearml-server ? are you using the community server ?

pip install clearml==0.17.5rc5

If you want to rename it (any pipeline), click on the "Full details" in the "Run Info" (right hand side panel), then in the full detail of the Pipeline Task you will be able to rename the pipeline execution
(Is renaming useful? should we add a right click to rename ?)

Hi PunyGoose16 ,
I think the website is probably the easiest 🙂
https://clear.ml/contact-us/
I think they get back to quite quickly

Verified.
BattyLizard6 can you open a github issue? I want to make sure this issue is addressed 🙂

DrabCockroach54 notice here there is no aarch64 wheel for anything other than python 3.5...
(and in both cases only py 3.5/3.6 builds, everything else will be built from code)
https://pypi.org/project/pycryptodome/#files

Hi @<1639799308809146368:profile|TritePigeon86>
Sounds awesome, how can we help?

Would the training script be able to register to the server when there is no public IP.
I guess it's more related to networking inside GCP, but just wanted to know if anyone tried it.

Hmm, as long as you can access it from within the LAN, it should work ...
Please update when you try it out 🙂

My use case is when I have a merge request for a model modification I need to provide several informations for our Quality Management System one is to show that the experiment is a success and the model has some improvement over the previous iteration.

Sounds likes good approach 🙂

Obviously I don't want the reviewer to see all ...

Maybe move publish the experiment and move it to a dedicated folder ? Then even if they see all other experiments, they are under "development" p...

Hi SteadyFox10
Short answer no 😞
Long answer, full permissions are available in the paid tier, along side a few more advanced features.
Fortunately in this specific use case, the community service allows you to share a single (or multiple) experiments with a read-only link. Would that work ?

now that I know I could use the right click I'll use it like in google drive etc.

That was the initial thought, but I think right clicking on a web page is not you "go to action", especially for Mac ppl ...

Hmm I guess that now that you mention it, not that obvious when I'm on a Mac as well, maybe we should have the archive button at the bottom as well..
SteadyFox10 What do you think?

BTW: the same hold for tagging multiple experiments at once

https://allegro.ai/clearml/docs/docs/faq/faq.html?highlight=clearml_config_file#clearml-configuration

SteadyFox10 TRAINS_CONFIG_FILE or CLEARML_CONFIG_FILE

😊

SteadyFox10 I suspect you are correct 🙂
CourageousLizard33 see also section (4) here:
https://github.com/allegroai/trains-server/blob/master/docs/install_linux_mac.md#launching-the-trains-server-docker-in-linux-or-macos

https://github.com/allegroai/trains/blob/master/docs/trains.conf#L47

:) yes on your gateway/firewall set http://demoapi.trains.allegro.ai to 127.0.0.1 . That's always good practice ;)

But I have no idea what will be input of step2.

What do you mean by that? the assumption is that somehow the output of step 1 will be passed (a string reference) to step 2, what am I missing ?

Hmm CourageousLizard33 seems you stumbled on a weird bug,
This piece of code only tries to get the username of the current UID, but since you are running inside a docker and probably set the environment UID but there is no "actual" UID by that number on /etc/passwd , and so it cannot resolve it.
I'm attaching a quick fix, please let me know if it solved the problem.
I'd like to make sure we have it in the next RC as soon as possible.

CourageousLizard33 so you have a Linux server running Ubuntu VM with Docker inside?
I would imagine that you could just run the docker on the host machine, no?
BTW, I think 8gb is a good recommendation for a VM it's reasonable enough to start with, I'll make sure we add it to the docs

CourageousLizard33 specifically section (4) is the issue (and it's related to any elastic docker, nothing specific to trains-server)
echo "vm.max_map_count=262144" > /tmp/99-trains.conf sudo mv /tmp/99-trains.conf /etc/sysctl.d/99-trains.conf sudo sysctl -w vm.max_map_count=262144 sudo service docker restartDid you try the above, and you are still getting the same error ?

CourageousLizard33 VM?! I thought we are talking fresh install on ubuntu 18.04?!
Is the Ubuntu in a VM? If so, I'm pretty sure 8GB will do, maybe less, but I haven't checked.
How much did you end up giving it?