We’d be using https in production

Nice 🙂
@<1687653458951278592:profile|StrangeStork48> , I was reading this thread trying to understand what exactly is the security concern/fear here, and I'm not sure I fully understand. Any chance you can elaborate ?

Assuming it was hashed, the seed would be stored on the same server, so knowing both would allow me the same access, no?

• Set hashed passwords with  pass_hashed: true
• Generate passwords using  python3 -c 'import bcrypt,base64; print(base64.b64encode(bcrypt.hashpw("password".encode(), bcrypt.gensalt())))'  (obviously, replace "password" with the actual password). The resulting b64 string should be placed in the password field for each user.
  For example, assuming your password is "123456":
• bash:

> python3 -c 'import bcrypt,base64; print(base64.b64encode(bcrypt.hashpw("123456".encode(), bcrypt.gensal...

Hi @<1687653458951278592:profile|StrangeStork48>

secrets manager per se,

Quick question, are you running the trains-server over http or https ?

Hi @<1687653458951278592:profile|StrangeStork48>
I have good news, v1.0 is out with hashed passwords support.

RC you can see on the main readme, (for some reason the Conda badge will show RC and the PyPi won't)
https://github.com/allegroai/clearml/

that clearml-agent needs to be installed from system python mentioned anywhere in the docs, if not I suggest it gets added.

You are right, I will check and fix if not 🙂

Thank you so much for helping.

My pleasure

Maybe hard code them into the code or pass them as env variable after the connect?
None
None

Yes, I think the API is probably the easiest:
from clearml.backend_api.session.client import APIClient client = APIClient() project_list = client.projects.get_all() print(project_list)

Hi SubstantialElk6
ClearML-Serving is already out with a new version, the ETA for the next ClearML-serving full 1.0 (which is the new redesign version) is the end of May

Hi JuicyDog96
The easiest way is:
from trains.backend_api.session.client import APIClient client = APIClient() client.projects.get_all()You can just run it from a python console and check what you are getting.
Full API is https://github.com/allegroai/trains/tree/master/trains/backend_api/services/v2_8

Is it also possible to specify different user/api_token for different hosts? For example I have a github and a private gitlab that I both want to be able to access.

ReassuredTiger98 my apologies I just realize you can use ~/.git-credentials for that. The agent will automatically map the host .git-credentials into the docker :)

No (this is deprecated and was removed because it was confusing)
https://github.com/allegroai/clearml-agent/blob/cec6420c8f40d92ab1cd6cbe5ca8f24cf351abd8/docs/clearml.conf#L101

(only works for pyroch because they have diff wheeks for diff cuda versions)

Yeah the doctring is always the most updated 🙂

BTW: the agent will resolve pytorch based on the install CUDA version.

Hi JollyChimpanzee19
I found this one:
https://clearml.slack.com/archives/CTK20V944/p1622134271306500

Hi @<1523701323046850560:profile|OutrageousSheep60>
What do you mean by "in clearml server" ? I do not see any reason a subprocess call from a Task will be an issue. What am I missing ?

TightElk12 I think this message belongs to a diff thread ;)

Hi UnevenDolphin73
I think there is an open issue on github, I'm not sure but I think there is already some internal progress

No worries, I'll see if I can replicate it anyhow

why are all defined components shown in the UI Results/Plots/PipelineDetails/ExecutionDetails section? Shouldn't it make more sense to show only the ones that are used in that pipeline?

They are listed there (because of the decorator, you basically "say" these are steps so they are listed), the actual resolving (i.e. which steps are actually being called) is done in "real-time"
Make sense ?

GiganticTurtle0 in the PipelineDecorator.component , did you pass helper_functions=[] with refrence to all the sub component ?

Hi GrotesqueOctopus42
In theory it can be built, the main hurdle is getting elk/mongo/redis containers for arm64 ...

This is what I think you should end up with
DiscreteParameterRange('General/dataset_url', values=["option 1 for url", "option 2 for url"])If args['dataset_url'] is a list, you should just do values=args['dataset_url']

The notebook path goes through a symlink a few levels up the file system (before hitting the repo root, though)

Hmm sounds interesting, how can I reproduce it?

The notebook kernel is also not the default kernel,

What do you mean?

Hi RoundMosquito25
What do you mean by "local commits" ?

It does not use key auth, instead sets up some weird password and then fails to auth:

AdventurousButterfly15 it ssh Into the container inside the container it sets new daemon with new random very long password
It will Not ssh to the host machine (i.e. the agent needs to run in docker mode, not venv mode), make sense ?

But it’s running in docker mode and it is trying to ssh into the host machine and failing

It is Not sshing to the machine it is sshing directly Into the container.
Notice the port is is sshing to is 10022 which is mapped into the container

set the following:
CLEARML_AGENT_DISABLE_SSH_MOUNT=1 clearml-agent daemon ...The issue is, it will automatically mount the .ssh of the host into the container, so that if you are using SSH to clone git you have credentials, in your case, it also mounts the configuration, hence failing to login.
I will make sure we add it to the configuration file, so it is more visible