Okay, let me see...

RoughTiger69
move the files locally (i.e. based on the example move folder b into folder a ) Create a new version with two parents ('a' and 'b') then sync the local root folder ('a' in your case). Only the meta-data should change (because the referenced files are already in one of the datasets)wdyt?

ClearML does

For example, the

Task

object is heavily overloaded and its documentation would benefit from being separated into logical units of work. It would also make it easier for the ClearML team to spot any formatting issues.

This is a very good point (the current documentation is basically docstring, but we should create a structured one)

... but some visualization/inline code with explanation is also very much welcome.

I'm assuming this connected with the previous po...

ContemplativeCockroach39 unfortunately No directly as part of clearml 😞
I can recommend the Nvidia triton serving (I'm hoping we will have the out-of-the-box integration soon)
mean while you can manually run it , see docs:
https://developer.nvidia.com/nvidia-triton-inference-server
docker here
https://ngc.nvidia.com/catalog/containers/nvidia:tritonserver

Are you running the agent in docker mode or venv mode?

👍

a task of queue B if the next task is of type A it will have to wait,

It seems you imply there are two types of Tasks and they need to be executed one after the other ?

CheerfulGorilla72 my guess is the Slack token does not have credentials for the private channel, could that be ?

releasing today 🙂

But there is no need for 2FA for cloning repo

Can't see any reason not to 🙂

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

It's much more than that, it's a way of life 🙂
But seriously now, it allows you to use any machine as part of your cluster, and send jobs for execution from the web UI (any machine, even just a standalong GPU machine under your desk, or any cloud GPU instance any mixing the two together:)

Maybe I need to change something here: 

apiserver.conf

Not sure, I'm still waiting on answer... It...

I'm not sure I follow the example... Are you sure this experiment continued a previous run?
What was the last iteration on the previous run ?

When are those keys used?

They are the default keys for internal access, basically just make up something, otherwise someoune could access the server with the default keys

I think the only way is using the API, with task.query_tasks and filter, would that have helped?

Interesting... TrickyRaccoon92 could it be the validation phase was creating a new Tensorboard file ?

This will set more time before the timeout right?

Correct.

task.freeze_monitor()
download()
task.defrost_monitor()

Currently there isn't, but that's a good ides.
What would be the argument of using it vs increasing the timeout ?
btw: setting the resource timeout to 99999 will basically mean that it will wait until the first reported iteration, Not that it will just sleep for 99999sec 🙂

Hi PungentLouse55

Hope you are not tired of me

Lol 🙂 No worries

I am using trains 0.16.1

Are you referring to the trains-server version or the python package ? (they are not the same and can be of totally different versions)

ValueError('Task object can only be updated if created or in_progress')

It seems the task is not "running" hence the error, could that be

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 🙂

yes

Hmm let me check something

Hi @<1523701066867150848:profile|JitteryCoyote63>

Hi, how does

agent.enable_git_ask_pass

works

basically it pushes the pass through stdin to git when it asks (it is a git feature)

SlipperyDove40 following on the missing section name, this seems like backwards compatibility issue. Try calling with backwards_compatibility=False
my_params = Task.get_parameters(backwards_compatibility=False)This should always add the section name prefix.

but this is not different from not using clearml-data,

ReassuredTiger98 just making sure we are on the same page. clearml-data immutability is fixed, the user cannot change the content of the dataset (it is actually compressed and uploaded). If you want to change it, you create a new child version

Let me verify something in the code,

JitteryCoyote63 any specific reason for the question?

JitteryCoyote63 I think I found the bug in clearml-task it adds it at the end instead of before everything else

Ok, I think figured it out.

Nice!

ClearML doesn't add all the imported packages needed to run the task to the Installed Packages

It does (but not derivative packages, that are used by the required packages, the derivative packages will be added when the agent is running it, because it creates a new clean venv and then it add the required packages, then it updates back with everything in pip freeze, because it now represents All the packages the Task needs)

Two questions:
Is t...