Nice 🙂

Can i log new lines to an old dataframe plot? any other suggestions?

Hi ChubbyLouse32
you mean to an already reported Table? or an artifact ? or a dataset ?

. It is not possible to specify the full output destination right?

Correct 😞

Hi WackyRabbit7 ,
Running in Docker mode provides you greater flexibility in terms of environment control, from switching cuda versions, to pre-compiled packages that are needed (think apt-get) etc. Specifically for DL if you are using multiple tensorflow versions, they are notorious for compiling against a specific CUDA version, and the only easy way to be able to switch between them would be different dockers. If your are a PyTorch user, then you are in luck, they have all the pytorch ver...

Hi ShallowArcticwolf27

Does the 

clearml-task

 cli command currently support remote repositories with that are intended to be used with ssh

It does 🙂

but the 

git@

 prefix used for gitlab's ssh it seems to default to looking for the repository locally

git@ is always the prefix for SSH repositories (it does not actually mean it uses it, it's what git will return when asked on the origin of the repository. The agent knows (if SSH credentials ...

Thanks MinuteGiraffe30 , fix will be pushed later today

delete logged images and texts though

logged images are also stored there?

We use nifty images, except for an 3D array the image also contains voxel spacing, and origin and direction in a world frame

Yep, make sense ... you can just upload them as debug samples from local files.
I guess the main difference is the context, debug samples (used for debugging) vs artifacts (might be useful from other Tasks / context)
https://github.com/allegroai/clearml/blob/6b9297660e0ed83a77bce3da2fab384c552206fd/examples/reporting/image_reporting.py#L36

Hi RoughHedgehog31
I'm assuming your git diff is just too big to be stored as is (probably some binary files)
it should not really have any effect on the execution, it just means the clearml-agent will not be able to reproduce the uncommitted changes.
Make sense ?

BitterStarfish58 could you open a GitHub issue on it? I really want to make sure we support it (and I think it should not be very difficult)

New python executable in /home/smjahad/.clearml/venvs-builds/3.6/bin/python2

This is the output of venv create this is odd.
Could it be that by accident you did:
pip install cleamrl-agentand not
pip3 install clearml-agentand now it is running on python2 (which would explain the error) ?
I would uninstall/reinstall on python3 to verify

Good news a dedicated class for exactly that will be out in a few days 🙂
Basically task scheduler and task trigger scheduler, running as a service cloning/launching tasks either based on time (cron alike) or based on a trigger).
wdyt?

Hi @<1573119962950668288:profile|ObliviousSealion5>

Hello, I don't really like the idea of providing my own github credentials to the ClearML agent. We have a local ClearML deployment.

if you own the agent, that should not be an issue,, no?

forward my SSH credentials using

ssh -A

and then starting the clearml agent?

When you are running the agent and you force git clonening with SSH, it will autmatically map the .ssh into the container for the git to use

Ba...

Nice debugging experience

Kudos on the work !
BTW, I feel weird to add an issue on their github, but someone should, this generic setup will break all sorts of things ...

For setting trains-server I would recommend the docker-compose, it is very easy to setup, and you just need a single fixed compute instance, details https://github.com/allegroai/trains-server/blob/master/docs/install_linux_mac.md With regards to the "low prio clusters", are you asking how they could be connected with the trains-agent or if running code that uses trains will work on them?

Hmm you either need to run with SUDO or make sure the running user has docker run permissions

I'll try to go with this option, I think its actually perfect for my needs

Great!

i’m just curious about how does trains server on different nodes communicate about the task queue

We start manual, we tell the agent just execute the task (notice we never enqueued it), if all goes well we will get to multi-node part 🙂

Interesting, do you think you could PR a "fixed" version ?
https://github.com/allegroai/clearml-web/blob/2b6aa6043c3f36e3349c6fe7235b77a3fddd[…]app/webapp-common/shared/single-graph/single-graph.component.ts

• This then looks for a module called foo , even though it’s just a namespaceI think this is the issue, are you using python package name spaces ?
  (this is a PEP feature that is really rarely used, and I have seen break too many times)
  Assuming you have from from foo.mod import what are you seeing in pip freeze ? I'd like to see if we can fix this, and better support namespaces

The problem is, the configuration is loaded at import time, so there is no "time" to pass anything other than environment variable.
That said if the only difference is server config you can use
Task.set_credentials

Okay this seems correct...
Can you share both yaml files (server & serving) and env file?

Hi RattySeagull0

I'm trying to execute trains-agent in docker mode with conda as package manager, is it supported?

It should, that said we really do not recommend using conda as package manager (it is a lot slower than pip, and can create an environment that will be very hard to reproduce due to internal "compatibility matrix" of conda, that might be changing from one conda version to another)

"trains_agent: ERROR: ERROR: package manager "conda" selected, but 'conda' executable...

Merged, is it working for you now?

Yep, everything (both conda and pip)

The only downside is that you cannot see it in the UI (or edit it).
You can now do:
data = {'datatask': 'idhere'} task.connect(data, 'DataSection')This will create another section named "DataSection" on the configuration tab. then you will be able to see/edit the input Task.id
JitteryCoyote63 what do you think?

I see TrickyFox41 try the following:
--args overrides="param=value"Notice this will change the Args/overrides argument that will be parsed by hydra to override it's params

like this.. But when I am cloning the pipeline and changing the parameters, it is running on default parameters, given when pipeline was 1st run

Just making sure, you are running the cloned pipeline with an agent. correct?
What is the clearml version you are using?
Is this reproducible with the pipeline example ?

RobustRat47 I think you have to use the latest clearml package for that (1.6.0)

SmarmySeaurchin8 it could be a switch, the problem is that when you have automatic stopping flows, they will abort a task, which is legitimate (e.g. should not considered failed)
How come you have aborted tasks in the pipeline ? If you want to abort the pipeline, you need to first abort the pipeline Task then the tasks themselves.