Ok sure. Thanks.

Going back to the open source, I think that adding the credentials as part of the source code might allow to have "credentials" auto populate as part of the remote execution, wdyt?

Not sure how this will work when i can't supply the credentials to ClearML programatically.

Likely network. Can you run a curl on ClearML server api server from jenkin stage and see if that gets through?

Hi SuccessfulKoala55 , is there a channel here that posts version updates?

I didn't track the version on this change in behaviour. But last I tried it was able to download the content after I provide the credentials.

It didn't work as expected.
` task init
task report iter 10

task init
task report iter 10

The second task pushed the reporting iteration to 20 instead. `

Thanks CostlyOstrich36 , how do i know how is the parts indexed in the first place? Or rather, how is chunk and parts defined? Say in the context of images, videos, text documents...etc.

yup. in this case it wasn't root. Removing that USER and -u in pip solves the problem. However, in our production images, we are required to remove root access.
` FROM nvidia/cuda:10.1-cudnn7-devel

ENV DEBIAN_FRONTEND noninteractive
RUN apt-get update && apt-get install -y
python3-opencv ca-certificates python3-dev git wget sudo ninja-build
RUN ln -sv /usr/bin/python3 /usr/bin/python

create a non-root user

ARG USER_ID=1000
RUN useradd -m --no-log-init --system --uid ${USER_ID} a...

Hi SuccessfulKoala55 , just to add, my clearml.conf (client) and clearml.agent.conf (agent) can have differing values. I'm not sure which one takes precedence and if this could be the cause.

Hi CostlyOstrich36 , thanks. I will check with the Enterprise team then.

Having same issues. Looks like Google DNS can't resolve the DNS at all.
` %nslookup app.clear.ml - 8.8.8.8
Server: 8.8.8.8
Address: 8.8.8.8#53

** server can't find app.clear.ml: SERVFAIL `

Ok thanks.

Hi SuccessfulKoala55 , thanks, tested the patch and its working as expected now.

which clearml.conf is it refering to? I'm executing on my client, which is then remotely executed by the agent. Both of them has ~/clearml.conf.

Got that thanks. Just to better understand. When clearml-data upload my recursive folder of image data, it convert it into a compressed form with a different folder structure than the original datasets.

When my software pull the data, i'm returned a str. How would we manipulate the data from there?

Any advice on this?

Its actually in your documentation. Its removed since 0.17 apparently.
https://allegro.ai/clearml/docs/docs/release_notes/ver_0_17.html#clearml-agent-0-17-2

And this is my logs, it tried to install something and encountered permission denied. It wouldn't if it obeyed the force_repo_requirements_txt.

1620664917916 Kahs-MacBook-Pro.local info ClearML Task: created new task id=024a421c0e174650a1c7ff64af756c26 ClearML results page: `
1620664920359 Kahs-MacBook-Pro.local info ClearML Mon...

Hi TimelyPenguin76 , i am adding a debug sample to an existing task using the above method. What should i put for the iteration? I do not want to overwrite existing ones but i do not know what's the last count. This is for both scalar and media reporting.

Hi, it's a preference from my developers. They preferred that the they install the python libraries into the images, load them up into the registry. In other words, they prefer to have libraries installed at image time.

I can't seem to find the fix to this. Ended up using an image that comes with torch installed.

Sorry i forgot to paste the logs.

Thanks this would be a good alternative before the enterprise version comes in. How is this different from argparser btw?

Ok thanks, that worked.

Hi this is the log. I didn't see any attempt from the agent to install virtualenv on the base image.
` 1618369068169 clearml-gpu-id-b926b4b809f544c49e99625380a1534b:gpuGPU-4ad68290-0daf-4634-6768-16fad73d47a3 DEBUG Current configuration (clearml_agent v0.17.2, location: /tmp/.clearml_agent.wgsmv2t9.cfg):

agent.worker_id = clearml-gpu-id-b926b4b809f544c49e99625380a1534b:gpuGPU-4ad68290-0daf-4634-6768-16fad73d47a3
agent.worker_name = clearml-gpu-id-b926b4b809f544c49e99625...

Hi, i tried the k8s-glue on my k8s setup and needed some clarifications on some of the arguments.
--queue. Does this only refer to default and service? How can i create new queue to which it can sync with the ClearML server? --ports-mode. I'm not sure what ports mode does. doc says "add a label to the pod which can be used as service". Which pod is it referring to in the first place? All args pertaining to --ports-mode. (E.g. base-pod-num, gateway-address...etc) --overrides-yaml. What is the ...

My assumption is that the agent will have pulled that off the client's clearml.conf.

Yes it is! But ClearML didn't support multi node training out of the box in a way that it streamline the process. So we are trying to figure out a way to do it.

I think the default action of clearml-agent k8s glue when running a task is to create a virtual env and installing the dependancies. So i'm just checking how to change that behaviour to look at global instead.

This is probably the whole script.

kubectl get nodes
pip install clearml-agent
python k8s_glue_example.py

So the context I'm asking is I realise I'll need to catalogue all the dataset ids created by ppl separately on a spreadsheet. And for each experiment, I'll need to go into the code commit to see which id is being used. But on the other hand, I thought I've seen advertised use cases where the experiment can be directly linked to the dataset id being used. The brain's a bit rusty to recall how it was done.