They don't have the same version. I do seem to notice that if the client is using version 3.8, during remote execution will try to use that same version despite the docker image not installed with that version.

Ok sure. Thanks.

Yeah that'll cover the first two points, but I don't see how it'll end up as a dataset catalogue as advertised.

Ok thanks. that explains alot. We have been doing this wrongly the whole time, thinking that the clearml.conf on the client side would be acknowledged by the remote agent execution. In reality, only the API section is utilised.

Do you have more info on vault?
Actually it only make sense if the entire department or organisation are saving their models in a common repo. In our case this is not possible due to client security (e.g. training data from clients can potentially be 'reverse engineered' from trained models in future). So each department and even projects will need their own repo.

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.

can you please verify that you have all the required packages installed locally ?

Its not installed on the image that runs the experiment. But its reflected in the requirements.txt.

what is the setting of 

agent.package_manager.system_site_packages

True.

Hi AgitatedDove14 . I'm trying out passing env via the code instead.
task.set_base_docker("nvcr.io/nvidia/tensorflow:19.11-tf2-py3 --env TRAINS_AGENT_GIT_USER=git_username_here --env TRAINS_AGENT_GIT_PASS=git_password_here")So the strange thing is when my k8sglue pulls a task, this happens.
` Pulling task xxxxxxxxxx launching on kubernetes cluster
Pushing task xxxxxxxxxx into temporary pending queue
Kubernetes scheduling task id=xxxxxxxxxxxx
skipping docker argument TRAINS_AGENT_GIT_USE...

Hi FriendlySquid61 , AgitatedDove14 , the issue and possible fix is in this issue raise. https://github.com/allegroai/clearml-agent/issues/51

Hi, any idea if i can acheive this? I just need a list of usernames.

I thought of another potential way but not sure if the SDK supports it.
We will perform manual save and upload of model using vanilla boto3 and credentials passed in as env var. Use ClearML SDK to update the Model Repo on the location of the model, without ClearML uploading it explicitly.Would the above work?

This is a env var?
CLEARML_CONFIG_FILE

clearml=1.0.3
python=3.8.10
clearml-data upload --id 12314jhg42342j4j --storagehttp://ecs.ai is an on-prem DELL EMC ECS that serves as our S3 storage configured with s self signed cert.

Any advice on this?

Hi AgitatedDove14 , i dug a bitt deeper. I saw this in installed packages in the original completed task. When the task is cloned, this is copied over and thus the problem. Can i ask, how ClearML create the list of installed packages? Why is it that some of them (E.g. attr is being pulled from @ file:///tmp/build/80754af9/attrs_1604765588209/work)

` absl-py==0.11.0
alabaster==0.7.12
antlr4-python3-runtime==4.8
apex==0.1
appdirs==1.4.4
argon2-cffi==20.1.0
ascii-graph==1.5.1
async-gener...

I can't seem to find the version number on the clearml web app. Is there a specific way?

I would like to run ClearML agent on kubernetes. So basically I need to run the image on a pod, but there isn't any information on how the agent would communicate with the code, nor how it would spawn more pods to run the task.

Then you pass the tolerations definition through a different pod template?

Yup.

Hi TimelyPenguin76 ,

If you notice in the last screenshot, it state the bucket name to be http://ecs.ai . It then it tries to open http://s3.amazonaws.com/ecs.ai/clearml-models/artifact/uploading_file?X-Amz-Algorithm= ....

Hi, Self-hosted using docker-compose.

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.

Hi AgitatedDove14 , that's what i am trying to figure out as well. The task has nothing to do with torch, and the requirements.txt doesn't have any torch packages as well.

Sorry i forgot to paste the logs.

Ok thanks, looking forward to it. Would you advise on the bug you encountered?

ok. Any idea what can go on between the setting up of clearml-agent and initialising the clearml-agent itself? Does the clearml-agent try to communicate with any internet address. From another perspective, it looks like a long time out issue. I happen to be deploying on a disconnected on-premise setup.

So these (PIP_INDEX_URL) weren't used when clearml starts running pip.

Just to put a ping for those on this side of the timezone to look at. Thanks.

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.

It would make sense on a very large resource cluster. Unfortunately we only have less than 50 GPUs to share across. A multi-tenant SAAS would cut the resources into even more smaller clusters and not help with efficiency. Or would you have a suggestion?

Hi, i will have to get back to you again. Need to check every client's repo to determine your hypothesis.