yes what happens in the case of the installation with pip wheels files?

Well, as long as you’re using a single node, it should indeed alleviate the shard disk size limit, but I’m not sure ES will handle that too well. In any case, you can’t change that for existing indices, you can modify the mapping template and reindex the existing index (you’ll need to index to another name, delete the original and create an alias to the original name as the new index can’t be renamed...)

Ok thanks!

Well, as long as you use a single node, multiple shards offer no sca...

I made sure before deleting the old index that the number of docs matched

I created a snapshot of both disks

Yes I am 🙂

AgitatedDove14 SuccessfulKoala55 I just saw that clearml-server 1.4.0 was released, congrats 🚀 🙌 Was this bug fixed with this new version?

Also, from https://lambdalabs.com/blog/install-tensorflow-and-pytorch-on-rtx-30-series/ :

As of 11/6/2020, you can't pip/conda install a TensorFlow or PyTorch version that runs on NVIDIA's RTX 30 series GPUs (Ampere). These GPUs require CUDA 11.1, and the current TensorFlow/PyTorch releases aren't built against CUDA 11.1. Right now, getting these libraries to work with 30XX GPUs requires manual compilation or NVIDIA docker containers.

But what wheel is downloading trains in that case?

Very nice! Maybe we could have this option as a toggle setting in the user profile page, so that by default we keep the current behaviour, and users like me can change it 😄 wdyt?

/opt/clearml/data/fileserver does not appear anywhere, sorry for the confusion - It’s the actual location where the files are stored

oh seems like it is not synced, thank you for noticing (it will be taken care immediately)

Thank you!

does not contain a specific wheel for cuda117 to x86, they use the pip defualt one

Yes so indeed they don't provide support for earlier cuda versions on latest torch versions. But I should still be able to install torch==1.11.0+cu115 even if I have cu117. Before that is what the clearml-agent was doing

That would be awesome 🎉

There is a pinned github thread on https://github.com/allegroai/clearml/issues/81 , seems to be the right place?

That said, v1.3.1 is already out, with what seems like a fix:

So you mean 1.3.1 should fix this bug?

This is new right? it detects the local package, uninstalls it and reinstalls it?

yes, that's also what I thought

I think my problem is that I am launching an experiment with python3.9 and I expect it to run in the agent with python3.8. The inconsistency is from my side, I should fix it and create the task with python3.8 with:
task.data.script.binary = "python3.8" task._update_script(convert_task.data.script)Or use python:3.9 when starting the agent

both are repos for python modules (experiment one and dependency of the experiment)

(I am not part of the awesome ClearML team, just a happy user 🙂 )

No 🙂

I get the same error when trying to run the task using clearml-agent services-mode with docker, so weird

Hey @<1523701205467926528:profile|AgitatedDove14> , Actually I just realised that I was confused by the fact that when the task is reset, because of the sorting it disappears, making it seem like it was deleted. I think it's a UX issue: When I click on reset.

• The pop shows "Deleting 100%"
• The task disappears in the list of tasks because of the sortingThis led me to thing that there was a bug and the task was deleted

Basically what I did is:
` if task_name is not None:
project_name = parent_task.get_project_name()
task = Task.get_task(project_name, task_name)
if task is not None:
return task

Otherwise here I create the Task `

yes 🙂

--- /data ---------- 48.4 GiB [##########] /elastic_7 1.8 GiB [ ] /shared 879.1 MiB [ ] /fileserver . 163.5 MiB [ ] /clearml_cache . 38.6 MiB [ ] /mongo 8.0 KiB [ ] /redis

I see 3 agents in the "Workers" tab

Yes, I will try 🙂

On clearml or clearml-server?

the deep learning AMI from nvidia (Ubuntu 18.04)

So in my use case each step would create a folder (potentially big) and would store it as an artifact. The last step should “merge” all the pervious folders. The idea is to split the work among multiple machines (in parallel). I would like to avoid that these potentially big folder artifacts are also stored in the pipeline task, because this one will be running on the services queue in the clearml-server instance, that will definitely not have enough space to handle all of them

yes but they are in plain text and I would like to avoid that