Would be very cool if you could include this use case!

` # Set the python version to use when creating the virtual environment and launching the experiment
# Example values: "/usr/bin/python3" or "/usr/local/bin/python3.6"
# The default is the python executing the clearml_agent
python_binary: ""
# ignore any requested python version (Default: False, if a Task was using a
# specific python version and the system supports multiple python the agent will use the requested python version)
# ignore_requested_python_version: ...

Sure, it’s because of a very annoying bug that I shared in this https://clearml.slack.com/archives/CTK20V944/p1648647503942759 , that I couldn’t solve so far.

I’m not sure you can downgrade that easily ...

Yea that’s what I thought, that’s a bit of pain for me now, I hope I can find a way to fix the bug somehow

what would be the name of these vars?

SuccessfulKoala55 I was able to recreate the indices in the new ES cluster. I specified number_of_shards: 4 for the events-log-d1bd92a3b039400cbafc60a7a5b1e52b index. I then copied the documents from the old ES using the _reindex API. The index is 7.5Gb on one shard.
Now I see that this index on the new ES cluster is ~19.4Gb 🤔 The index is divided into the 4 shards, but each shard is between 4.7Gb and 5Gb!
I was expecting to have the same index size as in the previous e...

I managed to do it by using logger.report_scalar, thanks!

Would you like me to open an issue for that or will you fix it?

And I can verify that ~/trains.conf exists in the su home folder

But you might want to double check

Actually I think I am approaching the problem from the wrong angle

Doing it the other way around works:
` cfg = OmegaConf.create(read_yaml(conf_yaml_path))
config = task.connect(cfg)
type(config)

<class 'omegaconf.dictconfig.DictConfig'> `

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

So you mean 1.3.1 should fix this bug?

erf, I have the same problem with ProxyDictPreWrite 😄 What is the use case of this one ?

CostlyOstrich36 super thanks for confirming! I have then the follow-up question: are the artifacts duplicated (copied)? or just referenced?

This works well when I run the agent in virtualenv mode (remove --docker )

This one doesn’t have _to_dict unfortunately

Not really because this is difficult to control: I use the AWS autoscaler with ubuntu AMI and when an instance is created, packages are updated, and I don't know which python version I get, + changing the python version of the OS is not really recommended

I mean that I have a taskA (controller) that is in charge of creating a taskB with the same argv parameters (I just change the entry point of taskB)

on /data or /opt/clearml? these are two different disks

I created a snapshot of both disks

line 13 is empty 🤔

my docker-compose for the master node of the ES cluster is the following:
` version: "3.6"
services:

elasticsearch:
container_name: clearml-elastic
environment:
ES_JAVA_OPTS: -Xms2g -Xmx2g
bootstrap.memory_lock: "true"
cluster.name: clearml-es
cluster.initial_master_nodes: clearml-es-n1, clearml-es-n2, clearml-es-n3
cluster.routing.allocation.node_initial_primaries_recoveries: "500"
cluster.routing.allocation.disk.watermark.low: 500mb
clust...

Does the agent install the nvidia-container toolkit, so that GPUs of the instance can be accessed from inside the docker running jupyterlab?

sorry, the clearml-session. The error is the one I shared at the beginning of this thread

Yes I did, I found the problem: docker-compose was using trains-server 0.15 because it didn't see the new version of trains-server. Hence I had trains-server 0.15 running with ES7.
-> I deleted all the containers and it successfully pulled trains-server 0.16. Now everything is running properly 🙂

continue_last_task is almost what I want, the only problem with it is that it will start the task even if the task is completed

nothing wrong from ClearML side 🙂

Yes, thanks!

I'll try to pass these values using the env vars

Alright, thanks for the answer! Seems legit then 🙂