It could be: I am running the clearml aws autoscaler in an ec2 instance having iam roles allowing for creating/deleting instances, but I get Warning! exception occurred: An error occurred (UnauthorizedOperation) when calling the RunInstances operation: You are not authorized to perform this operation. Encoded authorization failure message: ...
I suspect that since the agent is running in docker mode, the boto3 lib doesn’t automatically get the right permissions from the ec2-instance. To...

but it is set here, right?

No 🙂

yes, that's also what I thought

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

agent.package_manager.type = pip ... Using base prefix '/home/machine1/miniconda3/envs/py36' New python executable in /home/machine1/.trains/venvs-builds/3.6/bin/python3.6 Also creating executable in /home/machine1/.trains/venvs-builds/3.6/bin/python Installing setuptools, pip, wheel...

even if I move the Github workers internally where they could have access to the prod server, I am not sure I would like that, because it would pile up test data in the prod server that is not necessary

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?

Yes AgitatedDove14 🙂

Thanks!3. I don't know, I never used Highcharts 🙂

yes, the new project is the one where I changed the layout and that gets reset when I move an experiment there

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...

Otherwise I can try loading the file with custom loader, save as temp file, pass the temp file to connect_configuration, it will return me another temp file with overwritten config, and then pass this new file to OmegaConf

Thank you!

I am sorry to give infos that are not very precise, but it’s the best I can do - Is this bug happening only to me?

Hi AgitatedDove14 , initially I was doing this, but then I realised that with the approach you suggest all the packages of the local environment also end up in the “installed packages”, while in reality I only need the dependencies of the local package. That’s why I use _update_requirements , with this approach only the package required will be installed in the agent

I guess I’ll get used to it 😄

Hi CostlyOstrich36 , one more observation: it looks like when I don’t open the experiment in the webUI before it is finished, then I get all the logs correctly. It is when I open the experiment in the webUI while it is running that I don’t see all the logs.
So it looks like there is an effect of caching (the logs are retrieved only once, when I open the experiment for the first time), and not afterwards (or rarely). Is that possible?

Still the same problem 😞

Because it lives behind a VPN and github workers don’t have access to it

This one doesn’t have _to_dict unfortunately

OK but nowhere I specified that, I just checked my trains.conf file

Could you please point me to the relevant component? I am not familiar with typescript unfortunately 😞

This is the mapping of the faulty index:
` {
"events-plot-d1bd92a3b039400cbafc60a7a5b1e52b_new" : {
"mappings" : {
"dynamic" : "strict",
"properties" : {
"@timestamp" : {
"type" : "date"
},
"iter" : {
"type" : "long"
},
"metric" : {
"type" : "keyword"
},
"plot_data" : {
"type" : "binary"
},
"plot_len" : {
"type" : "long"
},
"plot_str" : {
...

ok, what is the 3.8 release? a server release? how does this number relates to the numbers above?

but then why do I have to do task.connect_configuration(read_yaml(conf_path))._to_dict() ?
Why not task.connect_configuration(read_yaml(conf_path)) simply?
I mean what is the benefit of returning ProxyDictPostWrite instead of a dict?

select multiple lines still works, you need to shift + click on the checkbox

DeterminedCrab71 Please check this screen recording

So I suppose clearml-agent is not responsible, because it finds a wheel for torch 1.11.0 with cu117. It just happens that this wheel doesn't work in ec2 g5 instances suprizingly. Either I'll hardcode the correct wheel or I'll upgrade torch to 1.13.0

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'> `

awesome thanks!