I opened an https://github.com/pytorch/ignite/issues/2343 in ignite’s repo and a https://github.com/pytorch/ignite/pull/2344 , could you please have a look? There might be a bug in clearml Task.init in distributed envs

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

/data/shared/miniconda3/bin/python /data/shared/miniconda3/bin/clearml-agent daemon --services-mode --detached --queue services --create-queue --docker ubuntu:18.04 --cpu-only

And I am wondering if only the main process (rank=0) should attach the ClearMLLogger or if all the processes within the node should do that

Now I am trying to restart the cluster with docker-compose and specifying the last volume, how can I do that?

I mean, inside a parent, do not show the project [parent] if there is nothing inside

line 13 is empty 🤔

I now have a different question: when installing torch from wheels files, I am guaranteed to have the corresponding cuda library and cudnn together right?

AgitatedDove14 So in the https://pytorch.org/ignite/_modules/ignite/handlers/early_stopping.html#EarlyStopping class I see that some infos are logged (in the __call__ function), and I would like to have these infos logged by clearml

that’s perfect, thanks!

Thanks SuccessfulKoala55 😁

AgitatedDove14 If I call explicitly task.get_logger().report_scalar("test", str(parse_args.local_rank), 1., 0) , this will log as expected one value per process, so reporting works

AgitatedDove14 After investigation, another program on the machine consumed all the memory available, most likely making the OS killing the agent/task

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...

Very good job! One note: in this version of the web-server, the experiments logo types are all blank, what was the reason to change them? Having a color code in the logos helps a lot to quickly check the nature of the different experiments tasks, isnt it?

Yes 🙂 Thanks!

Mmmh unfortunately not easily… I will try to debug deeper today, is there a way to resume a task from code to debug locally?
Something like replacing Task.init with Task.get_task so that Task.current_task is the same task as the output of Task.get_task

Yes that’s what I did initially, but eventually I decided that it’s too much complexity added for nothing really, I’d rather drop omegaconf and if one day clearml supports it out of the box take advantage of it

If I don’t start clearml-session , I can easily connect to the agent, so clearml-session is doing something that messes up the ssh config and prevent me from ssh into the agent afterwards

So when I create a task using `task = Task.init(project_name=config.get("project_name"), task_name=config.get("task_name"), task_type=Task.TaskTypes.training, output_uri=" s3://my-bucket ") locally, the artifact is correctly logged remotely, but when I create the task remotely (from an agent) the artifact is logged locally (in the agent machine, not on s3)

because I cannot locate libcudart or because cudnn_version = 0?

yes

Ho I wasn't aware of that new implementation, was it introduced silently? I don't remember reading it in the release notes! To answer your question: no, for gcp I used the old version, but for azure I will use this one, maybe send a PR if code is clean 👍

I don’t think it is, I was rather wondering how you handled it to understand potential sources of slow down in the training code

Ok, I got the following error when uploading the table as an artifact:
ValueError('Task object can only be updated if created or in_progress')

more than 120s?

Is it safe to turn off replication while a reindex operation is happening? the reindexing is rather slow and I am wondering if turning of replication will speed up the process

Yes 😞 😄

There is no need to add creds on the machine, since the EC2 instance has an attached IAM profile that grants access to s3. Boto3 is able retrieve the files from the s3 bucket