I tried deleting all the underlying resources: ec2 & ebs, and recreating it again.

@<1722061389024989184:profile|ResponsiveKoala38> I'm looking at the logs now (used "docker logs clearml-elastic").

The status seemed to had transitioned, but the it's not clear the error.

{"@timestamp":"2025-05-20T08:36:18.412Z", "log.level": "INFO", "message":"setting file [/usr/share/elasticsearch/config/operator/settings.json] not found, initializing [file_settings] as empty", "ecs.version": "1.2.0","service.name":"ES_ECS","event.dataset":"elasticsearch.server","process.thread.nam...

so, the same ClearML monitor error, but another issue now.

btw, the task logs the configuration, artifacts, etc.
I get this error at the end.

green open events-log-d1bd92a3b039400cbafc60a7a5b1e52b            Yh4BPGmgRZKU7STdCghmtw 1 0   96 0 175.1kb 175.1kb 175.1kb

I don't store anything on clearml server; everything is being stored in S3 and referenced by ClearML.

Thanks, @<1523701070390366208:profile|CostlyOstrich36> !

Currently, I'm using Task.set_script

In both cases, I need to clone the repo, and I'm trying to avoid that.

I'm looking for an automated way to detect repo, branch, and commit it

ecc2, after I ssh-ed the instance.

Alright, it's running...

it's behaving very strangely.

I'm trying to provision the instance, but something is off.
It's as if some functionalities are missing.

s3

@<1523701070390366208:profile|CostlyOstrich36>
I've updated the instance type to t3a.large.
The issue persisted.

That's a big context!
In general, I'm using standard functions; the script is running in SageMaker pipeline.
The model, however, is a composite, and consists of multiple primitive ones.

task = Task.init(
    project_name="icp",
    task_name=f"model_training_{client_name}",
    task_type=Task.TaskTypes.training,
    auto_connect_frameworks={'matplotlib': True, 'tensorflow': False, 
                             'tensorboard': False,
                            'pytorch': False, 'xgboos...

curl -XGET "localhost:9200/_cluster/allocation/explain?pretty"
curl: (7) Failed to connect to localhost port 9200 after 0 ms: Couldn't connect to server

It's the entire error repeating.
And, this happens at the end of the script.

I'm using the recommended instance (t3.large).

it's

ClearML Monitor: Could not detect iteration reporting, falling back to iterations as seconds-from-start
fzd6tw0x46-algo-1-lswt4  | 2025-05-20 10:02:08,177 - urllib3.connectionpool - WARNING - Retrying (Retry(total=2, connect=2, read=5, redirect=5, status=None)) after connection broken by 'ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x76f683ccb670>, 'Connection to "" timed out. (connect timeout=300.0)')': /
fzd6tw0x46-algo-1-lswt4  | 2025-05-20 10:02:08,178 - ur...

ok. Currently the ebs is 15 GB, is there a recommended size?

no, it's something else.

I commented out the above two line and I was still facing the issue.

I'm begining to think that there is something besides ClearML. I'll execute the training script on remote (SageMaker), instead of SageMaker local mode.

ClearML Task: created new task id=f08b012bce42420dba7cd166668f5e4b
2025-05-20 09:54:59,251 - clearml.Task - INFO - No repository found, storing script code instead
ClearML results page: /projects/184c6e8651d94b9088ae60ae3a9c8ace/experiments/f08b012bce42420dba7cd166668f5e4b/output/log
2025-05-20 12:55:02
ClearML Monitor: GPU monitoring failed getting GPU reading, switching off GPU monitoring
Starting the training.

....

ClearML Monitor: Could not detect iteration reporting, falling back...

I have been rerunning it since yesterday. The error persists.

I can try one more time though.

console (client).

I tested that theory before; I commented out these two lines

output_model = OutputModel(task=task, name="trained_model")
output_model.update_weights(register_uri=s3_model_uri)

The issue, however, persisted.

@<1722061389024989184:profile|ResponsiveKoala38> @<1523701070390366208:profile|CostlyOstrich36>
it's ClearML, I commented out clearml lines, and it ran successfully!

The.... are model-specific logs.

Also, it would be great if you could add a recommendation for EBS size in this guide ( None ),
The Elastic Search issue happened with 8 GB, and was resolved with 15 GB.

Also, (without CLearML) the model artifacts are uploaded/downloadable.

@<1722061389024989184:profile|ResponsiveKoala38> It's not resolved.

to close this thread, file server port wasn't configured
I added

        - IpProtocol: tcp
          FromPort: 8081
          ToPort: 8081
          CidrIp: 0.0.0.0/0

to cloudformation template, and it was resolved.

Thanks a bunch, guys
@<1722061389024989184:profile|ResponsiveKoala38> @<1523701070390366208:profile|CostlyOstrich36>

ok, I'm recreating the ec2 isntance to generate ssh key pair, then I'll check the elasticsearch logs.

{
  "error" : {
    "root_cause" : [
      {
        "type" : "illegal_argument_exception",
        "reason" : "No shard was specified in the request which means the response should explain a randomly-chosen unassigned shard, but there are no unassigned shards in this cluster. To explain the allocation of an assigned shard you must specify the target shard in the request. See 

 for more information."
      }
    ],
    "type" : "illegal_argument_exception",
    "reason" : "N...