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

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

I can try one more time though.

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

console (client).

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

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...

I need to ssh the instance, right?
I'll check it out.

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?

Alright, it's running...

@<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...

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...

it's behaving very strangely.

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

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

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.

{
  "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...

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

ecc2, after I ssh-ed the instance.

s3

no, it's something else.

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

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

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

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

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

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

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.

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

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.

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>

The.... are model-specific logs.

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.