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>

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, 'xgboost': False, 'scikit': False, 'fastai': False,
                            'lightgbm': False, 'hydra': True, 'detect_repository': True, 'tfdefines': False,
                            'joblib': False, 'megengine': False, 'catboost': False, 'gradio': False
    },
    output_uri=False
)

task.set_script(repository=repo_url, branch=branch_name, working_dir="./", commit=commit_id)
task.set_parameter("commit_id", commit_id)

task.connect_configuration()

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

....

task = Task.current_task()
if task is None:
    print("Warning: No ClearML task found. Metrics will not be logged to ClearML.")
    logger = None
else:
    logger = task.get_logger()

logger.report_matplotlib_figure()
logger.report_scalar()

Can you provide a standalone code snippet that reproduces this behaviour?

no, it's something else.

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

So you watered it down to these lines?

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

This is what causes the timeout errors? Did you define

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

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

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.

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.

s3

I assume that ec2-13-217-109-164.compute-1.amazonaws.com is the ec2 instance where the API is running?
Are you using the files server or S3 for storage? Can you verify on the storage itself that the artifacts are actually uploaded and are downloadable?

The.... are model-specific logs.

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 to iterations as seconds-from-start
2025-05-20 13:02:08
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 ec2-13-217-109-164.compute-1.amazonaws.com timed out. (connect timeout=300.0)')': /
2025-05-20 10:02:08,178 - 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 0x76f683cc9810>, 'Connection to ec2-13-217-109-164.compute-1.amazonaws.com timed out. (connect timeout=300.0)')': /
2025-05-20 10:02:08,178 - 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 0x76f683ccb6a0>, 'Connection to .compute-1.amazonaws.com timed out. (connect timeout=300.0)')': /
2025-05-20 13:04:25
2025-05-20 10:04:25,347 - urllib3.connectionpool - WARNING - Retrying (Retry(total=1, connect=1, read=5, redirect=5, status=None)) after connection broken by 'ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x76f683c32bf0>, 'Connection to .compute-1.amazonaws.com timed out. (connect timeout=300.0)')': /
2025-05-20 10:04:25,348 - urllib3.connectionpool - WARNING - Retrying (Retry(total=1, connect=1, read=5, redirect=5, status=None)) after connection broken by 'ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x76f683c32c20>, 'Connection to .compute-1.amazonaws.com timed out. (connect timeout=300.0)')': /
2025-05-20 10:04:25,348 - urllib3.connectionpool - WARNING - Retrying (Retry(total=1, connect=1, read=5, redirect=5, status=None)) after connection broken by 'ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x76f683c33040>, 'Connection to .compute-1.amazonaws.com timed out. (connect timeout=300.0)')': /
2025-05-20 13:06:48
2025-05-20 10:06:48,615 - urllib3.connectionpool - WARNING - Retrying (Retry(total=0, connect=0, read=5, redirect=5, status=None)) after connection broken by 'ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x76f683c32da0>, 'Connection to .compute-1.amazonaws.com timed out. (connect timeout=300.0)')': /
2025-05-20 10:06:48,616 - urllib3.connectionpool - WARNING - Retrying (Retry(total=0, connect=0, read=5, redirect=5, status=None)) after connection broken by 'ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x76f683c33f40>, 'Connection to .compute-1.amazonaws.com timed out. (connect timeout=300.0)')': /
2025-05-20 10:06:48,616 - urllib3.connectionpool - WARNING - Retrying (Retry(total=0, connect=0, read=5, redirect=5, status=None)) after 

Can you provide the full log?

console (client).

This seems something different not connected to ES. Where do you get these logs?

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.

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 - 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 0x76f683cc9810>, 'Connection to "" timed out. (connect timeout=300.0)')': /
fzd6tw0x46-algo-1-lswt4  | 2025-05-20 10:02:08,178 - urllib3.connectionpool - WARNING - Retrying (Retry(total=2, connect=2, read=5, redirect=5, status=None)) after connection broken by 'ConnectTimeoutError(<urlli

And are you still getting exactly this error?

<500/100: events.add_batch/v1.0 (General data error: err=1 document(s) failed to index., extra_info=[events-log-d1bd92a3b039400cbafc60a7a5b1e52b][0] primary shard is not active Timeout: [1m], request: [BulkShardRequest [[events-log-d1bd92a3b039400cbafc60a7a5b1e52b][0]] containing [index {[events-log-d1bd92a3b039400cbafc60a7a5b1e52b][f3abecd0f46f4bd289e0ac39662fd850], source[{"timestamp":1747654820464,"type":"log","task":"fd3d00d99d88427bbc576cba53db062d","level":"info","worker":"b1193fbdd662","msg":"Starting the training.\nClearML Monitor: GPU monitoring failed getting GPU reading, switching off GPU monitoring","model_event":false,"@timestamp":"2025-05-19T11:40:21.919Z","metric":"","variant":""}]}] and a refresh])>

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

What is the status that you get for the "events-log-d1bd92a3b039400cbafc60a7a5b1e52b" index?

In ES container please run "curl -XGET localhost:9200/_cat/indices"

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

Alright, it's running...

I would try it again.

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

I can try one more time though.

Did you try to run your job again?

No, it says that it does not detect any problematic shards. Given that output and the absence of the errors in the logs I would expect that you will not get the error anymore

{
  "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" : "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."
  },
  "status" : 400
}

this means that elasticsearch server hasn't started, right?