Probably the 9200 port is not mapped from the ES container in the docker compose
The easiest would be to perform "sudo docker exec -it clearml-elastic /bin/bash" and then run the curl command from inside the ES docker
Can you provide a standalone code snippet that reproduces this behaviour?
Also, (without CLearML) the model artifacts are uploaded/downloadable.
Hi @<1835488771542355968:profile|PerplexedShells66> , please inspect your Elasticsearch logs. Any errors or warnings there?
I have been rerunning it since yesterday. The error persists.
I can try one more time though.
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
it's behaving very strangely.
I'm trying to provision the instance, but something is off.
It's as if some functionalities are missing.
On what host did you run the curl command?
What is the status that you get for the "events-log-d1bd92a3b039400cbafc60a7a5b1e52b" index?
I don't store anything on clearml server; everything is being stored in S3 and referenced by ClearML.
no, it's something else.
I commented out the above two line and I was still facing the issue.
@<1722061389024989184:profile|ResponsiveKoala38> @<1523701070390366208:profile|CostlyOstrich36>
it's ClearML, I commented out clearml lines, and it ran successfully!
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>
@<1523701070390366208:profile|CostlyOstrich36>
I've updated the instance type to t3a.large.
The issue persisted.
In ES container please run "curl -XGET localhost:9200/_cat/indices"
Looks like elastic is failing to access a shard. Do you have visibility into machine utilization? How much RAM is elastic consuming?
Also, is this the entire error repeating or is there more context?
It depends on your usage. ES has some default watermarks that are activated when the amount of used space is above 85% and 90% (can be overwritten) of the storage. At some point it may transfer the index to a "readonly" state.
It's the entire error repeating.
And, this happens at the end of the script.
I'm using the recommended instance (t3.large).
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
@<1722061389024989184:profile|ResponsiveKoala38> It's not resolved.
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
I need to ssh the instance, right?
I'll check it out.
ok, I'm recreating the ec2 isntance to generate ssh key pair, then I'll check the elasticsearch 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.
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.
{
"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?
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])>