[Bug]: docker crash when inserts more data

[tadinhkien99]

Is there an existing issue for this?

• I have searched the existing issues

Environment

- Milvus version: 2.4.0
- Deployment mode(standalone or cluster): standalone
- MQ type(rocksmq, pulsar or kafka): docker   
- SDK version(e.g. pymilvus v2.0.0rc2): 2.4.0
- OS(Ubuntu or CentOS): window
- CPU/Memory: 64g ram
- GPU: 24g ram
- Others: docker 50gb/60gb ram

Current Behavior

When I insert upto 10M entities, docker crash then milvus disconnect. As I check because of cpu usage 100% and there no available RAM.

I use IVF_SQ8 index, each vectors 768 dimension.
I install milvus docker gpu version.
I use batchsize insert 10000 entities one time.

I think cpu and ram won't increase when we insert data?

Expected Behavior

Cpu and ram shouldn't OOM because only 10M entities

Steps To Reproduce

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Milvus Log

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Anything else?

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[yanliang567]

@tadinhkien99

1. if you are running IVF_SQ8, you don't need GPU image, try Milvus CPU image
2. according to my experience, for 768d vectors, please try to insert 1000 entities at a time
3. if it still reproduces to you, please off milvus logs for investigation. For Milvus installed with docker-compose, you can use docker-compose logs > milvus.log to export the logs.

/assign @tadinhkien99
/unassign

[tadinhkien99]

@tadinhkien99

1. if you are running IVF_SQ8, you don't need GPU image, try Milvus CPU image
2. according to my experience, for 768d vectors, please try to insert 1000 entities at a time
3. if it still reproduces to you, please off milvus logs for investigation. For Milvus installed with docker-compose, you can use docker-compose logs > milvus.log to export the logs.

/assign @tadinhkien99 /unassign

@yanliang567

1. Since I have a GPU, I prefer using it to enhance search performance. I chose IVF_SQ8 as it's the best quantization type for limited resources.

2. I tried again with a batch size of 1000 entities. With IVF_SQ8, I managed to insert a total of 4.3M entities, while IVF_PQ with m=8 allowed me to insert around 10M entities before Docker crashed.

3. I've attached the log file below for your review.
   
   milvus.log

[yanliang567]

I did not see any critical errors when the milvus crash, I guess there is a OOM with the container. could you please double check that? @tadinhkien99

/assign @congqixia
could you please also take a look

[tadinhkien99]

I did not see any critical errors when the milvus crash, I guess there is a OOM with the container. could you please double check that? @tadinhkien99

/assign @congqixia could you please also take a look
@yanliang567

I'm aware that Docker can encounter Out of Memory errors, but in this instance, I was merely adding entities into the system without conducting any searches. What could be causing the OOM issue under these circumstances?

Additionally, I need advice on the most effective index type for handling larger datasets. I have approximately 50 million entities to insert. I initially used IVF_PQ, but encountered an OOM error after inserting only 10 million entities. What would you recommend?

[xiaofan-luan]

1. are u using GPU index or cpu index?
2. if it's cpu index,I believe 50GB memory is far more enough than 10m data.
3. when you using IVFSQ8, why specify m? m is only for HNSW index.

[tadinhkien99]

1. are u using GPU index or cpu index?
2. if it's cpu index,I believe 50GB memory is far more enough than 10m data.
3. when you using IVFSQ8, why specify m? m is only for HNSW index.

1. I use cpu index type. But OOM on ram memory.
2. I deleted m param.

Now I use gpu cagra index rtx 4090 24gb ram. And it's fine for 4M entities.
Do you have any ideas to optimize milvus.yaml (2.4)?

[xiaofan-luan]

what part you need top optimize with milvus.yaml?

[tadinhkien99]

I want to use gpu index to save more and more entities. Around > 10M entities.
Also where I can setup run multiple gpus?
Thanks.

[xiaofan-luan]

you can use more GPU devices on single machine.

I think the document already cover multi device use case https://milvus.io/docs/install_standalone-helm-gpu.md

@Presburger can help if you hit any issue