triton-inference-server / triton-inference-server/server
Why does my tirton service response time keep increasing at high QPM?
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 11k
- Forks
- 1.8k
- Avg merge
- 3d 16h
- Merged PRs (30d)
- 28
Description
The vertical axis is ms, QPM=2000. Model work on the service is a xlm-roberta-large.onnx with dynamic batch_size and squence_length.
Here is the config.pbtxt.
`name: "xlm-roberta-large-onnx"
backend: "onnxruntime"
dynamic_batching {
}
max_batch_size: 16
input [
{
name: "input_ids"
data_type: TYPE_INT64
dims: [-1]
},
{
name: "attention_mask"
data_type: TYPE_INT64
dims: [-1]
}
]
output [
{
name: "embeds"
data_type: TYPE_FP32
dims: [512]
}
]
instance_group [
{
count: 1
kind: KIND_GPU
}
]
parameters {
key: "execution_mode"
value: { string_value: "1" }
}`
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the supplied config.pbtxt, the response-time graph, and the reported workload: QPM=2000 using xlm-roberta-large.onnx with dynamic batch_size and sequence_length. Reproduce the increasing latency if possible, then trace how the configuration and workload affect response time. Done means identifying the cause of the increase and documenting the relevant configuration or workload change.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- machine-learning
- Domain
- backend, machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100