init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>
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2026-08-27 15:11:51 +08:00
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commit 7f8a1b1f7a
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@@ -1,8 +1,8 @@
# Using EvalScope
This document will guide you have model inference stress testing and accuracy testing using [EvalScope](https://github.com/modelscope/evalscope).
This document will guide you through model inference stress testing and accuracy testing using [EvalScope](https://github.com/modelscope/evalscope).
## 1. Online serving
## 1. Online server
You can run docker container to start the vLLM server on a single NPU:
@@ -13,6 +13,7 @@ export DEVICE=/dev/davinci7
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--shm-size=1g \
--name vllm-ascend \
--device $DEVICE \
--device /dev/davinci_manager \
@@ -31,30 +32,30 @@ docker run --rm \
vllm serve Qwen/Qwen2.5-7B-Instruct --max_model_len 26240
```
If your service start successfully, you can see the info shown below:
If the vLLM server is started successfully, you can see information shown below:
```
```shell
INFO: Started server process [6873]
INFO: Waiting for application startup.
INFO: Application startup complete.
```
Once your server is started, you can query the model with input prompts in new terminal:
Once your server is started, you can query the model with input prompts in a new terminal:
```
```shell
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen2.5-7B-Instruct",
"prompt": "The future of AI is",
"max_tokens": 7,
"max_completion_tokens": 7,
"temperature": 0
}'
```
## 2. Install EvalScope using pip
You can install EvalScope by using:
You can install EvalScope as follows:
```bash
python3 -m venv .venv-evalscope
@@ -62,21 +63,21 @@ source .venv-evalscope/bin/activate
pip install gradio plotly evalscope
```
## 3. Run gsm8k accuracy test using EvalScope
## 3. Run GSM8K using EvalScope for accuracy testing
You can `evalscope eval` run gsm8k accuracy test:
You can use `evalscope eval` to run GSM8K (a grade-school math benchmark dataset) for accuracy testing:
```
```shell
evalscope eval \
--model Qwen/Qwen2.5-7B-Instruct \
--api-url http://localhost:8000/v1 \
--api-key EMPTY \
--eval-type service \
--eval-type server \
--datasets gsm8k \
--limit 10
```
After 1-2 mins, the output is as shown below:
After 1 to 2 minutes, the output is shown below:
```shell
+---------------------+-----------+-----------------+----------+-------+---------+---------+
@@ -86,7 +87,7 @@ After 1-2 mins, the output is as shown below:
+---------------------+-----------+-----------------+----------+-------+---------+---------+
```
See more detail in: [EvalScope doc - Model API Service Evaluation](https://evalscope.readthedocs.io/en/latest/get_started/basic_usage.html#model-api-service-evaluation).
See more details in [EvalScope doc - Model API Service Evaluation](https://evalscope.readthedocs.io/en/latest/get_started/basic_usage.html#model-api-service-evaluation).
## 4. Run model inference stress testing using EvalScope
@@ -98,9 +99,9 @@ pip install evalscope[perf] -U
### Basic usage
You can use `evalscope perf` run perf test:
You can use `evalscope perf` to run perf testing:
```
```shell
evalscope perf \
--url "http://localhost:8000/v1/chat/completions" \
--parallel 5 \
@@ -113,7 +114,7 @@ evalscope perf \
### Output results
After 1-2 mins, the output is as shown below:
After 1 to 2 minutes, the output is shown below:
```shell
Benchmarking summary:
@@ -172,4 +173,4 @@ Percentile results:
+------------+----------+---------+-------------+--------------+---------------+----------------------+
```
See more detail in: [EvalScope doc - Model Inference Stress Testing](https://evalscope.readthedocs.io/en/latest/user_guides/stress_test/quick_start.html#basic-usage).
See more detail in [EvalScope doc - Model Inference Stress Testing](https://evalscope.readthedocs.io/en/latest/user_guides/stress_test/quick_start.html#basic-usage).