本指南解释如何在 InfiniMetrics 中配置和运行测试。
测试规格以 JSON 格式提供。典型的配置文件如下所示:
{
"run_id": "unique_run_identifier",
"testcase": "hardware.cudaUnified.Comprehensive",
"config": {
"device": "nvidia",
"array_size": 67108864,
"output_dir": "./output"
},
"metrics": [
{"name": "hardware.mem_sweep_h2d"},
{"name": "hardware.stream_triad"}
]
}| 参数 | 类型 | 描述 | 默认值 |
|---|---|---|---|
run_id |
string | 唯一的测试运行标识符 | 必填 |
testcase |
string | 测试类型标识符 | 必填 |
config.device |
string | 加速器类型 (nvidia/amd/huawei/cambricon) | nvidia |
config.array_size |
int | STREAM 测试的数组大小 | 67108864 |
config.output_dir |
string | 输出目录路径 | ./output |
格式:<类别>.<框架>.<测试名称>
hardware- 硬件级测试operator- 算子级测试infer- 推理测试comm- 通信测试
cudaUnified- CUDA 统一内存测试infinicore- InfiniCore 算子测试infinilm- InfiniLM 推理测试vllm- vLLM 推理测试nccltest- NCCL 通信测试
hardware.cudaUnified.Comprehensiveoperator.infinicore.Matmulinfer.infinilm.directcomm.nccltest.AllReduce
python main.py input.json# 运行目录中的所有 JSON 配置
python main.py ./test_configs/python main.py input.json --verbose{
"run_id": "hw_test_001",
"testcase": "hardware.cudaUnified.Comprehensive",
"config": {
"device": "nvidia",
"array_size": 67108864,
"output_dir": "./output"
},
"metrics": [
{"name": "hardware.mem_bw_h2d"},
{"name": "hardware.stream_triad"}
]
}{
"run_id": "infer_test_001",
"testcase": "infer.infinilm.direct",
"config": {
"model_path": "/path/to/model",
"batch_size": 32,
"output_dir": "./output"
},
"metrics": [
{"name": "infer.throughput"},
{"name": "infer.latency"}
]
}