// 可以设置CPU数学库线程数math_threads,可加速推理。 // 注意:math_threads * 外部线程数 需要小于总的CPU的核心数目,否则会影响预测性能。 config->SetCpuMathLibraryNumThreads(10); 2-2 使用GPU推理 // EnableUseGpu后,模型将运行在GPU上。 // 第一个参数表示预先分配显存数目,第二个参数表示设备的ID。
tensor_array_batch_cleaner_.ResetTensorArray();//recover the cpu_math_library_num_threads to 1, in order to avoid thread//conflict when integrating it into deployment service.paddle::platform::SetNumThreads(1); #ifdef PADDLE_WITH_MKLDNN...#endif#ifdefined(PADDLE_WITH_MKLML)...#endifreturn...
[ INFO ] INFERENCE_NUM_THREADS: 44 [ INFO ] PERF_COUNT: NO [ INFO ] INFERENCE_PRECISION_HINT: f32 [ INFO ] PERFORMANCE_HINT: THROUGHPUT [ INFO ] EXECUTION_MODE_HINT: PERFORMANCE [ INFO ] PERFORMANCE_HINT_NUM_REQUESTS: 0 [ INFO ] ENABLE_CPU_PINNING: YES ...
飞桨在 paddle.vision.models 下内置了 CV 领域的一些经典模型,LeNet 就是其中之一,调用很方便,只需一行代码即可完成 LeNet 的网络构建和初始化。num_classes字段中定义分类的类别数,因为需要对 0 ~ 9 的十类数字进行分类,所以设置为 10。 # 模型组网并初始化网络 lenet = paddle.vision.models.LeNet(num_...
INFERENCE_NUM_THREADS: Maximum number of threads that can be used for inference tasks. Should be a non-negative number. Default is equal to number of cores. COMPILATION_NUM_THREADS: Maximum number of threads that can be used for compilation tasks. Should be a non-negative number. ...
15:44:57-341349 INFO accelerate launch --num_cpu_threads_per_process=20 "./train_db.py" --v2 --v_parameterization --enable_bucket --min_bucket_reso=256 --max_bucke 分享1赞 novelai吧 🌜彩耻厌战🌛 求助,关于Adetailer为什么我加载了adetailer跑出的图是这样的 分享39 eviews吧 爱吃鸭...
enable_mkldnn, FLAGS_cpu_threads,// FLAGS_cls_batch_num, "dynamic", FLAGS_precision,// ...
Client: Request count: 348 Throughput: 69.6 infer/sec p50 latency: 13936 usec p90 latency: 18682 usec p95 latency: 19673 usec p99 latency: 21859 usec Avg HTTP time: 14017 usec (send/recv 200 usec + response wait 13817 usec) Server: ...
input_layer_name='input0'input_shape=[1,3,416,416]data_shape=json.dumps({input_layer_name:input_shape})target_device='ml_c5'framework='PYTORCH'compiled_env={"MMS_DEFAULT_WORKERS_PER_MODEL":'1',"TVM_NUM_THREADS":'36',"COMPILEDMODEL":'True','MMS_MAX_RESPONSE_SIZE':'1000000...
For pre-processing, I use the following code: class ImagePipeline(Pipeline): def __init__(self, file_list, batch_size, num_threads, device_id): super(ImagePipeline, self).__init__(batch_size, num_threads, device_id) self.input = ops.readers.File(file_root="", file_list...