session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_EXTENDED); #defineUSE_CUDA#ifdefUSE_CUDAAF_INFO("USE CUDA, DEVICE_ID={:d}", DEVICE_ID);//OrtCUDAProviderOptions cuda_options;//cuda_options.device_id = DEVICE_ID;//cuda_options.cudnn_conv_algo_search = OrtCudnnConv...
3. **GraphOptimizationLevel**:控制图优化级别,这可以影响模型在运行时的性能和效率。 4. **Use feeds**:通过feed提供的输入数据来运行模型。 5. **Use fetches**:获取模型运行后的输出结果。 6. **Run options**:可以设置模型运行的特定选项。 了解`onnxruntime.SessionOptions()`的工作原理需要对其背后的...
int userGraphOptimizationLevel{ -1 }; booldo_copy_outputs{}; std::set<int> unused{}; }; 首先,我们来看一下computePrecision参数。这个参数用于指定GPU上计算的精度。我们可以指定浮点数的位数,如16-bit、32-bit或64-bit。较低的精度可以加速计算,但可能会对结果产生一些影响。默认情况下,computePrecision...
Implementaion: bool loadONNX() { const char* onnx_model_path_ = "model/melgan_dynamic.onnx"; Ort::Env env_(ORT_LOGGING_LEVEL_ERROR, "test"); Ort::SessionOptions session_options; session_options.SetIntraOpNumThreads(1); session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENA...
logger.error("There is no gpu for onnxruntime to do optimization.")returnonnx_model_path sess_options = onnxruntime.SessionOptions()ifopt_level ==1: sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_BASICelifopt_level ==2: ...
opt_level=tf.OptimizerOptions.L1, do_function_inlining=False))ifself._single_cpu_thread: config = tf.ConfigProto( intra_op_parallelism_threads=1, inter_op_parallelism_threads=1, allow_soft_placement=True, graph_options=graph_options, log_device_placement=False)else: ...
產品文件 開發語言 主題 這個主題有部分內容可能由機器翻譯。 D3d9types.h D3dcaps.h D3dhal.h D3dkmddi.h D3dkmdt.h D3dkmthk.h D3dukmdt.h D3dumddi.h Dispmprt.h Dxgiddi.h Dxgitype.h Dxva.h Iddcx.h Igpupvdev.h Ksmedia.h Netdispumdddi.h ...
GraphCachePolicies GraphDescriptorResult GraphFederatedProviderData GraphGlobalExtendedPropertyBatch GraphGroup GraphGroupCreationContext GraphGroupMailAddressCreationContext GraphGroupOriginIdCreationContext GraphGroupVstsCreationContext GraphMember GraphMembership GraphMembershipState GraphMembershipTraversal GraphProviderInfo...
However, we obtained good results with Nelder-Mead optimization (Nelder & Mead, 1965), as implemented in the optim-function in R, with a starting value of (a, b) that corresponds to a small non-reward rate. Fig. 3 The figure contains filled contour plots of the estimated non-reward ...
GraphX, for Graphs and graph computation for a broad scope of use cases from cognitive analytics to data exploration. Cluster creation in seconds, with dynamic autoscaling clusters, sharing them across teams. Programmatic cluster access using REST APIs. Instant access to the latest Apache Spark fea...