在开始阶段会编译third_party目录下的依赖包(基本是facebook和谷歌公司贡献的)。 #Facebook开源的cpuinfo,检测cpu信息的 third_party/cpuinfo #Facebook开源的神经网络模型交换格式, #目前Pytorch、caffe2、ncnn、coreml等都可以对接 third_party/onnx #FB (Facebook) + GEMM (General Matrix-Matrix Multiplication)...
third_party/onnx/onnx/onnx_onnx_torch.proto Writing /home/gemfield/github/pytorch/build/third_party/onnx/onnx/onnx_onnx_torch.proto3 Writing /home/gemfield/github/pytorch/build/third_party/onnx/onnx/onnx.pb.h generating /home/gemfield/github/pytorch/build/third_party/onnx/onnx/onnx_...
The interoperability Standard of Third-party Backend Integartion Mechanism Authors: @FFFrog @hipudding Summary As the top AI framework,PyTorch will see more and more backends wanting to integrate with it in the future. A universal third-...
而Caffe2则不用多说,caffe2则主要针对移动端设计了很多优化后的运算代码,模型融合、模型量化等等的代码,其后端有QNNPACK等一些针对移动端的底层运算库(有开发人员说GLOW也在caffe2后端考虑之内)。 third_party Pytorch毕竟是大型的深度学习库,所以需要的依赖库也是有很多的,其中有很多我们耳熟能详的数值计算库(eigen、...
cd /home/test/pytorch/third_party/sgx/linux-sgx git am ../0001* cd external/dnnl make sudo cp sgx_dnnl/lib/libsgx_dnnl.a /opt/alibaba/teesdk/intel/sgxsdk/lib64/libsgx_dnnl2.a sudo cp sgx_dnnl/include/* /opt/alibaba/teesdk/intel/sgxsdk/include/ ...
.github benchmarks binaries ci cpp docker docs examples frontend kubernetes model-archiver plugins requirements serving-sdk test third_party/google ts ts_scripts workflow-archiver .gitignore .gitmodules .pre-commit-config.yaml CODE_OF_CONDUCT.md ...
运行以下命令：scripts/install_third_party_dependencies.sh 使用如下命令激活环境：source scripts/activate_conda_env.sh 停用命令：source scripts/deactivate_conda_env.sh 在激活环境下，编译 OpenFold 的 CUDA 内核 python3 setup.py install 在 / usr/bin 路径下安装 HH-suite：# scripts/install_hh_suite.sh...
git submodule update --remote third_party/protobuf #这句必须要有,否则在编译时会报一个找不到protobuf.h的错误 1. 2. 3. 4. 5. 6. 树莓派是不支持CUDA和MKLDNN的,CUDA是nv的,MKLDNN是intel的, 我们拿树莓派也只做推理,分布式也不要了。所以我们设置以下的环境变量 ...
./third_party/tmp/lame-3.99.5/config.guess``./third_party/tmp/libmad-0.15.1b/config.guess:https://github.com/gcc-mirror/gcc/blob/master/config.guess 另见:#658 使用“BUILD_SOX”时对“tgetnum”的未定义引用 如果在 anaconda 环境中构建时遇到类似以下的错误:...
In scenarios that are performance-sensitive, you can deploy the model by using a processor. In scenarios that have custom requirements, such as when the model has third-party dependencies, or when the inference service requires preprocessing and post-processing, you can deploy the model by using...