DeepRec源代码编译&安装
开发环境准备
CPU Base Docker Image
GCC Version |
Python Version |
IMAGE |
|---|---|---|
7.5.0 |
3.6.9 |
alideeprec/deeprec-base:deeprec-base-cpu-py36-ubuntu18.04 |
9.4.0 |
3.8.10 |
alideeprec/deeprec-base:deeprec-base-cpu-py38-ubuntu20.04 |
11.2.0 |
3.8.6 |
alideeprec/deeprec-base:deeprec-base-cpu-py38-ubuntu22.04 |
GPU Base Docker Image
GCC Version |
Python Version |
CUDA VERSION |
IMAGE |
|---|---|---|---|
7.5.0 |
3.6.9 |
CUDA 11.6.1 |
alideeprec/deeprec-base:deeprec-base-gpu-py36-cu116-ubuntu18.04 |
9.4.0 |
3.8.10 |
CUDA 11.6.2 |
alideeprec/deeprec-base:deeprec-base-gpu-py38-cu116-ubuntu20.04 |
11.2.0 |
3.8.6 |
CUDA 11.7.1 |
alideeprec/deeprec-base:deeprec-base-gpu-py38-cu117-ubuntu22.04 |
CPU Dev Docker (with bazel cache)
GCC Version |
Python Version |
IMAGE |
|---|---|---|
7.5.0 |
3.6.9 |
alideeprec/deeprec-build:deeprec-dev-cpu-py36-ubuntu18.04 |
9.4.0 |
3.8.10 |
alideeprec/deeprec-build:deeprec-dev-cpu-py38-ubuntu20.04 |
GPU(cuda11.6) Dev Docker (with bazel cache)
GCC Version |
Python Version |
CUDA VERSION |
IMAGE |
|---|---|---|---|
7.5.0 |
3.6.9 |
CUDA 11.6.1 |
alideeprec/deeprec-build:deeprec-dev-gpu-py36-cu116-ubuntu18.04 |
9.4.0 |
3.8.10 |
CUDA 11.6.2 |
alideeprec/deeprec-build:deeprec-dev-gpu-py38-cu116-ubuntu20.04 |
代码编译
GPU Environment 为了更好的发挥GPU性能，根据编译/运行的GPU卡，配置不同的TF_CUDA_COMPUTE_CAPABILITIES
GPU architecture |
TF_CUDA_COMPUTE_CAPABILITIES |
|---|---|
Pascal (P100) |
6.0+6.1 |
Volta (V100) |
7.0 |
Turing (T4) |
7.5 |
Ampere (A10, A100) |
8.0+8.6 |
Hopper (H100, H800) |
9.0 |
如果希望编译出支持不同GPU卡上执行的版本，可以配置多个值，比如DeepRec中默认配置为"7.0,7.5,8.0,8.6" (当前CIBUILD使用A10卡）
如果编译的DeepRec需要执行在H100和A100的GPU卡上，配置环境变量TF_CUDA_COMPUTE_CAPABILITIES：
export TF_CUDA_COMPUTE_CAPABILITIES="8.0,8.6,9.0"
./configure
GPU/CPU版本编译
bazel build -c opt --config=opt //tensorflow/tools/pip_package:build_pip_package
GPU/CPU版本编译+ABI=0
bazel build --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --host_cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" -c opt --config=opt //tensorflow/tools/pip_package:build_pip_package
编译开启OneDNN + Eigen Threadpool工作线程池版本（CPU）
bazel build -c opt --config=opt --config=mkl_threadpool --define build_with_mkl_dnn_v1_only=true //tensorflow/tools/pip_package:build_pip_package
编译开启OneDNN + Eigen Threadpool工作线程池版本+ABI=0的版本 （CPU）
bazel build --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --host_cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" -c opt --config=opt --config=mkl_threadpool --define build_with_mkl_dnn_v1_only=true //tensorflow/tools/pip_package:build_pip_package
编译开启 Arm Compute Library (ACL) 版本（ARM CPU）
bazel build -c opt --config=opt --config=mkl_aarch64 //tensorflow/tools/pip_package:build_pip_package
生成Whl包
./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
安装Whl包
pip3 install /tmp/tensorflow_pkg/tensorflow-1.15.5+${version}-cp38-cp38m-linux_x86_64.whl
最新Release镜像
CPU镜像
x86_64:
alideeprec/deeprec-release:deeprec2304-cpu-py38-ubuntu20.04
arm64:
alideeprec/deeprec-release:deeprec2302-cpu-py38-ubuntu22.04-arm64
GPU CUDA11.6镜像
alideeprec/deeprec-release:deeprec2304-gpu-py38-cu116-ubuntu20.04
DeepRec Processor编译打包
配置.bazelrc（注意如果编译DeepRec请重新进行configure配置）
./configure serving
增加MKL相关配置
./configure serving --mkl
./configure serving --mkl_open_source_v1_only
./configure serving --mkl_threadpool
编译GPU版本
./configure serving --cuda
./configure serving --cuda_alios
更多细节请查看: serving/configure.py
编译processor库，会生成libserving_processor.so，用户可以加载该so，并且调用示例中的serving API进行predict
bazel build //serving/processor/serving:libserving_processor.so
单元测试
bazel test -- //serving/processor/... -//serving/processor/framework:lookup_manual_test
E2E测试细节请查看: serving/processor/tests/end2end/README