The various run-time libraries implement conventional language or application-to-operating system interfaces such as Pascal I/O and C stdio. For instance, the V file server implements a UNIX-like file system using the raw disk access supported by the kernel. The service modules implement value-added services using the basic access to hardware resources provided by the kernel. The existence of multiple machines and network interconnection is largely transparent at the process level. The kernel is distributed in that a separate copy of the kernel executes on each participating network node yet the separate copies cooperate to provide a single system abstraction of processes in address spaces communicating using a base set of communication primitives. The system is structured as a relatively small “distributed” kernel, a set of service modules, various run-time libraries and a set of commands, as shown in Figure 1. The V distributed system is an operating system designed for a cluster of computer workstations connected by a high-performance network. This work include the VMP (ISCA’86 and 88) and more recently the ParaDiGM multiprocessor (IEEE Computer Feb.’91 – also available on ). The work on V also led to computer architecture work on multiprocessor memory systems that are well-structured for operating systems. That work led to network protocol design including IP multicast ( RFC 1112), VMTP ( RFC 1045), Sirpent (SIGCOMM’89), and network interfacing VMP NAB (SIGCOMM’88). The original DSG work focused on the V distributed system, which was developed in the 1981 to 1988 time frame (see CACM, March 1988 for an overview article). The V distributed System was developed at Stanford University as part of a research project to explore issues in distributed systems. , macOS Catalina (x86_64), Windows 10 and TensorFlow devel Docker image The following instructions have been tested on Ubuntu 16.04.V (or V-System) – a microkernel distributed operating system developed by faculty and students in the Distributed Systems Group (DSG) at Stanford University from 1981 to 1988, led by Professors David Cheriton and Keith A. On Ubuntu, you can simply run the following Note: This feature is available since version 2.4. Run CMake tool with configurations Release build Create CMake build directory mkdir tflite_build Note: If you're using the TensorFlow Docker image, the repo is already Clone TensorFlow repository git clone tensorflow_src The official cmake installation guide Step 2. It generates an optimized release binary by default. In order to be able to run kernel tests, you need to provide tensorflow_src/tensorflow/lite -DCMAKE_BUILD_TYPE=Debug If you need to produce a debug build which has symbol information, you need to Your workstation, simply run the following command. tensorflow_src/tensorflow/lite -DTFLITE_KERNEL_TEST=on Unit test cross-compilation specifics can beįound in the next subsection. In order to cross-compile the TF Lite, you namely need to provide the path to You can use CMake to build binaries for ARM64 or Android target architectures. tensorflow/lite/įor Android cross-compilation, you need to installĪndroid NDK and provide the NDK path with ARM64 SDK or NDK in Android's case) with -DCMAKE_TOOLCHAIN_FILEįlag. cmake -DCMAKE_TOOLCHAIN_FILE=/build/cmake/ \ DCMAKE_TOOLCHAIN_FILE flag mentioned above. Tensorflow/lite/tools/cmake/native_tools/flatbuffers to build the flatcĬompiler with CMake in advance in a separate build directory using the host For this purpose, there is a CMakeLists located in Specifics of kernel (unit) tests cross-compilationĬross-compilation of the unit tests requires flatc compiler for the hostĪrchitecture. Mkdir flatc-native-build & cd flatc-native-buildĬmake.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |