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TensorFlow conda packages are available for Windows, Linux, and macOS. The Linux packages for the 1.10.0 release support a number of Linux distributions including older distributions such as CentOS 6. This is a further benefit of the conda packages: in spite of being labeled as manylinux1-compatible (works on many versions of linux), the wheels. Mac hardware must be a 2010 or a newer model, with Intel’s hardware support for memory management unit (MMU) virtualization, including Extended Page Tables (EPT) and Unrestricted Mode. You can check to see if your machine has this support by running the following command in a. I've tried these ways to download it with Python 3.6: pip install tensorflow-gpu2.0.0-alpha0 pip install tf-nightly-gpu-2.0-preview I'm able to install other versions of tensorflow-gpu but not the 2.0 version. Is anyone able to help me? This release version is built for Tensorflow 1.14.0rc1 for Python 2.7, 3.7 on Mac OS X 10.13 with GPU (CUDA) support. Supported CUDA compute capability: 3.0,3.5,5.0,5.2,6.1,7.0. (All possible capabilities on Mac OS. If you find anything missing, please open an issue.) CUDA 10.0 and CuDNN 7.4 are required.
TensorFlow provides a C API that can be used to buildbindings for other languages. The API is defined inc_api.hand designed for simplicity and uniformity rather than convenience.
Nightly Libtensorflow C packages
Libtensorflow packages are built nightly and uploaded to GCS for all supportedplatforms. They are uploaded to thelibtensorflow-nightly GCS bucketand are indexed by operating system and date built. For MacOS and Linux sharedobjects, we have ascriptthat renames the .so files versioned to the current date copied into thedirectory with the artifacts.
Supported Platforms
TensorFlow for C is supported on the following systems:
- Linux, 64-bit, x86
- macOS, Version 10.12.6 (Sierra) or higher
- Windows, 64-bit x86
Setup
Download
| TensorFlow C library | URL |
|---|---|
| Linux | |
| Linux CPU only | https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.3.1.tar.gz |
| Linux GPU support | https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.3.1.tar.gz |
| macOS | |
| macOS CPU only | https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.3.1.tar.gz |
| Windows | |
| Windows CPU only | https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.3.1.zip |
| Windows GPU only | https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-windows-x86_64-2.3.1.zip |
Extract
Extract the downloaded archive, which contains the header files to include inyour C program and the shared libraries to link against.
On Linux and macOS, you may want to extract to /usr/local/lib:
Linker
On Linux/macOS, if you extract the TensorFlow C library to a system directory,such as /usr/local, configure the linker with ldconfig:
If you extract the TensorFlow C library to a non-system directory, such as~/mydir, then configure the linker environmental variables:
Build
Example program
With the TensorFlow C library installed, create an example program with thefollowing source code (hello_tf.c):
Compile
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Compile the example program to create an executable, then run:
The command outputs: Hello from TensorFlow C library version number
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Success: The TensorFlow C library is configured.Tensorflow Download Mac Download
If the program doesn't build, make sure that gcc can access the TensorFlow Clibrary. If extracted to /usr/local, explicitly pass the library location tothe compiler:
Build from source
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TensorFlow is open source. Readthe instructions to build TensorFlow's C library from source code.