TensorFlow 1.12.0 更新总结

TensorFlow 1.12.0版本更新包括Keras模型可以直接输出为SavedModel格式,支持使用tf.data.Dataset进行评估,tf.data引入新API如window()和reduce(),以及对tf.contrib.linalg等模块的调整。

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TensorFlow 1.12.0 更新总结

Keras 更新

Keras 模型现在可以直接输出为 SavedModel 格式(tf.contrib.saved_model.save_keras_model()),输出的模型可以使用 TensorFlow Serving 来部署。
Keras 模型现在支持使用 tf.data.Dataset 作为数据源来进行模型评估。
tf.data 更新

现在使用 tf.data.Options()、tf.data.Dataset.options()、tf.data.Dataset.with_options() 分别可以表示、获取、设置数据输入管道的选项。
新的 tf.data.Dataset.reduce() API 允许用户使用自定义 reduce 函数来将一个有限数据集缩减为单个元素。
新的 tf.data.Dataset.window() API 允许用户创建有限窗口的输入数据集;结合 tf.data.Dataset.reduce() API,用户便可以实现自定义 batching。
tf.data 的 C++ 代码移到了 tensorflow::data 命名空间。
为 tf.data.Dataset.interleave 添加了 num_parallel_calls 参数。
tf.contrib.data 退化,取而代之的是 tf.data.experimental。
其他更新

去除了 tf.contrib.linalg,取而代之的是 tf.linalg。
使用 meta_graph_def.signature_def[signature_def_key] 取代了 tf.contrib.get_signature_def_by_key(metagraph_def, signature_def_key)。对应的,用户程序中对于 tf.contrib.get_signature_def_by_key 引发的 ValueError 的处理应该改为对 KeyError 的处理。
英文原版:https://github.com/tensorflow/tensorflow/blob/r1.12/RELEASE.md

自编译tensorflow1.python3.5,tensorflow1.12; 2.支持cuda10.0,cudnn7.3.1,TensorRT-5.0.2.6-cuda10.0-cudnn7.3; 3.无mkl支持; 软硬件硬件环境:Ubuntu16.04,GeForce GTX 1080 TI 配置信息: hp@dla:~/work/ts_compile/tensorflow$ ./configure WARNING: --batch mode is deprecated. Please instead explicitly shut down your Bazel server using the command "bazel shutdown". You have bazel 0.19.1 installed. Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3 Found possible Python library paths: /usr/local/lib/python3.5/dist-packages /usr/lib/python3/dist-packages Please input the desired Python library path to use. Default is [/usr/local/lib/python3.5/dist-packages] Do you wish to build TensorFlow with XLA JIT support? [Y/n]: XLA JIT support will be enabled for TensorFlow. Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: No OpenCL SYCL support will be enabled for TensorFlow. Do you wish to build TensorFlow with ROCm support? [y/N]: No ROCm support will be enabled for TensorFlow. Do you wish to build TensorFlow with CUDA support? [y/N]: y CUDA support will be enabled for TensorFlow. Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 10.0]: Please specify the location where CUDA 10.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda-10.0 Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7]: 7.3.1 Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda-10.0]: Do you wish to build TensorFlow with TensorRT support? [y/N]: y TensorRT support will be enabled for TensorFlow. Please specify the location where TensorRT is installed. [Default is /usr/lib/x86_64-linux-gnu]://home/hp/bin/TensorRT-5.0.2.6-cuda10.0-cudnn7.3/targets/x86_64-linux-gnu Please specify the locally installed NCCL version you want to use. [Default is to use https://github.com/nvidia/nccl]: Please specify a list of comma-separated Cuda compute capabilities you want to build with. You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 6.1,6.1,6.1]: Do you want to use clang as CUDA compiler? [y/N]: nvcc will be used as CUDA compiler. Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: Do you wish to build TensorFlow with MPI support? [y/N]: No MPI support will be enabled for TensorFlow. Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native -Wno-sign-compare]: Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: Not configuring the WORKSPACE for Android builds. Preconfigured Bazel build configs. You can use any of the below by adding "--config=" to your build command. See .bazelrc for more details. --config=mkl # Build with MKL support. --config=monolithic # Config for mostly static monolithic build. --config=gdr # Build with GDR support. --config=verbs # Build with libverbs support. --config=ngraph # Build with Intel nGraph support. --config=dynamic_kernels # (Experimental) Build kernels into separate shared objects. Preconfigured Bazel build configs to DISABLE default on features: --config=noaws # Disable AWS S3 filesystem support. --config=nogcp # Disable GCP support. --config=nohdfs # Disable HDFS support. --config=noignite # Disable Apacha Ignite support. --config=nokafka # Disable Apache Kafka support. --config=nonccl # Disable NVIDIA NCCL support. Configuration finished 编译: bazel build --config=opt --verbose_failures //tensorflow/tools/pip_package:build_pip_package 卸载已有tensorflow: hp@dla:~/temp$ sudo pip3 uninstall tensorflow 安装自己编译的成果: hp@dla:~/temp$ sudo pip3 install tensorflow-1.12.0-cp35-cp35m-linux_x86_64.whl
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