Source: tensorflow Maintainer: Debian Deep Learning Team Uploaders: Michael R. Crusoe , Andreas Tille , Wookey Section: science Priority: optional Build-Depends: debhelper-compat (= 13), dh-python, chrpath, rsync, android-libboringssl-dev, libcurl4-nss-dev, libdouble-conversion-dev, libfarmhash-dev, libgemmlowp-dev, libgif-dev, libgrpc++-dev (>= 1.23~), libgrpc-dev (>= 1.23~), libgtest-dev, libhighwayhash-dev, libhwloc-dev, libicu-dev, libjpeg62-turbo-dev, libjsoncpp-dev, liblmdb-dev, libdnnl-dev [amd64 arm64 ppc64el s390x], libnsync-dev, libpng-dev, libprotobuf-dev (>= 3.8~), libprotoc-dev (>= 3.8~), libre2-dev, libsnappy-dev, libsqlite3-dev, libssl-dev, libsuperlu-dev, bazel-bootstrap, protobuf-compiler (>= 3.8.0~), protobuf-compiler-grpc (>= 1.23~), python3, python3-all-dev, python3-future, python3-keras, python3-keras-applications, python3-keras-preprocessing, python3-mock, python3-numpy, python3-setuptools, python3-tqdm, python3-wheel, swig, zlib1g-dev, nasm Standards-Version: 4.5.0 Vcs-Browser: https://salsa.debian.org/deeplearning-team/tensorflow Vcs-Git: https://salsa.debian.org/deeplearning-team/tensorflow.git Homepage: https://tensorflow.org/ # MISSING features: tensorflow lite, tensorflow extended, tensorflow.js Package: libtensorflow-framework2 Architecture: any Multi-Arch: same Section: libs Depends: ${misc:Depends}, ${shlibs:Depends} # I copied some detail about this shared object from tensorflow/BUILD Description: Computation using data flow graphs for scalable machine learning TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. . This package ships shared object libtensorflow_framework.so.2.0 . A shared object which includes registration mechanisms for ops and kernels. Does not include the implementations of any ops or kernels. Instead, the library which loads libtensorflow_framework.so (e.g. _pywrap_tensorflow_internal.so for Python, libtensorflow.so for the C API) is responsible for registering ops with libtensorflow_framework.so. In addition to this core set of ops, user libraries which are loaded (via TF_LoadLibrary/tf.load_op_library) register their ops and kernels with this shared object directly. . For example, from Python tf.load_op_library loads a custom op library (via dlopen() on Linux), the library finds libtensorflow_framework.so (no filesystem search takes place, since libtensorflow_framework.so has already been loaded by pywrap_tensorflow) and registers its ops and kernels via REGISTER_OP and REGISTER_KERNEL_BUILDER (which use symbols from libtensorflow_framework.so), and pywrap_tensorflow can then use these ops. Since other languages use the same libtensorflow_framework.so, op libraries are language agnostic. #Package: libtensorflow2 #Architecture: any #Multi-Arch: same #Section: libs #Depends: ${misc:Depends}, # ${shlibs:Depends}, # libtensorflow-framework2 (= ${binary:Version}) #Description: Computation using data flow graphs for scalable machine learning (C) # TensorFlow is an open source software library for numerical computation # using data flow graphs. The graph nodes represent mathematical operations, # while the graph edges represent the multidimensional data arrays (tensors) # that flow between them. This flexible architecture enables you to deploy # computation to one or more CPUs or GPUs in a desktop, server, or mobile # device without rewriting code. # . # This package ships shared object libtensorflow.so.2.0, which exports # C API for TensorFlow (i.e. symbols named "*TF_*" or "*TFE_*"). Package: libtensorflow-cc2 Architecture: any Multi-Arch: same Section: libs Depends: ${misc:Depends}, ${shlibs:Depends}, libtensorflow-framework2 (= ${binary:Version}), #libtensorflow2 (= ${binary:Version}) Description: Computation using data flow graphs for scalable machine learning (C++) TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. . This package ships shared object libtensorflow_cc.so.2.0, which exports C++ API for TensorFlow (i.e. *tensorflow* *TF_* *TFE_* *pywrap_xla*). Package: libtensorflow-dev Architecture: any Multi-Arch: same Section: libdevel Depends: ${misc:Depends}, ${shlibs:Depends}, libtensorflow-framework2 (= ${binary:Version}), libtensorflow-cc2 (= ${binary:Version}) Description: Computation using data flow graphs for scalable machine learning (dev) TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. . This package ships C and C++ headers files for tensorflow. #Package: python3-tensorflow #Architecture: any #Multi-Arch: foreign #Depends: ${misc:Depends}, # ${shlibs:Depends}, # libtensorflow-framework2 (= ${binary:Version}) #Description: Computation using data flow graphs for scalable machine learning (Py3) # TensorFlow is an open source software library for numerical computation # using data flow graphs. The graph nodes represent mathematical operations, # while the graph edges represent the multidimensional data arrays (tensors) # that flow between them. This flexible architecture enables you to deploy # computation to one or more CPUs or GPUs in a desktop, server, or mobile # device without rewriting code. # . # This package ships Python3 interface of tensorflow.