Tensorflow internal
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TensorFlow is a dataflow-like model that runs on a wide variety of hardware platforms. It uses tensors and a directed graph to describe computations. Operations are abstract computations implemented by kernels that run on different devices like CPUs and GPUs. The core C++ implementation defines the framework and kernel functions, while the Python implementation focuses on operations, training, and providing APIs. Additional libraries like Keras, TensorFlow Slim, Skflow, PrettyTensor, and TFLearn build on TensorFlow to provide higher-level abstractions.
![How it works
■ Python-C++ connection with SWIG wrapper
10
[tensorflow.i] [py_func.i]
[py_func.h] [py_func.cc]
v v](https://image.slidesharecdn.com/tensorflow-internal-160527101311/85/Tensorflow-internal-10-320.jpg)
![C++ kernels
■ Inherit from OpKernel
■ Kernel is implemented per CPU / GPU [How to]
– GPU version uses CUDA library
13
[constant_op.h]
[constant_op.cc]
[constant_op_gpu.cu.cc]](https://image.slidesharecdn.com/tensorflow-internal-160527101311/85/Tensorflow-internal-13-320.jpg)
Tensorflow internal
- 2. Overview ■ Dataflow-like model ■ Runs on a wide variety of different H/W platform 2※ Source: tensorflow.org ※ Source: github.com/zer0n/deepframeworks
- 3. Basic concepts ■ Tensor – definition: an array with more than two axes – arbitrary dimensionality array ■ Directed graph describes T/F computation – node: instantiation of an Operation ■ Operation – an abstract computation – have attribute(s) ■ Kernel – particular implementation of an Operation – run on a type of device (e.g. CPU, GPU) ■ Variable – special Operation to persistent mutable Tensor ■ Session – Created to interact with T/F system 3 nodein out 0…* 0…* ※ Source: T/F white paper
- 4. Programming Model ■ Example T/F code and corresponding computation graph ■ Single machine and distributed system architecture 4※ Source: T/F white paper
- 5. Previous work ■ DistBelief – Downpour SGD – Sandblaster L-BFGS ■ Related to – Project Adam • MSR – Parameter Server project 5 ※ Source: Large Scale Distributed Deep Networks ※ Source: parameter server architecture github wiki ※ Source: Project Adam paper
- 6. Feature Comparison Feature Tensor Flow Theano Torch Caffe Chainer CNTK Run on Single Machine O O O O O O Run on Distributed Machines O X X X X O Symbolic differentiation O O X X O X Implemented by C++ O X X O X X 6 ※ Source: T/F white paper ■ For detail, refer to Wikipedia
- 7. Execution Mode ■ Single Device ■ Multi Device – Node placement – Cross-Device Communication ■ Distributed – Fault Tolerance • Error handling between Send-Receive node pair • Periodic health check to worker process 7
- 8. Programming Idioms ■ Programming Idioms – Data Parallel Training • sequential SGD – Model Parallel Training • Recurrent deep LSTM – Concurrent Steps 8
- 9. Code Metrics ■ Source – https://github.com/tensorflow/tensorflow ■ Code Summary – Total 114MB • 3373 files including C/C++, python, HTML, … – Top 5 languages for implementation • C++ and Python are the major languages • Protocol Buffers: provide mechanism for serializing structured data 9 language files blank comment code C++ 1092 46473 43399 276160 C/C++ Header 779 23457 44727 86274 Python 641 27622 46660 97570 Protocol Buffers 179 2217 7294 8724 Java 167 8296 17325 49374 C# 116 4285 8653 34347
- 10. How it works ■ Python-C++ connection with SWIG wrapper 10 [tensorflow.i] [py_func.i] [py_func.h] [py_func.cc] v v
- 11. Code Structure ■ C++ implementation under /core folder 11 Folder C/C++ Header C++ Protocol Buffers 총합계 ./tensorflow/core/client/ 511 511 ./tensorflow/core/common_runtime/ 1384 8526 9910 ./tensorflow/core/common_runtime/gpu/ 644 3674 4318 ./tensorflow/core/distributed_runtime/ 581 2579 3160 ./tensorflow/core/distributed_runtime/rpc/ 434 2759 3193 ./tensorflow/core/example/ 116 209 45 370 ./tensorflow/core/framework/ 3539 14022 451 18012 ./tensorflow/core/graph/ 952 5586 6538 ./tensorflow/core/kernels/ 9180 42188 11 51379 ./tensorflow/core/lib/core/ 573 1240 25 1838 ./tensorflow/core/lib/gtl/ 1452 1943 3395 ./tensorflow/core/lib/hash/ 36 400 436 ./tensorflow/core/lib/histogram/ 60 324 384 ./tensorflow/core/lib/io/ 340 2134 2474 ./tensorflow/core/lib/jpeg/ 78 767 845 ./tensorflow/core/lib/png/ 37 311 348 ./tensorflow/core/lib/random/ 690 856 1546 ./tensorflow/core/lib/strings/ 532 3111 3643 ./tensorflow/core/lib/wav/ 13 166 179 ./tensorflow/core/ops/ 9346 9346 ./tensorflow/core/ops/compat/ 25 204 229 ./tensorflow/core/platform/ 805 738 1543 ./tensorflow/core/platform/default/ 349 290 639 ./tensorflow/core/platform/posix/ 31 656 687 ./tensorflow/core/protobuf/ 333 333 ./tensorflow/core/public/ 202 202 ./tensorflow/core/user_ops/ 20 20 ./tensorflow/core/util/ 1354 4426 170 5950 ./tensorflow/core/util/ctc/ 600 298 898 ./tensorflow/core/util/sparse/ 504 498 1002 총합계 24511 107782 1035 133328
- 12. C++ framework ■ Key classes 12
- 13. C++ kernels ■ Inherit from OpKernel ■ Kernel is implemented per CPU / GPU [How to] – GPU version uses CUDA library 13 [constant_op.h] [constant_op.cc] [constant_op_gpu.cu.cc]
- 14. Code Structure ■ Python implementation under /python folder 14 Folder C/C++ Header C++ Protocol Buffers Python 총합계 ./tensorflow/python/ 168 168 ./tensorflow/python/client/ 33 475 2031 2539 ./tensorflow/python/framework/ 13 686 7097 7796 ./tensorflow/python/kernel_tests/ 25391 25391 ./tensorflow/python/lib/core/ 26 316 342 ./tensorflow/python/lib/io/ 52 75 31 158 ./tensorflow/python/ops/ 14995 14995 ./tensorflow/python/platform/ 888 888 ./tensorflow/python/platform/default / 389 389 ./tensorflow/python/summary/ 1168 1168 ./tensorflow/python/summary/impl/ 693 693 ./tensorflow/python/tools/ 280 280 ./tensorflow/python/training/ 6 7732 7738 ./tensorflow/python/user_ops/ 7 7 ./tensorflow/python/util/ 51 51 총합계 124 1552 6 60921 62603
- 15. Python Implementation ■ Operations ■ Trainings 15
- 16. Code Summary ■ The Python part – Various operations and trainings – API: • the most complete and the easiest to use ■ The C++ part – Framework and kernel functions – API: • offer some performance advantages • supports deployment to small devices such as Android 16
- 17. Meta Framework ■ Keras ■ TensorFlow Slim – a lightweight library for defining, training and evaluating models ■ Skflow – provide Scikit Learn style API ■ PrettyTensor – support a chainable object syntax to quickly define neural networks ■ TFLearn – a modular and transparent deep learning library 17