Deep learning is a method based on the representation and learning of data in machine learning. As the hottest topic at the moment, giants such as Google, Facebook, and Microsoft have made a series of researches on deep learning, and have been supporting the construction of open source deep learning framework. .
In the past year, under the blessing of these giants, the pattern of deep learning framework has changed dramatically: the new framework has emerged, the old framework has gradually withdrawn from the historical arena, and the link between the framework and the framework is closer, and the ecology is more Open. This year, Xiaobian is also paying attention to these developments and changes. Each of these topics has caused developers to discuss one after another:
Facebook open source PyTorch, become a strong enemy of TensorFlow
Theano completes its historical mission and retreats
The ONNX open platform is born, trying to promote an open development ecosystem
CNTK, Keras, MXNet are all ushered in major updates
Microsoft and Amazon launch Gluon deep learning library, Intel launched enhanced learning framework Coach
For mobile devices, Caffe2 and TensorFlow Lite are open source
Next, Xiaobian will take stock of the major developments and changes between the above machine learning frameworks to readers.
Facebook open source PyTorch, occupying the developer community with turbulence
In early 2017, Facebook launched a new machine learning toolkit, PyTorch, for the Python language based on the machine learning and scientific computing tool Torch. PyTorch is actually an alternative to NumPy, which supports GPUs and has more advanced features that can be used to build and train deep neural networks. Once released, it received widespread attention and discussion. After nearly a year of development, PyTorch has become one of the most important R&D tools for practitioners.
When PyTorch was released at the beginning of the year, Facebook said, "It is expected to assist or to some extent replace the existing Python math library (such as NumPy)." At present, PyTorch is used more and more widely, and this prediction becomes a reality.
PyTorch is a great choice for TensorFlow in the eyes of many developers.
Here are the main advantages of PyTorch:
It is in the ecosystem of Python, the largest language for machine learning, enabling developers to access a wide range of Python libraries and software. As a result, Python developers can write code in a style they are familiar with, without the need for a wrapper for external C or C++ libraries, using its specialized language.
There is no need to rebuild the entire network from scratch, it provides a faster way to improve existing neural networks - using dynamic computaTIonal graph structures instead of most open source frameworks such as TensorFlow, Caffe, CNTK, Theano The static calculation map used.
The API for data loading in PyTorch is well designed. Interfaces are clearly defined in data sets, samplers, and data loaders. The data loader receives the data set and the sampler and generates an iterator on the data set based on the scheduler's scheduling. Loading parallel data is as simple as passing a num_workers statement to the data loader.
A custom GPU memory allocator is used. This allows the developer's deep learning model to have "maximum memory performance" and train deeper neural networks than ever before.
GitHub address: https://github.com/pytorch/pytorch
#p TensorFlow introduces dynamic graph mechanism #e#
TensorFlow is a deep learning framework launched by Google at the end of 2015. It has a reputation in the developer community for the past two years and is now the most commonly used deep learning framework. It has full functions and a good community, so the usage rate has been consistently maintained. With the emergence of frameworks such as PyTorch, TensorFlow has been widely criticized for its confusing documentation and interfaces, and the cumbersome use.
In 2017, the development team continued to introduce new features to TensorFlow: TensorFlow 1.0 was released at the beginning of the year to fully support Keras; the Eager ExecuTIon dynamic graphing mechanism was introduced at the end of the year to make development easier and more intuitive.
Here are the advantages that TensorFlow has been widely praised for:
TensorFlow's Saver object is easy to use and offers more options for checkpoints (check-poinTIng).
In serialization, the main advantage of TensorFlow is that the entire graph can be saved as a protocol buffer. This includes parameters and operations. In addition, the diagram can be loaded in other supported languages ​​(C++, Java). This is critical for scheduling stacks that don't support Python. In theory, it can help when you want to run an old model after changing the model source code.
Support for mobile and embedded deployments, although deploying TensorFlow to Android or iOS requires a lot of work, but you don't have to rewrite the entire reasoning program of the model in Java or C++.
In addition, TensorFlow Serving supports high-performance server-side deployments that allow users to easily switch models without compromising service performance.
In addition, it introduced Eager ExecuTIon at the end of the year - an imperative, run-defined interface that, once invoked from Python, performs operations immediately, making TensorFlow's introductory learning easier and making development more intuitive .
GitHub address: https://github.com/tensorflow/tensorflow
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