Dynamic neural networks toolkits such as PyTorch, DyNet, and Chainer offer more flexibility for implementing models that cope with data of varying dimensions and structure, relative to toolkits that operate on statically declared computations (e.g., TensorFlow, CNTK, and Theano). However, existing toolkits - both static and dynamic - require that the developer organize the computations into the batches necessary for exploiting high-performance data-parallel algorithms and hardware. This batching task is generally difficult, but it becomes a major hurdle as architectures become complex. In this paper, we present an algorithm, and its implementation in the DyNet toolkit, for automatically batching operations. Developers simply write minibatch computations as aggregations of single instance computations, and the batching algorithm seamlessly executes them, on the fly, in computationally efficient batches. On a variety of tasks, we obtain throughput similar to manual batches, as well as comparable speedups over single-instance learning on architectures that are impractical to batch manually.

Summary: The authors of this paper extend neural network toolkit DyNet with automatic operation batching. Batching enables efficient utilization of CPUs and GPUs by turning matrix-vector products into matrix-matrix products and reducing kernel launch overhead (for GPUs) but it is commonly done manually. Manual batching is manageable for simple feed-forward-networks but it becomes increasingly a headache as we explore more flexible models that take variable-length input, tree-structured input, or networks that perform dynamic control decisions. Chainer, DyNet, and PyTorch are recently proposed neural network toolkits that allow user to dynamically define the computation graph using the syntax of the host language (if, while, etc in python). This is desirable as it avoids tookit specific constructions (e.g., cond in TensorFlow) and make the network definition intuitive but it tends to limit performance because the network construction and computation happens at the same time.

With the applications of artificial intelligence and deep learning (DL) on the rise, organisations seek easy and faster solutions to the problems presented by AI and deep learning. The challenge has always been about how to imitate the human brain and be able to deploy its logic artificially. Result: Neural Networks that are essentially designed on the human brain wiring. Neural Networks can be described as a set of algorithms that are loosely modelled on human brain. They are designed to recognise patterns.

PyTorch is a deep learning library developed by Facebook to develop machine learning models for NLP, Computer Vision and AI, to name a few. It was developed by Facebook's Artificial Intelligence Research Group and is used to run deep learning frameworks. PyTorch is an excellent framework for entering the actual machine learning and neural network building process. It is ideal for complex neural networks such as RNNNs, CNNs, LSTMs and neural networks that you want to design for a specific purpose. PyTorch is a very different kind of deep learning library (dynamic vs. static) that was adopted by many researchers if not most, and it's flexible approach and easy-to-understand style have won over newcomers and industry veterans alike.

Thanks to it, we don't need to worry about partial derivatives, chain rule, or anything like it. To illustrate how it works, let's say we're trying to fit a simple linear regression with a single feature x, using Mean Squared Error (MSE) as our loss: We need to create two tensors, one for each parameter our model needs to learn: b and w. Without PyTorch, we would have to start with our loss, and work the partial derivatives out to compute the gradients manually. Sure, it would be easy enough to do it for this toy problem, but we need something that can scale. So, how do we do it?

Dynamic neural networks toolkits such as PyTorch, DyNet, and Chainer offer more flexibility for implementing models that cope with data of varying dimensions and structure, relative to toolkits that operate on statically declared computations (e.g., TensorFlow, CNTK, and Theano). However, existing toolkits - both static and dynamic - require that the developer organize the computations into the batches necessary for exploiting high-performance data-parallel algorithms and hardware. This batching task is generally difficult, but it becomes a major hurdle as architectures become complex. In this paper, we present an algorithm, and its implementation in the DyNet toolkit, for automatically batching operations. Developers simply write minibatch computations as aggregations of single instance computations, and the batching algorithm seamlessly executes them, on the fly, in computationally efficient batches.

The year is coming to an end. I did not write nearly as much as I had planned to. But I'm hoping to change that next year, with more tutorials around Reinforcement Learning, Evolution, and Bayesian Methods coming to WildML! And what better way to start than with a summary of all the amazing things that happened in 2017? Looking back through my Twitter history and the WildML newsletter, the following topics repeatedly came up.

The year is coming to an end. I did not write nearly as much as I had planned to. But I'm hoping to change that next year, with more tutorials around Reinforcement Learning, Evolution, and Bayesian Methods coming to WildML! And what better way to start than with a summary of all the amazing things that happened in 2017? Looking back through my Twitter history and the WildML newsletter, the following topics repeatedly came up.

The year is coming to an end. I did not write nearly as much as I had planned to. But I'm hoping to change that next year, with more tutorials around Reinforcement Learning, Evolution, and Bayesian Methods coming to WildML! And what better way to start than with a summary of all the amazing things that happened in 2017? Looking back through my Twitter history and the WildML newsletter, the following topics repeatedly came up.

The year is coming to an end. I did not write nearly as much as I had planned to. But I'm hoping to change that next year, with more tutorials around Reinforcement Learning, Evolution, and Bayesian Methods coming to WildML! And what better way to start than with a summary of all the amazing things that happened in 2017? Looking back through my Twitter history and the WildML newsletter, the following topics repeatedly came up.