Towards automated neural design: An open source, distributed neural architecture research framework

09/20/2018 ∙ by George Kyriakides, et al. ∙ University of Macedonia 0

NORD (Neural Operations Research & Development) is an open source distributed deep learning architectural research framework, based on PyTorch, MPI and Horovod. It aims to make research of deep architectures easier for experts of different domains, in order to accelerate the process of finding better architectures, as well as study the best architectures generated for different datasets. Although currently under heavy development, the framework aims to allow the easy implementation of different design and optimization method families (optimization algorithms, meta-heuristics, reinforcement learning etc.) as well as the fair comparison between them. Furthermore, due to the computational resources required in order to optimize and evaluate network architectures, it leverage the use of distributed computing, while aiming to minimize the researcher's overhead required to implement it. Moreover, it strives to make the creation of architectures more intuitive, by implementing network descriptors, allowing to separately define the architecture's nodes and connections. In this paper, we present the framework's current state of development, while presenting its basic concepts, providing simple examples as well as their experimental results.

READ FULL TEXT VIEW PDF
POST COMMENT

Comments

There are no comments yet.

Authors

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

1. Introduction

Following the recent developments in deep learning, groups of researchers experimented with autonomous generation of network architectures (Miikkulainen et al., 2017),(Stanley and Miikkulainen, 2002),(Baker et al., 2016),(Zoph and Le, 2016),(Negrinho and Gordon, 2017)

. Although the results are promising, the computational resources required exceed those readily available to most academic and many industrial researchers. Thus, to make autonomous design of neural architectures widely available, more efficient methods have to be developed. Although probable, it is unlikely that such methods will be spontaneously created. It is more probable that new methods will be increasingly more efficient, leading to their incremental adoption, in direct proportion to available computing resources.

There are many points during an automated design process that can greatly impact (and increase) the required resources. The evaluation process of an architecture usually demands the most resources. Distributing the workload across many machines is certainly lucrative. Either by data parallelization/distribution where each node gets a slice of the training data or by model parallelization/distribution where each node gets a slice of the network’s parameters. The design algorithm itself may present inefficiencies, for example some reinforcement learning algorithms are sample-inefficient (OpenAI, 2017)

. Moreover, it may present opportunities for parallelization for example genetic algorithms are considered ”embarrassingly” parallel /citeAlba1999 . The algorithm that distributes the workload among many nodes can itself be a bottleneck, if it is not carefully implemented.

Naturally, in order to design the different components involved in the design process, expertise in different domains and an understanding of the whole process as a system are required. Outside of dedicated research groups, this is usually difficult to communicate and embrace. Furthermore, even if a single researcher, expert in a field relative to a certain component (ex. meta-heuristics) can understand the process as a system, fellow experts in different fields are needed in other to implement it and conduct research. Thus, a framework that allows the independent development of design-process components can greatly accelerate advances in the field.

In this paper we present an open source framework which is currently under development and aims to enable researchers of many different fields to contribute and further our understanding of deep neural architectures. First we present the framework’s basic concepts. Following, we provide simple examples, as well as discuss possible further directions and research. The framework is available at (Kyriakides, 2018)

2. Basic Concepts

In concordance to many well-established deep learning frameworks, NORD’s description of a network is based on directed graphs. The framework’s main concept is to provide an easy to understand programming model in order to efficiently define, optimize and evaluate a graph (i.e. neural network), as well as tools in order to analyze the generated architectures. In principle, as with any optimization method, the basic workflow consists of constructing a solution (network), evaluating it (training and testing) and using the evaluation feedback in order to generate another solution, until a certain criterion is met. In Figure 1 the workflow process is depicted using NORD’s basic components.

Figure 1. Basic workflow for automated architecture design.

2.1. Descriptors

In order to make the expression of a network more friendly to automated generation, we implemented descriptors, which allow the independent declaration of network nodes and connections within the graph. The first step to define a network using descriptors is to add the required layer types, along with their parameters and names. The second step is to define connections between the layers, using their names. This aims to make the programmatical and thus, autonomous, creation of complex neural architectures easier and more intuitive. In Figure 2 an example of declaring a simple branching convolutional network is given.

Furthermore, a sequential mode can be used, where each layer is automatically connected to the previously added layer, making the use of names optional.

Figure 2. Example of a descriptor.

2.2. Evaluators

The majority of computations required in order to discover good architectures lies within the evaluation process. An evaluator is an object that undertakes the process of assessing the quality of a descriptor’s architecture. An evaluator can have certain properties and operate under certain assumptions. For example, it may be executed in a distributed setting or perform certain operations in order to reduce the computational cost of evaluating a network. This enables the fair comparison between networks and design methods, as it is possible to control and replicate the way that networks are defined and evaluated.

2.3. Environments

An environment defines the distributed setting under which the current evaluator will operate. Using MPI as a reference, an Environment tends to the communications initialization, the definition of local and global ranks and finally ensures that the collective communications are terminated correctly. Furthermore, it provides functions to orderly print and log results.

2.4. Data Curators

Data curators are responsible for loading evaluation datasets and correctly distributing them amongst the workers. Curators provide the required data samplers to evaluators as well as the classes contained in the dataset.

3. Implementation

Our current implementation is based on a few well-established libraries, ensuring the correct and optimized execution of our framework. We use PyTorch (Paszke et al., 2017) for the implementation and training/evaluation of individual neural networks. In order to distribute the workload of training a specific network between many workers, we use Horovod (Sergeev and Balso, [n. d.])

. Finally,to orchestrate the collective communication of non-Tensor objects and information we use mpi4py, a fairly robust MPI library for python.

4. Examples

In this section we present some simple examples that we implemented to demonstrate the framework’s basic concepts, as well as to compare their results. The examples aim to design a fully convolutional 2-layer network and evaluate it on the CIFAR10 dataset (Krizhevsky and Hinton, 2009)], using a genetic algorithm. The networks generated consist of two convolutional and one classification layer. For the first two examples, ESA’s PyGMO(Biscani and Izzo, 2018) implementation of genetic algorithms was used, while the third example uses a custom implementation.

4.1. Local execution

Our first and simplest example entails the use of a genetic algorithm in order to evolve a small population of 2-layer convolutional networks on a single machine. We assume square filters, with sizes of 1 to 50 pixels, as well as 1 to 50 filters per layer. The genetic algorithm must thus evolve choromosomes with a length of 4, representing the size and number of filters for each layer. The algorithm is initialized with a crossover rate of 0.9 , mutation rate of 0.2 and uniform sampling from the possible value range. The population consists of 10 individuals and is evloved for 10 generations.

To evaluate each individual, we create a descriptor and add two Conv2d layers, with parameters the first and second pair of values contained in the chromosome, using the add_layer_sequential function. We then pass the descriptor to the evaluator, calling the descriptor_evaluate function. In Figure 3 the evaluation code is depicted.

Executed on a single machine with a GT730 graphics card, each generation needed almost 18 minutes to complete.

Figure 3. Evaluation of a solution.

4.2. Local execution with distributed evaluation

This is a typical use case for NORD. The implementation is the same as the above experiment, substituting only the evaluator type with the distributed version and executing the code within an Environment context manager. By utilizing 9 worker machines, identical to that of the first experiment, the average time required for each generation was 6 minutes. Due to the samll size of our networks, it is probable that more workers were employed than needed. This experiment’s purpose was not to exhibit an almost perfect speedup, but rather to explore the stability and ease of implementation for our framework.

4.3. Distributed execution with local evaluation

In this example, we implemented a distributed genetic algorithm with local network evaluation. Each individual is evaluated on a different machine locally, in contrast to the previous example where each individual is evaluated by distributing the workload (i.e. dataset) amongst many machines. In order to achieve this, we implemented a custom genetic algorithm, using MPI for the collective communication between the workers. The algorithm’s parameters remain the same with the previous two examples.

Using the same execution environment with the previous example, this experiment needed 7 minutes and 40 seconds for each generation on average, being slower than experiment 2. This is due to the fact that every generation ends when all networks have been evaluated, and thus making big networks bottleneck the process.

4.4. Experimental results

In this section we present and compare the results of our framework’s current implemented examples. First, we compare the solutions generated by each method, focusing on the accuracy achieved on the test set. Figure 4

depicts the mean accuracy (line plots), as well as the distribution (box plots) of accuracies for each generation. As it is evident, the first example produced the best solutions by running on a single machine. The second example, although producing stable solutions did not produce as high quality solutions as the first. Finally, the third example, although performing worse than the first and better than the second, required more generations in order to produce stable solutions, as the mean is below the third quantile of the solutions’ distributions for many generations.

Figure 4. Accuracy of solutions for each generation.

Although the local execution seems to greatly outperform the other two, it can be attributed to the fact that the experiments were not executed for many generations due to their requirements in computational resources. Furthermore, as it is seen in Figure 5 the local version of our experiments (162 min.) needs almost twice as much time to complete as the third version (76 min.) and almost three times as much as the ’NORD’ version with distributed evaluation (55 min.).

The distributed version of the experiment, although incurs less communication overhead, requires more time to complete due to the fact that it is a synchronous algorithm. For each generation, each worker must wait all the others to complete before computing the next generation’s solution.

Figure 5. Time required for each experiment.

In order to compare the solutions themeselves, we calculate the mean trainable parameters in each generation for each method. In Figure 6, it is evident that the local and ’NORD’ versions of our experiments, although starting with the same number of parameters, diverge for the majority of the generations, only to finally arrive at the same number of average trainable parameters. The distributed version once again exhibits an unstable behaviour. We should note that the local and ’NORD’ versions are executed using the same PyGMO implementation, while the distributed version uses a custom implementation and thus certain differences exist.

Figure 6. Average number of rainable parameters for each generation.

5. Limitations and future work

The framework is currently under heavy development, and many features will be added and refined. Community feedback and suggestions are welcomed and greatly appreciated. The examples demonstrated here are not meant to be cutting-edge solutions, but merely working prototypes. We aim to enrich the framework with tools to provide a fair and thorough implementation, evaluation and comparison of neural architecture design methods. Furthermore,

6. Conclusions

In this paper we presented a distributed deep learning architectural research framework, based on well-known and established libraries. We provided some simple examples, by building 2-layer fully convolutional networks for the CIFAR10 dataset. By implementing a local, distributed and local-execution-distributed-evaluation variant, we exhibited the basic use of our framework in its current state. By uncoupling the design, evaluation and analysis stages of the design process, we aim to enable more researchers to contribute to the expansion of our knowledge and understadning of deep neural networks.

References