TF-Replicator: Distributed Machine Learning for Researchers (1902.00465v1)

Abstract: We describe TF-Replicator, a framework for distributed machine learning designed for DeepMind researchers and implemented as an abstraction over TensorFlow. TF-Replicator simplifies writing data-parallel and model-parallel research code. The same models can be effortlessly deployed to different cluster architectures (i.e. one or many machines containing CPUs, GPUs or TPU accelerators) using synchronous or asynchronous training regimes. To demonstrate the generality and scalability of TF-Replicator, we implement and benchmark three very different models: (1) A ResNet-50 for ImageNet classification, (2) a SN-GAN for class-conditional ImageNet image generation, and (3) a D4PG reinforcement learning agent for continuous control. Our results show strong scalability performance without demanding any distributed systems expertise of the user. The TF-Replicator programming model will be open-sourced as part of TensorFlow 2.0 (see https://github.com/tensorflow/community/pull/25).

TF-Replicator: Distributed Machine Learning for Researchers

The paper presents TF-Replicator, a distributed machine learning framework designed to abstract the complexities of scaling models across multiple computing devices. This framework, implemented as an extension to TensorFlow, aims to simplify the construction of both data-parallel and model-parallel machine learning applications for researchers in the field.

Core Contributions and Features

TF-Replicator introduces a programming model that requires minimal distributed systems knowledge, allowing researchers to deploy models across various cluster architectures, including CPUs, GPUs, and TPUs, with synchronous or asynchronous training. Key features of TF-Replicator include:

• Flexible Deployment: The framework allows for easy deployment across different hardware configurations, supporting both in-graph and between-graph replications.
• Simplified API: Users can define model replicas using simple API functions, input_fn and step_fn, effectively abstracting the complexity of distributed data parallelism.
• Support for Advanced Models: TF-Replicator is not limited to traditional models but extends to multiple-loss methods and reinforcement learning agents, which have been traditionally hard to scale horizontally.

Experimental Benchmarks

The paper benchmarks TF-Replicator's scalability and usability through three distinct machine learning tasks:

1. Image Classification with ResNet-50: Demonstrating strong scalability, TF-Replicator efficiently trains a ResNet-50 on the ImageNet dataset across multiple GPUs and TPUs. Results indicate reductions in training time without hand-coding optimizations specific to the model.
2. Generative Adversarial Networks (GANs): Applying TF-Replicator to train a class-conditional SN-GAN, the framework successfully scales the model to larger batch sizes, leading to substantial improvements in Inception Scores, highlighting the framework’s capability in enhancing model quality through effective resource utilization.
3. Reinforcement Learning with D4PG: TF-Replicator enables the D4PG agent to achieve high performance on the DeepMind Control Suite by allowing scalable learning from pixel observations through data-parallel techniques.

Technical Implementation

TF-Replicator leverages TensorFlow’s computation graph to manage distributed processes, providing mechanisms for both in-graph and between-graph replication. The framework offers all-reduce primitives and supports both synchronous and asynchronous SGD. By abstracting communication patterns, TF-Replicator facilitates straightforward deployment across TPUs and GPUs.

Implications and Future Directions

The emergence of TF-Replicator signifies a notable progression in simplifying distributed machine learning frameworks. By providing a unified platform for scaling, the framework reduces the engineering overhead for academia and industry researchers diversifying their experimental setups across various hardware configurations. In terms of future advancements, integrating additional optimizations for emerging hardware and extending support for novel machine learning paradigms could further enhance the utility of TF-Replicator. The open-source contribution of this framework within TensorFlow 2.0 potentially impacts research accessibility and encourages widespread adoption.

Conclusion

TF-Replicator successfully addresses some of the key challenges in distributed machine learning by providing an intuitive interface for deploying large-scale models. It facilitates research and experimentation at scale without demanding extensive distributed system expertise, thus serving as an effective tool for advancing machine learning research efficiency and scalability.