Jeff Dean (Google Senior Fellow) – Large Scale Deep Learning with TensorFlow (part 2) (Oct 2016)
Chapters
00:00:31 Exploiting Model Parallelism in Machine Learning
TensorFlow Model Parallelism: Distributing training across multiple devices can decrease training time by decreasing step time, but communication between devices can be a limiting factor. Models with local connectivity, such as convolutional models, are well-suited for model parallelism, as communication is only required for activations on the boundary of partitions. Large models can be partially activated for specific examples, leading to more efficient computation.
Exploiting Model Parallelism: Single-core CPUs can utilize instructions that perform multiple multiply-add operations simultaneously. Thread parallelism enables the distribution of computation across multiple CPU cores. Model parallelism across devices is limited by PCIe bandwidth, but newer GPUs have customized interconnect networks for higher bandwidth. Network bandwidth and latency can be limiting factors for model parallelism across machines.
Using TensorFlow for Model Parallelism: TensorFlow allows hints to be specified for where different computations should be performed, either on a particular device or with general constraints. A placement algorithm, combined with a cost model, makes placement decisions based on execution times and tensor sizes. A machine learning-based approach to placement is being developed for generalizing to new graphs.
Different Kinds of Models in Google Products:
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Repurposing Model Architectures: General model architectures can be used for various tasks with modest changes or no changes, except for training on different data. This allows for efficient solutions that can be repurposed across different product areas or features.
Examples of Repurposed Models: Text Detection: A model trained to predict text in Street View images was repurposed to find rooftops in satellite imagery. Solar Energy Estimation: The same model could estimate solar panel area and energy generation potential based on roof angle and orientation. Diabetic Retinopathy Detection: A similar model was used to detect diabetic retinopathy in medical images.
Challenges in Healthcare: Board-certified ophthalmologists had only 60% agreement in grading diabetic retinopathy images from 1 to 5, indicating variance in human assessment. Intra-radio agreement was even lower at 65% three hours later. To reduce variance, a large number of ophthalmologists had to label each image.
AlphaGo and Pattern Recognition: AlphaGo’s work is similar to computer vision models in recognizing patterns and making predictions based on those patterns. The move evaluation neural network identifies interesting moves to consider, reducing the branching factor and making Monte Carlo tree search more effective.
00:12:14 Neural Net Applications in Search Ranking, Smart Reply, and Graph Algorithms
Search Ranking Function: Neural nets have been successfully deployed in Google’s search ranking function, enhancing matching based on meaning rather than surface forms of words. It became the third most important ranking signal, significantly improving search quality. Debugging tools were developed to help experts understand and interpret the model’s behavior, leading to its successful launch.
Sequence-to-Sequence Model: The sequence-to-sequence model has proven powerful in various machine learning tasks, such as image captioning, graph algorithms, and neural conversational models. For image captioning, the encoder is replaced with a convolutional neural model, using pixel activations to initialize caption generation. The model can generate plausible sentences for new images, but requires a large training set for optimal performance. The sequence-to-sequence model was adapted to solve graph algorithms like the traveling salesman problem and finding convex hulls, by training it to emit indirect references to input data.
Neural Conversational Models and Smart Reply: Neural conversational models extend the sequence-to-sequence approach to emulate good responses in dialogues between people. Google launched Smart Reply, based on the sequence-to-sequence model, which generates short replies to emails on mobile devices. Smart Reply processes 10% of all mobile inbox replies, predicting when a short reply is appropriate and applying the LSTM machinery accordingly.
Deep LSTM and Model Parallelism: Adding depth to recurrent models, such as LSTM, can improve performance. TensorFlow allows for easy implementation of deep LSTMs. Model parallelism can be achieved by assigning different layers of the LSTM to different GPUs, maximizing GPU utilization. This approach allows training of deep LSTMs with modest changes to TensorFlow code.
TensorFlow Queues: TensorFlow supports queues, enabling efficient data processing. Queues can be enqueued or dequeued, with operations waiting for a specific number of elements to become available if needed.
00:24:31 Queueing and Data Parallelism in TensorFlow
TensorFlow Queues: Queues in TensorFlow allow for input prefetching, separating out computation needed at different rates. Queues can group similar examples for training, such as grouping batches by sentence length for translation models. Modified queues can randomize or shuffle a large number of elements, which is useful when the data in the training set is not necessarily randomized.
Data Parallelism: TensorFlow allows control over data parallelism by specifying the number of parameter devices to spread parameters out over. A supervisor takes care of replica zero, which controls checkpointing and restoring of model parameters, as well as tracking the total number of global steps done by all replicas in the model. This allows for flexibility and control in data parallel training, though it can be clunky. Higher-level layers exist to make this process easier.
TensorFlow in Robotics: TensorFlow has been used extensively for research in robotics.
00:27:36 Advanced Techniques in Machine Learning for Robotics and Mobile Devices
The Robotic Learning Lab: Google acquired 20 robotic arms and set up a lab for parallel experimentation on robotics tasks. One project involved teaching robots to grasp objects from scratch using a supervised learning approach. The model received camera inputs and controlled six torque motor joints on the robot.
Precision Reduction: To reduce the amount of data sent across networks, the team experimented with reducing the precision of values. Lopping off 16 bits of the mantissa and sending the remaining 16 bits worked surprisingly well. This technique preserved the exponent bits of the full 32-bit floating point format and halved the network bandwidth requirement. The new format proved more amenable to reduced precision computation than the IEEE 16-bit format.
Quantization for Mobile Devices: TensorFlow models can be quantized to 8-bit fixed-point arithmetic for efficient execution on mobile devices. Quantization enables high-end smartphones to run an Inception model at six frames per second, albeit with significant battery drain. Mobile phone manufacturers are expected to develop more power-efficient hardware for accelerating neural networks on phones.
Integration with Robotic Operating Systems: TensorFlow is integrated with a custom robotic operating system to generate motor commands through lower-level robotic software. The system allows for easy addition of new operations for specific robots and supports both simulation and real-world robots.
Challenges with Integer Quantization: While quantization to fixed-point numbers is feasible for inference, it is more challenging for training. The dynamic range of weights changes during training, making it difficult to keep values within the appropriate range to avoid overflow or underflow.
Entry Points for Machine Learning: Google offers a range of machine learning products with different entry points for users with varying levels of expertise. Pre-trained models in different domains are available for use without any machine learning knowledge. For more sophisticated users, TensorFlow models can be retrained on custom datasets. Researchers can explore new model architectures and advance the field of machine learning.
Cloud Vision API: The Cloud Vision API allows users to analyze images without machine learning knowledge. It provides whole image classification, face detection, emotion assessment, and text recognition.
Cloud ML Product: The Cloud ML product enables users to train and deploy TensorFlow models. It automatically rewrites graphs to take advantage of multiple devices for faster training. It offers a managed inference service that scales computation based on the number of requests.
00:36:13 TensorFlow: Open Source Machine Learning Library
TensorFlow’s Impact and Applications: TensorFlow has gained popularity in the external community, with various implementations on GitHub. Examples include reinforcement learning games, neural artwork, character RNNs, Keras library integration, and neural captioning. A group is translating TensorFlow documentation into Mandarin.
Benefits of TensorFlow for Research: Model and data parallelism enable faster iteration and quicker turnaround times for research experiments and hypotheses. Researchers can quickly progress from an idea to results, enhancing productivity.
Deep Learning’s Broad Impact: Deep learning will have significant implications across various fields such as robotics, self-driving cars, healthcare, video, and dialogue systems. Encouraging research in deep learning due to its potential impact on technological advancements and societal progress.
Residency Program for Machine Learning Research: Google’s residency program allows individuals to spend a year with their research group, gaining hands-on experience in machine learning research. Residents work closely with Google researchers and learn the intricacies of conducting cutting-edge machine learning research.
Goals: To provide research opportunities for individuals with diverse backgrounds and expertise in machine learning, computer science, math, statistics, and related fields.
Program Structure: Participants are selected through an application process and accepted into a one-year program. During the program, scholars work on research projects under the mentorship of Google researchers. The aim is for scholars to publish two or three papers in top conferences and contribute to open-source repositories within the year.
Applicant Backgrounds: The program seeks individuals with bachelor’s, master’s, or Ph.D. degrees in relevant fields. About half of participants come directly from school, while the other half have post-school work experience.
Research Areas: Scholars work on a wide range of machine learning and AI-related topics, including: Computational biology Bio problems Finance
Opportunities: Scholars have access to Google’s computational resources and mentorship from experienced researchers. The program aims to foster collaboration among scholars with different backgrounds and expertise.
Application Process: Applications will open in the fall. More information will be available on the program website.
Abstract
Efficiency and Innovation in Machine Learning: Exploring the Advances and Applications of Parallelism and TensorFlow
In the rapidly evolving landscape of machine learning (ML) and artificial intelligence (AI), efficient training methods and versatile applications stand at the forefront of technological advancements. Key developments include the reduction of communication overhead in distributed training, the implementation of model parallelism, and the adoption of TensorFlow for enhanced parallelism in model training. These techniques not only improve the efficiency of large-scale computations but also find diverse applications in Google products, medical imaging, and even gaming. This article delves into these advancements, offering a comprehensive understanding of their significance and potential.
Reducing Communication Overhead in Distributed Training:
Distributed training, a method of reducing training time by spreading the workload across multiple devices or machines, faces the challenge of communication overhead. Convolutional models, with their local connectivity, are particularly adept at distributed training, minimizing data exchange. Innovative techniques like tower training and partial model activation further diminish this overhead, streamlining the training process.
TensorFlow Model Parallelism:
Distributing training across multiple devices can decrease training time by decreasing step time, but communication between devices can be a limiting factor. Models with local connectivity, such as convolutional models, are well-suited for model parallelism, as communication is only required for activations on the boundary of partitions. Large models can be partially activated for specific examples, leading to more efficient computation.
Model Parallelism: A Multi-Faceted Approach:
Model parallelism entails dividing a model across various devices, optimizing computation distribution. This approach manifests in several forms: instruction-level parallelism within a single core, thread parallelism across CPU cores, data parallelism by splitting data across devices, and model parallelism itself. Its efficacy is especially notable in large models with local receptive fields.
Exploiting Model Parallelism:
Single-core CPUs can utilize instructions that perform multiple multiply-add operations simultaneously. Thread parallelism enables the distribution of computation across multiple CPU cores. Model parallelism across devices is limited by PCIe bandwidth, but newer GPUs have customized interconnect networks for higher bandwidth. Network bandwidth and latency can be limiting factors for model parallelism across machines.
TensorFlow’s Role in Parallelism:
TensorFlow, a prominent ML framework, offers robust mechanisms for different parallelism types in model training. It employs sophisticated device placement algorithms and explores machine learning-based approaches for optimal computation assignment, significantly enhancing generalization capabilities for new models.
Parallelism in Google Products:
Parallelism has been instrumental in improving Google’s products, such as image search, speech recognition, and translation. By enabling efficient training of large models, it has brought significant advancements in both accuracy and processing efficiency.
Innovative Applications in Diverse Fields:
The principles of model parallelism and TensorFlow’s capabilities extend to various applications:
– Google Photos Clustering: Utilizes computer vision for content-based photo clustering, enabling users to search for specific objects without manual tagging.
– General Model Architectures: Demonstrates the adaptability of similar model structures across different problems, fostering efficient solutions across diverse product areas.
– Street View and Satellite Imagery: A text recognition model in Street View images was repurposed for rooftop detection in satellite imagery, aiding in solar panel potential estimation.
– Medical Imaging: Shows the versatility of underlying models in different medical imaging applications, like diabetic retinopathy detection, with results rivaling human assessment.
– Pathways: Research Scholars Program: The program provides research opportunities to individuals with diverse backgrounds in relevant fields. Scholars work on projects, often publishing papers in top conferences and contributing to open-source repositories.
Repurposing Model Architectures:
General model architectures can be used for various tasks with modest changes or no changes, except for training on different data. This allows for efficient solutions that can be repurposed across different product areas or features.
Examples of Repurposed Models:
– Text Detection: A model trained to predict text in Street View images was repurposed to find rooftops in satellite imagery.
– Solar Energy Estimation: The same model could estimate solar panel area and energy generation potential based on roof angle and orientation.
– Diabetic Retinopathy Detection: A similar model was used to detect diabetic retinopathy in medical images.
TensorFlow Queues for Efficient Data Handling:
– TensorFlow supports queues, enabling efficient data processing. Queues can be enqueued or dequeued, with operations waiting for a specific number of elements to become available if needed.
Breakthroughs in AI and Gaming:
– AlphaGo’s Neural Network: The move evaluation network in AlphaGo significantly reduces the branching factor in Monte Carlo tree search, leading to stronger gameplay.
– Neural Networks in Search Ranking: The incorporation of neural networks in Google’s search ranking marks a substantial improvement in search quality.
– AlphaGo and Pattern Recognition: AlphaGo’s work is similar to computer vision models in recognizing patterns and making predictions based on those patterns. The move evaluation neural network identifies interesting moves to consider, reducing the branching factor and making Monte Carlo tree search more effective.
Advances in Sequence-to-Sequence Models:
These models, excelling in mapping input to output sequences, have broad applications, including image captioning and neural conversational models. Their versatility extends to solving graph algorithms like the traveling salesman problem and convex hull finding.
– Search Ranking Function: Neural nets have been successfully deployed in Google’s search ranking function, enhancing matching based on meaning rather than surface forms of words. It became the third most important ranking signal, significantly improving search quality. Debugging tools were developed to help experts understand and interpret the model’s behavior, leading to its successful launch.
– Sequence-to-Sequence Model: The sequence-to-sequence model has proven powerful in various machine learning tasks, such as image captioning, graph algorithms, and neural conversational models. For image captioning, the encoder is replaced with a convolutional neural model, using pixel activations to initialize caption generation. The model can generate plausible sentences for new images, but requires a large training set for optimal performance. The sequence-to-sequence model was adapted to solve graph algorithms like the traveling salesman problem and finding convex hulls, by training it to emit indirect references to input data.
– Neural Conversational Models and Smart Reply: Neural conversational models extend the sequence-to-sequence approach to emulate good responses in dialogues between people. Google launched Smart Reply, based on the sequence-to-sequence model, which generates short replies to emails on mobile devices. Smart Reply processes 10% of all mobile inbox replies, predicting when a short reply is appropriate and applying the LSTM machinery accordingly.
Deep LSTM and TensorFlow’s Efficiency Tools:
Deepening LSTM models enhances their performance, with TensorFlow supporting model parallelism for concurrent running of different layers. TensorFlow’s queue mechanism and input prefetching further optimize data handling.
Efficiency Techniques in Network and Model:
Methods like precision reduction and model quantization are crucial for minimizing data transfer across networks and improving model efficiency, especially on mobile devices.
TensorFlow’s Expanding Influence:
TensorFlow’s open-source nature has spurred collaboration in the ML community. Its Cloud ML product simplifies training and serving TensorFlow models, enhancing accessibility for varied user expertise levels.
Expanding ML Accessibility and Deep Learning’s Impact:
Google’s offering of pre-trained models and TensorFlow’s diverse use cases, from games to neural artwork, signify the growing accessibility of ML. The profound impact of deep learning is evident across fields like robotics, healthcare, and dialogue systems.
Google’s Residency Program:
This initiative allows individuals from diverse backgrounds to conduct ML research, benefiting from Google’s computational resources and expertise, fostering innovation and interdisciplinary collaboration.
The advancements in machine learning, particularly in the fields of parallel processing and TensorFlow, are reshaping the landscape of technology and research. These innovations are not only accelerating computational processes but also broadening the horizons of ML applications, making this field more accessible and influential across diverse domains.
TensorFlow, a versatile machine learning framework, evolved from Google's DistBelief to address computational demands and enable efficient deep learning model development. TensorFlow's graph-based architecture and mixed execution model optimize computation and distribution across various hardware and distributed environments....
TensorFlow, an open-source machine learning library, has revolutionized research in speech and image recognition thanks to its scalability, flexibility, and real-world applicability. The framework's distributed systems approach and data parallelism techniques enable faster training and execution of complex machine learning models....
Machine learning has achieved breakthroughs in areas such as unsupervised learning, multitask learning, neural network architectures, and more. Asynchronous training accelerates the training process by running multiple model replicas in parallel and updating model parameters asynchronously....
TensorFlow, a versatile machine learning platform, has revolutionized problem-solving approaches, while transfer learning reduces data requirements and accelerates model development for diverse applications....
Deep learning revolutionizes NLP by unifying tasks under a single framework, enabling neural networks to learn end-to-end without explicit linguistic programming. Deep learning models excel in text generation, capturing long-range dependencies and producing fluent, coherent sentences, outshining traditional methods in machine translation and parsing....
TensorFlow and XLA's integration enhances machine learning research and development by offering flexibility, scalability, and performance optimizations for diverse hardware platforms. XLA's just-in-time compilation and TensorFlow's comprehensive capabilities empower users to explore complex ideas and create high-performance models effortlessly....
Deep neural networks have revolutionized computational capabilities in various domains, bringing about groundbreaking results in perception-based tasks and creating new opportunities for advancing artificial intelligence and machine learning. The challenges of scalability, interpretability, and robustness, however, demand ongoing exploration and research....