InfoQ Homepage TensorFlow Content on InfoQ
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TensorFlow 2.3 Features Pipeline Bottleneck Reduction and Improved Preprocessing
The TensorFlow project announced the release of version 2.3.0, featuring new mechanisms for reducing input pipeline bottlenecks, Keras layers for pre-processing, and memory profiling.
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Google Announces TensorFlow 2 Support in Object Detection API
Google announced support for TensorFlow 2 (TF2) in the TensorFlow Object Detection (OD) API. The release includes eager-mode compatible binaries, two new network architectures, and pre-trained weights for all supported models.
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Google ML Kit SDK Now Focuses on On-Device Machine Learning
Google has introduced a new ML Kit SDK aimed at working in standalone mode without requiring a tight integration with Firebase, as the original ML Kit SDK did. Additionally, it provides limited support for replacing its default models with custom ones for image labeling and object detection and tracking.
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Uber Open-Sources AI Abstraction Layer Neuropod
Uber open-sourced Neuropod, an abstraction layer for machine learning frameworks that allows researchers to build models in the framework of their choice while reducing the effort of integration, allowing the same production system to swap out models implemented in different frameworks. Neuropod currently supports several frameworks, including TensorFlow, PyTorch, Keras, and TorchScript.
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Google Open-Sources New Higher Performance TensorFlow Runtime
Google open-sourced the TensorFlow Runtime (TFRT), a new abstraction layer for their TensorFlow deep-learning framework that allows models to achieve better inference performance across different hardware platforms. Compared to the previous runtime, TFRT improves average inference latency by 28%.
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Google Releases Quantization Aware Training for TensorFlow Model Optimization
Google announced the release of the Quantization Aware Training (QAT) API for their TensorFlow Model Optimization Toolkit. QAT simulates low-precision hardware during the neural-network training process, adding the quantization error into the overall network loss metric, which causes the training process to minimize the effects of post-training quantization.
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Google Introduces TensorFlow Developer Certification
Google has launched a certification program for its deep-learning framework TensorFlow. The certification exam is administered using a PyCharm IDE plugin, and candidates who pass can be listed in Google's world-wide Certification Directory.
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Google Announces Beta Launch of Cloud AI Platform Pipelines
Google Cloud Platform (GCP) recently announced the beta launch of Cloud AI Platform Pipelines, a new product for automating and managing machine learning (ML) workflows, which leverages the open-source technologies TensorFlow Extended (TFX) and Kubeflow Pipelines (KFP).
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TensorFlow Quantum Joins Quantum Computing and Machine Learning
TensorFlow Quantum (TFQ) brings Google quantum computing framework Cirq and TensorFlow together to enable the creation of quantum machine learning (ML) models.
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Spotify Open-Sources Terraform Module for Kubeflow ML Pipelines
Spotify has open-sourced their Terraform module for running machine-learning pipeline software Kubeflow on Google Kubernetes Engine (GKE). By switching their in-house ML platform to Kubeflow, Spotify engineers have achieved faster time to production and are producing 7x more experiments than on the previous platform.
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Microsoft Open-Sources Project Petridish for Deep-Learning Optimization
A team from Microsoft Research and Carnegie Mellon University has open-sourced Project Petridish, a neural architecture search algorithm that automatically builds deep-learning models that are optimized to satisfy a variety of constraints. Using Petridish, the team achieved state-of-the-art results on the CIFAR-10 benchmark with only 2.2M parameters and five GPU-days of search time.
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Google Open-Sources ALBERT Natural Language Model
Google AI has open-source A Lite Bert (ALBERT), a deep-learning natural language processing (NLP) model, which uses 89% fewer parameters than the state-of-the-art BERT model, with little loss of accuracy. The model can also be scaled-up to achieve new state-of-the-art performance on NLP benchmarks.
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TensorFlow 2.1.0 Will Be the Last Version to Support Python 2
The TensorFlow project announced a release candidate for version 2.1.0. In addition to several improvements and bug fixes, this release will be the last version of the deep-learning framework to support Python 2.
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Google Introduces New Metrics for AI-Generated Audio and Video Quality
Google AI researchers published two new metrics for measuring the quality of audio and video generated by deep-learning networks, the Fréchet Audio Distance (FAD) and Fréchet Video Distance (FVD). The metrics have been shown to have a high correlation with human evaluations of quality.
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Machine Learning on Mobile and Edge Devices with TensorFlow Lite: Daniel Situnayake at QCon SF
At QCon SF, Daniel Situnayake presented "Machine learning on mobile and edge devices with TensorFlow Lite". TensorFlow Lite is a production-ready, cross-platform framework for deploying ML on mobile devices and embedded systems, and was the main topic of the presentation.