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Firectl is a basic command-line tool that lets you run arbitrary Firecracker MicroVMs via the command line. This lets you run a fully functional Firecracker MicroVM, including console access, read/write access to filesystems, and network connectivity.

This package is a Go library to interact with the Firecracker API. It is designed as an abstraction of the OpenAPI-generated client that allows for convenient manipulation of Firecracker VM from Go programs. There are some Firecracker features that are not yet supported by the SDK. These are tracked as GitHub issues with the firecracker-feature label. Contributions to address missing features are welcomed

This is the presentation website for Firecracker. We take pull request for content and FAQ improvements, as well as additons to the list of Firecracker integrations. When contributing to HTML pages in this repo, please format the entire file with the latest stable Prettier release, using the settings below for the HTML parser.

This repository provides resources for implementing a visual search engine. Visual search is the central component of an interface where instead of asking for something by voice or text, you show what you are looking for. When shown a real world, physical item, an AWS DeepLens device generates a feature vector representing that item. The feature vector generated by the AWS DeepLens device is sent to the

Secure Packager and Encoder Key Exchange (SPEKE) is part of the AWS Elemental content encryption protection strategy for media services customers. SPEKE defines the standard for communication between AWS Media Services and digital rights management (DRM) system key servers. SPEKE is used to supply keys to encrypt video on demand (VOD) content through AWS Elemental MediaConvert and for live content through

FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment.

The paper proposes an efficient signal processing system mainly consisting of an adaptation-based nonlinear echo cancellation (NLEC) layer and a joint perceptual subband residual echo suppression (SBRES) layer and noise reduction (SBNR) layer. The theoretical analyses, subjective and objective test results show that the proposed signal processing system can offer a significant improvement for automatic

The Alexa Meaning Representation Language (AMRL) is a compositional graph-based semantic representation that includes fine-grained types, properties, actions, and roles and can represent a wide variety of spoken language. AMRL increases the ability of virtual assistants to represent more complex requests, including logical and conditional statements as well as ones with nested clauses. Due to this representational

Training discriminative classifiers involves learning a conditional distribution p(yi|xi), given a set of feature vectors xi and the corresponding labels yi, i = 1..N. For a classifier to be generalizable and not overfit to training data, the resulting conditional distribution p(yi|xi) is desired to be smoothly varying over the inputs xi. Adversarial training procedures enforce this smoothness using manifold

This paper presents a novel deep neural network (DNN) architecture with highway blocks (HWs) using a complex discrete Fourier transform (DFT) feature for keyword spotting. In our previous work, we showed that the feed-forward DNN with a time-delayed bottleneck layer (TDB-DNN) directly trained from the audio input outperformed the model with the log-mel filter bank energy feature (LFBE), given a large amount

We propose to smooth out the calibration score, which measures how good a forecaster is, by combining nearby forecasts. While regular calibration can be guaranteed only by randomized forecasting procedures, we show that smooth calibration can be guaranteed by deterministic procedures. As a consequence, it does not matter if the forecasts are leaked, i.e., made known in advance: smooth calibration can nevertheless

Identifying sets of items that are equivalent to one another is a problem common to many fields. Systems addressing this generally have at their core a function s(di, dj ) for computing the similarity between pairs of records di, dj . The output of s() can be interpreted as a weighted graph where edges indicate the likelihood of two records matching. Partitioning this graph into equivalence classes is non-trivial

We argue for the necessity of managing the metadata and lineage of common artifacts in machine learning (ML). We discuss a recently presented lightweight system built for this task, which accelerates users in their ML workflows, and provides a basis for comparability and repeatability of ML experiments. This system tracks the lineage of produced artifacts in ML workloads and automatically extracts metadata

This paper describes a reference architecture for self-maintaining systems that can learn continually, as data arrives. In environments where data evolves, we need architectures that manage Machine Learning (ML) models in production, adapt to shifting data distributions, cope with outliers, retrain when necessary, and adapt to new tasks. This represents continual AutoML or Automatically Adaptive Machine

This paper introduces a brand new tree library treelite. The library is a toolbox to facilitate easy deployment of models and accelerate prediction performance. It has a Python wrapper that allows users to integrate it as part of their workflow. Treelite is able to read tree ensemble models that are trained by any tree libraries, including XGBoost [1], LightGBM [2], and scikit-learn [3]. Treelite is also

Amazon Aurora is a high-throughput cloud-native relational database offered as part of Amazon Web Services (AWS). One of the more novel differences between Aurora and other relational databases is how it pushes redo processing to a multi-tenant scale-out storage service, purpose-built for Aurora. Doing so reduces networking traffic, avoids checkpoints and crash recovery, enables failovers to replicas without

Linear models have been widely used in the industry for their low computation time, small memory footprint and interpretability. However, linear models are not capable of leveraging non-linear feature interactions in predicting the target. This limits their performance. A classical approach to overcome this limitation is to use combinations of the original features, referred to as higher-order features,

We present an end-of-utterance detector for real-time automatic speech recognition in far-field scenarios. The proposed system consists of three components: a long short-term memory (LSTM) neural network trained on acoustic features, an LSTM trained on 1-best recognition hypotheses of the automatic speech recognition (ASR) decoder, and a feedforward deep neural network (DNN) combining embeddings derived

In this paper, we explore the task of mapping spoken language utterances to one of thousands of natural language understanding domains in intelligent personal digital assistants (IPDAs). This scenario is observed for many mainstream IPDAs in industry that allow third parties to develop thousands of new domains to augment built-in ones to rapidly increase domain coverage and overall IPDA capabilities. We

Pitch detection is a fundamental problem in speech processing as F0 is used in a large number of applications. Recent papers have proposed deep learning for robust pitch tracking. In this letter, we consider voicing detection as a classification problem and F0 contour estimation as a regression problem. For both tasks, acoustic features from multiple domains and traditional machine learning methods are