Distributed inference. Faster examples with accelerated inference.

In order to remedy the computational issue of two-sample U-statistics while maintaining statistical efficiency for massive datasets, we consider distributed inference for two-sample U-statistics. We present FastServe, a distributed inference serving system for LLMs. It is noted that each device should load one same checkpoint file. multiprocessing as mp. Addressing this problem, we develop a scalable line with the standard distributed inference literature. Writing Distributed Applications with PyTorch shows examples of using c10d communication APIs. Aritra Mitra, John A. Dec 15, 2022 · In this paper, we focus on two key problems impacting deployment of distributed inference (DI) models on SC: resource allocation and cold start latency. Meanwhile, it provides opportunities Jan 30, 2021 · The distributed inference set-up makes it infeasible to obtain the top order statistics of the oracle sample. The inference technique uses observed map structure to infer unobserved map features. Abstract: Large-scale Transformer-based models trained for generation tasks (e. Distributed inference is a common use case, especially with natural language processing (NLP) models. In this paper, we focus on the variational message passing (VMP) applied to Gaussian graphical models Abstract. g. Monte Carlo algorithms, such as Markov chain Monte Carlo (MCMC) and Hamiltonian Monte Carlo (HMC), are routinely used for Bayesian inference; however, these algorithms are prohibitively slow in massive data settings because they require multiple passes through the full data in every iteration. An intuitive solution to reduce such overhead is to May 2, 2022 · This paper presents EdgeFlow, a new distributed inference mechanism designed for general DAG structured deep learning models, which partitions model layers into independent execution units with a new progressive model partitioning algorithm. In this attack, the goal of an adversary is to infer distributional information about the training data. The team then coordinates to explore both the inferred and observed portions of the map. Distributed inference means use multiple devices for prediction. Jun 12, 2023 · For distributed inference, there is a new predict_batch_udf API, which builds on the Spark Pandas UDF to provide a simpler interface for DL model inference. However, using these 50B+ models requires high-end hardware, making them inaccessible to most researchers. It can reduce SDXL latency by up to 6. Loading parts of a model onto each GPU and processing a single input at one time. Since these models generate a next token in an autoregressive manner, one has to run the model multiple times to process an inference request Distributed inference means use multiple devices for prediction. Our work has been accepted by CVPR 2024 as This guide will show you how to use 🤗 Accelerate and PyTorch Distributed for distributed inference. TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and more. distributed) enables researchers and practitioners to easily parallelize their computations across processes and clusters of machines. Conventional centralized methods—those in Distributed inference (e. FlexGen allow you to do pipeline parallelism with these 2 GPUs to accelerate the generation. This paper introduces distributed speculative inference (DSI), a novel distributed inference algorithm that is provably faster than speculative inference (SI) [leviathan2023fast, chen2023accelerating, miao2023specinfer] and traditional autoregressive inference (non-SI). 1× on 8 A100s. e. In MP, transmitting bulky intermediate data (orders of magnitude larger than input) between devices imposes huge communication overhead. Meanwhile, it provides opportunities for researchers to develop novel algorithms. See examples here. float16, use_safetensors=True. But to have scaled performance, you should have GPUs on distributed machines. 01, 2015) is the only other survey we found about distributed inference though the focus was on the presence of eavesdroppers, and it was Apr 8, 2024 · Message passing algorithms on graphical models offer a low-complexity and distributed paradigm for performing marginalization from a high-dimensional distribution. The distributed package included in PyTorch (i. Fundamentally, Bayesian inference uses prior knowledge, in the form of a prior distribution in Nov 17, 2023 · The basic idea of distributed learning is introduced and a selected review on various distributed learning methods are conducted, which are categorized by their statistical accuracy, computational efficiency, heterogeneity, and privacy. multiprocessing to set up the distributed process group and to spawn the processes for inference on each GPU. Following a divide-and-conquer algorithm, we first apply the Hill estimator to each machine and then take the average of the Hill estimates from all machines. ) is one of the primary applications of wireless sensor networks. Existing mechanisms divided the model across edge devices with the assumption that deep learning models are constructed with a chain of layers The distributed training tutorial and sample code can be referred to Distributed Parallel Training Example (Ascend). It supports model parallelism (MP) to fit large models that would otherwise not fit in GPU memory. May 1, 2023 · Regarding distributed inference, Ben-Nun et al. However, it is hard and even infeasible to calculate the empirical log-likelihood ratio statistic with massive data. For example, if you have 2 GPUs but the aggregated GPU memory is less than the model size, you still need offloading. Ernest Atta-Asiamah, Ernest Atta-Asiamah. This is the implementation of Distributed Inference via Decoupled CNN Structure (DeCNN). Individual robots select exploration poses by accounting for expected information gain and travel costs That is, the distributed inference should not lose any statistical e ciency as compared to the \oracle" single machine setting. Nov 27, 2017 · Distributed inference on large datasets – Needs and challenges. We show that under both combination strategies, agents are able to learn the truth exponentially fast, with a faster rate under log-linear fusion. To overcome these challenges, each sensor should compress and quantize its observations before sending them to a fusion center (FC) for global decision inference Elastic distributed inference. edu In this paper, we present a performance comparison between the capabilities of mobile phones and new hardware designed for Deep Learning Inference - the Coral TPU and the NVIDIA Jetson Nano. Collaborate on models, datasets and Spaces. , torch. At inference, you don’t need backward computation and you don’t want to modify the evaluation data. Bayesian inference ( / ˈbeɪziən / BAY-zee-ən or / ˈbeɪʒən / BAY-zhən) [1] is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. ai May 23, 2024 · Accelerating the inference of large language models (LLMs) is an important challenge in artificial intelligence. , send and isend ), which are used under the hood in all of the parallelism implementations. In this article, we propose DeCNN, a more effective Jul 14, 2021 · We can decompose your problem into two subproblems: 1) launching multiple processes to utilize all the 4 GPUs; 2) Partition the input data using DataLoader. An inference service can be used for inference by any authorized client. , all_reduce. Like other Experimental results show that our method dramatically improves the performance of distributed DNN inference in heterogeneous scenarios. distributed as dist. Distributed inference can fall into three brackets: Loading an entire model onto each GPU and sending chunks of a batch through each GPU’s model copy at a time. TGI implements many features, such as: Modern computer vision requires processing large amounts of data, both while training the model and/or during inference, once the model is deployed. When you have multiple microbatches to May 6, 2024 · Embedded distributed inference of Neural Networks has emerged as a promising approach for deploying machine-learning models on resource-constrained devices in an efficient and scalable manner. Distributed Inference of Deep Learning Models. A weighted distributed estimator is proposed to improve the statistical efficiency of the standard ”split-and-conquer" estimator for the common parameter shared by all the data blocks. . The programs in this repository allow for sending an image or video stream to an EDGE inference device, receive an inference result, and overlay the inference result onto the image or video stream. The combined pseudo posterior distribution replaces the full data posterior distribution in prediction and inference prob-lems. In the distributed Inference scenario, during the training phase, the integrated_save of CheckpointConfig interface should be set to False, which means that each device only saves the slice of model instead of the full model. So far, research on distribution inference has focused This guide will show you how to use 🤗 Accelerate and PyTorch Distributed for distributed inference. Inspired by the idea of divide-and-conquer, various distributed frameworks for statistical estimation and inference have been proposed. 500. Empirical likelihood is a very important nonparametric approach which is of wide application. Process of Distributed Inference May 29, 2019 · Distributed inference for degenerate U-statistics. This challenge becomes particularly acute in distributed setups, where traditional methods necessitate computing a debiased estimator on every DeepSpeed-Inference introduces several features to efficiently serve transformer-based PyTorch models. 5-2. By adopting the commonly used DaC approach, we formulate the distributed two-sample U-statistic. Those approaches directly utilize the availability of estimators from subsamples and can be carried out at almost no additional computational cost. and all_gather ) and P2P communication APIs (e. Independent samples from an unknown probability distribution on a domain of size are distributed across players, with each player holding one sample. The debiased estimator is a crucial tool in statistical inference for high-dimensional model parameters. This tutorial will be broken down into two parts showcasing how to use both 🤗 Accelerate and 🤗 Transformers (a higher API-level) to make use of this idea. In this paper, we focus on two key problems impacting deployment of distributed inference (DI) models on SC: resource allocation and cold start latency. We consider distributed statistical optimization and inference in the presence of heterogeneity among distributed data blocks. def run_inference(rank, world_size): # create default process group. Note that the oracle property compares the distributed estimator to the oracle estimator when the two estimators are constructed based on the same sample size. Distributed Llama allows you to run huge LLMs in-house. . Concerning distributed statistical inference, Tan et al. The inference task is distributed across a network of embedded devices, with each device contributing to the overall computation by performing a portion of the workload. The distributed inference platform is a set of tools for distributing inference tasks to a heterogeneous network of EDGE devices. We aim to avoid any condition on the number of machines (or the number of data batches). , 2014; Duan et al. This is typically known as online inference. to get started. Large language models (LLMs) are useful in many NLP tasks and become more capable with size, with the best open-source models having over 50 billion parameters. The hybrid scheduler identifies container allocation based on candidate Homogeneous distribution among the data blocks is assumed in the majority of the statistical distributed inference studies with a few exceptions (Zhao et al. Sep 5, 2023 · The resulting distributed Gaussian variational inference (DGVI) efficiently inverts a $1$-rank correction to the covariance matrix. In addition, we provide theoretical guarantee of the distributed statistical inference procedure. (Ben-Nun and Hoefler, Aug. Unfortunately, deploying and inferring large, compute- and memory-intensive CNNs on Internet of Things devices at the Edge is challenging as they typically have limited resources. The PyTorch distributed communication layer (C10D) offers both collective communication APIs (e. Our evaluation shows that SpecInfer outperforms existing LLM serving systems by 1. ← Big Model Inference 🤗 Accelerate's internal mechanism →. In addition, many devices suffer from limited resources to store or transmit data Feb 21, 2018 · This work proposes a technique for distributed multi-robot exploration that leverages novel methods of map inference. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. Experiments also confirm that our serverless solution can handle large distributed workloads and leverage high degrees of FaaS parallelism. We also describe a new distributed inference system, named DeepHome, that can distribute the machine learning inference tasks to multiple heterogeneous FasterTransformer [18]: It is an open-source production-grade distributed inference engine from NVIDIA, which optimizes for large transformer-based language models and is widely used in industry. Distributed Llama running Llama 2 70B on 8 Raspberry Pi 4B devices Distributed Inference Ascend Inference Application. [32] is an efficient distributed inference algorithm, which is robust against a moderate fraction of Byzantine nodes, and can improve the statistical efficiency over the vanilla MOM method. Hawkins , transportation editor with 10+ years of experience who covers EVs, public DeepSpeed brings together innovations in parallelism technology such as tensor, pipeline, expert and ZeRO-parallelism, and combines them with high performance custom inference kernels, communication optimizations and heterogeneous memory technologies to enable inference at an unprecedented scale, while achieving unparalleled latency, throughput and cost reduction. You should also initialize a [ DiffusionPipeline ]: "runwayml/stable-diffusion-v1-5", torch_dtype=torch. An adversary may use a distribution inference attack to infer the proportion of the training data having a specific value for some Recent years witnessed an increasing research attention in deploying deep learning models on edge devices for inference. We study the asymptotic learning rates of belief vectors in a distributed hypothesis testing problem under linear and log-linear combination rules. from_pretrained( "runwayml/stable-diffusion-v1-5", torch_dtype=torch. This also has other cases outside of just NLP, however for this tutorial we will focus Feb 8, 2023 · Motivated by the pervasiveness of artificial intelligence (AI) and the Internet of Things (IoT) in the current “smart everything” scenario, this article provides a comprehensive overview of the most recent research at the intersection of both domains, focusing on the design and development of specific mechanisms for enabling a collaborative inference across edge devices towards the in situ Apr 24, 2024 · ‘That’s 100 gigawatts of inference compute, distributed all around the world,’ Musk said. We Develop. Federated Learning, on the other hand, was introduced to mitigate challenges arising from classical distributed optimization. As one example, consider a financial organization that trains a loan scoring model on some of its historical data. 8×for distributed LLM inference and by 2. Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). We examine the gap between the rates in terms of network connectivity and information diversity. You can use a custom dataloader for evaluation similarly this example to avoid the problems. In some cases, more powerful Nov 17, 2020 · How can I inference model under distributed data parallel? I want to gather all predictions to calculate metrics and write result in one file. We’re on a journey to advance and democratize artificial intelligence through open source and open science. See full list on iqua. Pandas UDFs provide several advantages over row-based UDFs, including faster serialization of data through Apache Arrow and faster vectorized operations through Pandas . While these models deliver impressive performance in various natural language processing (NLP) tasks, their sheer magnitude poses challenges for inference on resource-constrained devices and large-scale distributed systems. You can easily configure your AI cluster by using a home router. Department of Statistics, North Dakota State University, Fargo, 58108 Distributed inference for multiple DNN models in IoT environments: poster Authors : YoungHwan Jin , HyungBin Park , and SuKyoung Lee Authors Info & Claims MobiHoc '22: Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing A review of distributed statistical inference Yuan Gaoa, Weidong Liub, Hansheng Wangc, Xiaozhou Wanga Yibo Yan a, Riquan Zhang ∗ aSchool of Statistics and Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE, East China Normal University, Shanghai, China; bSchool of Mathematical Sciences Mar 22, 2024 · The results show that when compared to server-based alternatives, FSD-Inference is significantly more cost-effective and scalable, and can even achieve competitive performance against optimized HPC solutions. Existing LLM serving systems use run-to-completion processing for inference jobs, which suffers from head-of-line blocking and long JCT. Finally, we derive a diagonalized version for online distributed inference in high-dimensional models, and apply it to multi-robot probabilistic mapping using indoor LiDAR data. Deep learning models are typically deployed at remote cloud servers and require users to upload local data for inference, incurring considerable overhead with respect to the time needed for transferring large volumes of data over the Internet. Moreover, we propose computationally efficient approaches to conducting inference in the distributed estimation setting described above. , GPT-3) have recently attracted huge interest, emphasizing the need for system support for serving models in this family. DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models Muyang Li*, Tianle Cai*, Jiaxin Cao, Qinsheng Zhang, Han Cai, Junjie Bai, Yangqing Jia, Ming-Yu Liu, Kai Li, and Song Han MIT, Princeton, Lepton AI, and NVIDIA In CVPR 2024. Dec 13, 2023 · Distributed Inference and Fine-tuning of Large Language Models Over The Internet. May 1, 2023 · Specially, the VRMOM (variance reduced median-of-means) estimator proposed by Tu et al. 🤗 Accelerate is a library designed to make it easy to train or run inference across distributed setups. We focus on scenarios where communication between agents is costly, and takes place over channels with finite bandwidth. 🤗 Accelerate. Loading parts of a model onto each GPU and using what is Jan 28, 2019 · DistributedSampler that modifies the dataloader so that the number of samples are evenly divisible by the number of GPUs. Apr 19, 2018 · Distributed Simulation and Distributed Inference. e. Specifically, on a cluster of three edge devices, the proposed scheme achieves DNN inference time acceleration speedup of 1. Theoretically, we show the consistency and asymptotic normality of both the one-step and two-step estimators. Although CP solves this problem, it has limitations on Distributed Inference and Fine-tuning of Large Language Models Over The Internet. Recent years witnessed an increasing research attention in deploying deep learning models on edge devices for inference. It supports both tensor par-allelism and pipeline parallelism for distributed execution. Sec- Jan 1, 2024 · For statistical inference, we utilize a random projection method to reduce the expensive communication cost. autonomous vehicles, cloud computing, smartphones). Distribution inference attacks pose a less obvious threat but can also be dangerous. training and inference, which are placed in "Training" and "Inference" folders separately. multiprocessing as mp from diffusers import DiffusionPipeline sd = DiffusionPipeline. Faster examples with accelerated inference. Not Found. The weighted t. 🤗 Accelerate is a library that enables the same PyTorch code to be run across any distributed configuration by adding just four lines of code! In short, training and inference at scale made simple, efficient and adaptable. Apr 27, 2024 · By leveraging tree-based speculative inference and verifi-cation, SpecInfer accelerates both distributed LLM inference across multiple GPUs and offloading-based LLM inference on one GPU. You should also initialize a DiffusionPipeline: import torch. Inference can be executed in real-time for tasks that require immediate feedback, such as fraud detection. For model inference of convolutional neural networks (CNNs), we nowadays witness a shift from the Cloud to the Edge. To address the two problems, we propose a hybrid scheduler for identifying the optimal server resource allocation policy. PiPPy can split pre-trained models into pipeline stages and distribute them onto multiple GPUs or even multiple hosts. We consider the problem of distributed inference where agents in a network observe a stream of private signals generated by an unknown state, and aim to uniquely identify this state from a finite set of hypotheses. To enable preemption at the level of each output token, FastServe uses iteration-level scheduling and the autoregressive pattern of LLM inference. float16, use_safetensors=True ) One of the biggest advancements 🤗 Accelerate provides is the concept of large model inference wherein you can perform inference on models that cannot fully fit on your graphics card. However, constructing such an estimator involves estimating the high-dimensional inverse Hessian matrix, incurring significant computational costs. Switch between documentation themes. Distributed-CNN-Inference. May 23, 2022 · PiPPy (Pipeline Parallelism for PyTorch) supports distributed inference. Our work has been contributed to IEEE Transactions on Parallel and Distributed Systems (TPDS). Distributed Inference Step The dispatcher, having distributed model partitions across compute nodes, now begins to send data to the first compute node in the chain. By Andrew J. 38–1. 6-3. The limited bandwidth and power resources of wireless sensors in distributed environments have resulted in new challenges in handling the ever-growing volume of transmissions generated by the Internet-of-Things (IoT) applications. Model parallelism has the potential to provide high throughput and low latency in distributed CNN inference. To fully utilize the information contained in big data, we propose a two-step procedure: (i) estimate Sep 18, 2022 · A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Although this condition is widely assumed in distributed inference literature (seeLian and Fan(2017) and Section2for more details), import torch import torch. , detection, estimation, learning, etc. Richards, Saurabh Bagchi, Shreyas Sundaram. To reduce the frequency of communication, we develop a novel To start, create a Python file and import torch. Due to limited capabilities and power constraints, it may be necessary to distribute the inference workload across multiple devices. The increased availability of massive data sets provides a unique opportunity to discover subtle patterns in their distributions, but also imposes overwhelming computational challenges. This paper presents an overview of recent results by the author and co-workers in this area. Jun 14, 2023 · Two major techniques are commonly used to meet real-time inference limitations when distributing models across resource-constrained IoT devices: 1) model parallelism (MP) and 2) class parallelism (CP). To do so, it leverages message passing semantics allowing each process to communicate data to any of the other processes. Data are distributed across different sites due to computing facility limitations or data privacy considerations. Canonne, Himanshu Tyagi. import torch. distributed as dist import torch. vllm. If data parallel or integrated save is used in training, the method of distributed inference is same with the above description. Each player can communicate bits to a central referee in a simultaneous message passing Distributed Inference with 🤗 Accelerate. Distribution inference attacks can pose serious risks when models are trained on private data, but are difficult to distinguish from the intrinsic purpose of statistical machine learning -- namely, to produce models that capture By producing near-optimal model partitions, our new algorithm seeks to improve the run-time performance of distributed inference as these partitions are distributed across the edge devices. May 9, 2023 · The interactive nature of these applications demand low job completion time (JCT) for model inference. It simplifies the process of setting up the distributed environment, allowing you to focus on your PyTorch code. Even for smaller models, MP can be used to reduce latency for inference. To further reduce latency and cost, we introduce inference-customized Sep 13, 2021 · This paper aims to provide a comprehensive review for related literature of various distributed frameworks for statistical estimation and inference, which includes parametric models, nonparametric model, and other frequently used models. Alternatively, inference can We consider the problem of distributed inference where agents in a network observe a stream of private signals generated by an unknown state, and aim to uniquely identify this state from a finite set of hypotheses. After a deep learning model has been trained, it’s put to work by running inference on new data. DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models. 72× without accuracy loss compared to a state-of-the-art scheme. Scenarios where images are captured and processed in physically separated locations are increasingly common (e. ece. [23] has proposed methods addressing distributed statistical inference problems in a convolution-smoothed quantile regression model, but the performances of the proposed distributed inference methods are sensitive to the selection of the bandwidth, especially when the local sample size is Distributed Inference with 🤗 Accelerate. Upon receiving data on its incoming socket, the node performs the necessary de-serialization and compression and runs that previous inference result through its model. DDP implementation in LLM. However, the convergence behaviors of message passing algorithms can be heavily affected by the adopted message update schedule. One approach to address this challenge is to leverage all available resources across multiple edge Jul 12, 2023 · Researchers from Peking University developed a distributed inference serving solution for LLMs called FastServe. The project uses TCP sockets to synchronize the state. The main challenge is the calculation of the To start, create a Python file and import torch. While SC has been shown effective for event-triggered web applications, the use of deep learning (DL) applications on SC is limited due to latency-sensitive DL applications and stateless SC. The focus is on results in distributed learning and sensor scheduling, but some issues relating to energy efficiency are also discussed briefly Jan 21, 2017 · Distributed inference for quantile regression processes. distributed and torch. 2019) have discussed DNN distributed inference in their review, and Kailkhura et al. 5×for offloading-based LLM Jan 23, 2024 · It is shown that the distributed empirical log-likelihood ratio statistic is asymptotically standard chi-squared under some mild conditions. 2. In this blog, we introduce DistriFusion, a training-free algorithm to harness multiple GPUs to accelerate diffusion model inference without sacrificing image quality. Testing. (Kailkhura, Nadendla and Varshney, Jun. inference, the new paradigm of distributed inference has been proposed, which distributes the workload across multiple edge devices to accelerate the inference process [9]. Oct 3, 2022 · 9 libraries for parallel & distributed training/inference of deep learning models: Megatron-LM, DeepSpeed, Hivemind, Colossol-AI, FairScale, Tensorflow-Mesh Setup. For example, by utilizing the partial receptive field property of convolution kernels, the input feature map of a convolution layer can be Sep 22, 2023 · With the rise of deep learning and the development of increasingly powerful models, pre-trained language models have grown in size. Dec 1, 2020 · It is promising to deploy CNN inference on local end-user devices for high-accuracy and time-sensitive applications. , 2021). The rapid emergence of massive datasets in various fields poses a serious challenge to traditional statistical methods. Users often want to send a number of different prompts, each to a different GPU, and then get the results back. The workflow of DeCNN includes two steps, i. toronto. Due to limited capabilities and Apr 2, 2020 · Distributed Inference with Sparse and Quantized Communication. Sep 13, 2021 · Distribution inference, sometimes called property inference, infers statistical properties about a training set from access to a model trained on that data. During inference, EdgeFlow orchestrates the intermediate results flowing through these units to fulfill the complicated layer dependencies. Tensor parallelism and pipeline parallelism support for distributed inference; Streaming outputs; OpenAI-compatible API server; Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs (Experimental) Prefix caching support (Experimental) Multi-lora support; vLLM seamlessly supports most popular open-source models on HuggingFace Jul 21, 2023 · Specifically, distributed inference approaches are complemented with necessary resource management and network orchestration in order to enable distributed inference in the field – hence paving the way towards a broad number of possible applications, such as autonomous driving, traffic optimization, medical applications, agriculture, and disclosure. Sep 9, 2019 · The article explores several levels of the problem: (1) a lightweight inter-FPGA communication protocol and routing layer to facilitate the communication between the different FPGAs, (2) the data partitioning and distribution strategies maximizing performance, (3) and an in depth analysis on how applications can be efficiently distributed over C. Dierent from standard statistics, extreme value statistics use observations in the tail only, for example, the Hill esti- docs. However, it is non-trivial to use model parallelism as the original CNN model is inherently tightly-coupled structure. It also supports distributed, per-stage materialization if the model does not fit in the memory of a single GPU. Demonstrating our framework’s generality, we extend posterior com-putations for (nondistributed) spatial process models with a stationary full-rank and a nonstationary low-rank GP priors to the distributed setting. Elastic distributed inference is a feature that is available in IBM Spectrum Conductor Deep Learning Impact which enables you to publish inference models as services and from which REST clients can consume the service by making requests. Jayadev Acharya, Clément L. ic bh ey ga xo rm yz vw aw tr