Modern applications span multiple clouds to reduce costs, avoid vendor lock-in, and leverage low-availability resources in another cloud. However, standard object stores operate within a single cloud, forcing users to manually manage data placement across clouds, i.e., navigate their diverse APIs and handle heterogeneous costs for network and storage. This is often a complex choice: users must either pay to store objects in a remote cloud, or pay to transfer them over the network based on application access patterns and cloud provider cost offerings. To address this, we present SkyStore, a unified object store that addresses cost-optimal data management across regions and clouds. SkyStore introduces a virtual object and bucket API to hide the complexity of interacting with multiple clouds. At its core, SkyStore has a novel TTL-based data placement policy that dynamically replicates and evicts objects according to application access patterns while optimizing for lower cost. Our evaluation shows that across various workloads, SkyStore reduces the overall cost by up to 6X over academic baselines and commercial alternatives like AWS multi-region buckets. SkyStore also has comparable latency, and its availability and fault tolerance are on par with standard cloud offerings.

Selecting suitable architecture parameters and training hyperparameters is essential for enhancing machine learning (ML) model performance. Several recent empirical studies conduct large-scale correlational analysis on neural networks (NNs) to search for effective generalization metrics that can guide this type of model selection. Effective metrics are typically expected to correlate strongly with test performance. In this paper, we expand on prior analyses by examining generalization-metric-based model selection with the following objectives: (i) focusing on natural language processing (NLP) tasks, as prior work primarily concentrates on computer vision (CV) tasks; (ii) considering metrics that directly predict test error instead of the generalization gap; (iii) exploring metrics that do not need access to data to compute. From these objectives, we are able to provide the first model selection results on large pretrained Transformers from Huggingface using generalization metrics. Our analyses consider (I) hundreds of Transformers trained in different settings, in which we systematically vary the amount of data, the model size and the optimization hyperparameters, (II) a total of 51 pretrained Transformers from eight families of Huggingface NLP models, including GPT2, BERT, etc., and (III) a total of 28 existing and novel generalization metrics. Despite their niche status, we find that metrics derived from the heavy-tail (HT) perspective are particularly useful in NLP tasks, exhibiting stronger correlations than other, more popular metrics. To further examine these metrics, we extend prior formulations relying on power law (PL) spectral distributions to exponential (EXP) and exponentially-truncated power law (E-TPL) families.

The GraphLab project spanned almost a decade and had profound academic and industrial impact on large-scale machine learning and graph processing systems. There were numerous papers written describing the innovations in GraphLab including the original vertex-centric [8] and edge-centric [3] programming abstractions, high-performance asynchronous execution engines [9], out-of-core graph computation [6], tabular graph-systems [4], and even new statistical inference algorithms [2] enabled by the GraphLab project. This work became the basis of multiple PhD theses [1, 5, 7]. The GraphLab open-source project had broad academic and industrial adoption and ultimately lead to the launch of Turi. In this talk, we tell the story of GraphLab, how it began and the key ideas behind it. We will focus on the approach to achieving scalable asynchronous systems in machine learning. During our talk, we will explore the impact that GraphLab has had on the development of graph processing systems, graph databases, and AI/ML; Additionally, we will share our insights and opinions into where we see the future of these fields heading. In the process, we highlight some of the lessons we learned and provide guidance for future students.

Feature stores (also sometimes referred to as embedding stores) are becoming ubiquitous in model serving systems: downstream applications query these stores for auxiliary inputs at inference-time. Stored features are derived by featurizing rapidly changing base data sources. Featurization can be costly prohibitively expensive to trigger on every data update, particularly for features that are vector embeddings computed by a model. Yet, existing systems naively apply a one-size-fits-all policy as to when/how to update these features, and do not consider query access patterns or impacts on prediction accuracy. This paper introduces RALF, which orchestrates feature updates by leveraging downstream error feedback to minimize feature store regret , a metric for how much featurization degrades downstream accuracy. We evaluate with representative feature store workloads, anomaly detection and recommendation, using real-world datasets. We run system experiments with a 275,077 key anomaly detection workload on 800 cores to show up to a 32.7% reduction in prediction error or up to 1.6X compute cost reduction with accuracy-aware scheduling.

Federated learning (FL) is a promising way to use the computing power of mobile devices while maintaining the privacy of users. Current work in FL, however, makes the unrealistic assumption that the users have ground-truth labels on their devices, while also assuming that the server has neither data nor labels. In this work, we consider the more realistic scenario where the users have only unlabeled data, while the server has some labeled data, and where the amount of labeled data is smaller than the amount of unlabeled data. We call this learning problem semi-supervised federated learning (SSFL). For SSFL, we demonstrate that a critical issue that affects the test accuracy is the large gradient diversity of the models from different users. Based on this, we investigate several design choices. First, we find that the so-called consistency regularization loss (CRL), which is widely used in semi-supervised learning, performs reasonably well but has large gradient diversity. Second, we find that Batch Normalization (BN) increases gradient diversity. Replacing BN with the recently-proposed Group Normalization (GN) can reduce gradient diversity and improve test accuracy. Third, we show that CRL combined with GN still has a large gradient diversity when the number of users is large. Based on these results, we propose a novel grouping-based model averaging method to replace the FedAvg averaging method. Overall, our grouping-based averaging, combined with GN and CRL, achieves better test accuracy than not just a contemporary paper on SSFL in the same settings (>10%), but also four supervised FL algorithms.

Dataframes have become universally popular as a means to represent data in various stages of structure, and manipulate it using a rich set of operators---thereby becoming an essential tool in the data scientists' toolbox. However, dataframe systems, such as pandas, scale poorly---and are non-interactive on moderate to large datasets. We discuss our experiences developing Modin, our first cut at a parallel dataframe system, which already has users across several industries and over 1M downloads. Modin translates pandas functions into a core set of operators that are individually parallelized via columnar, row-wise, or cell-wise decomposition rules that we formalize in this paper. We also introduce metadata independence to allow metadata---such as order and type---to be decoupled from the physical representation and maintained lazily. Using rule-based decomposition and metadata independence, along with careful engineering, Modin is able to support pandas operations across both rows and columns on very large dataframes---unlike Koalas and Dask DataFrames that either break down or are unable to support such operations, while also being much faster than pandas.

In modern Machine Learning, model training is an iterative, experimental process that can consume enormous computation resources and developer time. To aid in that process, experienced model developers log and visualize program variables during training runs. Exhaustive logging of all variables is infeasible, so developers are left to choose between slowing down training via extensive conservative logging, or letting training run fast via minimalist optimistic logging that may omit key information. As a compromise, optimistic logging can be accompanied by program checkpoints; this allows developers to add log statements post-hoc, and "replay" desired log statements from checkpoint---a process we refer to as hindsight logging. Unfortunately, hindsight logging raises tricky problems in data management and software engineering. Done poorly, hindsight logging can waste resources and generate technical debt embodied in multiple variants of training code. In this paper, we present methodologies for efficient and effective logging practices for model training, with a focus on techniques for hindsight logging. Our goal is for experienced model developers to learn and adopt these practices. To make this easier, we provide an open-source suite of tools for Fast Low-Overhead Recovery (flor) that embodies our design across three tasks: (i) efficient background logging in Python, (ii) adaptive periodic checkpointing, and (iii) an instrumentation library that codifies hindsight logging for efficient and automatic record-replay of model-training. Model developers can use each flor tool separately as they see fit, or they can use flor in hands-free mode, entrusting it to instrument their code end-to-end for efficient record-replay. Our solutions leverage techniques from physiological transaction logs and recovery in database systems. Evaluations on modern ML benchmarks demonstrate that flor can produce fast checkpointing with small user-specifiable overheads (e.g. 7%), and still provide hindsight log replay times orders of magnitude faster than restarting training from scratch.

Slow running or straggler tasks in distributed processing frameworks [1, 2] can be 6 to 8 times slower than the median task in a job on a production cluster [3], despite existing mitigation techniques. This leads to extended job completion times, inefficient use of resources, and increased costs. Recently, proactive straggler avoidance techniques [4] have explored the use of predictive models to improve task scheduling. However, to capture node and workload variability, separate models are built for every node and workload, requiring the time consuming collection of training data and limiting the applicability to new nodes and workloads. In this work, we observe that predictors for similar nodes or workloads are likely to be similar and can share information, suggesting a multi-task learning (MTL) based approach. We generalize the MTL formulation of [5] to capture commonalities in arbitrary groups. Using our formulation to predict stragglers allows us to reduce job completion times by up to 59% over Wrangler [4]. This large reduction arises from a 7 point increase in prediction accuracy. Further, we can get equal or better accuracy than [4] using a sixth of the training data, thus bringing the training time down from 4 hours to about 40 minutes. In addition, our formulation reduces the number of parameters by grouping our parameters into node- and workload-dependent factors. This helps us generalize to tasks with insufficient data and achieve significant gains over a naive MTL formulation [5].

MLI is an Application Programming Interface designed to address the challenges of building Machine Learning algorithms in a distributed setting based on data-centric computing. Its primary goal is to simplify the development of high-performance, scalable, distributed algorithms. Our initial results show that, relative to existing systems, this interface can be used to build distributed implementations of a wide variety of common Machine Learning algorithms with minimal complexity and highly competitive performance and scalability.

Latent variable techniques are pivotal in tasks ranging from predicting user click patterns and targeting ads to organizing the news and managing user generated content. Latent variable techniques like topic modeling, clustering, and subspace estimation provide substantial insight into the latent structure of complex data with little or no external guidance making them ideal for reasoning about large-scale, rapidly evolving datasets. Unfortunately, due to the data dependencies and global state introduced by latent variables and the iterative nature of latent variable inference, latent-variable techniques are often prohibitively expensive to apply to large-scale, streaming datasets.

While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently support many important data mining and machine learning algorithms and can lead to inefficient learning systems. To help fill this critical void, we introduced the GraphLab abstraction which naturally expresses asynchronous, dynamic, graph-parallel computation while ensuring data consistency and achieving a high degree of parallel performance in the shared-memory setting. In this paper, we extend the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees. We develop graph based extensions to pipelined locking and data versioning to reduce network congestion and mitigate the effect of network latency. We also introduce fault tolerance to the GraphLab abstraction using the classic Chandy-Lamport snapshot algorithm and demonstrate how it can be easily implemented by exploiting the GraphLab abstraction itself. Finally, we evaluate our distributed implementation of the GraphLab abstraction on a large Amazon EC2 deployment and show 1-2 orders of magnitude performance gains over Hadoop-based implementations.