RISECamp Behind the Scenes

Jey Kottalam blog

  RISECamp was held at UC Berkeley on September 7th and 8th. This post looks behind the scenes at the technical infrastructure used to provide a cloud-hosted cluster for each attendee with ready-to-use Jupyter notebooks requiring only a web browser to access. Background and Requirements RISECamp is the latest in a series of workshops held by RISELab (and its predecessor, AMPLab) showcasing the latest research from the lab. The sessions consist of talks on the latest research systems produced by the lab followed by tutorials and exercises for attendees to get hands-on practical experience using our latest technologies. In the past, attendees used their own laptops to perform the hands-on exercises, with each user setting up a local development environment and manually …

Fast Python Serialization with Ray and Apache Arrow

Robert Nishihara blog, Ray

This post was originally posted here. Robert Nishihara and Philipp Moritz are graduate students in the RISElab at UC Berkeley. This post elaborates on the integration between Ray and Apache Arrow. The main problem this addresses is data serialization. From Wikipedia, serialization is … the process of translating data structures or object state into a format that can be stored … or transmitted … and reconstructed later (possibly in a different computer environment). Why is any translation necessary? Well, when you create a Python object, it may have pointers to other Python objects, and these objects are all allocated in different regions of memory, and all of this has to make sense when unpacked by another process on another machine. Serialization and deserialization …

Ray: 0.2 Release

Robert Nishihara blog

This was originally posted on the Ray blog. We are pleased to announce the Ray 0.2 release. This release includes the following: substantial performance improvements to the Plasma object store an initial Jupyter notebook based web UI the start of a scalable reinforcement learning library fault tolerance for actors Plasma Since the last release, the Plasma object store has moved out of the Ray codebase and is now being developed as part of Apache Arrow (see the relevant documentation), so that it can be used as a standalone component by other projects to leverage high-performance shared memory. In addition, our Arrow-based serialization libraries have been moved into pyarrow (see the relevant documentation). In 0.2, we’ve increased the write throughput of the object store …

Low-Latency Model Serving with Clipper

Daniel Crankshaw blog

The mission of the RISELab is to develop technologies that enable applications to make low-latency decisions on live data with strong security. One of the first steps towards achieving this goal is to study techniques to evaluate machine learning models and quickly render predictions. This missing piece of machine learning infrastructure, the prediction serving system, is critical to delivering real-time and intelligent applications and services. As we studied the prediction-serving problem, two key challenges emerged. The first challenge is supporting the stringent performance demands of interactive serving workloads. As machine learning models improve they are increasingly being applied in business critical settings and user-facing interactive applications. This requires models to render predictions that can meet the strict latency requirements of …

Opaque: Secure Apache Spark SQL

Wenting Zheng blog, Security, Systems

As enterprises move to cloud-based analytics, the risk of cloud security breaches poses a serious threat. Encrypting data at rest and in transit is a major first step. However, data must still be decrypted in memory for processing, exposing it to any attacker who can observe memory contents. This is a challenging problem because security usually implies a tradeoff between performance and functionality. Cryptographic approaches like fully homomorphic encryption provide full functionality to a system, but are extremely slow. Systems like CryptDB utilize lighter cryptographic primitives to provide a practical database, but are limited in functionality. Recent developments in trusted hardware enclaves (such as Intel SGX) provide a much needed alternative. These hardware enclaves provide hardware-enforced shielded execution that allows …

Announcing Ground v0.1

Vikram Sreekanti blog, Ground, News, Open Source, Projects, Systems

We’re excited to be releasing v0.1 of the Ground project! Ground is a data context service. It is a central repository for all the information surrounding the use of data in an organization. Ground concerns itself with what data an organization has, where that data is, who (both human beings and software systems) is touching that data, and how that data is being modified and described. Above all, Ground aims to be an open-source, vendor neutral system that provides users an unopinionated metamodel and set of APIs that allow them to think about and interact with data context generated in their organization. Ground has many use cases, but we’re focused on two specific ones at present: Data Inventory: large organizations …

Reinforcement Learning brings together RISELab and Berkeley DeepDrive for a joint mini-retreat

Alexey Tumanov blog, Deep Learning, Reinforcement Learning, Systems

On May 2, RISELab and the Berkeley DeepDrive (BDD) lab held a joint, largely student-driven mini-retreat. The event was aimed at exploring research opportunities at the intersection of the BDD and RISE labs. The topical focus of the mini-retreat was emerging AI applications, such as Reinforcement Learning (RL), and computer systems to support such applications. Trevor Darrell kicked off the event with an introduction to the Berkeley DeepDrive lab, followed by Ion Stoica’s overview of RISE. The event offered a great opportunity for researchers from both labs to exchange ideas about their ongoing research activity and discover points of collaboration. Philipp Moritz started the first student talk session with an update on Ray — a distributed execution framework for emerging …

RISELab Announces 3 Open Source Releases

Joe Hellerstein blog, Clipper, Ground, Open Source, Projects, Ray, Systems

Part of the Berkeley tradition—and the RISELab mission—is to release open source software as part of our research agenda. Six months after launching the lab, we’re excited to announce initial v0.1 releases of three RISElab open-source systems: Clipper, Ground and Ray. Clipper is an open-source prediction-serving system. Clipper simplifies deploying models from a wide range of machine learning frameworks by exposing a common REST interface and automatically ensuring low-latency and high-throughput predictions.  In the 0.1 release, we focused on reliable support for serving models trained in Spark and Scikit-Learn.  In the next release we will be introducing support for TensorFlow and Caffe2 as well as online-personalization and multi-armed bandits.  We are providing active support for early users and will be following Github issues …

Making cities safer: data collection for Vision Zero

K. Shankari blog

A critical part of enabling cities to implement their Vision Zero policies – the goal of the current National Transportation Data Challenge – is to be able to generate open, multi-modal travel experience data. While existing datasets use police and hospital reports to provide a comprehensive picture of fatalities and life altering injuries, by their nature, they are sparse and resist use for prediction and prioritization. Further, changes to infrastructure to support Vision Zero policies frequently require balancing competing needs from different constituencies – protected bike lanes, dedicated signals and expanded sidewalks all raise concerns that automobile traffic will be severely impacted. A timeline of the El Monte/Marich intersection in Mountain View, from 2014 to 2017 provides an opportunity to …

Declarative Heterogeneity Handling for Datacenter and ML Resources

Alexey Tumanov blog, Systems

Challenge Heterogeneity in datacenter resources has become the fact of life. We identify and categorize a number of different types of heterogeneity. When talking about heterogeneity, we generally refer to static or dynamic attributes associated with individual resources. Previously the levels of heterogeneity were fairly benign and limited to a few different types of processor architectures. Now, however, it has become a common trend to deploy hardware accelerators (e.g., Tesla K40/K80, Google TPU, Intel Xeon PHI) and even FPGAs (e.g., Microsoft Catapult project). Nodes themselves are connected with heterogeneous interconnects, oftentimes with more than one interconnect option available (e.g., 40Gbps ethernet backbone, Infiniband, FPGA torus topology). The workloads we consolidate on top of this diverse hardware differ vastly in their success metrics (completion …

RISELab at Spark Summit

Ion Stoica blog

This year, Spark Summit East was held in Boston between February 7-9. With over 1,500 attendees, this was the largest Spark Summit ever outside the Bay Area. Apache Spark, developed in large at AMPLab (the precursor of RISELab), is now the de-facto standard of big data processing. Like the previous Spark summits, UC Berkeley had a very strong presence. Ion Stoica gave a keynote on RISELab, describing the lab’s research focus on addressing a long-standing grand challenge in computing: enable machines to act autonomously and intelligently, to rapidly and repeatedly take appropriate actions based on information in the world around them. The presentation also discussed some early results from two recent projects, Drizzle and Opaque, which had their own presentations …

Serverless Scientific Computing

Eric Jonas blog, Projects, Systems

For many scientific and engineering users, cloud infrastructure remains challenging to use. While many of their use cases are embarrassingly parallel, the challenges involved in provisioning and using stateful cloud services keep them trapped on their laptops or large shared workstations. Before getting started, a new cloud user confronts a bewildering number of choices. First, what instance type do they need ? How do they make the compute/memory tradeoff? How large do they want their cluster to be? Can they take advantage of dynamic market-based instances (spot instances) that can disappear at any time? What if they have 1000 small jobs, each of which takes a few minutes — what’s the most cost-effective way of allocating servers? What host operating …

RISELab Kicks Off

melissa mecca Administrative, blog

Berkeley’s computer science division has an ongoing tradition of 5-year collaborative research labs. In the fall of 2016 we closed out the most recent of the series: the AMPLab. We think it was a pretty big deal, and many agreed. One great thing about Berkeley is the endless supply of energy and ideas that flows through the place — always bringing changes, building on what came before. In that spirit, we’re fired up to announce the Berkeley RISELab, where we will focus intensely for five years on systems that provide Real-time Intelligence with Secure Execution. Context RISELab represents the next chapter in the ongoing story of data-intensive systems at Berkeley; a proactive step to move beyond Big Data analytics into …