Michio JP

Michio JP

1630662062

RAPIDS Graph Analytics Library

cuGraph - GPU Graph Analytics

The RAPIDS cuGraph library is a collection of GPU accelerated graph algorithms that process data found in GPU DataFrames. The vision of cuGraph is to make graph analysis ubiquitous to the point that users just think in terms of analysis and not technologies or frameworks. To realize that vision, cuGraph operates, at the Python layer, on GPU DataFrames, thereby allowing for seamless passing of data between ETL tasks in cuDF and machine learning tasks in cuML. Data scientists familiar with Python will quickly pick up how cuGraph integrates with the Pandas-like API of cuDF. Likewise, users familiar with NetworkX will quickly recognize the NetworkX-like API provided in cuGraph, with the goal to allow existing code to be ported with minimal effort into RAPIDS. For users familiar with C++/CUDA and graph structures, a C++ API is also provided. However, there is less type and structure checking at the C++ layer.

For more project details, see rapids.ai.

NOTE: For the latest stable README.md ensure you are on the latest branch.

As an example, the following Python snippet loads graph data and computes PageRank:

import cugraph

# read data into a cuDF DataFrame using read_csv
gdf = cudf.read_csv("graph_data.csv", names=["src", "dst"], dtype=["int32", "int32"])

# We now have data as edge pairs
# create a Graph using the source (src) and destination (dst) vertex pairs
G = cugraph.Graph()
G.from_cudf_edgelist(gdf, source='src', destination='dst')

# Let's now get the PageRank score of each vertex by calling cugraph.pagerank
df_page = cugraph.pagerank(G)

# Let's look at the PageRank Score (only do this on small graphs)
for i in range(len(df_page)):
    print("vertex " + str(df_page['vertex'].iloc[i]) +
        " PageRank is " + str(df_page['pagerank'].iloc[i]))

Getting cuGraph

There are 3 ways to get cuGraph :

  1. Quick start with Docker Repo
  2. Conda Installation
  3. Build from Source 

     

Currently Supported Features

As of Release 21.08 - including 21.08 nightly

Supported Algorithms

CategoryAlgorithmScaleNotes
Centrality   
 KatzMulti-GPU 
 Betweenness CentralitySingle-GPU 
 Edge Betweenness CentralitySingle-GPU 
Community   
 EgoNetSingle-GPU 
 LeidenSingle-GPU 
 LouvainMulti-GPUC++ README
 Ensemble Clustering for GraphsSingle-GPU 
 Spectral-Clustering - Balanced CutSingle-GPU 
 Spectral-Clustering - ModularitySingle-GPU 
 Subgraph ExtractionSingle-GPU 
 Triangle CountingSingle-GPU 
 K-TrussSingle-GPU 
Components   
 Weakly Connected ComponentsMulti-GPU 
 Strongly Connected ComponentsSingle-GPU 
Core   
 K-CoreSingle-GPU 
 Core NumberSingle-GPU 
Layout   
 Force Atlas 2Single-GPU 
Linear Assignment   
 HungarianSingle-GPUREADME
Link Analysis   
 PagerankMulti-GPUC++ README
 Personal PagerankMulti-GPUC++ README
 HITSSingle-GPUleverages Gunrock
Link Prediction   
 Jaccard SimilaritySingle-GPU 
 Weighted Jaccard SimilaritySingle-GPU 
 Overlap SimilaritySingle-GPU 
Sampling   
 Random Walks (RW)Single-GPU 
Traversal   
 Breadth First Search (BFS)Multi-GPUwith cutoff support 
C++ README
 Single Source Shortest Path (SSSP)Multi-GPUC++ README
 Traveling Salesperson Problem (TSP)Single-GPU 
Tree   
 Minimum Spanning TreeSingle-GPU 
 Maximum Spanning TreeSingle-GPU 
Other   
 RenumberingMulti-GPUmultiple columns, any data type
 SymmetrizeMulti-GPU 
    



 

Supported Graph

TypeDescription
GraphAn undirected Graph
DiGraphA Directed Graph
MultigraphA Graph with multiple edges between a vertex pair
MultiDigraphA Directed Graph with multiple edges between a vertex pair
  

ALL Algorithms support Graphs and MultiGraph (directed and undirected)



 

Supported Data Types

cuGraph supports graph creation with Source and Destination being expressed as:

  • cuDF DataFrame
  • Pandas DataFrame

cuGraph supports execution of graph algorithms from different graph objects

  • cuGraph Graph classes
  • NetworkX graph classes
  • CuPy sparse matrix
  • SciPy sparse matrix

cuGraph tries to match the return type based on the input type. So a NetworkX input will return the same data type that NetworkX would have.

cuGraph Notice

Vertex IDs are expected to be contiguous integers starting from 0. If your data doesn't match that restriction, we have a solution. cuGraph provides the renumber function, which is by default automatically called when data is addted to a graph. Input vertex IDs for the renumber function can be any type, can be non-contiguous, can be multiple columns, and can start from an arbitrary number. The renumber function maps the provided input vertex IDs to either 32- or 64-bit contiguous integers starting from 0.

Additionally, when using the auto-renumbering feature, vertices are automatically un-renumbered in results.

cuGraph is constantly being updated and improved. Please see the Transition Guide if errors are encountered with newer versions

Graph Sizes and GPU Memory Size

The amount of memory required is dependent on the graph structure and the analytics being executed. As a simple rule of thumb, the amount of GPU memory should be about twice the size of the data size. That gives overhead for the CSV reader and other transform functions. There are ways around the rule but using smaller data chunks.

SizeRecommended GPU Memory
500 million edges32 GB
250 million edges16 GB

The use of managed memory for oversubscription can also be used to exceed the above memory limitations. See the recent blog on Tackling Large Graphs with RAPIDS cuGraph and CUDA Unified Memory on GPUs: https://medium.com/rapids-ai/tackling-large-graphs-with-rapids-cugraph-and-unified-virtual-memory-b5b69a065d4



 

Quick Start

Please see the Docker Repository, choosing a tag based on the NVIDIA CUDA version you’re running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize all of the RAPIDS libraries: cuDF, cuML, and cuGraph.

Conda

It is easy to install cuGraph using conda. You can get a minimal conda installation with Miniconda or get the full installation with Anaconda.

Install and update cuGraph using the conda command:


# CUDA 11.0
conda install -c nvidia -c rapidsai -c numba -c conda-forge cugraph cudatoolkit=11.0

# CUDA 11.2
conda install -c nvidia -c rapidsai -c numba -c conda-forge cugraph cudatoolkit=11.2

Note: This conda installation only applies to Linux and Python versions 3.7/3.8.

Build from Source and Contributing

Please see our guide for building cuGraph from source

Please see our guide for contributing to cuGraph.

Documentation

Python API documentation can be generated from docs directory.

Open GPU Data Science

The RAPIDS suite of open source software libraries aims to enable execution of end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization but exposing that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

Apache Arrow on GPU

The GPU version of Apache Arrow is a common API that enables efficient interchange of tabular data between processes running on the GPU. End-to-end computation on the GPU avoids unnecessary copying and converting of data off the GPU, reducing compute time and cost for high-performance analytics common in artificial intelligence workloads. As the name implies, cuDF uses the Apache Arrow columnar data format on the GPU. Currently, a subset of the features in Apache Arrow are supported.

 

Download Details:
 

Author: rapidsai

Download Link: Download The Source Code
Official Website: https://github.com/rapidsai/cugraph
 

What is GEEK

Buddha Community

RAPIDS Graph Analytics Library
Art  Lind

Art Lind

1598817300

How Graph Analytics Can Transform Your Business

See how graph databases can offer data modeling and analysis capabilities your business can leverage to model real-world systems and answer challenging questions.

Introduction

Your business is operating in an ever more connected world where the understanding of complex relationships and interdependencies between different data points is crucial to many decision-making processes. This is the main reason why graph databases have gained a lot of interest in the past few years and have become that fastest-growing database category. They offer powerful data modeling and analysis capabilities your business can use to easily model real-world complex systems and answer challenging questions previously hard to address.

What Is a Graph Database?

You might not be aware of it, but many of the services you use on a daily basis are powered by a graph database. Such examples include Google’s search engine, Linkedin’s connection recommendations, UberEats food recommendations and Gmail’s autocomplete feature. Simply put, a graph database is a data management system specifically engineered and optimized to store and analyze complex networks of connected data where relationships are equally important to individual data points. As a result, they offer a highly efficient, flexible, and overall elegant way to discover connections and patterns within your data that are otherwise very hard to see.

Let’s take the example of an insurance fraud network.

#graph database #graph algorithms #graph analytics #data analytic

Luna  Mosciski

Luna Mosciski

1595932020

Graph Therapy: The Year of the Graph Newsletter, June/May 2020

Parts of the world are still in lockdown, while others are returning to some semblance of normalcy. Either way, while the last few months have given some things pause, they have boosted others. It seems like developments in the world of Graphs are among those that have been boosted.

An abundance of educational material on all things graph has been prepared and delivered online, and is now freely accessible, with more on the way.

Graph databases have been making progress and announcements, repositioning themselves by a combination of releasing new features, securing additional funds, and entering strategic partnerships.

A key graph database technology, RDF*, which enables compatibility between RDF and property graph databases, is gaining momentum and tool support.

And more cutting edge research combining graph AI and knowledge graphs is seeing the light, too. Buckle up and enjoy some graph therapy.


Stanford’s series of online seminars featured some of the world’s leading experts on all things graph. If you missed it, or if you’d like to have an overview of what was said, you can find summaries for each lecture in this series of posts by Bob Kasenchak and Ahren Lehnert. Videos from the lectures are available here.

Stanford Knowledge Graph Course Not-Quite-Live-Blog

Stanford University’s computer science department is offering a free class on Knowledge Graphs available to the public. Stanford is also making recordings of the class available via the class website.


Another opportunity to get up to speed with educational material: The entire program of the course “Information Service Engineering” at KIT - Karlsruhe Institute of Technology, is delivered online and made freely available on YouTube. It includes topics such as ontology design, knowledge graph programming, basic graph theory, and more.

Information Service Engineering at KIT

Knowledge representation as a prerequisite for knowledge graphs. Learn about knowledge representation, ontologies, RDF(S), OWL, SPARQL, etc.


Ontology may sound like a formal term, while knowledge graph is a more approachable one. But the 2 are related, and so is ontology and AI. Without a consistent, thoughtful approach to developing, applying, evolving an ontology, AI systems lack underpinning that would allow them to be smart enough to make an impact.

The ontology is an investment that will continue to pay off, argue Seth Earley and Josh Bernoff in Harvard Business Review, making the case for how businesses may benefit from a knowldge-centric approach

Is Your Data Infrastructure Ready for AI?

Even after multiple generations of investments and billions of dollars of digital transformations, organizations struggle to use data to improve customer service, reduce costs, and speed the core processes that provide competitive advantage. AI was supposed to help with that.


Besides AI, knowledge graphs have a part to play in the Cloud, too. State is good, and lack of support for Stateful Cloud-native applications is a roadblock for many enterprise use-cases, writes Dave Duggal.

Graph knowledge bases are an old idea now being revisited to model complex, distributed domains. Combining high-level abstraction with Cloud-native design principles offers efficient “Context-as-a-Service” for hydrating stateless services. Graph knowledge-based systems can enable composition of Cloud-native services into event-driven dataflow processes.

Kubernetes also touches upon Organizational Knowledge, and that may be modeled as a Knowledge Graph.

Graph Knowledge Base for Stateful Cloud-Native Applications

Extending graph knowledge bases to model distributed systems creates a new kind of information system, one intentionally designed for today’s IT challenges.


The Enterprise Knowledge Graph Foundation was recently established to define best practices and mature the marketplace for EKG adoption, with a launch webinar on June the 23rd.

The Foundation defines its mission as including adopting semantic standards, developing best practices for accelerated EKG deployment, curating a repository of reusable models and resources, building a mechanism for engagement and shared knowledge, and advancing the business cases for EKG adoption.

Enterprise Knowledge Graph Maturity Model

The Enterprise Knowledge Graph Maturity Model (EKG/MM) is the industry-standard definition of the capabilities required for an enterprise knowledge graph. It establishes standard criteria for measuring progress and sets out the practical questions that all involved stakeholders ask to ensure trust, confidence and usage flexibility of data. Each capability area provides a business summary denoting its importance, a definition of the added value from semantic standards and scoring criteria based on five levels of defined maturity.


Enterprise Knowledge Graphs is what the Semantic Web Company (SWC) and Ontotext have been about for a long time, too. Two of the vendors in this space that have been around for the longer time just announced a strategic partnership: Ontotext, a graph database and platform provider, meets SWC, a management and added value layer that sits on top.

SWC and Ontotext CEOs emphasize how their portfolios are complementary, while the press release states that the companies have implemented a seamless integration of the PoolParty Semantic Suite™ v.8 with the GraphDB™ and Ontotext Platform, which offers benefits for many use cases.

#database #artificial intelligence #graph databases #rdf #graph analytics #knowledge graph #graph technology

Ruth  Nabimanya

Ruth Nabimanya

1621327800

Graphs and Knowledge Connexions. The Year of the Graph Newsletter, Autumn 2020

As 2020 is coming to an end, let’s see it off in style. Our journey in the world of Graph Analytics, Graph Databases, Knowledge Graphs and Graph AI culminate.

The representation of the relationships among data, information, knowledge and --ultimately-- wisdom, known as the data pyramid, has long been part of the language of information science. Digital transformation has made this relevant beyond the confines of information science. COVID-19 has brought years’ worth of digital transformation in just a few short months.

In this new knowledge-based digital world, encoding and making use of business and operational knowledge is the key to making progress and staying competitive. So how do we go from data to information, and from information to knowledge? This is the key question Knowledge Connexions aims to address.

Graphs in all shapes and forms are a key part of this.


Knowledge Connexions is a visionary event featuring a rich array of technological building blocks to support the transition to a knowledge-based economy: Connecting data, people and ideas, building a global knowledge ecosystem.

The Year of the Graph will be there, in the workshop “From databases to platforms: the evolution of Graph databases”. George Anadiotis, Alan Morrison, Steve Sarsfield, Juan Sequeda and Steven Xi bring many years of expertise in the domain, and will analyze Graph Databases from all possible angles.

This is the first step in the relaunch of the Year of the Graph Database Report. Year of the Graph Newsletter subscribers just got a 25% discount code. To be always in the know, subscribe to the newsletter, and follow the newly launched Year of the Graph account on Twitter! In addition to getting the famous YotG news stream every day, you will also get a 25% discount code.

#database #machine learning #artificial intelligence #data science #graph databases #graph algorithms #graph analytics #emerging technologies #knowledge graphs #semantic technologies

Luna  Mosciski

Luna Mosciski

1595924640

Graph Therapy: The Year of the Graph Newsletter, June/May 2020

Parts of the world are still in lockdown, while others are returning to some semblance of normalcy. Either way, while the last few months have given some things pause, they have boosted others. It seems like developments in the world of Graphs are among those that have been boosted.

An abundance of educational material on all things graph has been prepared and delivered online, and is now freely accessible, with more on the way.

Graph databases have been making progress and announcements, repositioning themselves by a combination of releasing new features, securing additional funds, and entering strategic partnerships.

A key graph database technology, RDF*, which enables compatibility between RDF and property graph databases, is gaining momentum and tool support.

And more cutting edge research combining graph AI and knowledge graphs is seeing the light, too. Buckle up and enjoy some graph therapy.


Stanford’s series of online seminars featured some of the world’s leading experts on all things graph. If you missed it, or if you’d like to have an overview of what was said, you can find summaries for each lecture in this series of posts by Bob Kasenchak and Ahren Lehnert. Videos from the lectures are available here.

Stanford Knowledge Graph Course Not-Quite-Live-Blog

Stanford University’s computer science department is offering a free class on Knowledge Graphs available to the public. Stanford is also making recordings of the class available via the class website.


Another opportunity to get up to speed with educational material: The entire program of the course “Information Service Engineering” at KIT - Karlsruhe Institute of Technology, is delivered online and made freely available on YouTube. It includes topics such as ontology design, knowledge graph programming, basic graph theory, and more.

Information Service Engineering at KIT

Knowledge representation as a prerequisite for knowledge graphs. Learn about knowledge representation, ontologies, RDF(S), OWL, SPARQL, etc.


Ontology may sound like a formal term, while knowledge graph is a more approachable one. But the 2 are related, and so is ontology and AI. Without a consistent, thoughtful approach to developing, applying, evolving an ontology, AI systems lack underpinning that would allow them to be smart enough to make an impact.

The ontology is an investment that will continue to pay off, argue Seth Earley and Josh Bernoff in Harvard Business Review, making the case for how businesses may benefit from a knowldge-centric approach

Is Your Data Infrastructure Ready for AI?

Even after multiple generations of investments and billions of dollars of digital transformations, organizations struggle to use data to improve customer service, reduce costs, and speed the core processes that provide competitive advantage. AI was supposed to help with that.


Besides AI, knowledge graphs have a part to play in the Cloud, too. State is good, and lack of support for Stateful Cloud-native applications is a roadblock for many enterprise use-cases, writes Dave Duggal.

Graph knowledge bases are an old idea now being revisited to model complex, distributed domains. Combining high-level abstraction with Cloud-native design principles offers efficient “Context-as-a-Service” for hydrating stateless services. Graph knowledge-based systems can enable composition of Cloud-native services into event-driven dataflow processes.

Kubernetes also touches upon Organizational Knowledge, and that may be modeled as a Knowledge Graph.

Graph Knowledge Base for Stateful Cloud-Native Applications

Extending graph knowledge bases to model distributed systems creates a new kind of information system, one intentionally designed for today’s IT challenges.


The Enterprise Knowledge Graph Foundation was recently established to define best practices and mature the marketplace for EKG adoption, with a launch webinar on June the 23rd.

The Foundation defines its mission as including adopting semantic standards, developing best practices for accelerated EKG deployment, curating a repository of reusable models and resources, building a mechanism for engagement and shared knowledge, and advancing the business cases for EKG adoption.

Enterprise Knowledge Graph Maturity Model

The Enterprise Knowledge Graph Maturity Model (EKG/MM) is the industry-standard definition of the capabilities required for an enterprise knowledge graph. It establishes standard criteria for measuring progress and sets out the practical questions that all involved stakeholders ask to ensure trust, confidence and usage flexibility of data. Each capability area provides a business summary denoting its importance, a definition of the added value from semantic standards and scoring criteria based on five levels of defined maturity.


Enterprise Knowledge Graphs is what the Semantic Web Company (SWC) and Ontotext have been about for a long time, too. Two of the vendors in this space that have been around for the longer time just announced a strategic partnership: Ontotext, a graph database and platform provider, meets SWC, a management and added value layer that sits on top.

SWC and Ontotext CEOs emphasize how their portfolios are complementary, while the press release states that the companies have implemented a seamless integration of the PoolParty Semantic Suite™ v.8 with the GraphDB™ and Ontotext Platform, which offers benefits for many use cases.

#database #artificial intelligence #graph databases #rdf #graph analytics #knowledge graph #graph technology

Jackson  Crist

Jackson Crist

1618209540

Measuring Crop Health Using Deep Learning – Notes From Tiger Analytics

Agrochemical companies manufacture a range of offerings for yield maximisation, pest resistance, hardiness, water quality and availability and other challenges facing farmers. These companies need to measure the efficacy of their products in real-world conditions, not just controlled experimental environments. Single-crop farms are divided into plots and a specific intervention performed in each. For example, hybrid seeds are sown in one plot while another is treated with fertilisers, and so on. The relative performance of each treatment is assessed by tracking the plants’ health in the plot where that treatment was administered.

#featured #deep learning solution #tiger analytics #tiger analytics deep learning #tiger analytics deep learning solution #tiger analytics machine learning #tiger analytics ml #tiger analytics ml-powered digital twin