GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba

A One-Stop Large-Scale Graph Computing System from Alibaba

GraphScope is a unified distributed graph computing platform that provides a one-stop environment for performing diverse graph operations on a cluster of computers through a user-friendly Python interface. GraphScope makes multi-staged processing of large-scale graph data on compute clusters simple by combining several important pieces of Alibaba technology: including GRAPE, MaxGraph, and Graph-Learn (GL) for analytics, interactive, and graph neural networks (GNN) computation, respectively, and the Vineyard store that offers efficient in-memory data transfers.

Visit our website at graphscope.io to learn more.

Getting Started

We provide a Playground with a managed JupyterLab. Try GraphScope straight away in your browser!

GraphScope supports run in standalone mode or on clusters managed by Kubernetes within containers. For quickly getting started, let's begin with the standalone mode.

Installation for Standalone Mode

GraphScope pre-compiled package is distributed as a python package and can be easily installed with pip.

pip3 install graphscope

Note that graphscope requires Python >= 3.6 and pip >= 19.0. The package is built for and tested on the most popular Linux (Ubuntu 18.04+ / CentOS 7+) and macOS (version 10.15+) distributions. For Windows users, you may want to install Ubuntu on WSL2 to use this package.

Next, we will walk you through a concrete example to illustrate how GraphScope can be used by data scientists to effectively analyze large graphs.

Demo: Node Classification on Citation Network

ogbn-mag is a heterogeneous network composed of a subset of the Microsoft Academic Graph. It contains 4 types of entities(i.e., papers, authors, institutions, and fields of study), as well as four types of directed relations connecting two entities.

Given the heterogeneous ogbn-mag data, the task is to predict the class of each paper. Node classification can identify papers in multiple venues, which represent different groups of scientific work on different topics. We apply both the attribute and structural information to classify papers. In the graph, each paper node contains a 128-dimensional word2vec vector representing its content, which is obtained by averaging the embeddings of words in its title and abstract. The embeddings of individual words are pre-trained. The structural information is computed on-the-fly.

Loading a graph

GraphScope models graph data as property graph, in which the edges/vertices are labeled and have many properties. Taking ogbn-mag as example, the figure below shows the model of the property graph.

sample-of-property-graph

This graph has four kinds of vertices, labeled as paper, author, institution and field_of_study. There are four kinds of edges connecting them, each kind of edges has a label and specifies the vertex labels for its two ends. For example, cites edges connect two vertices labeled paper. Another example is writes, it requires the source vertex is labeled author and the destination is a paper vertex. All the vertices and edges may have properties. e.g., paper vertices have properties like features, publish year, subject label, etc.

To load this graph to GraphScope with our retrieval module, please use these code:

import graphscope
from graphscope.dataset import load_ogbn_mag

g = load_ogbn_mag()

We provide a set of functions to load graph datasets from ogb and snap for convenience. Please find all the available graphs here. If you want to use your own graph data, please refer this doc to load vertices and edges by labels.

Interactive query

Interactive queries allow users to directly explore, examine, and present graph data in an exploratory manner in order to locate specific or in-depth information in time. GraphScope adopts a high-level language called Gremlin for graph traversal, and provides efficient execution at scale.

In this example, we use graph traversal to count the number of papers two given authors have co-authored. To simplify the query, we assume the authors can be uniquely identified by ID 2 and 4307, respectively.

# get the endpoint for submitting Gremlin queries on graph g.
interactive = graphscope.gremlin(g)

# count the number of papers two authors (with id 2 and 4307) have co-authored
papers = interactive.execute("g.V().has('author', 'id', 2).out('writes').where(__.in('writes').has('id', 4307)).count()").one()

Graph analytics

Graph analytics is widely used in real world. Many algorithms, like community detection, paths and connectivity, centrality are proven to be very useful in various businesses. GraphScope ships with a set of built-in algorithms, enables users easily analysis their graph data.

Continuing our example, below we first derive a subgraph by extracting publications in specific time out of the entire graph (using Gremlin!), and then run k-core decomposition and triangle counting to generate the structural features of each paper node.

Please note that many algorithms may only work on homogeneous graphs, and therefore, to evaluate these algorithms over a property graph, we need to project it into a simple graph at first.

# extract a subgraph of publication within a time range
sub_graph = interactive.subgraph("g.V().has('year', inside(2014, 2020)).outE('cites')")

# project the projected graph to simple graph.
simple_g = sub_graph.project(vertices={"paper": []}, edges={"cites": []})

ret1 = graphscope.k_core(simple_g, k=5)
ret2 = graphscope.triangles(simple_g)

# add the results as new columns to the citation graph
sub_graph = sub_graph.add_column(ret1, {"kcore": "r"})
sub_graph = sub_graph.add_column(ret2, {"tc": "r"})

In addition, users can write their own algorithms in GraphScope. Currently, GraphScope supports users to write their own algorithms in Pregel model and PIE model.

Graph neural networks (GNNs)

Graph neural networks (GNNs) combines superiority of both graph analytics and machine learning. GNN algorithms can compress both structural and attribute information in a graph into low-dimensional embedding vectors on each node. These embeddings can be further fed into downstream machine learning tasks.

In our example, we train a GCN model to classify the nodes (papers) into 349 categories, each of which represents a venue (e.g. pre-print and conference). To achieve this, first we launch a learning engine and build a graph with features following the last step.


# define the features for learning
paper_features = []
for i in range(128):
    paper_features.append("feat_" + str(i))

paper_features.append("kcore")
paper_features.append("tc")

# launch a learning engine.
lg = graphscope.graphlearn(sub_graph, nodes=[("paper", paper_features)],
                  edges=[("paper", "cites", "paper")],
                  gen_labels=[
                      ("train", "paper", 100, (0, 75)),
                      ("val", "paper", 100, (75, 85)),
                      ("test", "paper", 100, (85, 100))
                  ])

Then we define the training process, and run it.

# Note: Here we use tensorflow as NN backend to train GNN model. so please
# install tensorflow.
import graphscope.learning
from graphscope.learning.examples import GCN
from graphscope.learning.graphlearn.python.model.tf.trainer import LocalTFTrainer
from graphscope.learning.graphlearn.python.model.tf.optimizer import get_tf_optimizer

# supervised GCN.

def train(config, graph):
    def model_fn():
        return GCN(
            graph,
            config["class_num"],
            config["features_num"],
            config["batch_size"],
            val_batch_size=config["val_batch_size"],
            test_batch_size=config["test_batch_size"],
            categorical_attrs_desc=config["categorical_attrs_desc"],
            hidden_dim=config["hidden_dim"],
            in_drop_rate=config["in_drop_rate"],
            neighs_num=config["neighs_num"],
            hops_num=config["hops_num"],
            node_type=config["node_type"],
            edge_type=config["edge_type"],
            full_graph_mode=config["full_graph_mode"],
        )
    graphscope.learning.reset_default_tf_graph()
    trainer = LocalTFTrainer(
        model_fn,
        epoch=config["epoch"],
        optimizer=get_tf_optimizer(
            config["learning_algo"], config["learning_rate"], config["weight_decay"]
        ),
    )
    trainer.train_and_evaluate()


config = {
    "class_num": 349,  # output dimension
    "features_num": 130,  # 128 dimension + kcore + triangle count
    "batch_size": 500,
    "val_batch_size": 100,
    "test_batch_size": 100,
    "categorical_attrs_desc": "",
    "hidden_dim": 256,
    "in_drop_rate": 0.5,
    "hops_num": 2,
    "neighs_num": [5, 10],
    "full_graph_mode": False,
    "agg_type": "gcn",  # mean, sum
    "learning_algo": "adam",
    "learning_rate": 0.0005,
    "weight_decay": 0.000005,
    "epoch": 20,
    "node_type": "paper",
    "edge_type": "cites",
}

train(config, lg)

A python script with the entire process is availabe here, you may try it out by yourself.

Processing Large Graph on Kubernetes Cluster

GraphScope is designed for processing large graphs, which are usually hard to fit in the memory of a single machine. With Vineyard as the distributed in-memory data manager, GraphScope supports run on a cluster managed by Kubernetes(k8s).

To continue this tutorial, please ensure that you have a k8s managed cluster and know the credentials for the cluster. (e.g., address of k8s API server, usually stored a ~/.kube/config file.)

Alternatively, you can set up a local k8s cluster for testing with Kind. We provide a script for setup this environment.

# for usage, type -h
./scripts/install_deps.sh --k8s

If you did not install the graphscope package in the above step, you can install a subset of the whole package with client functions only.

pip3 install graphscope-client

Next, let's revisit the example by running on a cluster instead.

how-it-works

The figure shows the flow of execution in the cluster mode. When users run code in the python client, it will:

  • Step 1. Create a session or workspace in GraphScope.
  • Step 2 - Step 5. Load a graph, query, analysis and run learning task on this graph via Python interface. These steps are the same to local mode, thus users process huge graphs in a distributed setting just like analysis a small graph on a single machine.(Note that graphscope.gremlin and graphscope.graphlearn need to be changed to sess.gremlin and sess.graphlearn, respectively. sess is the name of the Session instance user created.)
  • Step 6. Close the session.

Creating a session

To use GraphScope in a distributed setting, we need to establish a session in a python interpreter.

For convenience, we provide several demo datasets, and an option mount_dataset to mount the dataset in the graphscope cluster. The datasets will be mounted to location you assigned in the pods. If you want to use your own data on k8s cluster, please refer to this.

import graphscope

sess = graphscope.session(mount_dataset="/dataset")

For macOS, the session needs to establish with the LoadBalancer service type (which is NodePort by default).

import graphscope

sess = graphscope.session(mount_dataset="/dataset", k8s_service_type="LoadBalancer")

A session tries to launch a coordinator, which is the entry for the back-end engines. The coordinator manages a cluster of resources (k8s pods), and the interactive/analytical/learning engines ran on them. For each pod in the cluster, there is a vineyard instance at service for distributed data in memory.

Loading a graph and processing computation tasks

Similar to the standalone mode, we can still use the functions to load a graph easily.

from graphscope.dataset import load_ogbn_mag

# Note we have mounted the demo datasets to /dataset,
# There are several datasets including ogbn_mag_small,
# User can attach to the engine container and explore the directory.
g = load_ogbn_mag(sess, "/dataset/ogbn_mag_small/")

Here, the g is loaded in parallel via vineyard and stored in vineyard instances in the cluster managed by the session.

Next, we can conduct graph queries with Gremlin, invoke various graph algorithms, or run graph-based neural network tasks like we did in the standalone mode. We do not repeat code here, but a .ipynb processing the classification task on k8s is available on the Playground.

Closing the session

Another additional step in the distribution is session close. We close the session after processing all graph tasks.

sess.close()

This operation will notify the backend engines and vineyard to safely unload graphs and their applications, Then, the coordinator will dealloc all the applied resources in the k8s cluster.

Please note that we have not hardened this release for production use and it lacks important security features such as authentication and encryption, and therefore it is NOT recommended for production use (yet)!

Development

Building on local

To build graphscope Python package and the engine bineries, you need to install some dependencies and build tools.

./scripts/install_deps.sh --dev

# With argument --cn to speed up the download if you are in China.
./scripts/install_deps.sh --dev --cn 

Then you can build GraphScope with pre-configured make commands.

# to make graphscope whole package, including python package + engine binaries.
make graphscope

# or make the engine components
# make gie
# make gae
# make gle

Building Docker images

GraphScope ships with a Dockerfile that can build docker images for releasing. The images are built on a builder image with all dependencies installed and copied to a runtime-base image. To build images with latest version of GraphScope, go to the root directory and run this command.

# by default, the built image is tagged as graphscope/graphscope:SHORTSHA
make graphscope-image

Building client library

GraphScope python interface is separate with the engines image. If you are developing python client and not modifying the protobuf files, the engines image doesn't require to be rebuilt.

You may want to re-install the python client on local.

make client

Note that the learning engine client has C/C++ extensions modules and setting up the build environment is a bit tedious. By default the locally-built client library doesn't include the support for learning engine. If you want to build client library with learning engine enabled, please refer Build Python Wheels.

Testing

To verify the correctness of your developed features, your code changes should pass our tests.

You may run the whole test suite with commands:

make test

Documentation

Documentation can be generated using Sphinx. Users can build the documentation using:

# build the docs
make graphscope-docs

# to open preview on local
open docs/_build/latest/html/index.html

The latest version of online documentation can be found at https://graphscope.io/docs

Publications

  • Wenfei Fan, Tao He, Longbin Lai, Xue Li, Yong Li, Zhao Li, Zhengping Qian, Chao Tian, Lei Wang, Jingbo Xu, Youyang Yao, Qiang Yin, Wenyuan Yu, Jingren Zhou, Diwen Zhu, Rong Zhu. GraphScope: A Unified Engine For Big Graph Processing. The 47th International Conference on Very Large Data Bases (VLDB), industry, 2021.
  • Jingbo Xu, Zhanning Bai, Wenfei Fan, Longbin Lai, Xue Li, Zhao Li, Zhengping Qian, Lei Wang, Yanyan Wang, Wenyuan Yu, Jingren Zhou. GraphScope: A One-Stop Large Graph Processing System. The 47th International Conference on Very Large Data Bases (VLDB), demo, 2021

Contributing

Any contributions you make are greatly appreciated!

  • Join in the Slack channel for discussion.
  • Please report bugs by submitting a GitHub issue.
  • Please submit contributions using pull requests.

Download Details:
Author: alibaba
Source Code: https://github.com/alibaba/GraphScope
License: Apache-2.0 License

#rust  #rustlang  #machinelearing 

What is GEEK

Buddha Community

GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba
Ruth  Nabimanya

Ruth Nabimanya

1620633584

System Databases in SQL Server

Introduction

In SSMS, we many of may noticed System Databases under the Database Folder. But how many of us knows its purpose?. In this article lets discuss about the System Databases in SQL Server.

System Database

Fig. 1 System Databases

There are five system databases, these databases are created while installing SQL Server.

  • Master
  • Model
  • MSDB
  • Tempdb
  • Resource
Master
  • This database contains all the System level Information in SQL Server. The Information in form of Meta data.
  • Because of this master database, we are able to access the SQL Server (On premise SQL Server)
Model
  • This database is used as a template for new databases.
  • Whenever a new database is created, initially a copy of model database is what created as new database.
MSDB
  • This database is where a service called SQL Server Agent stores its data.
  • SQL server Agent is in charge of automation, which includes entities such as jobs, schedules, and alerts.
TempDB
  • The Tempdb is where SQL Server stores temporary data such as work tables, sort space, row versioning information and etc.
  • User can create their own version of temporary tables and those are stored in Tempdb.
  • But this database is destroyed and recreated every time when we restart the instance of SQL Server.
Resource
  • The resource database is a hidden, read only database that holds the definitions of all system objects.
  • When we query system object in a database, they appear to reside in the sys schema of the local database, but in actually their definitions reside in the resource db.

#sql server #master system database #model system database #msdb system database #sql server system databases #ssms #system database #system databases in sql server #tempdb system database

GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba

A One-Stop Large-Scale Graph Computing System from Alibaba

GraphScope is a unified distributed graph computing platform that provides a one-stop environment for performing diverse graph operations on a cluster of computers through a user-friendly Python interface. GraphScope makes multi-staged processing of large-scale graph data on compute clusters simple by combining several important pieces of Alibaba technology: including GRAPE, MaxGraph, and Graph-Learn (GL) for analytics, interactive, and graph neural networks (GNN) computation, respectively, and the Vineyard store that offers efficient in-memory data transfers.

Visit our website at graphscope.io to learn more.

Getting Started

We provide a Playground with a managed JupyterLab. Try GraphScope straight away in your browser!

GraphScope supports run in standalone mode or on clusters managed by Kubernetes within containers. For quickly getting started, let's begin with the standalone mode.

Installation for Standalone Mode

GraphScope pre-compiled package is distributed as a python package and can be easily installed with pip.

pip3 install graphscope

Note that graphscope requires Python >= 3.6 and pip >= 19.0. The package is built for and tested on the most popular Linux (Ubuntu 18.04+ / CentOS 7+) and macOS (version 10.15+) distributions. For Windows users, you may want to install Ubuntu on WSL2 to use this package.

Next, we will walk you through a concrete example to illustrate how GraphScope can be used by data scientists to effectively analyze large graphs.

Demo: Node Classification on Citation Network

ogbn-mag is a heterogeneous network composed of a subset of the Microsoft Academic Graph. It contains 4 types of entities(i.e., papers, authors, institutions, and fields of study), as well as four types of directed relations connecting two entities.

Given the heterogeneous ogbn-mag data, the task is to predict the class of each paper. Node classification can identify papers in multiple venues, which represent different groups of scientific work on different topics. We apply both the attribute and structural information to classify papers. In the graph, each paper node contains a 128-dimensional word2vec vector representing its content, which is obtained by averaging the embeddings of words in its title and abstract. The embeddings of individual words are pre-trained. The structural information is computed on-the-fly.

Loading a graph

GraphScope models graph data as property graph, in which the edges/vertices are labeled and have many properties. Taking ogbn-mag as example, the figure below shows the model of the property graph.

sample-of-property-graph

This graph has four kinds of vertices, labeled as paper, author, institution and field_of_study. There are four kinds of edges connecting them, each kind of edges has a label and specifies the vertex labels for its two ends. For example, cites edges connect two vertices labeled paper. Another example is writes, it requires the source vertex is labeled author and the destination is a paper vertex. All the vertices and edges may have properties. e.g., paper vertices have properties like features, publish year, subject label, etc.

To load this graph to GraphScope with our retrieval module, please use these code:

import graphscope
from graphscope.dataset import load_ogbn_mag

g = load_ogbn_mag()

We provide a set of functions to load graph datasets from ogb and snap for convenience. Please find all the available graphs here. If you want to use your own graph data, please refer this doc to load vertices and edges by labels.

Interactive query

Interactive queries allow users to directly explore, examine, and present graph data in an exploratory manner in order to locate specific or in-depth information in time. GraphScope adopts a high-level language called Gremlin for graph traversal, and provides efficient execution at scale.

In this example, we use graph traversal to count the number of papers two given authors have co-authored. To simplify the query, we assume the authors can be uniquely identified by ID 2 and 4307, respectively.

# get the endpoint for submitting Gremlin queries on graph g.
interactive = graphscope.gremlin(g)

# count the number of papers two authors (with id 2 and 4307) have co-authored
papers = interactive.execute("g.V().has('author', 'id', 2).out('writes').where(__.in('writes').has('id', 4307)).count()").one()

Graph analytics

Graph analytics is widely used in real world. Many algorithms, like community detection, paths and connectivity, centrality are proven to be very useful in various businesses. GraphScope ships with a set of built-in algorithms, enables users easily analysis their graph data.

Continuing our example, below we first derive a subgraph by extracting publications in specific time out of the entire graph (using Gremlin!), and then run k-core decomposition and triangle counting to generate the structural features of each paper node.

Please note that many algorithms may only work on homogeneous graphs, and therefore, to evaluate these algorithms over a property graph, we need to project it into a simple graph at first.

# extract a subgraph of publication within a time range
sub_graph = interactive.subgraph("g.V().has('year', inside(2014, 2020)).outE('cites')")

# project the projected graph to simple graph.
simple_g = sub_graph.project(vertices={"paper": []}, edges={"cites": []})

ret1 = graphscope.k_core(simple_g, k=5)
ret2 = graphscope.triangles(simple_g)

# add the results as new columns to the citation graph
sub_graph = sub_graph.add_column(ret1, {"kcore": "r"})
sub_graph = sub_graph.add_column(ret2, {"tc": "r"})

In addition, users can write their own algorithms in GraphScope. Currently, GraphScope supports users to write their own algorithms in Pregel model and PIE model.

Graph neural networks (GNNs)

Graph neural networks (GNNs) combines superiority of both graph analytics and machine learning. GNN algorithms can compress both structural and attribute information in a graph into low-dimensional embedding vectors on each node. These embeddings can be further fed into downstream machine learning tasks.

In our example, we train a GCN model to classify the nodes (papers) into 349 categories, each of which represents a venue (e.g. pre-print and conference). To achieve this, first we launch a learning engine and build a graph with features following the last step.


# define the features for learning
paper_features = []
for i in range(128):
    paper_features.append("feat_" + str(i))

paper_features.append("kcore")
paper_features.append("tc")

# launch a learning engine.
lg = graphscope.graphlearn(sub_graph, nodes=[("paper", paper_features)],
                  edges=[("paper", "cites", "paper")],
                  gen_labels=[
                      ("train", "paper", 100, (0, 75)),
                      ("val", "paper", 100, (75, 85)),
                      ("test", "paper", 100, (85, 100))
                  ])

Then we define the training process, and run it.

# Note: Here we use tensorflow as NN backend to train GNN model. so please
# install tensorflow.
import graphscope.learning
from graphscope.learning.examples import GCN
from graphscope.learning.graphlearn.python.model.tf.trainer import LocalTFTrainer
from graphscope.learning.graphlearn.python.model.tf.optimizer import get_tf_optimizer

# supervised GCN.

def train(config, graph):
    def model_fn():
        return GCN(
            graph,
            config["class_num"],
            config["features_num"],
            config["batch_size"],
            val_batch_size=config["val_batch_size"],
            test_batch_size=config["test_batch_size"],
            categorical_attrs_desc=config["categorical_attrs_desc"],
            hidden_dim=config["hidden_dim"],
            in_drop_rate=config["in_drop_rate"],
            neighs_num=config["neighs_num"],
            hops_num=config["hops_num"],
            node_type=config["node_type"],
            edge_type=config["edge_type"],
            full_graph_mode=config["full_graph_mode"],
        )
    graphscope.learning.reset_default_tf_graph()
    trainer = LocalTFTrainer(
        model_fn,
        epoch=config["epoch"],
        optimizer=get_tf_optimizer(
            config["learning_algo"], config["learning_rate"], config["weight_decay"]
        ),
    )
    trainer.train_and_evaluate()


config = {
    "class_num": 349,  # output dimension
    "features_num": 130,  # 128 dimension + kcore + triangle count
    "batch_size": 500,
    "val_batch_size": 100,
    "test_batch_size": 100,
    "categorical_attrs_desc": "",
    "hidden_dim": 256,
    "in_drop_rate": 0.5,
    "hops_num": 2,
    "neighs_num": [5, 10],
    "full_graph_mode": False,
    "agg_type": "gcn",  # mean, sum
    "learning_algo": "adam",
    "learning_rate": 0.0005,
    "weight_decay": 0.000005,
    "epoch": 20,
    "node_type": "paper",
    "edge_type": "cites",
}

train(config, lg)

A python script with the entire process is availabe here, you may try it out by yourself.

Processing Large Graph on Kubernetes Cluster

GraphScope is designed for processing large graphs, which are usually hard to fit in the memory of a single machine. With Vineyard as the distributed in-memory data manager, GraphScope supports run on a cluster managed by Kubernetes(k8s).

To continue this tutorial, please ensure that you have a k8s managed cluster and know the credentials for the cluster. (e.g., address of k8s API server, usually stored a ~/.kube/config file.)

Alternatively, you can set up a local k8s cluster for testing with Kind. We provide a script for setup this environment.

# for usage, type -h
./scripts/install_deps.sh --k8s

If you did not install the graphscope package in the above step, you can install a subset of the whole package with client functions only.

pip3 install graphscope-client

Next, let's revisit the example by running on a cluster instead.

how-it-works

The figure shows the flow of execution in the cluster mode. When users run code in the python client, it will:

  • Step 1. Create a session or workspace in GraphScope.
  • Step 2 - Step 5. Load a graph, query, analysis and run learning task on this graph via Python interface. These steps are the same to local mode, thus users process huge graphs in a distributed setting just like analysis a small graph on a single machine.(Note that graphscope.gremlin and graphscope.graphlearn need to be changed to sess.gremlin and sess.graphlearn, respectively. sess is the name of the Session instance user created.)
  • Step 6. Close the session.

Creating a session

To use GraphScope in a distributed setting, we need to establish a session in a python interpreter.

For convenience, we provide several demo datasets, and an option mount_dataset to mount the dataset in the graphscope cluster. The datasets will be mounted to location you assigned in the pods. If you want to use your own data on k8s cluster, please refer to this.

import graphscope

sess = graphscope.session(mount_dataset="/dataset")

For macOS, the session needs to establish with the LoadBalancer service type (which is NodePort by default).

import graphscope

sess = graphscope.session(mount_dataset="/dataset", k8s_service_type="LoadBalancer")

A session tries to launch a coordinator, which is the entry for the back-end engines. The coordinator manages a cluster of resources (k8s pods), and the interactive/analytical/learning engines ran on them. For each pod in the cluster, there is a vineyard instance at service for distributed data in memory.

Loading a graph and processing computation tasks

Similar to the standalone mode, we can still use the functions to load a graph easily.

from graphscope.dataset import load_ogbn_mag

# Note we have mounted the demo datasets to /dataset,
# There are several datasets including ogbn_mag_small,
# User can attach to the engine container and explore the directory.
g = load_ogbn_mag(sess, "/dataset/ogbn_mag_small/")

Here, the g is loaded in parallel via vineyard and stored in vineyard instances in the cluster managed by the session.

Next, we can conduct graph queries with Gremlin, invoke various graph algorithms, or run graph-based neural network tasks like we did in the standalone mode. We do not repeat code here, but a .ipynb processing the classification task on k8s is available on the Playground.

Closing the session

Another additional step in the distribution is session close. We close the session after processing all graph tasks.

sess.close()

This operation will notify the backend engines and vineyard to safely unload graphs and their applications, Then, the coordinator will dealloc all the applied resources in the k8s cluster.

Please note that we have not hardened this release for production use and it lacks important security features such as authentication and encryption, and therefore it is NOT recommended for production use (yet)!

Development

Building on local

To build graphscope Python package and the engine bineries, you need to install some dependencies and build tools.

./scripts/install_deps.sh --dev

# With argument --cn to speed up the download if you are in China.
./scripts/install_deps.sh --dev --cn 

Then you can build GraphScope with pre-configured make commands.

# to make graphscope whole package, including python package + engine binaries.
make graphscope

# or make the engine components
# make gie
# make gae
# make gle

Building Docker images

GraphScope ships with a Dockerfile that can build docker images for releasing. The images are built on a builder image with all dependencies installed and copied to a runtime-base image. To build images with latest version of GraphScope, go to the root directory and run this command.

# by default, the built image is tagged as graphscope/graphscope:SHORTSHA
make graphscope-image

Building client library

GraphScope python interface is separate with the engines image. If you are developing python client and not modifying the protobuf files, the engines image doesn't require to be rebuilt.

You may want to re-install the python client on local.

make client

Note that the learning engine client has C/C++ extensions modules and setting up the build environment is a bit tedious. By default the locally-built client library doesn't include the support for learning engine. If you want to build client library with learning engine enabled, please refer Build Python Wheels.

Testing

To verify the correctness of your developed features, your code changes should pass our tests.

You may run the whole test suite with commands:

make test

Documentation

Documentation can be generated using Sphinx. Users can build the documentation using:

# build the docs
make graphscope-docs

# to open preview on local
open docs/_build/latest/html/index.html

The latest version of online documentation can be found at https://graphscope.io/docs

Publications

  • Wenfei Fan, Tao He, Longbin Lai, Xue Li, Yong Li, Zhao Li, Zhengping Qian, Chao Tian, Lei Wang, Jingbo Xu, Youyang Yao, Qiang Yin, Wenyuan Yu, Jingren Zhou, Diwen Zhu, Rong Zhu. GraphScope: A Unified Engine For Big Graph Processing. The 47th International Conference on Very Large Data Bases (VLDB), industry, 2021.
  • Jingbo Xu, Zhanning Bai, Wenfei Fan, Longbin Lai, Xue Li, Zhao Li, Zhengping Qian, Lei Wang, Yanyan Wang, Wenyuan Yu, Jingren Zhou. GraphScope: A One-Stop Large Graph Processing System. The 47th International Conference on Very Large Data Bases (VLDB), demo, 2021

Contributing

Any contributions you make are greatly appreciated!

  • Join in the Slack channel for discussion.
  • Please report bugs by submitting a GitHub issue.
  • Please submit contributions using pull requests.

Download Details:
Author: alibaba
Source Code: https://github.com/alibaba/GraphScope
License: Apache-2.0 License

#rust  #rustlang  #machinelearing 

dia adalyn

1612246777

Open your own B2B e-commerce app like Alibaba tailored with rich features

Are you a dedicated entrepreneur who is looking to start a B2B website like Alibaba? Then you are in the right place to buy Appdupe’s Alibaba clone script enriched with customizable and B2B marketplace features. Our Alibaba clone app development is well-crafted and neatly designed with an intuitive interface that keeps your app intact with your users for a longer duration. We built a high-quality Alibaba clone software enabling every app user to connect on the social marketplace. Hence, wait no longer to build an app like Alibaba using our user-friendly clone app services.

Read More, https://www.appdupe.com/alibaba-clone

#alibaba clone #alibaba clone app #alibaba clone script #alibaba clone app development #alibaba clone software #alibaba clone open source

Maddy Bris

Maddy Bris

1599132316

5Kw Solar System in Brisbane

1 August 2020, Sunny Sky solarannounced you to launch a residential solar power system in Queensland, Australia. There are different sizes of houses with different energy requirements so one solar power system cannot fulfill every type of electricity need.

Whether energy need is low or higher they have announced a wide range of solar power system in Brisbane that includes 5KW solar panel system, 6Kw solar panel system, 10Kw solar panel system, and many more so that everyone can enjoy the benefits of solar energy.

Residential Solar Power System needs to be flexible because of the changing requirement of energy. As we know our energy needs hikes up in the summers more than winters because we use air conditioners, refrigerators (also used in winters but less than summers), fans. In winter we drop down these usages so the energy needs to go up and down according to the weather changing.

Some households have a high energy need, some have low, and mostly have the normal or average of high and low. Sunny Sky Solar offers expert’s advice to all the customers on call or personally because it is important to analyze the energy need, budget, location, and many other things before buying a solar power system for your home sweet home.

Their professionals analyze all these things and suggest you the best residential solar power system in Brisbane to reduce the energy costs and clean the environment as solar energy is green & clean energy.

At this time of announcing the residential solar panel system, the representative of Sunny Sky Solar has talked about some advantages of a residential solar power system. He said “get update yourself by the time is important because the latest technology will save you lots of money and time. The solar power system is the best technology in this era that can give you lots of benefits. Don’t get upset with the initial cost because after installing a solar power system at your house it will repay you the initial cost in two to three years. So, you are going to invest in a great deal if you are purchasing a solar panel system in Brisbane.”

He also added “Residential solar power system can save your pocket from getting loose every month for heavy electricity bills. You will earn money by producing solar energy and feeding your power supply grid as government, and mostly all the power suppliers give benefits to producing solar energy. You can easily earn money by feeding the power grid with your excess produced solar energy. You will use solar energy and save the excess by feeding the power grid this way.”

Sunny Sky Solar offering an efficient range of residential and commercial solar power system that includes 5KW solar panel system, 6.6Kw solar panel system, 10Kw solar panel system, and there are many more that you can select according to your energy needs and budget.
They provide expert assistance that will help you in choosing the best solar system for your house. Their experienced professionals work under the guidance of experts who ensures the perfections and safety at the time of installing and after the installation.

Installing a solar power system at your place will be more convenient with them because they work under the expert’s supervision that makes them perfect and faster. They ensure safety first at the time of installing because at that time family members are around the installing site and accidents can happen.

They also ensure the quality of products they used in installing and other solar products. If the products will be durable and efficient, the system will produce more electricity with higher efficiency for a longer period.
The main thing that matters while installing a solar power system at a residence is the roof situation, Sunny Sky Solar doesn’t work for doing business only. They first check the place or analyze from your information that your location is safe for installing a solar power system or not. If the find any problem they will suggest repairing it first because if you will put the solar power system at a less secure place and the solar system’s weight can damage it then repairing that place first should your main priority.
This shows their loyalty and caring behavior towards the customers.

#solar panel system #solar panel system in brisbane #5kw solar panel system #5kw solar panel system #10kw solar panel system #10kw solar panel system in brisbane

Secret Email System Review - Recommended or Not?

Matt Bacak’s secret email system is one of the most successful products in the WarriorPlus marketplace in recent memory. My secret email system review will not try to hard sell you on the product – I mean, it’s pretty cheap, so if you’re going to buy it, you’re going to buy it. Instead, I’ll concentrate on explaining the benefits of email marketing and how to get the most out of Matt’s system.

Nowadays, digital marketing is essential for every business. But what is the best strategy? There are many different points of view, but one thing is certain: emails are essential. Email marketing is one of the most efficient and cost-effective ways to promote a business online, and it is simple and inexpensive to get started. The most important thing is to understand your audience and deliver content and offers that are truly relevant to them.

The PDF Download of an Honest Secret Email System Review
The front-end product
What Matt Bacak is selling, which has been promoted by such capable affiliates as Curt Maly, is a PDF ebook that you can download immediately after purchasing. However, there are a number of bonuses included to sweeten the deal. You get access to Mr. Bacak’s private Facebook group, and instead of a simple PDF download, you get a massive zip file full of useful files and videos.
Now that we know what we’re up against, let’s get into this secret email system review!

What is Included in the Secret Email System Download?
Here is a list of everything you get inside the zip file sold at the front end of the Secret Email System:

Matt Bacak’s 3x Formula Calculator (plus a video explaining how to use it)
1000 email swipe files in text format (swipe files or “swipes” are like templates you can repurpose in a variety of ways).
A 1.5-hour video session

Free access to Matt’s high-converting leadpages lead generation template
A massive book of swipe files (in PDF format)
A copy of Matt’s book, Secrets of the Internet Millionaire Mind,
A video tutorial on how to select “irresistible offers” from affiliate marketplaces.

The PDF version of The Secret Email System
The Checklist for the Secret Email System PDF
Text files containing instructions for joining the Facebook group and other bonuses
Matt was charging less than $6 for all of that value last time I checked. He is demonstrating his many years of experience in internet marketing by creating an irresistible offer that people will want to buy and affiliates will want to promote. As a result, the Secret Email System has sold more copies on Warrior Plus than any other product in recent memory.

Examine everything included in the secret email system
Who is Matt Bacak, and why should I listen to him?
Many consider Matt Bacak to be an internet marketing legend, and email marketing is his primary focus. My first encounter with Matt came in the form of some Facebook ads he ran. Matt explained who he was in the video ad (which featured a little guy dancing in the background) and invited me to visit his blog, which I did. He demonstrated a thorough understanding of online business, so it’s no surprise that he put together the ultimate email marketing package.
headshot of Matt Bacak

Overall, Matt’s ad was one of the strangest Facebook ads I’ve ever seen. It was also one of the most effective and memorable. I didn’t buy whatever Matt was selling that day, but I read his blog and remembered his name and who he was. When I saw Curt Maly running ads for Matt Bacak’s Secret Email System months later, it made a big difference.

When I saw that the price was under $6 and that the bonuses were included, I knew I had to buy the product. I didn’t buy it right away because I was too busy, but it stayed in the back of my mind until I had the opportunity to do so.
If it isn’t obvious, I’ll explain: the reason you should listen to Matt Bacak is that he knows how to get inside people’s heads and stay there, both as a marketer and as a public figure.

Is the Secret Email System Training of Good Quality?
At first glance, the training does not appear to be groundbreaking, but this is because the creator is unconcerned about flashy packaging. You literally get a zip file full of stuff that most people would put on a membership website. I can see how this would irritate some people who are used to flashy ClickFunnels and Kajabi courses.

If that describes you, you’re missing out. Matt’s training isn’t flashy, but it describes a solid system that most businesses can implement in some way. As the name implies, it all revolves around building a list and emailing it on a regular basis. Did I ruin the surprise?
Front end offer and upsells from a secret email system
Bonuses from the Secret Email System (and a Bonus From Me)
I’ve already outlined everything you get in the zip file that serves as the funnel’s front-end offer. Everything else, other than the Secret Email System PDF itself, is considered a bonus, and the total value could easily be in the hundreds of dollars.

That’s why purchasing this product was such a no-brainer for me. I already knew how to write good marketing emails, but I really wanted to look inside Matt’s system.
In addition to everything else, you’ll get lifetime access to Matt’s private Facebook community. He answers questions from people here on a daily basis, and it can be a great place to learn.

The truth is that you get so much value and stuff from purchasing this product that adding another bonus is almost pointless. But I’m a bonus machine, so be prepared.
In 2020, I published my first book on email marketing, How to Build Your First Money Making Email List. You’re already getting a lot of reading material, but if you purchase Matt’s product through my link, I’ll add it to the stack. Most of the books I write sell for $27, so this just adds to the ridiculous valuation of this sub-six-dollar product.
Bonuses Bonuses for Matt Bacak’s Secret Email System
Will This Product Really Help You Make Money Online?

It all depends on whether or not you use the secret email system. According to multiple sources, Matt Bacak is in charge of millions of dollars in sales for both himself and his clients. And the best thing about this guy is that he’s upfront and honest, and he puts his money where his mouth is. What I mean is that he doesn’t hold anything back in the books he writes. That is another reason he has amassed such a large and devoted fan base.

Finally, if your business can profit from email marketing or if you want to use email marketing to become an influential public figure, I believe this ebook can assist you. It helped me improve my understanding of the business side of being an affiliate marketer and is far more valuable than the price tag. This product will be especially useful if you want to get started in affiliate marketing with a small investment.

matt bacak’s business model
Going Beyond My Review – Secret Email System
The book itself goes beyond email marketing, but I don’t want to give too much away. Instead, I’ll go over some of the finer points of lead generation quickly so you can get started building your email list as soon as possible.

Now, I’m guessing that roughly 90% of people reading this review are affiliate marketers or are interested in affiliate marketing. As a result, I’m going to focus on lead generation strategies used by many successful affiliates. If you want to learn more about my favorite affiliate marketing strategies, click on that link to read my in-depth guide.

The Most Effective Methods for Building an Email List
Here’s a rundown of some of the best (and quickest) ways to build an email list. First, you’ll need a way to collect emails, and it must be a high-converting method. My favorite lead generation tools are:
ConvertBox (on-site messaging software/advanced popup builder)
ConversioBot (website chatbot platform)

You may have noticed that the majority of them are chatbots. Chatbots, on the other hand, are one of the best ways to not only capture an email address, but also to obtain additional customer information and even directly sell products.
The following are the most effective ways to drive traffic to these tools:
Facebook ads (particularly effective when paired with ConvertBox)
Google Ads and YouTube Ads (a killer combination with ConvertBox or Conversiobot)

Influencer Marketing
Facebook organic marketing
Search Engine Optimization
Secret Email System sales page Matt Bacak

If you can master even one of those traffic methods and use it to drive people to a high-converting optin or sales funnel, you’ll be well on your way to creating a recurring income.
Whether or not you choose to purchase this product through my link, I wish you the best of luck with your online business. If you do purchase the system, I hope to see you in the Facebook community! Please feel free to contact me via message or email at any time.

And if you do get the ebook through my link, please let me know so I can send you a copy of my book as a bonus!

Frequently Asked Questions (FAQs) About The Secret Email System
Here are some frequently asked product-related questions.
Is the secret email system bonus worth it?

In my opinion, the front end product and the majority of the bonuses are worth the price. I would have paid more just to gain access to Matt’s Facebook group!
What are the benefits of a hidden email system?
The main benefit is that you will learn one of the highest ROI business practices (email marketing) from someone who has built a seven-figure online business.

What do you call email marketing?
Email marketing is an important component of digital marketing for many businesses. Email marketing software is frequently referred to as an autoresponder, but a good email marketing platform will have more functionality.

Is this a legitimate way to make money online?
My secret email system review says it’s a great way to make money online as long as your online business uses marketing emails. It does require a list, but Matt teaches several methods for creating one.

Visit The Officail Website

#secret email system matt bacak #secret email system review #secret email system #secret email system bonus #secret email system bonuses #secret email system reviews