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com.tencent.angel:client

A stand alone industrial serving system for angel.

License

License
Categories

Categories

CLI User Interface
GroupId

GroupId

com.tencent.angel
ArtifactId

ArtifactId

client
Last Version

Last Version

0.1.0
Release Date

Release Date
Type

Type

jar
Description

Description

A stand alone industrial serving system for angel.

Download client

Filename Size
client-0.1.0.pom
client-0.1.0.jar 10 KB
client-0.1.0-sources.jar 5 KB
client-0.1.0-javadoc.jar 349 KB
Browse

How to add to project

<!-- https://jarcasting.com/artifacts/com.tencent.angel/client/ -->
<dependency>
    <groupId>com.tencent.angel</groupId>
    <artifactId>client</artifactId>
    <version>0.1.0</version>
</dependency>
// https://jarcasting.com/artifacts/com.tencent.angel/client/
implementation 'com.tencent.angel:client:0.1.0'
// https://jarcasting.com/artifacts/com.tencent.angel/client/
implementation ("com.tencent.angel:client:0.1.0")
'com.tencent.angel:client:jar:0.1.0'
<dependency org="com.tencent.angel" name="client" rev="0.1.0">
  <artifact name="client" type="jar" />
</dependency>
@Grapes(
@Grab(group='com.tencent.angel', module='client', version='0.1.0')
)
libraryDependencies += "com.tencent.angel" % "client" % "0.1.0"
[com.tencent.angel/client "0.1.0"]

Dependencies

compile (17)

Group / Artifact Type Version
com.tencent.angel : common jar 0.1.0
org.scala-lang : scala-library jar 2.11.8
org.scala-lang : scala-reflect jar 2.11.8
org.scala-lang : scala-compiler jar 2.11.8
org.glassfish.jersey.bundles : jaxrs-ri jar 2.4
com.typesafe.scala-logging : scala-logging_2.11 jar 3.9.0
io.grpc : grpc-all jar 1.17.1
io.grpc : grpc-core jar 1.17.1
io.grpc : grpc-services jar 1.17.1
commons-io : commons-io jar 2.4
org.slf4j : slf4j-log4j12 jar 1.6.2
org.slf4j : slf4j-api jar 1.6.2
com.sun.jersey : jersey-servlet jar 1.19.4
com.sun.jersey : jersey-json jar 1.19.4
com.sun.jersey : jersey-client jar 1.19.4
org.eclipse.jetty.aggregate : jetty-all-server jar 8.2.0.v20160908
org.apache.hadoop : hadoop-common jar 2.7.2

test (2)

Group / Artifact Type Version
org.scalatest : scalatest_2.11 jar 3.0.3
junit : junit jar 4.4

Project Modules

There are no modules declared in this project.

Angel Serving

Angel Serving is standalone industrial serving system for machine/deep learning models, it is designed to flexible and high-performance.

Architecture

Angel Serving Architecture

Features

  • One can access Angel Serving through gRPC and Restful API

  • Angel Serving is a general machine learning serving framework which means models from other training platform can server on Angel Serving. There is a pluggable mechanism for the third party platform join in, now we support: Angel, PyTorch and PMML format. Through the PMML format, Angel can server Spark and XGBoost models.

  • Similar to TensorFlow Serving, we provide fine grain version control: earliest, latest and specified versions.

  • Apart from version control, angel serving also provide fine grain service monitoring:

    • QPS: Query per second
    • Success/Total requests
    • Response time distribution
    • Average response Time

Serve a model in 60 seconds

# Download the Angel Serving Docker image and repo
docker pull tencentangel/serving

git clone https://github.com/Angel-ML/serving.git
# Location of demo models
TESTDATA="$(pwd)/serving/models/angel/lr/lr-model"

# Start Angel Serving container and open the REST API port
docker run -t --rm -p 8501:8501 \
    -v "$TESTDATA:/models" \
    -e MODEL_NAME=lr \
    -e MODEL_PLATFORM=angel \
    tencentangel/serving &

# Query the model using the predict API
curl -H "Content-Type: application/json" -X POST -d '{"instances": [[0.51483303, 0.99900955, 0.9477888, 0.6912188, 0.41446745, 0.2525878, 0.6014038, 0.46847868, 0.12854028, 0.8306037, 0.3461753, 0.1129151, 0.6229094, 0.90299904, 0.50834644, 0.34843314, 0.95900637, 0.9437762, 0.31707388, 0.73501045, 0.05600065, 0.47225082, 0.28908283, 0.7371853, 0.55928135, 0.81367457, 0.91782594, 0.008230567, 0.9317811, 0.0061050057, 0.7060979, 0.51740277, 0.07297987, 0.34826308, 0.43395072, 0.5017575, 0.73248106, 0.7576818, 0.43087876, 0.9380423, 0.5226082, 0.9813176, 0.20717019, 0.42229313, 0.8274106, 0.6791944, 0.48174334, 0.77374876, 0.56179315, 0.6584269, 0.7635249, 0.9949779, 0.84034514, 0.7586089, 0.74443096, 0.21172583, 0.7850719, 0.5341459, 0.84134424, 0.06459451, 0.1270392, 0.41439575, 0.98234355, 0.5515572, 0.9594097, 0.18379861, 0.8221523, 0.23739898, 0.07713032, 0.66251403, 0.84977543, 0.905998, 0.21836805, 0.40002906, 0.6271626, 0.37708586, 0.20958215, 0.051997364, 0.6841619, 0.22454417, 0.34285623, 0.19205093, 0.35783356, 0.29280972, 0.19194472, 0.42898583, 0.27232456, 0.12662607, 0.74165606, 0.43464816, 0.8310301, 0.012846947, 0.9810947, 0.43377626, 0.3608846, 0.22756284, 0.6404164, 0.7243295, 0.68765146, 0.12439847, 0.25675082, 0.26143825, 0.41246158, 0.867953, 0.2895738, 0.3916427, 0.93816304, 0.27819514, 0.1989426, 0.62377095, 0.9969712, 0.4159639, 0.70966166, 0.29150474, 0.6492832, 0.10598481, 0.44674253, 0.03885162, 0.25127923, 0.60202503, 0.6067293, 0.94750637, 0.97315085]]}' \
  localhost:8501/v1/models/lr/versions/6:predict

# Returns => {"predictions": [{"trueLabel":"0.0","proba":"0.024818711775534966","pred":"-3.671025514602661","attached":"NaN","predLabel":"-1.0","sid":"0"}]}

Setup

  1. Compile Environment Requirements

    • jdk >=1.8
    • maven >= 3.0.5
    • protobuf >= 3.5.1

  2. Source Code Download

    git clone https://github.com/Angel-ML/serving.git

  3. Compile

    Run the following command in the root directory of the source code

    mvn clean package -Dmaven.test.skip=true

    After compiling, a distribution package named serving-0.1.0-bin.zip will be generated under dist/target in the root directory.

  4. Distribution Package Unpacking the distribution package, subdirectories will be generated under the root directory:

    • bin: contains Angel Serving start scripts.
    • conf: contains system config files.
    • lib: contains jars for Angel Serving and dependencies.
    • models: contains trained example models.
    • docs: contains user manual and restful api documentation.

Deployment Guide

  1. Execution Environment Requirements

    • jdk >= 1.8
    • set JAVA_HOME

  2. Start Server

    Run the serving-submit with args to start Angel Serving, example:

    $SERVING_HOME/bin/serving-submit \
   --port 8500 \
   --rest_api_port 8501 \
   --model_base_path $SERVING_HOME/models/angel/lr/lr-model/ \
   --model_name lr \ 
   --model_platform angel \
   --enable_metric_summary true

Documentation

com.tencent.angel

Angel

Angel is a high-performance and full-stack distributed ML platform. It is an LF AI Graduation project.

Versions

Version
0.1.0