com.intel.analytics.zoo:analytics-zoo-bigdl_0.12.1-spark_2.1.1

A distributed analytics library for Apache Spark.

License

License

GroupId

GroupId

com.intel.analytics.zoo
ArtifactId

ArtifactId

analytics-zoo-bigdl_0.12.1-spark_2.1.1
Last Version

Last Version

0.9.0
Release Date

Release Date

Type

Type

jar
Description

Description

A distributed analytics library for Apache Spark.

Download analytics-zoo-bigdl_0.12.1-spark_2.1.1

How to add to project

<!-- https://jarcasting.com/artifacts/com.intel.analytics.zoo/analytics-zoo-bigdl_0.12.1-spark_2.1.1/ -->
<dependency>
    <groupId>com.intel.analytics.zoo</groupId>
    <artifactId>analytics-zoo-bigdl_0.12.1-spark_2.1.1</artifactId>
    <version>0.9.0</version>
</dependency>
// https://jarcasting.com/artifacts/com.intel.analytics.zoo/analytics-zoo-bigdl_0.12.1-spark_2.1.1/
implementation 'com.intel.analytics.zoo:analytics-zoo-bigdl_0.12.1-spark_2.1.1:0.9.0'
// https://jarcasting.com/artifacts/com.intel.analytics.zoo/analytics-zoo-bigdl_0.12.1-spark_2.1.1/
implementation ("com.intel.analytics.zoo:analytics-zoo-bigdl_0.12.1-spark_2.1.1:0.9.0")
'com.intel.analytics.zoo:analytics-zoo-bigdl_0.12.1-spark_2.1.1:jar:0.9.0'
<dependency org="com.intel.analytics.zoo" name="analytics-zoo-bigdl_0.12.1-spark_2.1.1" rev="0.9.0">
  <artifact name="analytics-zoo-bigdl_0.12.1-spark_2.1.1" type="jar" />
</dependency>
@Grapes(
@Grab(group='com.intel.analytics.zoo', module='analytics-zoo-bigdl_0.12.1-spark_2.1.1', version='0.9.0')
)
libraryDependencies += "com.intel.analytics.zoo" % "analytics-zoo-bigdl_0.12.1-spark_2.1.1" % "0.9.0"
[com.intel.analytics.zoo/analytics-zoo-bigdl_0.12.1-spark_2.1.1 "0.9.0"]

Dependencies

compile (22)

Group / Artifact Type Version
joda-time : joda-time jar 2.9.9
org.scala-lang : scala-library jar 2.11.8
com.typesafe.akka : akka-actor_2.11 jar 2.5.26
com.fasterxml.jackson.module : jackson-module-scala_2.11 jar 2.10.2
com.intel.analytics.bigdl : bigdl-SPARK_2.1 jar 0.12.1
ml.dmlc : xgboost4j_2.11 jar 1.2.0-SNAPSHOT
ml.dmlc : xgboost4j-spark_2.11 jar 1.2.0-SNAPSHOT
org.elasticsearch : elasticsearch-hadoop jar 7.7.0
org.tensorflow : libtensorflow jar 1.15.0
org.tensorflow : proto jar 1.15.0
black.ninia : jep jar 3.9.0
org.tensorflow : tensorflow-hadoop jar 1.15.0
com.intel.analytics.zoo : zoo-core-dist-all jar 0.9.0
redis.clients : jedis jar 3.3.0
org.yaml : snakeyaml jar 1.25
commons-lang : commons-lang jar 2.6
com.typesafe.akka : akka-http_2.11 jar 10.1.11
com.typesafe.akka : akka-stream_2.11 jar 2.5.26
io.dropwizard.metrics : metrics-core jar 4.1.2
com.fasterxml.jackson.core : jackson-databind jar 2.10.2
com.google.guava : guava jar 15.0
org.codehaus.janino : commons-compiler jar 3.0.9

provided (12)

Group / Artifact Type Version
org.scala-lang : scala-compiler jar 2.11.8
org.scala-lang : scala-reflect jar 2.11.8
org.scala-lang : scala-actors jar 2.11.8
org.scala-lang : scalap jar 2.11.8
org.apache.spark : spark-mllib_2.11 jar 2.1.1
org.apache.flink : flink-scala_2.11 jar 1.11.2
org.apache.flink : flink-streaming-scala_2.11 jar 1.11.2
org.apache.flink : flink-clients_2.11 jar 1.11.2
org.apache.flink : flink-table-api-scala-bridge_2.11 jar 1.11.2
org.apache.flink : flink-table-common jar 1.11.2
org.apache.flink : flink-table-planner-blink_2.11 jar 1.11.2
org.apache.flink : flink-csv jar 1.11.2

test (3)

Group / Artifact Type Version
org.scalatest : scalatest_2.11 jar 3.0.7
org.reflections : reflections jar 0.9.9-RC1
com.github.fppt : jedis-mock jar 0.1.16

Project Modules

There are no modules declared in this project.


A unified Data Analytics and AI platform for distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray


What is Analytics Zoo?

Analytics Zoo seamless scales TensorFlow, Keras and PyTorch to distributed big data (using Spark, Flink & Ray).


  • End-to-end pipeline for applying AI models (TensorFlow, PyTorch, OpenVINO, etc.) to distributed big data

    • Write TensorFlow or PyTorch inline with Spark code for distributed training and inference.
    • Native deep learning (TensorFlow/Keras/PyTorch/BigDL) support in Spark ML Pipelines.
    • Directly run Ray programs on big data cluster through RayOnSpark.
    • Plain Java/Python APIs for (TensorFlow/PyTorch/BigDL/OpenVINO) Model Inference.
  • High-level ML workflow for automating machine learning tasks

    • Cluster Serving for automatically distributed (TensorFlow/PyTorch/Caffe/OpenVINO) model inference .
    • Scalable AutoML for time series prediction.
  • Built-in models for Recommendation, Time Series, Computer Vision and NLP applications.


Why use Analytics Zoo?

You may want to develop your AI solutions using Analytics Zoo if:

  • You want to easily apply AI models (e.g., TensorFlow, Keras, PyTorch, BigDL, OpenVINO, etc.) to distributed big data.
  • You want to transparently scale your AI applications from a single laptop to large clusters with "zero" code changes.
  • You want to deploy your AI pipelines to existing YARN or K8S clusters WITHOUT any modifications to the clusters.
  • You want to automate the process of applying machine learning (such as feature engineering, hyperparameter tuning, model selection, distributed inference, etc.).

How to use Analytics Zoo?

com.intel.analytics.zoo

Versions

Version
0.9.0