Apache Spark
Apache Spark is an open source distributed general-purpose cluster-computing framework. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.
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Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
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Hey!
Currently, there are no plans to add support for it (from core team), but we are happy to merge and help with contribution from the community.
For driver implementer, take a look at
https://github.com/cube-js/cube.js/blob/master/CONTRIBUTING.md#implementing-sql-dialect
Example of driver implementation
Who are interested in prepari
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macOS development environment setup: Easy-to-understand instructions with automated setup scripts for developer tools like Vim, Sublime Text, Bash, iTerm, Python data analysis, Spark, Hadoop MapReduce, AWS, Heroku, JavaScript web development, Android development, common data stores, and dev-based OS X defaults.
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H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
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Alluxio, data orchestration for analytics and machine learning in the cloud
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PipelineAI Kubeflow Distribution
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BigDL: Distributed Deep Learning Framework for Apache Spark
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TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.
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酷玩 Spark: Spark 源代码解析、Spark 类库等
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Hi, if my spark app is using 2 storage type, both S3 and Azure Data Lake Store Gen2, could I put spark.delta.logStore.class=org.apache.spark.sql.delta.storage.AzureLogStore, org.apache.spark.sql.delta.storage.S3SingleDriverLogStore
Thanks in advance
Interactive and Reactive Data Science using Scala and Spark.
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The Hunting ELK
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Used Spark version
2.4.3
Used Spark Job Server version
(Released version, git branch or docker image version)
0.9.0-SNAPSHOT
Deployed mode
(client/cluster on Spark Standalone/YARN/Mesos/EMR or default)
client spark standalone
Actual (wrong) behavior
curl -d "input.string = a b c a b see hello world ssdsds " 'localhost:8090/jobs?appName=test&classPath=spark.jobserver.WordCo
I have a simple regression task (using a LightGBMRegressor) where I want to penalize negative predictions more than positive ones. Is there a way to achieve this with the default regression LightGBM objectives (see https://lightgbm.readthedocs.io/en/latest/Parameters.html)? If not, is it somehow possible to define (many example for default LightGBM model) and pass a custom regression objective?
A better compressed bitset in Java
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Feb 6, 2021 - Java
Created by Matei Zaharia
Released May 26, 2014
- Repository
- apache/spark
- Website
- spark.apache.org
- Wikipedia
- Wikipedia


At this moment relu_layer op doesn't allow threshold configuration, and legacy RELU op allows that.
We should add configuration option to relu_layer.