Comments on: Run Spark jobs on Azure Batch – Azure AZTK

By: Run Apache Spark on AKS with Azure Storage integration – tsmatz

Run Apache Spark on AKS with Azure Storage integration – tsmatz — Tue, 08 Dec 2020 08:20:34 +0000

[…] RDD and MLlib libraries, which is not recommended now in Spark. (This code is the same one in my early post.) Please see here (Databricks tutorial) for Spark dataframe and SparkML […]

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By: Don Pablo

Don Pablo — Mon, 11 Jun 2018 12:08:01 +0000

Awesome tutorial.

Is there any documentation where a comparison between AZTK and Data Factory is made?

Thanks a lot.

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By: Azure Machine Learning Services ステップ・バイ・ステップ | @Subaru

Azure Machine Learning Services ステップ・バイ・ステップ | @Subaru — Fri, 25 May 2018 08:58:57 +0000

[…] ml experimentation submit コマンドを使うことで、作成したコード (Python または pyspark スクリプト) の単一環境でのイテレートや複数環境での実行結果 […]

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By: Azure Databricks tutorial for TensorFlow developers (TensorFlowOnSpark) – Tsmatz

Wed, 09 May 2018 10:19:31 +0000

[…] to show how it scales for your reference. (Currently you can run GPU accelerated Spark jobs using aztk in […]

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By: Ram Krishnan

Ram Krishnan — Tue, 08 May 2018 17:37:44 +0000

Hi, Have you been able to successfully run more than one Notebook on AZTK from Jupyter without having to shut down the kernel for the previous notebook. To elaborate, assume you have two notebooks. You open the first one. jupyter autostarts the PySpark kernel to enable runs on AZTK. You run that notebook. Before you run the second notebook (assume that the second has some dependencies on the first notebook), are you forced to shut down the kernel for the first notebook.

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By: 3 of approaches : SQL Server R and Python Workloads (Machine Learning Services) | Tsmatz

Fri, 02 Mar 2018 11:44:09 +0000

[…] If the size of data is so large, it’ll hung up … (Please use air-flight dataset in my previous post – the size of data is 500 MB with 2 million […]

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