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Posted to commits@systemml.apache.org by ni...@apache.org on 2016/05/17 01:10:40 UTC
incubator-systemml git commit: [SYSTEMML-594] Adding tutorial to run
SystemML on Bluemix/DSW using Zeppelin/Jupyter
Repository: incubator-systemml
Updated Branches:
refs/heads/master c334c2c85 -> bcdc9da51
[SYSTEMML-594] Adding tutorial to run SystemML on Bluemix/DSW using
Zeppelin/Jupyter
Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml/commit/bcdc9da5
Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/bcdc9da5
Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml/diff/bcdc9da5
Branch: refs/heads/master
Commit: bcdc9da51b85388f7221394bc6273ff654e9f102
Parents: c334c2c
Author: Niketan Pansare <np...@us.ibm.com>
Authored: Mon May 16 18:07:26 2016 -0700
Committer: Niketan Pansare <np...@us.ibm.com>
Committed: Mon May 16 18:08:37 2016 -0700
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samples/images/bluemix_screen.jpeg | Bin 0 -> 103436 bytes
samples/images/bluemix_spark_screen.jpeg | Bin 0 -> 116357 bytes
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samples/images/bluemix_spark_screen4.jpeg | Bin 0 -> 160587 bytes
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samples/images/datascientistworkbench1.jpeg | Bin 0 -> 133176 bytes
samples/images/datascientistworkbench2.jpeg | Bin 0 -> 175770 bytes
samples/import-nb-bluemix.md | 26 +++++
samples/import-nb-datascientistworkbench.md | 18 ++++
samples/jupyter-notebooks/tutorial1.ipynb | 103 +++++++++++++++++++
.../zeppelin-notebooks/tutorial1_zeppelin.json | 1 +
12 files changed, 148 insertions(+)
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http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/bcdc9da5/samples/images/bluemix_screen.jpeg
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http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/bcdc9da5/samples/import-nb-bluemix.md
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diff --git a/samples/import-nb-bluemix.md b/samples/import-nb-bluemix.md
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@@ -0,0 +1,26 @@
+## General setup to run one of the Jupyter notebooks on IBM Bluemix:
+
+* Clone the repository to download the notebooks
+```
+git clone https://github.com/apache/incubator-systemml.git
+```
+
+* Log on to https://console.ng.bluemix.net/ and create Apache Spark service:
+
+
+
+* Go to Apache Spark service dashboard and click on notebook button:
+
+
+
+* Create a new notebook:
+
+
+
+* Upload the notebook from this tutorial you want run on bluemix:
+
+
+
+* Hurray, we now have a scala notebook running on bluemix:
+
+
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/bcdc9da5/samples/import-nb-datascientistworkbench.md
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diff --git a/samples/import-nb-datascientistworkbench.md b/samples/import-nb-datascientistworkbench.md
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@@ -0,0 +1,18 @@
+## General setup to run one of the Zeppelin notebooks on https://datascientistworkbench.com/:
+
+* Clone the repository to download the notebooks
+```
+git clone https://github.com/apache/incubator-systemml.git
+```
+
+* Create Zeppelin notebook
+
+
+
+* Upload Zeppelin notebook
+
+
+
+* Hurray, we now have a scala notebook running on datascientist workbench:
+
+
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/bcdc9da5/samples/jupyter-notebooks/tutorial1.ipynb
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diff --git a/samples/jupyter-notebooks/tutorial1.ipynb b/samples/jupyter-notebooks/tutorial1.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Starting download from https://sparktc.ibmcloud.com/repo/latest/SystemML.jar\n",
+ "Finished download of SystemML.jar\n"
+ ]
+ }
+ ],
+ "source": [
+ "%AddJar https://sparktc.ibmcloud.com/repo/latest/SystemML.jar"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import org.apache.sysml.api.MLContext"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import org.apache.spark.sql.SQLContext\n",
+ "val sqlCtx = new SQLContext(sc)\n",
+ "val ml = new MLContext(sc)\n",
+ "val dml = \"\"\"\n",
+ "X = rand(rows=100, cols=10)\n",
+ "sumX = sum(X)\n",
+ "outMatrix = matrix(sumX, rows=1, cols=1)\n",
+ "write(outMatrix, \" \", format=\"csv\")\n",
+ "\"\"\"\n",
+ "ml.reset()\n",
+ "ml.registerOutput(\"outMatrix\")\n",
+ "val out = ml.executeScript(dml)\n",
+ "val outMatrix = out.getDF(sqlCtx, \"outMatrix\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+---+------------------+\n",
+ "| ID| C1|\n",
+ "+---+------------------+\n",
+ "|0.0|507.71224689601286|\n",
+ "+---+------------------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "outMatrix.show"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Scala 2.10",
+ "language": "scala",
+ "name": "spark"
+ },
+ "language_info": {
+ "name": "scala"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/bcdc9da5/samples/zeppelin-notebooks/tutorial1_zeppelin.json
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diff --git a/samples/zeppelin-notebooks/tutorial1_zeppelin.json b/samples/zeppelin-notebooks/tutorial1_zeppelin.json
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+{"paragraphs":[{"config":{"colWidth":12,"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true,"editorMode":"ace/mode/scala"},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1460062914890_499572682","id":"20160407-210154_742995576","dateCreated":"Apr 7, 2016 9:01:54 PM","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:354","text":"%dep\r\nz.load(\"org.apache.systemml:systemml:0.9.0-incubating\")","dateUpdated":"Apr 8, 2016 12:30:17 AM","dateFinished":"Apr 7, 2016 9:04:46 PM","dateStarted":"Apr 7, 2016 9:04:46 PM","result":{"code":"SUCCESS","type":"TEXT","msg":"res1: org.apache.zeppelin.spark.dep.Dependency = org.apache.zeppelin.spark.dep.Dependency@2652117f\n"}},{"config":{"colWidth":12,"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true,"editorMode":"ace/mode/scala"},"settings":{"params":{},"forms":{}},"jobName":"paragraph
_1460063047961_207364377","id":"20160407-210407_1127760007","dateCreated":"Apr 7, 2016 9:04:07 PM","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:375","dateUpdated":"Apr 7, 2016 9:04:46 PM","dateFinished":"Apr 7, 2016 9:05:10 PM","dateStarted":"Apr 7, 2016 9:04:46 PM","result":{"code":"SUCCESS","type":"TEXT","msg":"import org.apache.sysml.api.MLContext\n"},"text":"import org.apache.sysml.api.MLContext"},{"config":{"colWidth":12,"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true,"editorMode":"ace/mode/scala"},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1460063086400_-566304364","id":"20160407-210446_523705226","dateCreated":"Apr 7, 2016 9:04:46 PM","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:395","dateUpdated":"Apr 7, 2016 9:44:43 PM","dateFinished":"Apr 7, 2016 9:44:44 PM","dateStarted":"Apr 7, 2016 9:44:43 PM","result":{"code":"SUCCESS","type":"TEXT","
msg":"import org.apache.spark.sql.SQLContext\nsqlCtx: org.apache.spark.sql.SQLContext = org.apache.spark.sql.SQLContext@44252a8a\nml: org.apache.sysml.api.MLContext = org.apache.sysml.api.MLContext@f5ff11c\ndml: String = \n\"\nX = rand(rows=100, cols=10)\nsumX = sum(X)\noutMatrix = matrix(sumX, rows=1, cols=1)\nwrite(outMatrix, \" \", format=\"csv\")\n\"\nout: org.apache.sysml.api.MLOutput = org.apache.sysml.api.MLOutput@7f2976c4\noutMatrix: org.apache.spark.sql.DataFrame = [ID: double, C1: double]\n"},"text":"import org.apache.spark.sql.SQLContext\nval sqlCtx = new SQLContext(sc)\nval ml = new MLContext(sc)\nval dml = \"\"\"\nX = rand(rows=100, cols=10)\nsumX = sum(X)\noutMatrix = matrix(sumX, rows=1, cols=1)\nwrite(outMatrix, \" \", format=\"csv\")\n\"\"\"\nml.reset()\nml.registerOutput(\"outMatrix\")\nval out = ml.executeScript(dml)\nval outMatrix = out.getDF(sqlCtx, \"outMatrix\")"},{"config":{"colWidth":12,"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"value
s":[],"groups":[],"scatter":{}},"enabled":true,"editorMode":"ace/mode/scala"},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1460063826194_1965196735","id":"20160407-211706_2075868632","dateCreated":"Apr 7, 2016 9:17:06 PM","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:413","dateUpdated":"Apr 7, 2016 9:45:23 PM","dateFinished":"Apr 7, 2016 9:45:23 PM","dateStarted":"Apr 7, 2016 9:45:23 PM","result":{"code":"SUCCESS","type":"TEXT","msg":"+---+------------------+\n| ID| C1|\n+---+------------------+\n|0.0|508.60328663270093|\n+---+------------------+\n\n"},"text":"outMatrix.show()"},{"config":{"colWidth":12,"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true,"editorMode":"ace/mode/sh"},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1460065523381_66686365","id":"20160407-214523_1983686952","dateCreated":"Apr 7, 2016 9:45:23 PM","status":"FINISHED","progressUpda
teIntervalMs":500,"$$hashKey":"object:506","dateUpdated":"Apr 7, 2016 9:50:27 PM","dateFinished":"Apr 7, 2016 9:50:31 PM","dateStarted":"Apr 7, 2016 9:50:27 PM","result":{"code":"SUCCESS","type":"TEXT","msg":"--2016-04-07 21:50:28-- https://sparktc.ibmcloud.com/repo/latest/SystemML.jar\nResolving sparktc.ibmcloud.com (sparktc.ibmcloud.com)... 169.54.146.42\nConnecting to sparktc.ibmcloud.com (sparktc.ibmcloud.com)|169.54.146.42|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 6299395 (6.0M) [application/x-java-archive]\nSaving to: 'SystemML.jar'\n\n 0K .......... .......... .......... .......... .......... 0% 261K 23s\n 50K .......... .......... .......... .......... .......... 1% 390K 19s\n 100K .......... .......... .......... .......... .......... 2% 777K 15s\n 150K .......... .......... .......... .......... .......... 3% 391K 15s\n 200K .......... .......... .......... .......... .......... 4% 779K 14s\n 250K .......... .....
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\n 3500K .......... .......... .......... .......... .......... 57% 69.7M 2s\n 3550K .......... .......... .......... .......... .......... 58% 75.6M 2s\n 3600K .......... .......... .......... .......... .......... 59% 64.1M 2s\n 3650K .......... .......... .......... .......... .......... 60% 84.3M 2s\n 3700K .......... .......... .......... .......... .......... 60% 813K 2s\n 3750K .......... .......... .......... .......... .......... 61% 68.7M 2s\n 3800K .......... .......... .......... .......... .......... 62% 59.3M 1s\n 3850K .......... .......... .......... .......... .......... 63% 78.6M 1s\n 3900K .......... .......... .......... .......... .......... 64% 79.4M 1s\n 3950K .......... .......... .......... .......... .......... 65% 819K 1s\n 4000K .......... .......... .......... .......... .......... 65% 32.7M 1s\n 4050K .......... .......... .......... .......... .......... 66% 62.5M 1s\n 4100K .......... .......... .......... .......... .......... 67% 80.1
M 1s\n 4150K .......... .......... .......... .......... .......... 68% 67.0M 1s\n 4200K .......... .......... .......... .......... .......... 69% 71.9M 1s\n 4250K .......... .......... .......... .......... .......... 69% 827K 1s\n 4300K .......... .......... .......... .......... .......... 70% 44.6M 1s\n 4350K .......... .......... .......... .......... .......... 71% 77.6M 1s\n 4400K .......... .......... .......... .......... .......... 72% 62.9M 1s\n 4450K .......... .......... .......... .......... .......... 73% 78.9M 1s\n 4500K .......... .......... .......... .......... .......... 73% 832K 1s\n 4550K .......... .......... .......... .......... .......... 74% 39.8M 1s\n 4600K .......... .......... .......... .......... .......... 75% 51.5M 1s\n 4650K .......... .......... .......... .......... .......... 76% 79.2M 1s\n 4700K .......... .......... .......... .......... .......... 77% 80.0M 1s\n 4750K .......... .......... .......... .......... .......... 78%
83.1M 1s\n 4800K .......... .......... .......... .......... .......... 78% 828K 1s\n 4850K .......... .......... .......... .......... .......... 79% 42.5M 1s\n 4900K .......... .......... .......... .......... .......... 80% 58.9M 1s\n 4950K .......... .......... .......... .......... .......... 81% 81.3M 1s\n 5000K .......... .......... .......... .......... .......... 82% 70.3M 1s\n 5050K .......... .......... .......... .......... .......... 82% 82.1M 1s\n 5100K .......... .......... .......... .......... .......... 83% 831K 1s\n 5150K .......... .......... .......... .......... .......... 84% 68.6M 1s\n 5200K .......... .......... .......... .......... .......... 85% 48.0M 0s\n 5250K .......... .......... .......... .......... .......... 86% 73.5M 0s\n 5300K .......... .......... .......... .......... .......... 86% 71.6M 0s\n 5350K .......... .......... .......... .......... .......... 87% 74.0M 0s\n 5400K .......... .......... .......... .......... ..........
88% 67.7M 0s\n 5450K .......... .......... .......... .......... .......... 89% 831K 0s\n 5500K .......... .......... .......... .......... .......... 90% 38.5M 0s\n 5550K .......... .......... .......... .......... .......... 91% 80.7M 0s\n 5600K .......... .......... .......... .......... .......... 91% 58.1M 0s\n 5650K .......... .......... .......... .......... .......... 92% 80.2M 0s\n 5700K .......... .......... .......... .......... .......... 93% 81.6M 0s\n 5750K .......... .......... .......... .......... .......... 94% 814K 0s\n 5800K .......... .......... .......... .......... .......... 95% 77.0M 0s\n 5850K .......... .......... .......... .......... .......... 95% 80.6M 0s\n 5900K .......... .......... .......... .......... .......... 96% 85.3M 0s\n 5950K .......... .......... .......... .......... .......... 97% 83.9M 0s\n 6000K .......... .......... .......... .......... .......... 98% 69.3M 0s\n 6050K .......... .......... .......... .......... .......
... 99% 834K 0s\n 6100K .......... .......... .......... .......... .......... 99% 77.1M 0s\n 6150K . 100% 3343G=3.0s\n\n2016-04-07 21:50:31 (1.99 MB/s) - 'SystemML.jar' saved [6299395/6299395]\n\n"},"text":"%sh\nwget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar"},{"config":{"colWidth":12,"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true,"editorMode":"ace/mode/scala"},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1460065827533_-970219022","id":"20160407-215027_1832456962","dateCreated":"Apr 7, 2016 9:50:27 PM","status":"ERROR","progressUpdateIntervalMs":500,"$$hashKey":"object:528","dateUpdated":"Apr 7, 2016 9:51:02 PM","dateFinished":"Apr 7, 2016 9:51:02 PM","dateStarted":"Apr 7, 2016 9:51:02 PM","result":{"code":"ERROR","type":"TEXT","msg":"Must be used before SparkInterpreter (%spark) initialized\nHint: put this paragraph before any Spark
code and restart Zeppelin/Interpreter"},"text":"%dep\nz.load(\"SystemML.jar\")"},{"config":{"colWidth":12,"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1460065862398_1266603814","id":"20160407-215102_2146717979","dateCreated":"Apr 7, 2016 9:51:02 PM","status":"READY","progressUpdateIntervalMs":500,"$$hashKey":"object:550"}],"name":"Test MLContext in Zeppelin","id":"2BF3FUMPS","angularObjects":{"2BGXRRNEQ":[],"2BGXC9DMN":[],"2BHJKJYEK":[],"2BGF74GHC":[]},"config":{"looknfeel":"default"},"info":{}}
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