| {"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,"$$hashKey":"object:395","dateUpdated":"Apr 7, 2016 9:44:43 PM","dateFinished":"Apr 7, 2016 9:44:44 PM","dateStarted":"Apr 7, 2016 9:44:43 PM","result":{"code":"SUCCESS","type":"TEXT","msg":"import org.apache.spark.sql.SQLContext\nsqlCtx: org.apache.spark.sql.SQLContext = org.apache.spark.sql.SQLContext@44252a8a\nml: org.apache.sysml.api.MLContext = org.apache.sysml.api.MLContext@f5ff11c\ndml: String = \n\"\nX = rand(rows=100, cols=10)\nsumX = sum(X)\noutMatrix = matrix(sumX, rows=1, cols=1)\nwrite(outMatrix, \" \", format=\"csv\")\n\"\nout: org.apache.sysml.api.MLOutput = org.apache.sysml.api.MLOutput@7f2976c4\noutMatrix: org.apache.spark.sql.DataFrame = [ID: double, C1: double]\n"},"text":"import org.apache.spark.sql.SQLContext\nval sqlCtx = new SQLContext(sc)\nval ml = new MLContext(sc)\nval dml = \"\"\"\nX = rand(rows=100, cols=10)\nsumX = sum(X)\noutMatrix = matrix(sumX, rows=1, cols=1)\nwrite(outMatrix, \" \", format=\"csv\")\n\"\"\"\nml.reset()\nml.registerOutput(\"outMatrix\")\nval out = ml.executeScript(dml)\nval outMatrix = out.getDF(sqlCtx, \"outMatrix\")"},{"config":{"colWidth":12,"graph":{"mode":"table","height":300,"optionOpen":false,"keys":[],"values":[],"groups":[],"scatter":{}},"enabled":true,"editorMode":"ace/mode/scala"},"settings":{"params":{},"forms":{}},"jobName":"paragraph_1460063826194_1965196735","id":"20160407-211706_2075868632","dateCreated":"Apr 7, 2016 9:17:06 PM","status":"FINISHED","progressUpdateIntervalMs":500,"$$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","progressUpdateIntervalMs":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 .......... .......... .......... .......... .......... 4% 777K 12s\n 300K .......... .......... .......... .......... .......... 5% 781K 12s\n 350K .......... .......... .......... .......... .......... 6% 780K 11s\n 400K .......... .......... .......... .......... .......... 7% 782K 11s\n 450K .......... .......... .......... .......... .......... 8% 781K 10s\n 500K .......... .......... .......... .......... .......... 8% 784K 10s\n 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.......... .......... .......... .......... .......... 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":{}} |