From 598b174c170f41816e772364bac60db8ae0bc22c Mon Sep 17 00:00:00 2001 From: rebeccadias Date: Fri, 10 Jul 2020 19:08:03 +0530 Subject: [PATCH 01/11] Research commit1 --- gulpfile.js | 1 + package-lock.json | 298 ++++++++++++++++++++++++++++++++++++---------- package.json | 4 +- 3 files changed, 241 insertions(+), 62 deletions(-) diff --git a/gulpfile.js b/gulpfile.js index aac521c95..436a8695c 100644 --- a/gulpfile.js +++ b/gulpfile.js @@ -7,6 +7,7 @@ const gutil = require('gulp-util'); const browserSync = require('browser-sync'); const sourcemaps = require('gulp-sourcemaps'); const jekyll = process.platform === 'win32' ? 'jekyll.bat':'jekyll'; +//const jekyll = process.platform === 'win64' ? 'jekyll.bat':'jekyll'; // PATHS const config = { diff --git a/package-lock.json b/package-lock.json index 691c300f1..325abd51a 100644 --- a/package-lock.json +++ b/package-lock.json @@ -84,9 +84,9 @@ "dev": true }, "ajv": { - "version": "6.12.0", - "resolved": "https://registry.npmjs.org/ajv/-/ajv-6.12.0.tgz", - "integrity": "sha512-D6gFiFA0RRLyUbvijN74DWAjXSFxWKaWP7mldxkVhyhAV3+SWA9HEJPHQ2c9soIeTFJqcSdFDGFgdqs1iUU2Hw==", + "version": "6.12.3", + "resolved": "https://registry.npmjs.org/ajv/-/ajv-6.12.3.tgz", + "integrity": "sha512-4K0cK3L1hsqk9xIb2z9vs/XU+PGJZ9PNpJRDS9YLzmNdX6jmVPfamLvTJr0aDAusnHyCHO6MjzlkAsgtqp9teA==", "dev": true, "requires": { "fast-deep-equal": "^3.1.1", @@ -444,9 +444,9 @@ "dev": true }, "aws4": { - "version": "1.9.1", - "resolved": "https://registry.npmjs.org/aws4/-/aws4-1.9.1.tgz", - "integrity": "sha512-wMHVg2EOHaMRxbzgFJ9gtjOOCrI80OHLG14rxi28XwOW8ux6IiEbRCGGGqCtdAIg4FQCbW20k9RsT4y3gJlFug==", + "version": "1.10.0", + "resolved": "https://registry.npmjs.org/aws4/-/aws4-1.10.0.tgz", + "integrity": "sha512-3YDiu347mtVtjpyV3u5kVqQLP242c06zwDOgpeRnybmXlYYsLbtTrUBUm8i8srONt+FWobl5aibnU1030PeeuA==", "dev": true }, "axios": { @@ -1392,6 +1392,11 @@ "integrity": "sha1-p2o+0YVb56ASu4rBbLgPPADcKPA=", "dev": true }, + "dom-walk": { + "version": "0.1.2", + "resolved": "https://registry.npmjs.org/dom-walk/-/dom-walk-0.1.2.tgz", + "integrity": "sha512-6QvTW9mrGeIegrFXdtQi9pk7O/nSK6lSdXW2eqUspN5LWD7UTji2Fqw5V2YLjBpHEoU9Xl/eUWNpDeZvoyOv2w==" + }, "duplexer": { "version": "0.1.1", "resolved": "https://registry.npmjs.org/duplexer/-/duplexer-0.1.1.tgz", @@ -1495,6 +1500,12 @@ "integrity": "sha1-WQxhFWsK4vTwJVcyoViyZrxWsh0=", "dev": true }, + "emoji-regex": { + "version": "7.0.3", + "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-7.0.3.tgz", + "integrity": "sha512-CwBLREIQ7LvYFB0WyRvwhq5N5qPhc6PMjD6bYggFlI5YyDgl+0vxq5VHbMOFqLg7hfWzmu8T5Z1QofhmTIhItA==", + "dev": true + }, "encodeurl": { "version": "1.0.2", "resolved": "https://registry.npmjs.org/encodeurl/-/encodeurl-1.0.2.tgz", @@ -1886,9 +1897,9 @@ } }, "fast-deep-equal": { - "version": "3.1.1", - "resolved": "https://registry.npmjs.org/fast-deep-equal/-/fast-deep-equal-3.1.1.tgz", - "integrity": "sha512-8UEa58QDLauDNfpbrX55Q9jrGHThw2ZMdOky5Gl1CDtVeJDPVrG4Jxx1N8jw2gkWaff5UUuX1KJd+9zGe2B+ZA==", + "version": "3.1.3", + "resolved": "https://registry.npmjs.org/fast-deep-equal/-/fast-deep-equal-3.1.3.tgz", + "integrity": "sha512-f3qQ9oQy9j2AhBe/H9VC91wLmKBCCU/gDOnKNAYG5hswO7BLKj09Hc5HYNz9cGI++xlpDCIgDaitVs03ATR84Q==", "dev": true }, "fast-json-stable-stringify": { @@ -2882,6 +2893,15 @@ "object.defaults": "^1.1.0" } }, + "global": { + "version": "4.4.0", + "resolved": "https://registry.npmjs.org/global/-/global-4.4.0.tgz", + "integrity": "sha512-wv/LAoHdRE3BeTGz53FAamhGlPLhlssK45usmGFThIi4XqnBmjKQ16u+RNbP7WvigRZDxUsM0J3gcQ5yicaL0w==", + "requires": { + "min-document": "^2.19.0", + "process": "^0.11.10" + } + }, "global-modules": { "version": "1.0.0", "resolved": "https://registry.npmjs.org/global-modules/-/global-modules-1.0.0.tgz", @@ -2907,13 +2927,13 @@ } }, "globule": { - "version": "1.3.1", - "resolved": "https://registry.npmjs.org/globule/-/globule-1.3.1.tgz", - "integrity": "sha512-OVyWOHgw29yosRHCHo7NncwR1hW5ew0W/UrvtwvjefVJeQ26q4/8r8FmPsSF1hJ93IgWkyv16pCTz6WblMzm/g==", + "version": "1.3.2", + "resolved": "https://registry.npmjs.org/globule/-/globule-1.3.2.tgz", + "integrity": "sha512-7IDTQTIu2xzXkT+6mlluidnWo+BypnbSoEVVQCGfzqnl5Ik8d3e1d4wycb8Rj9tWW+Z39uPWsdlquqiqPCd/pA==", "dev": true, "requires": { "glob": "~7.1.1", - "lodash": "~4.17.12", + "lodash": "~4.17.10", "minimatch": "~3.0.2" } }, @@ -2945,9 +2965,9 @@ }, "dependencies": { "gulp-cli": { - "version": "2.2.0", - "resolved": "https://registry.npmjs.org/gulp-cli/-/gulp-cli-2.2.0.tgz", - "integrity": "sha512-rGs3bVYHdyJpLqR0TUBnlcZ1O5O++Zs4bA0ajm+zr3WFCfiSLjGwoCBqFs18wzN+ZxahT9DkOK5nDf26iDsWjA==", + "version": "2.3.0", + "resolved": "https://registry.npmjs.org/gulp-cli/-/gulp-cli-2.3.0.tgz", + "integrity": "sha512-zzGBl5fHo0EKSXsHzjspp3y5CONegCm8ErO5Qh0UzFzk2y4tMvzLWhoDokADbarfZRL2pGpRp7yt6gfJX4ph7A==", "dev": true, "requires": { "ansi-colors": "^1.0.1", @@ -2958,7 +2978,7 @@ "copy-props": "^2.0.1", "fancy-log": "^1.3.2", "gulplog": "^1.0.0", - "interpret": "^1.1.0", + "interpret": "^1.4.0", "isobject": "^3.0.1", "liftoff": "^3.1.0", "matchdep": "^2.0.0", @@ -2966,14 +2986,14 @@ "pretty-hrtime": "^1.0.0", "replace-homedir": "^1.0.0", "semver-greatest-satisfied-range": "^1.1.0", - "v8flags": "^3.0.1", + "v8flags": "^3.2.0", "yargs": "^7.1.0" } }, "yargs": { - "version": "7.1.0", - "resolved": "https://registry.npmjs.org/yargs/-/yargs-7.1.0.tgz", - "integrity": "sha1-a6MY6xaWFyf10oT46gA+jWFU0Mg=", + "version": "7.1.1", + "resolved": "https://registry.npmjs.org/yargs/-/yargs-7.1.1.tgz", + "integrity": "sha512-huO4Fr1f9PmiJJdll5kwoS2e4GqzGSsMT3PPMpOwoVkOK8ckqAewMTZyA6LXVQWflleb/Z8oPBEvNsMft0XE+g==", "dev": true, "requires": { "camelcase": "^3.0.0", @@ -2988,16 +3008,17 @@ "string-width": "^1.0.2", "which-module": "^1.0.0", "y18n": "^3.2.1", - "yargs-parser": "^5.0.0" + "yargs-parser": "5.0.0-security.0" } }, "yargs-parser": { - "version": "5.0.0", - "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-5.0.0.tgz", - "integrity": "sha1-J17PDX/+Bcd+ZOfIbkzZS/DhIoo=", + "version": "5.0.0-security.0", + "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-5.0.0-security.0.tgz", + "integrity": "sha512-T69y4Ps64LNesYxeYGYPvfoMTt/7y1XtfpIslUeK4um+9Hu7hlGoRtaDLvdXb7+/tfq4opVa2HRY5xGip022rQ==", "dev": true, "requires": { - "camelcase": "^3.0.0" + "camelcase": "^3.0.0", + "object.assign": "^4.1.0" } } } @@ -3529,9 +3550,9 @@ "dev": true }, "interpret": { - "version": "1.2.0", - "resolved": "https://registry.npmjs.org/interpret/-/interpret-1.2.0.tgz", - "integrity": "sha512-mT34yGKMNceBQUoVn7iCDKDntA7SC6gycMAWzGx1z/CMCTV7b2AAtXlo3nRyHZ1FelRkQbQjprHSYGwzLtkVbw==", + "version": "1.4.0", + "resolved": "https://registry.npmjs.org/interpret/-/interpret-1.4.0.tgz", + "integrity": "sha512-agE4QfB2Lkp9uICn7BAqoscw4SZP9kTE2hxiFI3jBPmXJfdqiahTbUuKGsMoN2GtqL9AxhYioAcVvgsb1HvRbA==", "dev": true }, "invert-kv": { @@ -3789,9 +3810,9 @@ "dev": true }, "js-base64": { - "version": "2.5.2", - "resolved": "https://registry.npmjs.org/js-base64/-/js-base64-2.5.2.tgz", - 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"integrity": "sha1-J17PDX/+Bcd+ZOfIbkzZS/DhIoo=", + "version": "13.1.2", + "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-13.1.2.tgz", + "integrity": "sha512-3lbsNRf/j+A4QuSZfDRA7HRSfWrzO0YjqTJd5kjAq37Zep1CEgaYmrH9Q3GwPiB9cHyd1Y1UwggGhJGoxipbzg==", "dev": true, "requires": { - "camelcase": "^3.0.0" + "camelcase": "^5.0.0", + "decamelize": "^1.2.0" } } } @@ -6692,9 +6868,9 @@ "dev": true }, "v8flags": { - "version": "3.1.3", - "resolved": "https://registry.npmjs.org/v8flags/-/v8flags-3.1.3.tgz", - "integrity": "sha512-amh9CCg3ZxkzQ48Mhcb8iX7xpAfYJgePHxWMQCBWECpOSqJUXgY26ncA61UTV0BkPqfhcy6mzwCIoP4ygxpW8w==", + "version": "3.2.0", + "resolved": "https://registry.npmjs.org/v8flags/-/v8flags-3.2.0.tgz", + "integrity": "sha512-mH8etigqMfiGWdeXpaaqGfs6BndypxusHHcv2qSHyZkGEznCd/qAXCWWRzeowtL54147cktFOC4P5y+kl8d8Jg==", "dev": true, "requires": { "homedir-polyfill": "^1.0.1" diff --git a/package.json b/package.json index a7ecb9007..89300c171 100644 --- a/package.json +++ b/package.json @@ -3,7 +3,9 @@ "version": "0.1.0", "description": "", "main": "index.js", - "dependencies": {}, + "dependencies": { + "global": "^4.4.0" + }, "devDependencies": { "browser-sync": "^2.26.7", "gulp": "^4.0.2", From 813c3585d3a09d287c6ca95ee6302e9f3b5a5eb3 Mon Sep 17 00:00:00 2001 From: rebeccadias Date: Fri, 10 Jul 2020 19:14:29 +0530 Subject: [PATCH 02/11] Create research.html Basic template for research.html --- _src/research.html | 227 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 227 insertions(+) create mode 100644 _src/research.html diff --git a/_src/research.html b/_src/research.html new file mode 100644 index 000000000..10c552171 --- /dev/null +++ b/_src/research.html @@ -0,0 +1,227 @@ + + + + + + + + + + Research + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

Apache SystemML™

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+ + + + + + + +
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+
+

Apache SystemML™ Research

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+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+ Apache Incubator +
+ +
+ Copyright © 2015 + The Apache Software Foundation. + Licensed under the Apache License, Version 2.0. +
+ Apache and the Apache Feather logo are trademarks of The Apache Software Foundation. +
+
+
+ + +
+ + + + From 6085f1ad31ffd4997d6b875b3fba55d80b78831a Mon Sep 17 00:00:00 2001 From: rebeccadias Date: Fri, 10 Jul 2020 11:59:08 +0530 Subject: [PATCH 03/11] Edit in research.html file Added the code that renders the navbar and footer template --- _src/research.html | 247 ++++++--------------------------------------- _src/roadmap.html | 2 +- 2 files changed, 31 insertions(+), 218 deletions(-) diff --git a/_src/research.html b/_src/research.html index 10c552171..bb7e4c40c 100644 --- a/_src/research.html +++ b/_src/research.html @@ -1,227 +1,40 @@ +--- +layout: page +title: Research +description: Apache SystemML Roadmap +group: nav-right +--- - - - - - - - - - Research - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-

Apache SystemML™

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Apache SystemML™ Research

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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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- Apache Incubator -
- -
- Copyright © 2015 - The Apache Software Foundation. - Licensed under the Apache License, Version 2.0. -
- Apache and the Apache Feather logo are trademarks of The Apache Software Foundation. -
+

{{ site.data.project.name }} Research

- -
- - - - +
+
+

Some Content here

+
+
\ No newline at end of file diff --git a/_src/roadmap.html b/_src/roadmap.html index f247051cf..d9da438ce 100644 --- a/_src/roadmap.html +++ b/_src/roadmap.html @@ -139,7 +139,7 @@

Prior Releases

  • Additional Layers in NN library: average pooling, upsampling, low-rank fully connected
  • New Capabilities/Features such as dense matrix blocks >16GB, additional ParFor result aggregation operations, UDFs callable in expressions, zero rows/columns matrices, matrix-matrix multiplication over compressed matrices
  • Extended Caffe2DML and Keras2DML APIs
    • -
  • Compiler & Runtime enhancements +
  • Compiler & Runtime enhancements
  • Performance improvements
    • From abbbd3c1b1cecf7025df5f088fe34ce933c10a2f Mon Sep 17 00:00:00 2001 From: rebeccadias Date: Fri, 10 Jul 2020 18:33:39 +0530 Subject: [PATCH 04/11] Adding contents to research.html page --- _src/research.html | 162 +++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 157 insertions(+), 5 deletions(-) diff --git a/_src/research.html b/_src/research.html index bb7e4c40c..27599e310 100644 --- a/_src/research.html +++ b/_src/research.html @@ -1,7 +1,7 @@ --- layout: page title: Research -description: Apache SystemML Roadmap +description: Apache SystemML Research group: nav-right --- + + +
    @@ -34,7 +168,25 @@

    {{ site.data.project.name }} Research

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    Some Content here

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    \ No newline at end of file +
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    2017

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    Lorem ipsum dolor sit amet, quo ei simul congue exerci, ad nec admodum perfecto mnesarchum, vim ea mazim fierent detracto. Ea quis iuvaret expetendis his, te elit voluptua dignissim per, habeo iusto primis ea eam.

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    2016

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    Lorem ipsum dolor sit amet, quo ei simul congue exerci, ad nec admodum perfecto mnesarchum, vim ea mazim fierent detracto. Ea quis iuvaret expetendis his, te elit voluptua dignissim per, habeo iusto primis ea eam.

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    \ No newline at end of file From 0ea0d803e956c605bba62b4571590a2c1cbe6dd2 Mon Sep 17 00:00:00 2001 From: rebeccadias Date: Sat, 11 Jul 2020 00:33:28 +0530 Subject: [PATCH 05/11] Adding Content to research.html --- _src/research.html | 254 ++++++++++++++++++--------------------------- 1 file changed, 102 insertions(+), 152 deletions(-) diff --git a/_src/research.html b/_src/research.html index 27599e310..1d7ceb2bb 100644 --- a/_src/research.html +++ b/_src/research.html @@ -24,139 +24,8 @@ --> - -
    @@ -166,27 +35,108 @@

    {{ site.data.project.name }} Research

    +
    + + +
    +
    +
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    2018

    +

    Compressed Linear Algebra for Large-Scale Machine Learning + Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald + VLDB Journal, 2018 + [paper (author version)]

    +

    On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML + Matthias Boehm, Berthold Reinwald, Dylan Hutchison, Alexandre V. Evfimievski, Prithviraj Sen + CoRR,abs/1801.00829, 2018 + [paper (preprint)]

    +
    +
    +
    +
    +

    2017

    +

    Scaling Machine Learning via Compressed Linear Algebra + Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald + SIGMOD Record 46(1), 2017 + [paper]

    +

    SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning + Tarek Elgamal, Shangyu Luo, Matthias Boehm, Alexandre V. Evfimievski, Shirish Tatikonda, Berthold Reinwald, Prithviraj Sen: + CIDR, 2017 + [paper, slides]

    +
    +
    +
    +
    +

    2016

    +

    Compressed Linear Algebra for Large-Scale Machine Learning + Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald + PVLDB 9(12), 2016

    +

    Declarative Machine Learning - A Classification of Basic Properties and Types + Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare, Berthold Reinwald + CoRR,abs/1605.05826, 2016

    +

    SystemML: Declarative Machine Learning on Spark (Industrial) + Matthias Boehm, Michael W. Dusenberry, Deron Eriksson, Alexandre V. Evfimievski, Faraz Makari Manshadi, Niketan Pansare, Berthold Reinwald, Frederick R. Reiss, Prithviraj Sen, Arvind C. Surve, Shirish Tatikonda + PVLDB 9(13), 2016

    +
    +
    +
    +
    +

    2015

    +

    On Optimizing Machine Learning Workloads via Kernel Fusion + Arash Ashari, Shirish Tatikonda, Matthias Boehm, Berthold Reinwald, Keith Campbell, John Keenleyside, P. Sadayappan + PPoPP, 2015

    +

    Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs + Matthias Boehm + CoRR,abs/1503.06384, 2015

    +

    Resource Elasticity for Large-Scale Machine Learning + Botong Huang, Matthias Boehm, Yuanyuan Tian, Berthold Reinwald, Shirish Tatikonda, Frederick R. Reiss + SIGMOD, 2015

    +
    +
    +
    +
    +

    2014

    +

    Large Scale Discriminative Metric Learning + Peter D. Kirchner, Matthias Boehm, Berthold Reinwald, Daby M. Sow, Michael Schmidt, Deepak S. Turaga, Alain Biem + ParLearning, 2014

    +

    Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML + Matthias Boehm, Shirish Tatikonda, Berthold Reinwald, Prithviraj Sen, Yuanyuan Tian, Douglas Burdick, Shivakumar Vaithyanathan + PVLDB 7(7), 2014

    +

    SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs + Matthias Boehm, Douglas R. Burdick, Alexandre V. Evfimievski, Berthold Reinwald, Frederick R. Reiss, Prithviraj Sen, Shirish Tatikonda, Yuanyuan Tian + IEEE Data Eng. Bull. 37(3), 2014

    +
    +
    +
    +
    +

    2013

    +

    Compiling Machine Learning Algorithms with SystemML (Poster) + Matthias Boehm, Douglas Burdick, Alexandre V. Evfimievski, Berthold Reinwald, Prithviraj Sen, Shirish Tatikonda, Yuanyuan Tian + SOCC, 2013

    +
    +
    +
    +
    +

    2012

    +

    Scalable and Numerically Stable Descriptive Statistics in SystemML + Y. Tian, S. Tatikonda, B. Reinwald + Data Engineering (ICDE), 2012 IEEE 28th International Conference on, pp. 1351--1359

    +
    +
    +
    +
    +

    2011

    +

    SystemML: Declarative machine learning on MapReduce + A. Ghoting, R. Krishnamurthy, E. Pednault, B. Reinwald, V. Sindhwani, S. Tatikonda, Y. Tian, S. Vaithyanathan + Data Engineering (ICDE), 2011 IEEE 27th International Conference on, pp. 231--242

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    2017

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    2016

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    -
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    + + + + -
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    \ No newline at end of file +
    \ No newline at end of file From 008daa7432008759f18cd55071b9010c116e4a6f Mon Sep 17 00:00:00 2001 From: rebeccadias Date: Sat, 11 Jul 2020 12:39:09 +0530 Subject: [PATCH 06/11] Adding Styles to research.html --- _src/research.html | 172 +++++++++++++++++++++++++++++++++++++-------- 1 file changed, 144 insertions(+), 28 deletions(-) diff --git a/_src/research.html b/_src/research.html index 1d7ceb2bb..4b0cf96cb 100644 --- a/_src/research.html +++ b/_src/research.html @@ -24,8 +24,131 @@ --> - - +
    @@ -35,12 +158,11 @@

    {{ site.data.project.name }} Research

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    2018

    Compressed Linear Algebra for Large-Scale Machine Learning Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald @@ -52,8 +174,8 @@

    2018

    [paper (preprint)]

    -
    -
    +
    +

    2017

    Scaling Machine Learning via Compressed Linear Algebra Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald @@ -65,8 +187,8 @@

    2017

    [paper, slides]

    -
    -
    +
    +

    2016

    Compressed Linear Algebra for Large-Scale Machine Learning Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald @@ -79,8 +201,8 @@

    2016

    PVLDB 9(13), 2016

    -
    -
    +
    +

    2015

    On Optimizing Machine Learning Workloads via Kernel Fusion Arash Ashari, Shirish Tatikonda, Matthias Boehm, Berthold Reinwald, Keith Campbell, John Keenleyside, P. Sadayappan @@ -93,8 +215,8 @@

    2015

    SIGMOD, 2015

    -
    -
    +
    +

    2014

    Large Scale Discriminative Metric Learning Peter D. Kirchner, Matthias Boehm, Berthold Reinwald, Daby M. Sow, Michael Schmidt, Deepak S. Turaga, Alain Biem @@ -107,24 +229,24 @@

    2014

    IEEE Data Eng. Bull. 37(3), 2014

    -
    -
    +
    +

    2013

    Compiling Machine Learning Algorithms with SystemML (Poster) Matthias Boehm, Douglas Burdick, Alexandre V. Evfimievski, Berthold Reinwald, Prithviraj Sen, Shirish Tatikonda, Yuanyuan Tian SOCC, 2013

    -
    -
    +
    +

    2012

    Scalable and Numerically Stable Descriptive Statistics in SystemML Y. Tian, S. Tatikonda, B. Reinwald Data Engineering (ICDE), 2012 IEEE 28th International Conference on, pp. 1351--1359

    -
    -
    +
    +

    2011

    SystemML: Declarative machine learning on MapReduce A. Ghoting, R. Krishnamurthy, E. Pednault, B. Reinwald, V. Sindhwani, S. Tatikonda, Y. Tian, S. Vaithyanathan @@ -132,11 +254,5 @@

    2011

    +
    --> - - - - - - -
    \ No newline at end of file From cd12adeb744fd6896e85b03343b1c3ae26856065 Mon Sep 17 00:00:00 2001 From: rebeccadias Date: Mon, 13 Jul 2020 15:34:00 +0530 Subject: [PATCH 07/11] Update research.html --- _src/research.html | 218 ++++++++++----------------------------------- 1 file changed, 47 insertions(+), 171 deletions(-) diff --git a/_src/research.html b/_src/research.html index 4b0cf96cb..b727e4193 100644 --- a/_src/research.html +++ b/_src/research.html @@ -23,132 +23,7 @@ {% endcomment %} --> - - +
    @@ -160,49 +35,50 @@

    {{ site.data.project.name }} Research

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    2017

    -

    Scaling Machine Learning via Compressed Linear Algebra - Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald - SIGMOD Record 46(1), 2017 - [paper]

    -

    SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning - Tarek Elgamal, Shangyu Luo, Matthias Boehm, Alexandre V. Evfimievski, Shirish Tatikonda, Berthold Reinwald, Prithviraj Sen: - CIDR, 2017 - [paper, slides]

    +

    + Scaling Machine Learning via Compressed Linear Algebra Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald + SIGMOD Record 46(1), 2017 +

    +

    SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning + Tarek Elgamal, Shangyu Luo, Matthias Boehm, Alexandre V. Evfimievski, Shirish Tatikonda, Berthold Reinwald, Prithviraj Sen: + CIDR, 2017 +

    -
    -
    -
    + + +

    2016

    Compressed Linear Algebra for Large-Scale Machine Learning - Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald - PVLDB 9(12), 2016

    -

    Declarative Machine Learning - A Classification of Basic Properties and Types - Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare, Berthold Reinwald - CoRR,abs/1605.05826, 2016

    -

    SystemML: Declarative Machine Learning on Spark (Industrial) - Matthias Boehm, Michael W. Dusenberry, Deron Eriksson, Alexandre V. Evfimievski, Faraz Makari Manshadi, Niketan Pansare, Berthold Reinwald, Frederick R. Reiss, Prithviraj Sen, Arvind C. Surve, Shirish Tatikonda - PVLDB 9(13), 2016

    + Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald + PVLDB 9(12), 2016

    +

    Declarative Machine Learning - A Classification of Basic Properties and Types + Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare, Berthold Reinwald + CoRR,abs/1605.05826, 2016

    +

    SystemML: Declarative Machine Learning on Spark (Industrial) + Matthias Boehm, Michael W. Dusenberry, Deron Eriksson, Alexandre V. Evfimievski, Faraz Makari Manshadi, Niketan Pansare, Berthold Reinwald, Frederick R. Reiss, Prithviraj Sen, Arvind C. Surve, Shirish Tatikonda + PVLDB 9(13), 2016

    -
    -
    -
    + + +

    2015

    On Optimizing Machine Learning Workloads via Kernel Fusion Arash Ashari, Shirish Tatikonda, Matthias Boehm, Berthold Reinwald, Keith Campbell, John Keenleyside, P. Sadayappan @@ -214,9 +90,9 @@

    2015

    Botong Huang, Matthias Boehm, Yuanyuan Tian, Berthold Reinwald, Shirish Tatikonda, Frederick R. Reiss SIGMOD, 2015

    -
    -
    -
    + + +

    2014

    Large Scale Discriminative Metric Learning Peter D. Kirchner, Matthias Boehm, Berthold Reinwald, Daby M. Sow, Michael Schmidt, Deepak S. Turaga, Alain Biem @@ -228,31 +104,31 @@

    2014

    Matthias Boehm, Douglas R. Burdick, Alexandre V. Evfimievski, Berthold Reinwald, Frederick R. Reiss, Prithviraj Sen, Shirish Tatikonda, Yuanyuan Tian IEEE Data Eng. Bull. 37(3), 2014

    -
    -
    -
    + + +

    2013

    Compiling Machine Learning Algorithms with SystemML (Poster) Matthias Boehm, Douglas Burdick, Alexandre V. Evfimievski, Berthold Reinwald, Prithviraj Sen, Shirish Tatikonda, Yuanyuan Tian SOCC, 2013

    -
    -
    -
    +> + +

    2012

    Scalable and Numerically Stable Descriptive Statistics in SystemML Y. Tian, S. Tatikonda, B. Reinwald Data Engineering (ICDE), 2012 IEEE 28th International Conference on, pp. 1351--1359

    -
    -
    -
    + + +

    2011

    SystemML: Declarative machine learning on MapReduce A. Ghoting, R. Krishnamurthy, E. Pednault, B. Reinwald, V. Sindhwani, S. Tatikonda, Y. Tian, S. Vaithyanathan Data Engineering (ICDE), 2011 IEEE 27th International Conference on, pp. 231--242

    -
    +
    --> From 36749bda852c11e49f2b35e8fa1317938447da31 Mon Sep 17 00:00:00 2001 From: rebeccadias Date: Tue, 14 Jul 2020 15:17:02 +0530 Subject: [PATCH 08/11] Update #2 research.html --- _src/research.html | 123 ++++++++++++++++++++++++++------------------- 1 file changed, 70 insertions(+), 53 deletions(-) diff --git a/_src/research.html b/_src/research.html index b727e4193..31188cf57 100644 --- a/_src/research.html +++ b/_src/research.html @@ -36,9 +36,11 @@

    {{ site.data.project.name }} Research

    - - + + -
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    2016

    -

    Compressed Linear Algebra for Large-Scale Machine Learning + +

  • +

    2016

    +

    Compressed Linear Algebra for Large-Scale Machine Learning Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald PVLDB 9(12), 2016

    Declarative Machine Learning - A Classification of Basic Properties and Types @@ -75,60 +81,71 @@

    2016

    SystemML: Declarative Machine Learning on Spark (Industrial) Matthias Boehm, Michael W. Dusenberry, Deron Eriksson, Alexandre V. Evfimievski, Faraz Makari Manshadi, Niketan Pansare, Berthold Reinwald, Frederick R. Reiss, Prithviraj Sen, Arvind C. Surve, Shirish Tatikonda PVLDB 9(13), 2016

    -
    • + + -
    -

    2015

    -

    On Optimizing Machine Learning Workloads via Kernel Fusion - Arash Ashari, Shirish Tatikonda, Matthias Boehm, Berthold Reinwald, Keith Campbell, John Keenleyside, P. Sadayappan - PPoPP, 2015

    -

    Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs - Matthias Boehm - CoRR,abs/1503.06384, 2015

    -

    Resource Elasticity for Large-Scale Machine Learning - Botong Huang, Matthias Boehm, Yuanyuan Tian, Berthold Reinwald, Shirish Tatikonda, Frederick R. Reiss - SIGMOD, 2015

    -
    + +
  • +

    2015

    +

    On Optimizing Machine Learning Workloads via Kernel Fusion +Arash Ashari, Shirish Tatikonda, Matthias Boehm, Berthold Reinwald, Keith Campbell, John Keenleyside, P. Sadayappan +PPoPP, 2015

    +

    Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs +Matthias Boehm +CoRR,abs/1503.06384, 2015

    +

    Resource Elasticity for Large-Scale Machine Learning +Botong Huang, Matthias Boehm, Yuanyuan Tian, Berthold Reinwald, Shirish Tatikonda, Frederick R. Reiss +SIGMOD, 2015

    + +
    • -
    + +

  • 2014

    -

    Large Scale Discriminative Metric Learning - Peter D. Kirchner, Matthias Boehm, Berthold Reinwald, Daby M. Sow, Michael Schmidt, Deepak S. Turaga, Alain Biem - ParLearning, 2014

    -

    Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML - Matthias Boehm, Shirish Tatikonda, Berthold Reinwald, Prithviraj Sen, Yuanyuan Tian, Douglas Burdick, Shivakumar Vaithyanathan - PVLDB 7(7), 2014

    -

    SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs - Matthias Boehm, Douglas R. Burdick, Alexandre V. Evfimievski, Berthold Reinwald, Frederick R. Reiss, Prithviraj Sen, Shirish Tatikonda, Yuanyuan Tian - IEEE Data Eng. Bull. 37(3), 2014

    -
    • +

    Large Scale Discriminative Metric Learning +Peter D. Kirchner, Matthias Boehm, Berthold Reinwald, Daby M. Sow, Michael Schmidt, Deepak S. Turaga, Alain Biem +ParLearning, 2014

    +

    Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML +Matthias Boehm, Shirish Tatikonda, Berthold Reinwald, Prithviraj Sen, Yuanyuan Tian, Douglas Burdick, Shivakumar Vaithyanathan +PVLDB 7(7), 2014

    +

    SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs +Matthias Boehm, Douglas R. Burdick, Alexandre V. Evfimievski, Berthold Reinwald, Frederick R. Reiss, Prithviraj Sen, Shirish Tatikonda, Yuanyuan Tian +IEEE Data Eng. Bull. 37(3), 2014

    + + -
    + +

  • 2013

    -

    Compiling Machine Learning Algorithms with SystemML (Poster) - Matthias Boehm, Douglas Burdick, Alexandre V. Evfimievski, Berthold Reinwald, Prithviraj Sen, Shirish Tatikonda, Yuanyuan Tian - SOCC, 2013

    -
    • -> +

    Compiling Machine Learning Algorithms with SystemML (Poster) +Matthias Boehm, Douglas Burdick, Alexandre V. Evfimievski, Berthold Reinwald, Prithviraj Sen, Shirish Tatikonda, Yuanyuan Tian +SOCC, 2013

    + + + -
    -

    2012

    -

    Scalable and Numerically Stable Descriptive Statistics in SystemML - Y. Tian, S. Tatikonda, B. Reinwald - Data Engineering (ICDE), 2012 IEEE 28th International Conference on, pp. 1351--1359

    -
    - -
    -

    2011

    -

    SystemML: Declarative machine learning on MapReduce - A. Ghoting, R. Krishnamurthy, E. Pednault, B. Reinwald, V. Sindhwani, S. Tatikonda, Y. Tian, S. Vaithyanathan - Data Engineering (ICDE), 2011 IEEE 27th International Conference on, pp. 231--242

    -
    +
  • +

    2012

    +

    Scalable and Numerically Stable Descriptive Statistics in SystemML + Y. Tian, S. Tatikonda, B. Reinwald + Data Engineering (ICDE), 2012 IEEE 28th International Conference on, pp. 1351--1359

     +
    • + + + +
  • +

    2011

    +

    SystemML: Declarative machine learning on MapReduce + A. Ghoting, R. Krishnamurthy, E. Pednault, B. Reinwald, V. Sindhwani, S. Tatikonda, Y. Tian, S. Vaithyanathan + Data Engineering (ICDE), 2011 IEEE 27th International Conference on, pp. 231--242

     +
    • + +
    --> From 0ac19bba6f6e068c299624df3b61694544d20501 Mon Sep 17 00:00:00 2001 From: rebeccadias Date: Wed, 15 Jul 2020 12:47:08 +0530 Subject: [PATCH 09/11] Create notebooks.html --- _src/notebooks.html | 13348 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 13348 insertions(+) create mode 100644 _src/notebooks.html diff --git a/_src/notebooks.html b/_src/notebooks.html new file mode 100644 index 000000000..fa0bfec51 --- /dev/null +++ b/_src/notebooks.html @@ -0,0 +1,13348 @@ + + + + +simple + + + + + + + + + + + + + + + + + + + + + + +
    +
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    + + + +
    +View on TensorFlow.org +Run in Google Colab +View source on GitHub
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    Copyright © 2020 Google Inc.
    + +
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    In [ ]:
    +
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    +
    #@title Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
    + +
    +
    +
    + +
    +
    +
    +
    +

    Preprocess data with TensorFlow Transform

    The Feature Engineering Component of TensorFlow Extended (TFX)

    +

    This example colab notebook provides a very simple example of how TensorFlow Transform (tf.Transform) can be used to preprocess data using exactly the same code for both training a model and serving inferences in production.

    +

    TensorFlow Transform is a library for preprocessing input data for TensorFlow, including creating features that require a full pass over the training dataset. For example, using TensorFlow Transform you could:

    +
      +
    • Normalize an input value by using the mean and standard deviation
      • +
    • Convert strings to integers by generating a vocabulary over all of the input values
      • +
    • Convert floats to integers by assigning them to buckets, based on the observed data distribution
      • +
    +

    TensorFlow has built-in support for manipulations on a single example or a batch of examples. tf.Transform extends these capabilities to support full passes over the entire training dataset.

    +

    The output of tf.Transform is exported as a TensorFlow graph which you can use for both training and serving. Using the same graph for both training and serving can prevent skew, since the same transformations are applied in both stages.

    + +
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    Python check and imports

    First, we'll make sure that we're using Python 3. Then, we'll go ahead and install and import the stuff we need.

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    In [ ]:
    +
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    import sys
+
+# Confirm that we're using Python 3
+assert sys.version_info.major is 3, 'Oops, not running Python 3. Use Runtime > Change runtime type'
+
    + +
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    In [ ]:
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    import argparse
+import os
+import pprint
+import tempfile
+import urllib.request
+import zipfile
+
+print("Installing dependencies for Colab environment")
+!pip install -Uq grpcio==1.26.0
+
+import tensorflow as tf
+
+print('Installing Apache Beam')
+!pip install -Uq apache_beam==2.16.0
+import apache_beam as beam
+
+print('Installing TensorFlow Transform')
+!pip install -Uq tensorflow-transform==0.15.0
+import tensorflow_transform as tft
+
+import apache_beam.io.iobase
+import tensorflow_transform.beam as tft_beam
+from tensorflow_transform.tf_metadata import dataset_metadata
+from tensorflow_transform.tf_metadata import dataset_schema
+
    + +
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    + +
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    Data: Create some dummy data

    We'll create some simple dummy data for our simple example:

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      +
    • raw_data is the initial raw data that we're going to preprocess
      • +
    • raw_data_metadata contains the schema that tells us the types of each of the columns in raw_data. In this case, it's very simple.
      • +
    + +
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    In [ ]:
    +
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    raw_data = [
+      {'x': 1, 'y': 1, 's': 'hello'},
+      {'x': 2, 'y': 2, 's': 'world'},
+      {'x': 3, 'y': 3, 's': 'hello'}
+  ]
+
+raw_data_metadata = dataset_metadata.DatasetMetadata(
+    dataset_schema.from_feature_spec({
+        'y': tf.io.FixedLenFeature([], tf.float32),
+        'x': tf.io.FixedLenFeature([], tf.float32),
+        's': tf.io.FixedLenFeature([], tf.string),
+    }))
+
    + +
    +
    +
    + +
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    +
    +
    +

    Transform: Create a preprocessing function

    The preprocessing function is the most important concept of tf.Transform. A preprocessing function is where the transformation of the dataset really happens. It accepts and returns a dictionary of tensors, where a tensor means a Tensor or SparseTensor. There are two main groups of API calls that typically form the heart of a preprocessing function:

    +
      +
    1. TensorFlow Ops: Any function that accepts and returns tensors, which usually means TensorFlow ops. These add TensorFlow operations to the graph that transforms raw data into transformed data one feature vector at a time. These will run for every example, during both training and serving.
      2. +
    2. TensorFlow Transform Analyzers: Any of the analyzers provided by tf.Transform. Analyzers also accept and return tensors, but unlike TensorFlow ops they only run once, during training, and typically make a full pass over the entire training dataset. They create tensor constants, which are added to your graph. For example, tft.min computes the minimum of a tensor over the training dataset. tf.Transform provides a fixed set of analyzers, but this will be extended in future versions.
      3. +
    +

    Caution: When you apply your preprocessing function to serving inferences, the constants that were created by analyzers during training do not change. If your data has trend or seasonality components, plan accordingly.

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    In [ ]:
    +
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    +
    def preprocessing_fn(inputs):
+    """Preprocess input columns into transformed columns."""
+    x = inputs['x']
+    y = inputs['y']
+    s = inputs['s']
+    x_centered = x - tft.mean(x)
+    y_normalized = tft.scale_to_0_1(y)
+    s_integerized = tft.compute_and_apply_vocabulary(s)
+    x_centered_times_y_normalized = (x_centered * y_normalized)
+    return {
+        'x_centered': x_centered,
+        'y_normalized': y_normalized,
+        's_integerized': s_integerized,
+        'x_centered_times_y_normalized': x_centered_times_y_normalized,
+    }
+
    + +
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    + +
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    +

    Putting it all together

    Now we're ready to transform our data. We'll use Apache Beam with a direct runner, and supply three inputs:

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      +
    1. raw_data - The raw input data that we created above
      2. +
    2. raw_data_metadata - The schema for the raw data
      3. +
    3. preprocessing_fn - The function that we created to do our transformation
      4. +
    + +
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    +
    In [ ]:
    +
    +
    +
    def main():
+  # Ignore the warnings
+  with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
+    transformed_dataset, transform_fn = (  # pylint: disable=unused-variable
+        (raw_data, raw_data_metadata) | tft_beam.AnalyzeAndTransformDataset(
+            preprocessing_fn))
+
+  transformed_data, transformed_metadata = transformed_dataset  # pylint: disable=unused-variable
+
+  print('\nRaw data:\n{}\n'.format(pprint.pformat(raw_data)))
+  print('Transformed data:\n{}'.format(pprint.pformat(transformed_data)))
+
+if __name__ == '__main__':
+  main()
+
    + +
    +
    +
    + +
    +
    +
    +
    +

    Is this the right answer?

    Previously, we used tf.Transform to do this:

    + +
    x_centered = x - tft.mean(x)
+y_normalized = tft.scale_to_0_1(y)
+s_integerized = tft.compute_and_apply_vocabulary(s)
+x_centered_times_y_normalized = (x_centered * y_normalized)
    +

    x_centered

    With input of [1, 2, 3] the mean of x is 2, and we subtract it from x to center our x values at 0. So our result of [-1.0, 0.0, 1.0] is correct.

    +

    y_normalized

    We wanted to scale our y values between 0 and 1. Our input was [1, 2, 3] so our result of [0.0, 0.5, 1.0] is correct.

    +

    s_integerized

    We wanted to map our strings to indexes in a vocabulary, and there were only 2 words in our vocabulary ("hello" and "world"). So with input of ["hello", "world", "hello"] our result of [0, 1, 0] is correct.

    +

    x_centered_times_y_normalized

    We wanted to create a new feature by crossing x_centered and y_normalized using multiplication. Note that this multiplies the results, not the original values, and our new result of [-0.0, 0.0, 1.0] is correct.

    + +
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    +
    + + + + + + From 1850e878ab4b806131c856ad88b3cc6e29c1bfa7 Mon Sep 17 00:00:00 2001 From: rebeccadias Date: Wed, 15 Jul 2020 16:09:09 +0530 Subject: [PATCH 10/11] Update notebooks.html --- _src/notebooks.html | 58 ++++++++++++++++++++++++++------------------- 1 file changed, 34 insertions(+), 24 deletions(-) diff --git a/_src/notebooks.html b/_src/notebooks.html index fa0bfec51..e14da4382 100644 --- a/_src/notebooks.html +++ b/_src/notebooks.html @@ -1,26 +1,30 @@ - - - +--- +layout: page +title: Notebooks +description: Apache SystemML Notebooks +group: nav-right +--- + + +