[jira] [Updated] (SYSTEMML-853) Create Python wrapper API for new MLContext API
[ https://issues.apache.org/jira/browse/SYSTEMML-853?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Deron Eriksson updated SYSTEMML-853: Assignee: Manoj Kumar (was: Deron Eriksson) > Create Python wrapper API for new MLContext API > --- > > Key: SYSTEMML-853 > URL: https://issues.apache.org/jira/browse/SYSTEMML-853 > Project: SystemML > Issue Type: Task > Components: APIs >Reporter: Deron Eriksson >Assignee: Manoj Kumar > > The new MLContext API needs a Python wrapper API to access MLContext from > Python. This will allow Jupyter notebooks to use the new API. > The old Python API is located at > src/main/java/org/apache/sysml/api/python/SystemML.py > The new MLContext API is located in the org.apache.sysml.api.mlcontext > package. > Examples of using the new MLContext API from Spark Shell can be found at > http://apache.github.io/incubator-systemml/spark-mlcontext-programming-guide.html -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Closed] (SYSTEMML-856) Add Gemfile to docs for Bundler
[ https://issues.apache.org/jira/browse/SYSTEMML-856?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Deron Eriksson closed SYSTEMML-856. --- > Add Gemfile to docs for Bundler > --- > > Key: SYSTEMML-856 > URL: https://issues.apache.org/jira/browse/SYSTEMML-856 > Project: SystemML > Issue Type: Task > Components: Documentation >Reporter: Deron Eriksson >Assignee: Mike Dusenberry >Priority: Minor > Fix For: SystemML 0.11 > > > GitHub describes setting up a documentation site locally using Bundler. See > https://help.github.com/articles/setting-up-your-github-pages-site-locally-with-jekyll/ > SystemML docs also describe generating documentation site using Bundler. See > http://apache.github.io/incubator-systemml/contributing-to-systemml.html > Bundler would like a Gemfile in the docs folder. -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Resolved] (SYSTEMML-856) Add Gemfile to docs for Bundler
[ https://issues.apache.org/jira/browse/SYSTEMML-856?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Deron Eriksson resolved SYSTEMML-856. - Resolution: Fixed Fix Version/s: SystemML 0.11 Fixed by commit https://github.com/apache/incubator-systemml/commit/24d57ad2a170d2f9ee34bca7ee896c728e555f1c > Add Gemfile to docs for Bundler > --- > > Key: SYSTEMML-856 > URL: https://issues.apache.org/jira/browse/SYSTEMML-856 > Project: SystemML > Issue Type: Task > Components: Documentation >Reporter: Deron Eriksson >Assignee: Mike Dusenberry >Priority: Minor > Fix For: SystemML 0.11 > > > GitHub describes setting up a documentation site locally using Bundler. See > https://help.github.com/articles/setting-up-your-github-pages-site-locally-with-jekyll/ > SystemML docs also describe generating documentation site using Bundler. See > http://apache.github.io/incubator-systemml/contributing-to-systemml.html > Bundler would like a Gemfile in the docs folder. -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Updated] (SYSTEMML-856) Add Gemfile to docs for Bundler
[ https://issues.apache.org/jira/browse/SYSTEMML-856?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Deron Eriksson updated SYSTEMML-856: Assignee: Mike Dusenberry (was: Deron Eriksson) > Add Gemfile to docs for Bundler > --- > > Key: SYSTEMML-856 > URL: https://issues.apache.org/jira/browse/SYSTEMML-856 > Project: SystemML > Issue Type: Task > Components: Documentation >Reporter: Deron Eriksson >Assignee: Mike Dusenberry >Priority: Minor > > GitHub describes setting up a documentation site locally using Bundler. See > https://help.github.com/articles/setting-up-your-github-pages-site-locally-with-jekyll/ > SystemML docs also describe generating documentation site using Bundler. See > http://apache.github.io/incubator-systemml/contributing-to-systemml.html > Bundler would like a Gemfile in the docs folder. -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Created] (SYSTEMML-856) Add Gemfile to docs for Bundler
Deron Eriksson created SYSTEMML-856: --- Summary: Add Gemfile to docs for Bundler Key: SYSTEMML-856 URL: https://issues.apache.org/jira/browse/SYSTEMML-856 Project: SystemML Issue Type: Task Components: Documentation Reporter: Deron Eriksson Assignee: Deron Eriksson Priority: Minor GitHub describes setting up a documentation site locally using Bundler. See https://help.github.com/articles/setting-up-your-github-pages-site-locally-with-jekyll/ SystemML docs also describe generating documentation site using Bundler. See http://apache.github.io/incubator-systemml/contributing-to-systemml.html Bundler would like a Gemfile in the docs folder. -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Closed] (SYSTEMML-854) Change Python MLContext to support new MLContext
[ https://issues.apache.org/jira/browse/SYSTEMML-854?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Deron Eriksson closed SYSTEMML-854. --- > Change Python MLContext to support new MLContext > > > Key: SYSTEMML-854 > URL: https://issues.apache.org/jira/browse/SYSTEMML-854 > Project: SystemML > Issue Type: Task >Reporter: Niketan Pansare > Fix For: SystemML 0.11 > > -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Resolved] (SYSTEMML-854) Change Python MLContext to support new MLContext
[ https://issues.apache.org/jira/browse/SYSTEMML-854?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Deron Eriksson resolved SYSTEMML-854. - Resolution: Duplicate Fix Version/s: SystemML 0.11 Duplicate of SYSTEMML-853 > Change Python MLContext to support new MLContext > > > Key: SYSTEMML-854 > URL: https://issues.apache.org/jira/browse/SYSTEMML-854 > Project: SystemML > Issue Type: Task >Reporter: Niketan Pansare > Fix For: SystemML 0.11 > > -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Updated] (SYSTEMML-855) Add a "Get Started" tutorial for Python users
[ https://issues.apache.org/jira/browse/SYSTEMML-855?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Niketan Pansare updated SYSTEMML-855: - Description: As an example, this tutorial could have following sections: 1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support: wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar pyspark --master local[*] --driver-class-path SystemML.jar OR Use pip installer. 2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description 3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL: https://github.com/apache/incubator-systemml/pull/197 import numpy as np from sklearn import datasets diabetes = datasets.load_diabetes() diabetes_X = diabetes.data[:, np.newaxis, 2] diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:] diabetes_y_train = diabetes.target[:-20] diabetes_y_test = diabetes.target[-20:] 4. Explain how to use our algorithm instead: http://apache.github.io/incubator-systemml/algorithms-regression.html#examples 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed. By the end of tutorial, the programmer should understand at very high-level: 1. Moving to SystemML is painless. Almost as simple as changing "import" 2. SystemML has a sophisticated optimizer that allows it to adapt to different data/cluster characteristics and allows the code and algorithm to scale. was: As an example, this tutorial could have following sections: 1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support: wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar pyspark --master local[*] --driver-class-path SystemML.jar OR Use pip installer. 2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description 3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL: https://github.com/apache/incubator-systemml/pull/197 import numpy as np from sklearn import datasets diabetes = datasets.load_diabetes() diabetes_X = diabetes.data[:, np.newaxis, 2] diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:] diabetes_y_train = diabetes.target[:-20] diabetes_y_test = diabetes.target[-20:] 4. Explain how to use our algorithm instead: http://apache.github.io/incubator-systemml/algorithms-regression.html#examples 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed. > Add a "Get Started" tutorial for Python users > - > > Key: SYSTEMML-855 > URL: https://issues.apache.org/jira/browse/SYSTEMML-855 > Project: SystemML > Issue Type: Task >Reporter: Niketan Pansare > > As an example, this tutorial could have following sections: > 1. Steps to start Python shell (or cloud service like datascientistworkbench) > with SystemML support: > wget > https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py > wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar > pyspark --master local[*] --driver-class-path SystemML.jar > OR > Use pip installer. > 2. Give context for one of the algorithm: For example: Linear regression. We > can borrow the technical detail from > http://apache.github.io/incubator-systemml/algorithms-regression.html#description > 3. Explain steps to download data we will use and how to implement Linear > regression DS using embedded Python DSL: > https://github.com/apache/incubator-systemml/pull/197 > import numpy as np > from sklearn import datasets > diabetes = datasets.load_diabetes() > diabetes_X = diabetes.data[:, np.newaxis, 2] > diabetes_X_train = diabetes_X[:-20] > diabetes_X_test = diabetes_X[-20:] > diabetes_y_train = diabetes.target[:-20] > diabetes_y_test = diabetes.target[-20:] > 4. Explain how to use our algorithm instead: > http://apache.github.io/incubator-systemml/algorithms-regression.html#examples > 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's > embedded DSL or SystemML's mllearn, increase the data size. For example: use > t
[jira] [Updated] (SYSTEMML-855) Add a "Get Started" tutorial for Python users
[ https://issues.apache.org/jira/browse/SYSTEMML-855?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Niketan Pansare updated SYSTEMML-855: - Description: As an example, this tutorial could have following sections: 1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support: wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar 2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description 3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL: https://github.com/apache/incubator-systemml/pull/197 import numpy as np from sklearn import datasets diabetes = datasets.load_diabetes() diabetes_X = diabetes.data[:, np.newaxis, 2] diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:] diabetes_y_train = diabetes.target[:-20] diabetes_y_test = diabetes.target[-20:] 4. Explain how to use our algorithm instead: http://apache.github.io/incubator-systemml/algorithms-regression.html#examples 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed. was: As an example, this tutorial could have following sections: 1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support: wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar 2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description 3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL: https://github.com/apache/incubator-systemml/pull/197 import numpy as np from sklearn import datasets # Load the diabetes dataset diabetes = datasets.load_diabetes() # Use only one feature diabetes_X = diabetes.data[:, np.newaxis, 2] # Split the data into training/testing sets diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:] # Split the targets into training/testing sets diabetes_y_train = diabetes.target[:-20] diabetes_y_test = diabetes.target[-20:] 4. Explain how to use our algorithm instead: http://apache.github.io/incubator-systemml/algorithms-regression.html#examples 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed. > Add a "Get Started" tutorial for Python users > - > > Key: SYSTEMML-855 > URL: https://issues.apache.org/jira/browse/SYSTEMML-855 > Project: SystemML > Issue Type: Task >Reporter: Niketan Pansare > > As an example, this tutorial could have following sections: > 1. Steps to start Python shell (or cloud service like datascientistworkbench) > with SystemML support: > wget > https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py > wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar > 2. Give context for one of the algorithm: For example: Linear regression. We > can borrow the technical detail from > http://apache.github.io/incubator-systemml/algorithms-regression.html#description > 3. Explain steps to download data we will use and how to implement Linear > regression DS using embedded Python DSL: > https://github.com/apache/incubator-systemml/pull/197 > import numpy as np > from sklearn import datasets > diabetes = datasets.load_diabetes() > diabetes_X = diabetes.data[:, np.newaxis, 2] > diabetes_X_train = diabetes_X[:-20] > diabetes_X_test = diabetes_X[-20:] > diabetes_y_train = diabetes.target[:-20] > diabetes_y_test = diabetes.target[-20:] > 4. Explain how to use our algorithm instead: > http://apache.github.io/incubator-systemml/algorithms-regression.html#examples > 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's > embedded DSL or SystemML's mllearn, increase the data size. For example: use > twitter feed. -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Updated] (SYSTEMML-855) Add a "Get Started" tutorial for Python users
[ https://issues.apache.org/jira/browse/SYSTEMML-855?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Niketan Pansare updated SYSTEMML-855: - Description: As an example, this tutorial could have following sections: 1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support: wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar pyspark --master local[*] --driver-class-path SystemML.jar OR Use pip installer. 2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description 3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL: https://github.com/apache/incubator-systemml/pull/197 import numpy as np from sklearn import datasets diabetes = datasets.load_diabetes() diabetes_X = diabetes.data[:, np.newaxis, 2] diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:] diabetes_y_train = diabetes.target[:-20] diabetes_y_test = diabetes.target[-20:] 4. Explain how to use our algorithm instead: http://apache.github.io/incubator-systemml/algorithms-regression.html#examples 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed. was: As an example, this tutorial could have following sections: 1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support: wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar OR Use pip installer. 2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description 3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL: https://github.com/apache/incubator-systemml/pull/197 import numpy as np from sklearn import datasets diabetes = datasets.load_diabetes() diabetes_X = diabetes.data[:, np.newaxis, 2] diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:] diabetes_y_train = diabetes.target[:-20] diabetes_y_test = diabetes.target[-20:] 4. Explain how to use our algorithm instead: http://apache.github.io/incubator-systemml/algorithms-regression.html#examples 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed. > Add a "Get Started" tutorial for Python users > - > > Key: SYSTEMML-855 > URL: https://issues.apache.org/jira/browse/SYSTEMML-855 > Project: SystemML > Issue Type: Task >Reporter: Niketan Pansare > > As an example, this tutorial could have following sections: > 1. Steps to start Python shell (or cloud service like datascientistworkbench) > with SystemML support: > wget > https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py > wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar > pyspark --master local[*] --driver-class-path SystemML.jar > OR > Use pip installer. > 2. Give context for one of the algorithm: For example: Linear regression. We > can borrow the technical detail from > http://apache.github.io/incubator-systemml/algorithms-regression.html#description > 3. Explain steps to download data we will use and how to implement Linear > regression DS using embedded Python DSL: > https://github.com/apache/incubator-systemml/pull/197 > import numpy as np > from sklearn import datasets > diabetes = datasets.load_diabetes() > diabetes_X = diabetes.data[:, np.newaxis, 2] > diabetes_X_train = diabetes_X[:-20] > diabetes_X_test = diabetes_X[-20:] > diabetes_y_train = diabetes.target[:-20] > diabetes_y_test = diabetes.target[-20:] > 4. Explain how to use our algorithm instead: > http://apache.github.io/incubator-systemml/algorithms-regression.html#examples > 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's > embedded DSL or SystemML's mllearn, increase the data size. For example: use > twitter feed. -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Updated] (SYSTEMML-855) Add a "Get Started" tutorial for Python users
[ https://issues.apache.org/jira/browse/SYSTEMML-855?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Niketan Pansare updated SYSTEMML-855: - Description: As an example, this tutorial could have following sections: 1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support: wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar 2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description 3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL: https://github.com/apache/incubator-systemml/pull/197 import numpy as np from sklearn import datasets # Load the diabetes dataset diabetes = datasets.load_diabetes() # Use only one feature diabetes_X = diabetes.data[:, np.newaxis, 2] # Split the data into training/testing sets diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:] # Split the targets into training/testing sets diabetes_y_train = diabetes.target[:-20] diabetes_y_test = diabetes.target[-20:] 4. Explain how to use our algorithm instead: http://apache.github.io/incubator-systemml/algorithms-regression.html#examples 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed. was: As an example, this tutorial could have following sections: 1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support: wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar 2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description 3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL: https://github.com/apache/incubator-systemml/pull/197 import numpy as np from sklearn import datasets # Load the diabetes dataset diabetes = datasets.load_diabetes() # Use only one feature diabetes_X = diabetes.data[:, np.newaxis, 2] # Split the data into training/testing sets diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:] # Split the targets into training/testing sets diabetes_y_train = diabetes.target[:-20] diabetes_y_test = diabetes.target[-20:] 4. Explain how to use our algorithm instead: http://apache.github.io/incubator-systemml/algorithms-regression.html#examples 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed. > Add a "Get Started" tutorial for Python users > - > > Key: SYSTEMML-855 > URL: https://issues.apache.org/jira/browse/SYSTEMML-855 > Project: SystemML > Issue Type: Task >Reporter: Niketan Pansare > > As an example, this tutorial could have following sections: > 1. Steps to start Python shell (or cloud service like datascientistworkbench) > with SystemML support: > wget > https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py > wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar > 2. Give context for one of the algorithm: For example: Linear regression. We > can borrow the technical detail from > http://apache.github.io/incubator-systemml/algorithms-regression.html#description > 3. Explain steps to download data we will use and how to implement Linear > regression DS using embedded Python DSL: > https://github.com/apache/incubator-systemml/pull/197 > import numpy as np > from sklearn import datasets > # Load the diabetes dataset > diabetes = datasets.load_diabetes() > # Use only one feature > diabetes_X = diabetes.data[:, np.newaxis, 2] > # Split the data into training/testing sets > diabetes_X_train = diabetes_X[:-20] > diabetes_X_test = diabetes_X[-20:] > # Split the targets into training/testing sets > diabetes_y_train = diabetes.target[:-20] > diabetes_y_test = diabetes.target[-20:] > 4. Explain how to use our algorithm instead: > http://apache.github.io/incubator-systemml/algorithms-regression.html#examples > 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's > embedded DSL or SystemML's mllearn, increase the data size. For example: use > twitter feed. -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Updated] (SYSTEMML-855) Add a "Get Started" tutorial for Python users
[ https://issues.apache.org/jira/browse/SYSTEMML-855?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Niketan Pansare updated SYSTEMML-855: - Description: As an example, this tutorial could have following sections: 1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support: wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar OR Use pip installer. 2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description 3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL: https://github.com/apache/incubator-systemml/pull/197 import numpy as np from sklearn import datasets diabetes = datasets.load_diabetes() diabetes_X = diabetes.data[:, np.newaxis, 2] diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:] diabetes_y_train = diabetes.target[:-20] diabetes_y_test = diabetes.target[-20:] 4. Explain how to use our algorithm instead: http://apache.github.io/incubator-systemml/algorithms-regression.html#examples 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed. was: As an example, this tutorial could have following sections: 1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support: wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar 2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description 3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL: https://github.com/apache/incubator-systemml/pull/197 import numpy as np from sklearn import datasets diabetes = datasets.load_diabetes() diabetes_X = diabetes.data[:, np.newaxis, 2] diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:] diabetes_y_train = diabetes.target[:-20] diabetes_y_test = diabetes.target[-20:] 4. Explain how to use our algorithm instead: http://apache.github.io/incubator-systemml/algorithms-regression.html#examples 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed. > Add a "Get Started" tutorial for Python users > - > > Key: SYSTEMML-855 > URL: https://issues.apache.org/jira/browse/SYSTEMML-855 > Project: SystemML > Issue Type: Task >Reporter: Niketan Pansare > > As an example, this tutorial could have following sections: > 1. Steps to start Python shell (or cloud service like datascientistworkbench) > with SystemML support: > wget > https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py > wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar > OR > Use pip installer. > 2. Give context for one of the algorithm: For example: Linear regression. We > can borrow the technical detail from > http://apache.github.io/incubator-systemml/algorithms-regression.html#description > 3. Explain steps to download data we will use and how to implement Linear > regression DS using embedded Python DSL: > https://github.com/apache/incubator-systemml/pull/197 > import numpy as np > from sklearn import datasets > diabetes = datasets.load_diabetes() > diabetes_X = diabetes.data[:, np.newaxis, 2] > diabetes_X_train = diabetes_X[:-20] > diabetes_X_test = diabetes_X[-20:] > diabetes_y_train = diabetes.target[:-20] > diabetes_y_test = diabetes.target[-20:] > 4. Explain how to use our algorithm instead: > http://apache.github.io/incubator-systemml/algorithms-regression.html#examples > 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's > embedded DSL or SystemML's mllearn, increase the data size. For example: use > twitter feed. -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Created] (SYSTEMML-855) Add a "Get Started" tutorial for Python users
Niketan Pansare created SYSTEMML-855: Summary: Add a "Get Started" tutorial for Python users Key: SYSTEMML-855 URL: https://issues.apache.org/jira/browse/SYSTEMML-855 Project: SystemML Issue Type: Task Reporter: Niketan Pansare As an example, this tutorial could have following sections: 1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support: wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar 2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description 3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL: https://github.com/apache/incubator-systemml/pull/197 ``` import numpy as np from sklearn import datasets # Load the diabetes dataset diabetes = datasets.load_diabetes() # Use only one feature diabetes_X = diabetes.data[:, np.newaxis, 2] # Split the data into training/testing sets diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:] # Split the targets into training/testing sets diabetes_y_train = diabetes.target[:-20] diabetes_y_test = diabetes.target[-20:] ``` 4. Explain how to use our algorithm instead: http://apache.github.io/incubator-systemml/algorithms-regression.html#examples 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed. -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Updated] (SYSTEMML-855) Add a "Get Started" tutorial for Python users
[ https://issues.apache.org/jira/browse/SYSTEMML-855?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Niketan Pansare updated SYSTEMML-855: - Description: As an example, this tutorial could have following sections: 1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support: wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar 2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description 3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL: https://github.com/apache/incubator-systemml/pull/197 import numpy as np from sklearn import datasets # Load the diabetes dataset diabetes = datasets.load_diabetes() # Use only one feature diabetes_X = diabetes.data[:, np.newaxis, 2] # Split the data into training/testing sets diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:] # Split the targets into training/testing sets diabetes_y_train = diabetes.target[:-20] diabetes_y_test = diabetes.target[-20:] 4. Explain how to use our algorithm instead: http://apache.github.io/incubator-systemml/algorithms-regression.html#examples 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed. was: As an example, this tutorial could have following sections: 1. Steps to start Python shell (or cloud service like datascientistworkbench) with SystemML support: wget https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar 2. Give context for one of the algorithm: For example: Linear regression. We can borrow the technical detail from http://apache.github.io/incubator-systemml/algorithms-regression.html#description 3. Explain steps to download data we will use and how to implement Linear regression DS using embedded Python DSL: https://github.com/apache/incubator-systemml/pull/197 ``` import numpy as np from sklearn import datasets # Load the diabetes dataset diabetes = datasets.load_diabetes() # Use only one feature diabetes_X = diabetes.data[:, np.newaxis, 2] # Split the data into training/testing sets diabetes_X_train = diabetes_X[:-20] diabetes_X_test = diabetes_X[-20:] # Split the targets into training/testing sets diabetes_y_train = diabetes.target[:-20] diabetes_y_test = diabetes.target[-20:] ``` 4. Explain how to use our algorithm instead: http://apache.github.io/incubator-systemml/algorithms-regression.html#examples 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's embedded DSL or SystemML's mllearn, increase the data size. For example: use twitter feed. > Add a "Get Started" tutorial for Python users > - > > Key: SYSTEMML-855 > URL: https://issues.apache.org/jira/browse/SYSTEMML-855 > Project: SystemML > Issue Type: Task >Reporter: Niketan Pansare > > As an example, this tutorial could have following sections: > 1. Steps to start Python shell (or cloud service like datascientistworkbench) > with SystemML support: > wget > https://raw.githubusercontent.com/apache/incubator-systemml/master/src/main/java/org/apache/sysml/api/python/SystemML.py > wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar > 2. Give context for one of the algorithm: For example: Linear regression. We > can borrow the technical detail from > http://apache.github.io/incubator-systemml/algorithms-regression.html#description > 3. Explain steps to download data we will use and how to implement Linear > regression DS using embedded Python DSL: > https://github.com/apache/incubator-systemml/pull/197 > > import numpy as np > from sklearn import datasets > # Load the diabetes dataset > diabetes = datasets.load_diabetes() > # Use only one feature > diabetes_X = diabetes.data[:, np.newaxis, 2] > # Split the data into training/testing sets > diabetes_X_train = diabetes_X[:-20] > diabetes_X_test = diabetes_X[-20:] > # Split the targets into training/testing sets > diabetes_y_train = diabetes.target[:-20] > diabetes_y_test = diabetes.target[-20:] > > 4. Explain how to use our algorithm instead: > http://apache.github.io/incubator-systemml/algorithms-regression.html#examples > 5. To explain tradeoffs of using NumPy or Scikit-Learn v/s SystemML's > embedded DSL or SystemML's mllearn, increase the data size. For example: use > twitter feed. -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Created] (SYSTEMML-854) Change Python MLContext to support new MLContext
Niketan Pansare created SYSTEMML-854: Summary: Change Python MLContext to support new MLContext Key: SYSTEMML-854 URL: https://issues.apache.org/jira/browse/SYSTEMML-854 Project: SystemML Issue Type: Task Reporter: Niketan Pansare -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Updated] (SYSTEMML-853) Create Python wrapper API for new MLContext API
[ https://issues.apache.org/jira/browse/SYSTEMML-853?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Deron Eriksson updated SYSTEMML-853: Description: The new MLContext API needs a Python wrapper API to access MLContext from Python. This will allow Jupyter notebooks to use the new API. The old Python API is located at src/main/java/org/apache/sysml/api/python/SystemML.py The new MLContext API is located in the org.apache.sysml.api.mlcontext package. Examples of using the new MLContext API from Spark Shell can be found at http://apache.github.io/incubator-systemml/spark-mlcontext-programming-guide.html was: The new MLContext API needs a Python wrapper API to access MLContext from Python. This will allow Jupyter notebooks to use the new API. The old Python API is located at src/main/java/org/apache/sysml/api/python/SystemML.py The new MLContext API is located in the org.apache.sysml.api.mlcontext package. > Create Python wrapper API for new MLContext API > --- > > Key: SYSTEMML-853 > URL: https://issues.apache.org/jira/browse/SYSTEMML-853 > Project: SystemML > Issue Type: Task > Components: APIs >Reporter: Deron Eriksson >Assignee: Deron Eriksson > > The new MLContext API needs a Python wrapper API to access MLContext from > Python. This will allow Jupyter notebooks to use the new API. > The old Python API is located at > src/main/java/org/apache/sysml/api/python/SystemML.py > The new MLContext API is located in the org.apache.sysml.api.mlcontext > package. > Examples of using the new MLContext API from Spark Shell can be found at > http://apache.github.io/incubator-systemml/spark-mlcontext-programming-guide.html -- This message was sent by Atlassian JIRA (v6.3.4#6332)
[jira] [Created] (SYSTEMML-853) Create Python wrapper API for new MLContext API
Deron Eriksson created SYSTEMML-853: --- Summary: Create Python wrapper API for new MLContext API Key: SYSTEMML-853 URL: https://issues.apache.org/jira/browse/SYSTEMML-853 Project: SystemML Issue Type: Task Components: APIs Reporter: Deron Eriksson Assignee: Deron Eriksson The new MLContext API needs a Python wrapper API to access MLContext from Python. This will allow Jupyter notebooks to use the new API. The old Python API is located at src/main/java/org/apache/sysml/api/python/SystemML.py The new MLContext API is located in the org.apache.sysml.api.mlcontext package. -- This message was sent by Atlassian JIRA (v6.3.4#6332)
