[jira] (SYSTEMML-1220) dmlFromResource capability in Python (equivalent to Scala
Title: Message Title Berthold Reinwald created an issue SystemML / SYSTEMML-1220 dmlFromResource capability in Python (equivalent to Scala Issue Type: Improvement Affects Versions: SystemML 0.13 Assignee: Unassigned Components: APIs Created: 31/Jan/17 08:19 Due Date: 01/Mar/17 Priority: Minor Reporter: Berthold Reinwald an equivalent mechanism to dmlFromResource in Scala is missing in Python Add Comment
[jira] (SYSTEMML-1221) predict in LinearRegression Mllearn wrapper fails in Python
Title: Message Title Berthold Reinwald created an issue SystemML / SYSTEMML-1221 predict in LinearRegression Mllearn wrapper fails in Python Issue Type: Bug Assignee: Unassigned Components: APIs Created: 31/Jan/17 08:30 Due Date: 01/Mar/17 Priority: Major Reporter: Berthold Reinwald from pyspark.sql import SQLContext from systemml.mllearn import LinearRegression sqlCtx = SQLContext(sc) regr = LinearRegression(sqlCtx) Train the model using the training sets regr.fit(diabetes_X_train, diabetes_y_train) regr.predict(diabetes_X_test) Traceback (most recent call last): File "/gpfs/fs01/user/s97f-e8039831bd320d-6262032d0bcc/.local/lib/python2.7/site-packages/systemml/mllearn/estimators.py", line 170, in predict retNumPy = self.decode(convertToNumPyArr(self.sc, self.model.transform(convertToMatrixBlock(self.sc, X File "/usr/local/src/spark160master/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py", line 813, in _call_ answer, self.gateway_client, self.target_id, self.
[jira] (SYSTEMML-1221) predict in LinearRegression Mllearn wrapper fails in Python
[ https://issues.apache.org/jira/browse/SYSTEMML-1221?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15847252#comment-15847252 ] Felix Schüler commented on SYSTEMML-1221: - I believe this is a duplicate of SYSTEMML-1199 and the root cause for not detecting this earlier is that the Scala tests have never been included in any test-runs as described in SYSTEMML-1200 > predict in LinearRegression Mllearn wrapper fails in Python > --- > > Key: SYSTEMML-1221 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1221 > Project: SystemML > Issue Type: Bug > Components: APIs >Reporter: Berthold Reinwald > > from pyspark.sql import SQLContext > from systemml.mllearn import LinearRegression > sqlCtx = SQLContext(sc) > regr = LinearRegression(sqlCtx) > # Train the model using the training sets > regr.fit(diabetes_X_train, diabetes_y_train) > regr.predict(diabetes_X_test) > Traceback (most recent call last): > File > "/gpfs/fs01/user/s97f-e8039831bd320d-6262032d0bcc/.local/lib/python2.7/site-packages/systemml/mllearn/estimators.py", > line 170, in predict > retNumPy = self.decode(convertToNumPyArr(self.sc, > self.model.transform(convertToMatrixBlock(self.sc, X > File > "/usr/local/src/spark160master/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py", > line 813, in __call__ > answer, self.gateway_client, self.target_id, self.name) > File "/usr/local/src/spark160master/spark/python/pyspark/sql/utils.py", > line 45, in deco > return f(*a, **kw) > File > "/usr/local/src/spark160master/spark/python/lib/py4j-0.9-src.zip/py4j/protocol.py", > line 308, in get_return_value > format(target_id, ".", name), value) > Py4JJavaError: An error occurred while calling o132.transform. > : org.apache.sysml.api.mlcontext.MLContextException: Exception when executing > script > at org.apache.sysml.api.mlcontext.MLContext.execute(MLContext.java:307) > at org.apache.sysml.api.mlcontext.MLContext.execute(MLContext.java:276) > at > org.apache.sysml.api.ml.BaseSystemMLRegressorModel$class.baseTransform(BaseSystemMLRegressor.scala:65) > at > org.apache.sysml.api.ml.LinearRegressionModel.baseTransform(LinearRegression.scala:83) > at > org.apache.sysml.api.ml.LinearRegressionModel.transform(LinearRegression.scala:95) > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) > at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:95) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:55) > at java.lang.reflect.Method.invoke(Method.java:507) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381) > at py4j.Gateway.invoke(Gateway.java:259) > at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133) > at py4j.commands.CallCommand.execute(CallCommand.java:79) > at py4j.GatewayConnection.run(GatewayConnection.java:209) > at java.lang.Thread.run(Thread.java:785) > Caused by: org.apache.sysml.api.mlcontext.MLContextException: Exception > occurred while validating script > at > org.apache.sysml.api.mlcontext.ScriptExecutor.validateScript(ScriptExecutor.java:553) > at > org.apache.sysml.api.mlcontext.ScriptExecutor.execute(ScriptExecutor.java:307) > at org.apache.sysml.api.mlcontext.MLContext.execute(MLContext.java:299) > ... 15 more > Caused by: org.apache.sysml.parser.LanguageException: Invalid Parameters : > ERROR: null -- line 206, column 12 -- Mismatch in matrix dimensions of > parameters for function TABLE > at > org.apache.sysml.parser.Expression.raiseValidateError(Expression.java:556) > at > org.apache.sysml.parser.BuiltinFunctionExpression.checkMatchingDimensions(BuiltinFunctionExpression.java:1447) > at > org.apache.sysml.parser.BuiltinFunctionExpression.checkMatchingDimensions(BuiltinFunctionExpression.java:1428) > at > org.apache.sysml.parser.BuiltinFunctionExpression.validateExpression(BuiltinFunctionExpression.java:677) > at > org.apache.sysml.parser.StatementBlock.validate(StatementBlock.java:567) > at > org.apache.sysml.parser.IfStatementBlock.validate(IfStatementBlock.java:70) > at > org.apache.sysml.parser.IfStatementBlock.validate(IfStatementBlock.java:70) > at > org.apache.sysml.parser.IfStatementBlock.validate(IfStatementBlock.java:77) > at > org.apache.sysml.parser.IfStatementBlock.validate(IfStatementBlock.java:70) > at > org.apache.sysml.parser.DMLTranslator.validateParseTree(DMLTranslator.java:140) > at > org.apache.sysml.api.mlcontext.ScriptExecutor.validateScript(ScriptExecutor.java:551) > ... 17 mor
[jira] (SYSTEMML-1220) dmlFromResource capability in Python (equivalent to Scala
[ https://issues.apache.org/jira/browse/SYSTEMML-1220?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Deron Eriksson reassigned SYSTEMML-1220: Assignee: Deron Eriksson > dmlFromResource capability in Python (equivalent to Scala > - > > Key: SYSTEMML-1220 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1220 > Project: SystemML > Issue Type: Improvement > Components: APIs >Affects Versions: SystemML 0.13 >Reporter: Berthold Reinwald >Assignee: Deron Eriksson >Priority: Minor > > an equivalent mechanism to dmlFromResource in Scala is missing in Python -- This message was sent by Atlassian JIRA (v6.3.15#6346)
[jira] (SYSTEMML-1140) Sparse/Caching performance bugs related to deep learning scripts
[ https://issues.apache.org/jira/browse/SYSTEMML-1140?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15847630#comment-15847630 ] Matthias Boehm commented on SYSTEMML-1140: -- Could you please specify the workloads to reproduce the mentioned issues? > Sparse/Caching performance bugs related to deep learning scripts > > > Key: SYSTEMML-1140 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1140 > Project: SystemML > Issue Type: Bug >Affects Versions: SystemML 1.0 >Reporter: Niketan Pansare >Priority: Blocker > > We have identified two performance bugs that frequently occurs in deep > learning script. > First, we repeatedly perform unnecessary conversion to sparse format. Also, > the operations such as matrix multiplication (including BLAS and CuBLAS) are > optimized for dense. > > Second, even with large memory budget, we sometimes spend almost 20-30% time > in caching. > [~mboehm7] [~reinwald] [~mwdus...@us.ibm.com] I am labeling this bug as > blocker for SystemML 1.0. Please feel free to assign this issue to yourself. -- This message was sent by Atlassian JIRA (v6.3.15#6346)
[jira] [Commented] (SYSTEMML-1140) Sparse/Caching performance bugs related to deep learning scripts
[ https://issues.apache.org/jira/browse/SYSTEMML-1140?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15847664#comment-15847664 ] Niketan Pansare commented on SYSTEMML-1140: --- Sorry, I forgot to update this JIRA with series of improvements related to this PR: 1. Many CP convolution operators now have sparse support (except im2col). However, since CuDNN doesnot have a sparse equivalent, we only support dense convolution on GPU. 2. Fused operators such as relu_maxpooling and relu_backward has been added to reduce the conversion overhead of sparsity-introducing operators such as relu. In fact, the performance of relu_maxpooling is exactly same as that of maxpooling in CP, making relu a no-op in the fused implementation :) [~mboehm7] I used Mike's Lenet script with MNIST dataset as an example. Please see https://github.com/apache/incubator-systemml/blob/master/scripts/staging/SystemML-NN/examples/Example%20-%20MNIST%20LeNet.ipynb ... Here is the Cache statistics from a sample run after adding the above mentioned fused operators (date: Jan 13th, 2017): Cache hits (Mem, WB, FS, HDFS): 1096424/0/0/2. Cache writes (WB, FS, HDFS): 603950/15/8. Cache times (ACQr/m, RLS, EXP): 3.659/0.456/273.799/1.275 sec. I have seen anywhere betweeh 250 to 500 seconds spent in Cache times. You can also use Mike's Breast Cancer Project as an example workload. > Sparse/Caching performance bugs related to deep learning scripts > > > Key: SYSTEMML-1140 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1140 > Project: SystemML > Issue Type: Bug >Affects Versions: SystemML 1.0 >Reporter: Niketan Pansare >Priority: Blocker > > We have identified two performance bugs that frequently occurs in deep > learning script. > First, we repeatedly perform unnecessary conversion to sparse format. Also, > the operations such as matrix multiplication (including BLAS and CuBLAS) are > optimized for dense. > > Second, even with large memory budget, we sometimes spend almost 20-30% time > in caching. > [~mboehm7] [~reinwald] [~mwdus...@us.ibm.com] I am labeling this bug as > blocker for SystemML 1.0. Please feel free to assign this issue to yourself. -- This message was sent by Atlassian JIRA (v6.3.15#6346)
[jira] [Commented] (SYSTEMML-1140) Sparse/Caching performance bugs related to deep learning scripts
[ https://issues.apache.org/jira/browse/SYSTEMML-1140?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15847687#comment-15847687 ] Matthias Boehm commented on SYSTEMML-1140: -- OK thanks, as it's on algorithm it will take a while but I'll look into it next week. > Sparse/Caching performance bugs related to deep learning scripts > > > Key: SYSTEMML-1140 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1140 > Project: SystemML > Issue Type: Bug >Affects Versions: SystemML 1.0 >Reporter: Niketan Pansare >Priority: Blocker > > We have identified two performance bugs that frequently occurs in deep > learning script. > First, we repeatedly perform unnecessary conversion to sparse format. Also, > the operations such as matrix multiplication (including BLAS and CuBLAS) are > optimized for dense. > > Second, even with large memory budget, we sometimes spend almost 20-30% time > in caching. > [~mboehm7] [~reinwald] [~mwdus...@us.ibm.com] I am labeling this bug as > blocker for SystemML 1.0. Please feel free to assign this issue to yourself. -- This message was sent by Atlassian JIRA (v6.3.15#6346)
[jira] [Commented] (SYSTEMML-1140) Sparse/Caching performance bugs related to deep learning scripts
[ https://issues.apache.org/jira/browse/SYSTEMML-1140?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15847691#comment-15847691 ] Niketan Pansare commented on SYSTEMML-1140: --- Thanks [~mboehm7]. That will also help speedup the breast cancer use-case as well :) > Sparse/Caching performance bugs related to deep learning scripts > > > Key: SYSTEMML-1140 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1140 > Project: SystemML > Issue Type: Bug >Affects Versions: SystemML 1.0 >Reporter: Niketan Pansare >Priority: Blocker > > We have identified two performance bugs that frequently occurs in deep > learning script. > First, we repeatedly perform unnecessary conversion to sparse format. Also, > the operations such as matrix multiplication (including BLAS and CuBLAS) are > optimized for dense. > > Second, even with large memory budget, we sometimes spend almost 20-30% time > in caching. > [~mboehm7] [~reinwald] [~mwdus...@us.ibm.com] I am labeling this bug as > blocker for SystemML 1.0. Please feel free to assign this issue to yourself. -- This message was sent by Atlassian JIRA (v6.3.15#6346)
[jira] [Commented] (SYSTEMML-1140) Sparse/Caching performance bugs related to deep learning scripts
[ https://issues.apache.org/jira/browse/SYSTEMML-1140?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15847738#comment-15847738 ] Mike Dusenberry commented on SYSTEMML-1140: --- Awesome, thanks [~mboehm7]. I agree with [~niketanpansare] that this will greatly help with the breast cancer project as well. > Sparse/Caching performance bugs related to deep learning scripts > > > Key: SYSTEMML-1140 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1140 > Project: SystemML > Issue Type: Bug >Affects Versions: SystemML 1.0 >Reporter: Niketan Pansare >Priority: Blocker > > We have identified two performance bugs that frequently occurs in deep > learning script. > First, we repeatedly perform unnecessary conversion to sparse format. Also, > the operations such as matrix multiplication (including BLAS and CuBLAS) are > optimized for dense. > > Second, even with large memory budget, we sometimes spend almost 20-30% time > in caching. > [~mboehm7] [~reinwald] [~mwdus...@us.ibm.com] I am labeling this bug as > blocker for SystemML 1.0. Please feel free to assign this issue to yourself. -- This message was sent by Atlassian JIRA (v6.3.15#6346)
[jira] [Commented] (SYSTEMML-1221) predict in LinearRegression Mllearn wrapper fails in Python
[ https://issues.apache.org/jira/browse/SYSTEMML-1221?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15847737#comment-15847737 ] Berthold Reinwald commented on SYSTEMML-1221: - Thanks, Felix. You are right. Closing this in favor of SYSTEMML-1200 > predict in LinearRegression Mllearn wrapper fails in Python > --- > > Key: SYSTEMML-1221 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1221 > Project: SystemML > Issue Type: Bug > Components: APIs >Reporter: Berthold Reinwald > > from pyspark.sql import SQLContext > from systemml.mllearn import LinearRegression > sqlCtx = SQLContext(sc) > regr = LinearRegression(sqlCtx) > # Train the model using the training sets > regr.fit(diabetes_X_train, diabetes_y_train) > regr.predict(diabetes_X_test) > Traceback (most recent call last): > File > "/gpfs/fs01/user/s97f-e8039831bd320d-6262032d0bcc/.local/lib/python2.7/site-packages/systemml/mllearn/estimators.py", > line 170, in predict > retNumPy = self.decode(convertToNumPyArr(self.sc, > self.model.transform(convertToMatrixBlock(self.sc, X > File > "/usr/local/src/spark160master/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py", > line 813, in __call__ > answer, self.gateway_client, self.target_id, self.name) > File "/usr/local/src/spark160master/spark/python/pyspark/sql/utils.py", > line 45, in deco > return f(*a, **kw) > File > "/usr/local/src/spark160master/spark/python/lib/py4j-0.9-src.zip/py4j/protocol.py", > line 308, in get_return_value > format(target_id, ".", name), value) > Py4JJavaError: An error occurred while calling o132.transform. > : org.apache.sysml.api.mlcontext.MLContextException: Exception when executing > script > at org.apache.sysml.api.mlcontext.MLContext.execute(MLContext.java:307) > at org.apache.sysml.api.mlcontext.MLContext.execute(MLContext.java:276) > at > org.apache.sysml.api.ml.BaseSystemMLRegressorModel$class.baseTransform(BaseSystemMLRegressor.scala:65) > at > org.apache.sysml.api.ml.LinearRegressionModel.baseTransform(LinearRegression.scala:83) > at > org.apache.sysml.api.ml.LinearRegressionModel.transform(LinearRegression.scala:95) > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) > at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:95) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:55) > at java.lang.reflect.Method.invoke(Method.java:507) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381) > at py4j.Gateway.invoke(Gateway.java:259) > at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133) > at py4j.commands.CallCommand.execute(CallCommand.java:79) > at py4j.GatewayConnection.run(GatewayConnection.java:209) > at java.lang.Thread.run(Thread.java:785) > Caused by: org.apache.sysml.api.mlcontext.MLContextException: Exception > occurred while validating script > at > org.apache.sysml.api.mlcontext.ScriptExecutor.validateScript(ScriptExecutor.java:553) > at > org.apache.sysml.api.mlcontext.ScriptExecutor.execute(ScriptExecutor.java:307) > at org.apache.sysml.api.mlcontext.MLContext.execute(MLContext.java:299) > ... 15 more > Caused by: org.apache.sysml.parser.LanguageException: Invalid Parameters : > ERROR: null -- line 206, column 12 -- Mismatch in matrix dimensions of > parameters for function TABLE > at > org.apache.sysml.parser.Expression.raiseValidateError(Expression.java:556) > at > org.apache.sysml.parser.BuiltinFunctionExpression.checkMatchingDimensions(BuiltinFunctionExpression.java:1447) > at > org.apache.sysml.parser.BuiltinFunctionExpression.checkMatchingDimensions(BuiltinFunctionExpression.java:1428) > at > org.apache.sysml.parser.BuiltinFunctionExpression.validateExpression(BuiltinFunctionExpression.java:677) > at > org.apache.sysml.parser.StatementBlock.validate(StatementBlock.java:567) > at > org.apache.sysml.parser.IfStatementBlock.validate(IfStatementBlock.java:70) > at > org.apache.sysml.parser.IfStatementBlock.validate(IfStatementBlock.java:70) > at > org.apache.sysml.parser.IfStatementBlock.validate(IfStatementBlock.java:77) > at > org.apache.sysml.parser.IfStatementBlock.validate(IfStatementBlock.java:70) > at > org.apache.sysml.parser.DMLTranslator.validateParseTree(DMLTranslator.java:140) > at > org.apache.sysml.api.mlcontext.ScriptExecutor.validateScript(ScriptExecutor.java:551) > ... 17 more > UnboundLocalErrorTraceback (most recent call last) > in () > > 1 regr.predict(diabetes_X_test) > /gpfs/fs01/u
[jira] [Closed] (SYSTEMML-1221) predict in LinearRegression Mllearn wrapper fails in Python
[ https://issues.apache.org/jira/browse/SYSTEMML-1221?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Berthold Reinwald closed SYSTEMML-1221. --- Resolution: Duplicate Fix Version/s: SystemML 0.13 > predict in LinearRegression Mllearn wrapper fails in Python > --- > > Key: SYSTEMML-1221 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1221 > Project: SystemML > Issue Type: Bug > Components: APIs >Reporter: Berthold Reinwald > Fix For: SystemML 0.13 > > > from pyspark.sql import SQLContext > from systemml.mllearn import LinearRegression > sqlCtx = SQLContext(sc) > regr = LinearRegression(sqlCtx) > # Train the model using the training sets > regr.fit(diabetes_X_train, diabetes_y_train) > regr.predict(diabetes_X_test) > Traceback (most recent call last): > File > "/gpfs/fs01/user/s97f-e8039831bd320d-6262032d0bcc/.local/lib/python2.7/site-packages/systemml/mllearn/estimators.py", > line 170, in predict > retNumPy = self.decode(convertToNumPyArr(self.sc, > self.model.transform(convertToMatrixBlock(self.sc, X > File > "/usr/local/src/spark160master/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py", > line 813, in __call__ > answer, self.gateway_client, self.target_id, self.name) > File "/usr/local/src/spark160master/spark/python/pyspark/sql/utils.py", > line 45, in deco > return f(*a, **kw) > File > "/usr/local/src/spark160master/spark/python/lib/py4j-0.9-src.zip/py4j/protocol.py", > line 308, in get_return_value > format(target_id, ".", name), value) > Py4JJavaError: An error occurred while calling o132.transform. > : org.apache.sysml.api.mlcontext.MLContextException: Exception when executing > script > at org.apache.sysml.api.mlcontext.MLContext.execute(MLContext.java:307) > at org.apache.sysml.api.mlcontext.MLContext.execute(MLContext.java:276) > at > org.apache.sysml.api.ml.BaseSystemMLRegressorModel$class.baseTransform(BaseSystemMLRegressor.scala:65) > at > org.apache.sysml.api.ml.LinearRegressionModel.baseTransform(LinearRegression.scala:83) > at > org.apache.sysml.api.ml.LinearRegressionModel.transform(LinearRegression.scala:95) > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) > at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:95) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:55) > at java.lang.reflect.Method.invoke(Method.java:507) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381) > at py4j.Gateway.invoke(Gateway.java:259) > at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133) > at py4j.commands.CallCommand.execute(CallCommand.java:79) > at py4j.GatewayConnection.run(GatewayConnection.java:209) > at java.lang.Thread.run(Thread.java:785) > Caused by: org.apache.sysml.api.mlcontext.MLContextException: Exception > occurred while validating script > at > org.apache.sysml.api.mlcontext.ScriptExecutor.validateScript(ScriptExecutor.java:553) > at > org.apache.sysml.api.mlcontext.ScriptExecutor.execute(ScriptExecutor.java:307) > at org.apache.sysml.api.mlcontext.MLContext.execute(MLContext.java:299) > ... 15 more > Caused by: org.apache.sysml.parser.LanguageException: Invalid Parameters : > ERROR: null -- line 206, column 12 -- Mismatch in matrix dimensions of > parameters for function TABLE > at > org.apache.sysml.parser.Expression.raiseValidateError(Expression.java:556) > at > org.apache.sysml.parser.BuiltinFunctionExpression.checkMatchingDimensions(BuiltinFunctionExpression.java:1447) > at > org.apache.sysml.parser.BuiltinFunctionExpression.checkMatchingDimensions(BuiltinFunctionExpression.java:1428) > at > org.apache.sysml.parser.BuiltinFunctionExpression.validateExpression(BuiltinFunctionExpression.java:677) > at > org.apache.sysml.parser.StatementBlock.validate(StatementBlock.java:567) > at > org.apache.sysml.parser.IfStatementBlock.validate(IfStatementBlock.java:70) > at > org.apache.sysml.parser.IfStatementBlock.validate(IfStatementBlock.java:70) > at > org.apache.sysml.parser.IfStatementBlock.validate(IfStatementBlock.java:77) > at > org.apache.sysml.parser.IfStatementBlock.validate(IfStatementBlock.java:70) > at > org.apache.sysml.parser.DMLTranslator.validateParseTree(DMLTranslator.java:140) > at > org.apache.sysml.api.mlcontext.ScriptExecutor.validateScript(ScriptExecutor.java:551) > ... 17 more > UnboundLocalErrorTraceback (most recent call last) > in () > > 1 regr.predict(diabetes_X_test) > /gpfs/fs01/user/s97f-e8039831bd320d-6262
