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Posted to commits@systemml.apache.org by na...@apache.org on 2017/04/21 21:52:30 UTC
[2/2] incubator-systemml git commit: Use Apache Commons CLI to parse
command line arguments in DMLScript
Use Apache Commons CLI to parse command line arguments in DMLScript
- Added unit tests
- changed scripts to accept "-config " instead of "-config="
- Removed DMLScriptTest{1,2}
- Modified bin/systemml script to print a better help message
- Removed extraneous ZPackageSuite for DMLScriptTest{1,2}
Closes #440
Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml/commit/32924dc6
Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/32924dc6
Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml/diff/32924dc6
Branch: refs/heads/master
Commit: 32924dc6027df1973b37a8688b7d0cacbdefd4bf
Parents: f73673d
Author: Nakul Jindal <na...@gmail.com>
Authored: Fri Apr 21 14:50:56 2017 -0700
Committer: Nakul Jindal <na...@gmail.com>
Committed: Fri Apr 21 14:50:57 2017 -0700
----------------------------------------------------------------------
bin/systemml | 84 +--
bin/systemml.bat | 4 +-
docs/algorithms-classification.md | 44 +-
docs/algorithms-clustering.md | 14 +-
docs/algorithms-descriptive-statistics.md | 14 +-
docs/algorithms-matrix-factorization.md | 18 +-
docs/algorithms-regression.md | 36 +-
docs/algorithms-survival-analysis.md | 16 +-
docs/hadoop-batch-mode.md | 16 +-
docs/spark-batch-mode.md | 4 +-
docs/standalone-guide.md | 2 +-
docs/troubleshooting-guide.md | 2 +-
scripts/sparkDML.sh | 2 +-
.../java/org/apache/sysml/api/DMLScript.java | 576 ++++++++++++-------
.../java/org/apache/sysml/api/MLContext.java | 49 +-
.../org/apache/sysml/api/jmlc/Connection.java | 2 +-
.../sysml/api/mlcontext/ScriptExecutor.java | 2 +-
.../org/apache/sysml/parser/ParserFactory.java | 16 +-
.../java/org/apache/sysml/utils/Explain.java | 32 +-
.../org/apache/sysml/yarn/DMLYarnClient.java | 3 +-
src/main/resources/scripts/sparkDML.sh | 2 +-
src/main/standalone/runStandaloneSystemML.bat | 4 +-
src/main/standalone/runStandaloneSystemML.sh | 2 +-
.../test/integration/AutomatedTestBase.java | 19 +-
.../functions/dmlscript/DMLScriptTest1.java | 125 ----
.../functions/dmlscript/DMLScriptTest2.java | 151 -----
.../functions/misc/DataTypeChangeTest.java | 14 +-
.../parfor/ParForDependencyAnalysisTest.java | 2 +-
.../TransformFrameEncodeDecodeTest.java | 15 +-
.../sysml/test/unit/CLIOptionsParserTest.java | 415 +++++++++++++
.../functions/dmlscript/ZPackageSuite.java | 37 --
31 files changed, 992 insertions(+), 730 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/32924dc6/bin/systemml
----------------------------------------------------------------------
diff --git a/bin/systemml b/bin/systemml
index 0ccee2d..44ab45e 100755
--- a/bin/systemml
+++ b/bin/systemml
@@ -20,32 +20,22 @@
#
#-------------------------------------------------------------
+
# error help print
-printUsageExit()
+printSimpleUsage()
{
cat << EOF
Usage: $0 <dml-filename> [arguments] [-help]
- -help - Print this usage message and exit
+ -help - Print detailed help message
EOF
exit 1
}
-# Script internally invokes 'java -Xmx4g -Xms4g -Xmn400m [Custom-Java-Options] -jar StandaloneSystemML.jar -f <dml-filename> -exec singlenode -config=SystemML-config.xml [Optional-Arguments]'
-while getopts "h:" options; do
- case $options in
- h ) echo Warning: Help requested. Will exit after usage message
- printUsageExit
- ;;
- \? ) echo Warning: Help requested. Will exit after usage message
- printUsageExit
- ;;
- * ) echo Error: Unexpected error while processing options
- esac
-done
+# Script internally invokes 'java -Xmx4g -Xms4g -Xmn400m [Custom-Java-Options] -jar StandaloneSystemML.jar -f <dml-filename> -exec singlenode -config=SystemML-config.xml [Optional-Arguments]'
if [ -z "$1" ] ; then
echo "Wrong Usage.";
- printUsageExit;
+ printSimpleUsage
fi
@@ -98,24 +88,6 @@ then
fi
-# Peel off first argument so that $@ contains arguments to DML script
-SCRIPT_FILE=$1
-shift
-
-# if the script file path was omitted, try to complete the script path
-if [ ! -f "$SCRIPT_FILE" ]
-then
- SCRIPT_FILE_NAME=$(basename $SCRIPT_FILE)
- SCRIPT_FILE_FOUND=$(find "$PROJECT_ROOT_DIR/scripts" -name "$SCRIPT_FILE_NAME")
- if [ ! "$SCRIPT_FILE_FOUND" ]
- then
- echo "Could not find DML script: $SCRIPT_FILE"
- printUsageExit;
- else
- SCRIPT_FILE=$SCRIPT_FILE_FOUND
- echo "DML script: $SCRIPT_FILE"
- fi
-fi
# add hadoop libraries which were generated by the build to the classpath
@@ -149,13 +121,57 @@ if [ -f "${PROJECT_ROOT_DIR}/conf/systemml-env.sh" ]; then
fi
fi
+
+printUsageExit()
+{
+CMD="\
+java ${SYSTEMML_DEFAULT_JAVA_OPTS} \
+org.apache.sysml.api.DMLScript \
+-help"
+# echo ${CMD}
+eval ${CMD}
+exit 0
+}
+
+while getopts "h:" options; do
+ case $options in
+ h ) echo Warning: Help requested. Will exit after usage message
+ printUsageExit
+ ;;
+ \? ) echo Warning: Help requested. Will exit after usage message
+ printUsageExit
+ ;;
+ * ) echo Error: Unexpected error while processing options
+ esac
+done
+
+# Peel off first argument so that $@ contains arguments to DML script
+SCRIPT_FILE=$1
+shift
+
+# if the script file path was omitted, try to complete the script path
+if [ ! -f "$SCRIPT_FILE" ]
+then
+ SCRIPT_FILE_NAME=$(basename $SCRIPT_FILE)
+ SCRIPT_FILE_FOUND=$(find "$PROJECT_ROOT_DIR/scripts" -name "$SCRIPT_FILE_NAME")
+ if [ ! "$SCRIPT_FILE_FOUND" ]
+ then
+ echo "Could not find DML script: $SCRIPT_FILE"
+ printSimpleUsage
+ else
+ SCRIPT_FILE=$SCRIPT_FILE_FOUND
+ echo "DML script: $SCRIPT_FILE"
+ fi
+fi
+
+
# Invoke the jar with options and arguments
CMD="\
java ${SYSTEMML_DEFAULT_JAVA_OPTS} \
org.apache.sysml.api.DMLScript \
-f '$SCRIPT_FILE' \
-exec singlenode \
--config='$PROJECT_ROOT_DIR/conf/SystemML-config.xml' \
+-config '$PROJECT_ROOT_DIR/conf/SystemML-config.xml' \
$@"
eval ${CMD}
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/32924dc6/bin/systemml.bat
----------------------------------------------------------------------
diff --git a/bin/systemml.bat b/bin/systemml.bat
index 3fc86a8..e16a2a1 100755
--- a/bin/systemml.bat
+++ b/bin/systemml.bat
@@ -117,7 +117,7 @@ set CMD=java -Xmx4g -Xms2g -Xmn400m ^
org.apache.sysml.api.DMLScript ^
-f %SCRIPT_FILE% ^
-exec singlenode ^
- -config="%PROJECT_ROOT_DIR%\conf\SystemML-config.xml" ^
+ -config "%PROJECT_ROOT_DIR%\conf\SystemML-config.xml" ^
%DML_OPT_ARGS%
:: execute the java command
@@ -141,7 +141,7 @@ GOTO Msg
:Msg
ECHO Usage: runStandaloneSystemML.bat ^<dml-filename^> [arguments] [-help]
-ECHO Script internally invokes 'java -Xmx4g -Xms4g -Xmn400m -jar jSystemML.jar -f ^<dml-filename^> -exec singlenode -config=SystemML-config.xml [Optional-Arguments]'
+ECHO Script internally invokes 'java -Xmx4g -Xms4g -Xmn400m -jar jSystemML.jar -f ^<dml-filename^> -exec singlenode -config SystemML-config.xml [Optional-Arguments]'
GOTO ExitErr
:ExitErr
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/32924dc6/docs/algorithms-classification.md
----------------------------------------------------------------------
diff --git a/docs/algorithms-classification.md b/docs/algorithms-classification.md
index b029e0a..ed56c34 100644
--- a/docs/algorithms-classification.md
+++ b/docs/algorithms-classification.md
@@ -165,7 +165,7 @@ val prediction = model.transform(X_test_df)
--conf spark.akka.frameSize=128
SystemML.jar
-f MultiLogReg.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=<file>
@@ -336,7 +336,7 @@ prediction.show()
--conf spark.akka.frameSize=128
SystemML.jar
-f MultiLogReg.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/Y.mtx
@@ -532,7 +532,7 @@ val model = svm.fit(X_train_df)
--conf spark.akka.frameSize=128
SystemML.jar
-f l2-svm.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=<file>
@@ -579,7 +579,7 @@ val prediction = model.transform(X_test_df)
--conf spark.akka.frameSize=128
SystemML.jar
-f l2-svm-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=[file]
@@ -661,7 +661,7 @@ using a held-out test set. Note that this is an optional argument.
--conf spark.akka.frameSize=128
SystemML.jar
-f l2-svm.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/y.mtx
@@ -695,7 +695,7 @@ using a held-out test set. Note that this is an optional argument.
--conf spark.akka.frameSize=128
SystemML.jar
-f l2-svm-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/y.mtx
@@ -800,7 +800,7 @@ val model = svm.fit(X_train_df)
--conf spark.akka.frameSize=128
SystemML.jar
-f m-svm.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=<file>
@@ -847,7 +847,7 @@ val prediction = model.transform(X_test_df)
--conf spark.akka.frameSize=128
SystemML.jar
-f m-svm-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=[file]
@@ -1014,7 +1014,7 @@ prediction.show()
--conf spark.akka.frameSize=128
SystemML.jar
-f m-svm.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/y.mtx
@@ -1048,7 +1048,7 @@ prediction.show()
--conf spark.akka.frameSize=128
SystemML.jar
-f m-svm-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/y.mtx
@@ -1153,7 +1153,7 @@ val model = nb.fit(X_train_df)
--conf spark.akka.frameSize=128
SystemML.jar
-f naive-bayes.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=<file>
@@ -1198,7 +1198,7 @@ val prediction = model.transform(X_test_df)
--conf spark.akka.frameSize=128
SystemML.jar
-f naive-bayes-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=[file]
@@ -1289,7 +1289,7 @@ metrics.f1_score(newsgroups_test.target, pred, average='weighted')
--conf spark.akka.frameSize=128
SystemML.jar
-f naive-bayes.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/y.mtx
@@ -1321,7 +1321,7 @@ metrics.f1_score(newsgroups_test.target, pred, average='weighted')
--conf spark.akka.frameSize=128
SystemML.jar
-f naive-bayes-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/y.mtx
@@ -1420,7 +1420,7 @@ implementation is well-suited to handle large-scale data and builds a
--conf spark.akka.frameSize=128
SystemML.jar
-f decision-tree.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=<file>
@@ -1458,7 +1458,7 @@ implementation is well-suited to handle large-scale data and builds a
--conf spark.akka.frameSize=128
SystemML.jar
-f decision-tree-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=[file]
@@ -1558,7 +1558,7 @@ SystemML Language Reference for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f decision-tree.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/Y.mtx
@@ -1593,7 +1593,7 @@ SystemML Language Reference for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f decision-tree-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/Y.mtx
@@ -1828,7 +1828,7 @@ for classification in parallel.
--conf spark.akka.frameSize=128
SystemML.jar
-f random-forest.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=<file>
@@ -1871,7 +1871,7 @@ for classification in parallel.
--conf spark.akka.frameSize=128
SystemML.jar
-f random-forest-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=[file]
@@ -1994,7 +1994,7 @@ SystemML Language Reference for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f random-forest.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/Y.mtx
@@ -2032,7 +2032,7 @@ To compute predictions:
--conf spark.akka.frameSize=128
SystemML.jar
-f random-forest-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/Y.mtx
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/32924dc6/docs/algorithms-clustering.md
----------------------------------------------------------------------
diff --git a/docs/algorithms-clustering.md b/docs/algorithms-clustering.md
index 023a7f5..0c91fa1 100644
--- a/docs/algorithms-clustering.md
+++ b/docs/algorithms-clustering.md
@@ -134,7 +134,7 @@ apart is a "false negative"�etc.
--conf spark.akka.frameSize=128
SystemML.jar
-f Kmeans.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
C=[file]
@@ -168,7 +168,7 @@ apart is a "false negative"�etc.
--conf spark.akka.frameSize=128
SystemML.jar
-f Kmeans-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=[file]
C=[file]
@@ -260,7 +260,7 @@ standard output
--conf spark.akka.frameSize=128
SystemML.jar
-f Kmeans.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
k=5
@@ -289,7 +289,7 @@ standard output
--conf spark.akka.frameSize=128
SystemML.jar
-f Kmeans.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
k=5
@@ -322,7 +322,7 @@ To predict Y given X and C:
--conf spark.akka.frameSize=128
SystemML.jar
-f Kmeans-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
C=/user/ml/C.mtx
@@ -348,7 +348,7 @@ given X and C:
--conf spark.akka.frameSize=128
SystemML.jar
-f Kmeans-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
C=/user/ml/C.mtx
@@ -373,7 +373,7 @@ labels prY:
--conf spark.akka.frameSize=128
SystemML.jar
-f Kmeans-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs spY=/user/ml/Y.mtx
prY=/user/ml/PredY.mtx
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/32924dc6/docs/algorithms-descriptive-statistics.md
----------------------------------------------------------------------
diff --git a/docs/algorithms-descriptive-statistics.md b/docs/algorithms-descriptive-statistics.md
index 1ddf01a..f45ffae 100644
--- a/docs/algorithms-descriptive-statistics.md
+++ b/docs/algorithms-descriptive-statistics.md
@@ -130,7 +130,7 @@ to compute the mean of a categorical attribute like \u2018Hair Color\u2019.
--conf spark.akka.frameSize=128
SystemML.jar
-f Univar-Stats.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
TYPES=<file>
@@ -169,7 +169,7 @@ be stored. The format of the output matrix is defined by
--conf spark.akka.frameSize=128
SystemML.jar
-f Univar-Stats.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
TYPES=/user/ml/types.mtx
@@ -590,7 +590,7 @@ attributes like \u2018Hair Color\u2019.
--conf spark.akka.frameSize=128
SystemML.jar
-f bivar-stats.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
index1=<file>
@@ -659,7 +659,7 @@ are defined in [**Table�2**](algorithms-descriptive-statistics.html#table2).
--conf spark.akka.frameSize=128
SystemML.jar
-f bivar-stats.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
index1=/user/ml/S1.mtx
@@ -1152,7 +1152,7 @@ becomes reversed and amplified (from $+0.1$ to $-0.5$) if we ignore the months.
--conf spark.akka.frameSize=128
SystemML.jar
-f stratstats.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Xcid=[file]
@@ -1360,7 +1360,7 @@ SystemML Language Reference for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f stratstats.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Xcid=/user/ml/Xcid.mtx
@@ -1388,7 +1388,7 @@ SystemML Language Reference for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f stratstats.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/Data.mtx
Xcid=/user/ml/Xcid.mtx
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/32924dc6/docs/algorithms-matrix-factorization.md
----------------------------------------------------------------------
diff --git a/docs/algorithms-matrix-factorization.md b/docs/algorithms-matrix-factorization.md
index 51eb614..9af8c19 100644
--- a/docs/algorithms-matrix-factorization.md
+++ b/docs/algorithms-matrix-factorization.md
@@ -61,7 +61,7 @@ top-$K$ (for a given value of $K$) principal components.
--conf spark.akka.frameSize=128
SystemML.jar
-f PCA.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs INPUT=<file>
K=<int>
@@ -124,7 +124,7 @@ SystemML Language Reference for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f PCA.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs INPUT=/user/ml/input.mtx
K=10
@@ -154,7 +154,7 @@ SystemML Language Reference for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f PCA.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs INPUT=/user/ml/test_input.mtx
K=10
@@ -262,7 +262,7 @@ problems.
--conf spark.akka.frameSize=128
SystemML.jar
-f ALS.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs V=<file>
L=<file>
@@ -296,7 +296,7 @@ problems.
--conf spark.akka.frameSize=128
SystemML.jar
-f ALS_predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=<file>
@@ -327,7 +327,7 @@ problems.
--conf spark.akka.frameSize=128
SystemML.jar
-f ALS_topk_predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=<file>
@@ -436,7 +436,7 @@ SystemML Language Reference for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f ALS.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs V=/user/ml/V
L=/user/ml/L
@@ -472,7 +472,7 @@ To compute predicted ratings for a given list of users and items:
--conf spark.akka.frameSize=128
SystemML.jar
-f ALS_predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X
Y=/user/ml/Y
@@ -506,7 +506,7 @@ predicted ratings for a given list of users:
--conf spark.akka.frameSize=128
SystemML.jar
-f ALS_topk_predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X
Y=/user/ml/Y
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/32924dc6/docs/algorithms-regression.md
----------------------------------------------------------------------
diff --git a/docs/algorithms-regression.md b/docs/algorithms-regression.md
index 31f7ec2..494693c 100644
--- a/docs/algorithms-regression.md
+++ b/docs/algorithms-regression.md
@@ -107,7 +107,7 @@ y_test = lr.fit(df_train)
--conf spark.akka.frameSize=128
SystemML.jar
-f LinearRegDS.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=<file>
@@ -152,7 +152,7 @@ y_test = lr.fit(df_train)
--conf spark.akka.frameSize=128
SystemML.jar
-f LinearRegCG.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=<file>
@@ -258,7 +258,7 @@ print("Residual sum of squares: %.2f" % np.mean((regr.predict(diabetes_X_test) -
--conf spark.akka.frameSize=128
SystemML.jar
-f LinearRegDS.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/Y.mtx
@@ -316,7 +316,7 @@ print("Residual sum of squares: %.2f" % np.mean((regr.predict(diabetes_X_test) -
--conf spark.akka.frameSize=128
SystemML.jar
-f LinearRegCG.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/Y.mtx
@@ -557,7 +557,7 @@ lowest AIC is computed.
--conf spark.akka.frameSize=128
SystemML.jar
-f StepLinearRegDS.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=<file>
@@ -628,7 +628,7 @@ SystemML Language Reference for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f StepLinearRegDS.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/Y.mtx
@@ -760,7 +760,7 @@ distributions and link functions, see below for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f GLM.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=<file>
@@ -898,7 +898,7 @@ if no maximum limit provided
--conf spark.akka.frameSize=128
SystemML.jar
-f GLM.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/Y.mtx
@@ -1235,7 +1235,7 @@ distribution family is supported (see below for details).
--conf spark.akka.frameSize=128
SystemML.jar
-f StepGLM.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=<file>
@@ -1340,7 +1340,7 @@ SystemML Language Reference for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f StepGLM.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
Y=/user/ml/Y.mtx
@@ -1486,7 +1486,7 @@ this step outside the scope of `GLM-predict.dml` for now.
--conf spark.akka.frameSize=128
SystemML.jar
-f GLM-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
Y=[file]
@@ -1625,7 +1625,7 @@ unknown (which sets it to�`1.0`).
--conf spark.akka.frameSize=128
SystemML.jar
-f GLM-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs dfam=1
vpow=0.0
@@ -1661,7 +1661,7 @@ unknown (which sets it to�`1.0`).
--conf spark.akka.frameSize=128
SystemML.jar
-f GLM-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs dfam=1
vpow=0.0
@@ -1695,7 +1695,7 @@ unknown (which sets it to�`1.0`).
--conf spark.akka.frameSize=128
SystemML.jar
-f GLM-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs dfam=2
link=2
@@ -1730,7 +1730,7 @@ unknown (which sets it to�`1.0`).
--conf spark.akka.frameSize=128
SystemML.jar
-f GLM-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs dfam=2
link=3
@@ -1763,7 +1763,7 @@ unknown (which sets it to�`1.0`).
--conf spark.akka.frameSize=128
SystemML.jar
-f GLM-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs dfam=3
X=/user/ml/X.mtx
@@ -1798,7 +1798,7 @@ unknown (which sets it to�`1.0`).
--conf spark.akka.frameSize=128
SystemML.jar
-f GLM-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs dfam=1
vpow=1.0
@@ -1837,7 +1837,7 @@ unknown (which sets it to�`1.0`).
--conf spark.akka.frameSize=128
SystemML.jar
-f GLM-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs dfam=1
vpow=2.0
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/32924dc6/docs/algorithms-survival-analysis.md
----------------------------------------------------------------------
diff --git a/docs/algorithms-survival-analysis.md b/docs/algorithms-survival-analysis.md
index a5e641e..239ab08 100644
--- a/docs/algorithms-survival-analysis.md
+++ b/docs/algorithms-survival-analysis.md
@@ -62,7 +62,7 @@ censored and uncensored survival times.
--conf spark.akka.frameSize=128
SystemML.jar
-f KM.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
TE=<file>
@@ -157,7 +157,7 @@ SystemML Language Reference for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f KM.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
TE=/user/ml/TE
@@ -194,7 +194,7 @@ SystemML Language Reference for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f KM.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
TE=/user/ml/TE
@@ -466,7 +466,7 @@ may be categorical (ordinal or nominal) as well as continuous-valued.
--conf spark.akka.frameSize=128
SystemML.jar
-f Cox.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
TE=<file>
@@ -508,7 +508,7 @@ may be categorical (ordinal or nominal) as well as continuous-valued.
--conf spark.akka.frameSize=128
SystemML.jar
-f Cox-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=<file>
RT=<file>
@@ -617,7 +617,7 @@ SystemML Language Reference for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f Cox.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
TE=/user/ml/TE
@@ -656,7 +656,7 @@ SystemML Language Reference for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f Cox.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X.mtx
TE=/user/ml/TE
@@ -696,7 +696,7 @@ SystemML Language Reference for details.
--conf spark.akka.frameSize=128
SystemML.jar
-f Cox-predict.dml
- -config=SystemML-config.xml
+ -config SystemML-config.xml
-exec hybrid_spark
-nvargs X=/user/ml/X-sorted.mtx
RT=/user/ml/recoded-timestamps.csv
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/32924dc6/docs/hadoop-batch-mode.md
----------------------------------------------------------------------
diff --git a/docs/hadoop-batch-mode.md b/docs/hadoop-batch-mode.md
index ddc1c1f..3af7c0c 100644
--- a/docs/hadoop-batch-mode.md
+++ b/docs/hadoop-batch-mode.md
@@ -49,11 +49,11 @@ refer to the Hadoop documentation.
SystemML can be invoked in Hadoop Batch mode using the following syntax:
- hadoop jar SystemML.jar [-? | -help | -f <filename>] (-config=<config_filename>) ([-args | -nvargs] <args-list>)
+ hadoop jar SystemML.jar [-? | -help | -f <filename>] (-config <config_filename>) ([-args | -nvargs] <args-list>)
The `SystemML.jar` file is specified to Hadoop using the `jar` option.
The DML script to invoke is specified after the `-f` argument. Configuration settings can be passed to SystemML
-using the optional `-config=` argument. DML scripts can optionally take named arguments (`-nvargs`) or positional
+using the optional `-config ` argument. DML scripts can optionally take named arguments (`-nvargs`) or positional
arguments (`-args`). Named arguments are preferred over positional arguments. Positional arguments are considered
to be deprecated. All the primary algorithm scripts included with SystemML use named arguments.
@@ -69,11 +69,11 @@ to be deprecated. All the primary algorithm scripts included with SystemML use n
In a clustered environment, it is *highly* recommended that SystemML configuration settings are specified
in a `SystemML-config.xml` file. By default, SystemML will look for this file in the current working
-directory (`./SystemML-config.xml`). This location can be overridden by the `-config=` argument.
+directory (`./SystemML-config.xml`). This location can be overridden by the `-config ` argument.
**Example #3: DML Invocation with Configuration File Explicitly Specified and Named Arguments**
- hadoop jar systemml/SystemML.jar -f systemml/algorithms/Kmeans.dml -config=/conf/SystemML-config.xml -nvargs X=X.mtx k=5
+ hadoop jar systemml/SystemML.jar -f systemml/algorithms/Kmeans.dml -config /conf/SystemML-config.xml -nvargs X=X.mtx k=5
For recommended SystemML configuration settings in a clustered environment, please see
[Recommended Hadoop Cluster Configuration Settings](hadoop-batch-mode.html#recommended-hadoop-cluster-configuration-settings).
@@ -170,7 +170,7 @@ arguments to the DML script were specified following the `-nvargs` option.
In the console output, we see a warning that no default SystemML config file was found in the current working directory.
In a distributed environment on a large data set, it is highly advisable to specify configuration settings in a SystemML config file for
-optimal performance. The location of the SystemML config file can be explicitly specified using the `-config=` argument.
+optimal performance. The location of the SystemML config file can be explicitly specified using the `-config ` argument.
The OptimizerUtils warning occurs because parallel multi-threaded text reads in Java versions less than 1.8 result
in thread contention issues, so only a single thread reads matrix data in text formats.
@@ -859,7 +859,7 @@ A description of the named arguments that can be passed in to this script can be
`genRandData4Kmeans.dml` file. For data, I'll generate a matrix `X.mtx` consisting of 1 million rows and 100 features. I'll explicitly reference my `SystemML-config.xml` file, since I'm
executing SystemML in Hadoop from my home directory rather than from the SystemML project root directory.
- [hadoop@host1 ~]$ hadoop jar systemml-{{site.SYSTEMML_VERSION}}/SystemML.jar -f genRandData4Kmeans.dml -config=systemml-{{site.SYSTEMML_VERSION}}/SystemML-config.xml -nvargs nr=1000000 nf=100 nc=10 dc=10.0 dr=1.0 fbf=100.0 cbf=100.0 X=X.mtx C=C.mtx Y=Y.mtx YbyC=YbyC.mtx
+ [hadoop@host1 ~]$ hadoop jar systemml-{{site.SYSTEMML_VERSION}}/SystemML.jar -f genRandData4Kmeans.dml -config systemml-{{site.SYSTEMML_VERSION}}/SystemML-config.xml -nvargs nr=1000000 nf=100 nc=10 dc=10.0 dr=1.0 fbf=100.0 cbf=100.0 X=X.mtx C=C.mtx Y=Y.mtx YbyC=YbyC.mtx
After the data generation has finished, I'll check HDFS for the amount of space used. The 1M-row matrix `X.mtx`
requires about 2.8GB of space.
@@ -895,7 +895,7 @@ Here we can see the `X.mtx` data files.
Next, I'll run the `Kmeans.dml` algorithm on the 1M-row matrix `X.mtx`.
- [hadoop@host1 ~]$ hadoop jar systemml-{{site.SYSTEMML_VERSION}}/SystemML.jar -f systemml-{{site.SYSTEMML_VERSION}}/algorithms/Kmeans.dml -config=/systemml-{{site.SYSTEMML_VERSION}}/SystemML-config.xml -nvargs X=X.mtx k=5 C=Centroids.mtx
+ [hadoop@host1 ~]$ hadoop jar systemml-{{site.SYSTEMML_VERSION}}/SystemML.jar -f systemml-{{site.SYSTEMML_VERSION}}/algorithms/Kmeans.dml -config /systemml-{{site.SYSTEMML_VERSION}}/SystemML-config.xml -nvargs X=X.mtx k=5 C=Centroids.mtx
We can see the `Centroids.mtx` data file has been written to HDFS.
@@ -916,7 +916,7 @@ We can see the `Centroids.mtx` data file has been written to HDFS.
Now that we have trained our model, next we will test our model. We can do this with
the `Kmeans-predict.dml` script.
- [hadoop@host1 ~]$ hadoop jar systemml-{{site.SYSTEMML_VERSION}}/SystemML.jar -f systemml-{{site.SYSTEMML_VERSION}}/algorithms/Kmeans-predict.dml -config=systemml-{{site.SYSTEMML_VERSION}}/SystemML-config.xml -nvargs X=X.mtx C=Centroids.mtx prY=PredY.mtx O=stats.txt
+ [hadoop@host1 ~]$ hadoop jar systemml-{{site.SYSTEMML_VERSION}}/SystemML.jar -f systemml-{{site.SYSTEMML_VERSION}}/algorithms/Kmeans-predict.dml -config systemml-{{site.SYSTEMML_VERSION}}/SystemML-config.xml -nvargs X=X.mtx C=Centroids.mtx prY=PredY.mtx O=stats.txt
In the file system, we can see that the `PredY.mtx` matrix was created.
The `stats.txt` file lists statistics about the results.
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/32924dc6/docs/spark-batch-mode.md
----------------------------------------------------------------------
diff --git a/docs/spark-batch-mode.md b/docs/spark-batch-mode.md
index c199b1f..39bcd3e 100644
--- a/docs/spark-batch-mode.md
+++ b/docs/spark-batch-mode.md
@@ -43,10 +43,10 @@ mode in more depth.
SystemML can be invoked in Hadoop Batch mode using the following syntax:
- spark-submit SystemML.jar [-? | -help | -f <filename>] (-config=<config_filename>) ([-args | -nvargs] <args-list>)
+ spark-submit SystemML.jar [-? | -help | -f <filename>] (-config <config_filename>) ([-args | -nvargs] <args-list>)
The DML script to invoke is specified after the `-f` argument. Configuration settings can be passed to SystemML
-using the optional `-config=` argument. DML scripts can optionally take named arguments (`-nvargs`) or positional
+using the optional `-config ` argument. DML scripts can optionally take named arguments (`-nvargs`) or positional
arguments (`-args`). Named arguments are preferred over positional arguments. Positional arguments are considered
to be deprecated. All the primary algorithm scripts included with SystemML use named arguments.
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/32924dc6/docs/standalone-guide.md
----------------------------------------------------------------------
diff --git a/docs/standalone-guide.md b/docs/standalone-guide.md
index 2c2092d..586e56e 100644
--- a/docs/standalone-guide.md
+++ b/docs/standalone-guide.md
@@ -605,5 +605,5 @@ script (`runStandaloneSystemML.sh` or `runStandaloneSystemML.bat`) to increase
the memory available to the JVM, i.e:
java -Xmx16g -Xms4g -Xmn1g -cp ${CLASSPATH} org.apache.sysml.api.DMLScript \
- -f ${SCRIPT_FILE} -exec singlenode -config=SystemML-config.xml \
+ -f ${SCRIPT_FILE} -exec singlenode -config SystemML-config.xml \
$@
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/32924dc6/docs/troubleshooting-guide.md
----------------------------------------------------------------------
diff --git a/docs/troubleshooting-guide.md b/docs/troubleshooting-guide.md
index 629bcf5..4731f51 100644
--- a/docs/troubleshooting-guide.md
+++ b/docs/troubleshooting-guide.md
@@ -91,7 +91,7 @@ They can also be configured on a **per SystemML-task basis** by inserting the fo
Note: The default `SystemML-config.xml` is located in `<path to SystemML root>/conf/`. It is passed to SystemML using the `-config` argument:
- hadoop jar SystemML.jar [-? | -help | -f <filename>] (-config=<config_filename>) ([-args | -nvargs] <args-list>)
+ hadoop jar SystemML.jar [-? | -help | -f <filename>] (-config <config_filename>) ([-args | -nvargs] <args-list>)
See [Invoking SystemML in Hadoop Batch Mode](hadoop-batch-mode.html) for details of the syntax.
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/32924dc6/scripts/sparkDML.sh
----------------------------------------------------------------------
diff --git a/scripts/sparkDML.sh b/scripts/sparkDML.sh
index 5548859..7bea639 100755
--- a/scripts/sparkDML.sh
+++ b/scripts/sparkDML.sh
@@ -116,7 +116,7 @@ $SPARK_HOME/bin/spark-submit \
${conf} \
${SYSTEMML_HOME}/SystemML.jar \
-f ${f} \
- -config=${SYSTEMML_HOME}/SystemML-config.xml \
+ -config ${SYSTEMML_HOME}/SystemML-config.xml \
-exec hybrid_spark \
$explain \
$stats \
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/32924dc6/src/main/java/org/apache/sysml/api/DMLScript.java
----------------------------------------------------------------------
diff --git a/src/main/java/org/apache/sysml/api/DMLScript.java b/src/main/java/org/apache/sysml/api/DMLScript.java
index 84bbf77..ce60d55 100644
--- a/src/main/java/org/apache/sysml/api/DMLScript.java
+++ b/src/main/java/org/apache/sysml/api/DMLScript.java
@@ -35,6 +35,15 @@ import java.util.HashSet;
import java.util.Map;
import java.util.Scanner;
+import org.apache.commons.cli.AlreadySelectedException;
+import org.apache.commons.cli.CommandLine;
+import org.apache.commons.cli.CommandLineParser;
+import org.apache.commons.cli.HelpFormatter;
+import org.apache.commons.cli.Option;
+import org.apache.commons.cli.OptionBuilder;
+import org.apache.commons.cli.OptionGroup;
+import org.apache.commons.cli.Options;
+import org.apache.commons.cli.PosixParser;
import org.apache.commons.lang.StringUtils;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
@@ -105,66 +114,77 @@ public class DMLScript
HYBRID_SPARK, // execute matrix operations in CP or Spark
SPARK // execute matrix operations in Spark
}
-
- public static RUNTIME_PLATFORM rtplatform = OptimizerUtils.getDefaultExecutionMode();
- public static boolean STATISTICS = false; //default statistics
- public static int STATISTICS_COUNT = 10; //default statistics maximum heavy hitter count
- public static boolean ENABLE_DEBUG_MODE = false; //default debug mode
- public static boolean USE_LOCAL_SPARK_CONFIG = false; //set default local spark configuration - used for local testing
- public static String DML_FILE_PATH_ANTLR_PARSER = null;
- public static ExplainType EXPLAIN = ExplainType.NONE; //default explain
+
+ /**
+ * Set of DMLOptions that can be set through the command line
+ * and {@link org.apache.sysml.api.mlcontext.MLContext}
+ * The values have been initialized with the default values
+ * Despite there being a DML and PyDML, this class is named DMLOptions
+ * to keep it consistent with {@link DMLScript} and {@link DMLOptions}
+ */
+ public static class DMLOptions {
+ public Map<String, String> argVals = new HashMap<>(); // Arguments map containing either named arguments or arguments by position for a DML program
+ public String configFile = null; // Path to config file if default config and default config is to be overriden
+ public boolean clean = false; // Whether to clean up all SystemML working directories (FS, DFS)
+ public boolean stats = false; // Whether to record and print the statistics
+ public int statsCount = 10; // Default statistics count
+ public Explain.ExplainType explainType = Explain.ExplainType.NONE; // Whether to print the "Explain" and if so, what type
+ public DMLScript.RUNTIME_PLATFORM execMode = OptimizerUtils.getDefaultExecutionMode(); // Execution mode standalone, MR, Spark or a hybrid
+ public boolean gpu = false; // Whether to use the GPU
+ public boolean forceGPU = false; // Whether to ignore memory & estimates and always use the GPU
+ public boolean debug = false; // to go into debug mode to be able to step through a program
+ public ScriptType scriptType = ScriptType.DML; // whether the script is a DML or PyDML script
+ public String filePath = null; // path to script
+ public String script = null; // the script itself
+ public boolean help = false; // whether to print the usage option
+
+ public final static DMLOptions defaultOptions = new DMLOptions();
+
+ @Override
+ public String toString() {
+ return "DMLOptions{" +
+ "argVals=" + argVals +
+ ", configFile='" + configFile + '\'' +
+ ", clean=" + clean +
+ ", stats=" + stats +
+ ", statsCount=" + statsCount +
+ ", explainType=" + explainType +
+ ", execMode=" + execMode +
+ ", gpu=" + gpu +
+ ", forceGPU=" + forceGPU +
+ ", debug=" + debug +
+ ", scriptType=" + scriptType +
+ ", filePath='" + filePath + '\'' +
+ ", script='" + script + '\'' +
+ ", help=" + help +
+ '}';
+ }
+ }
+
+ public static RUNTIME_PLATFORM rtplatform = DMLOptions.defaultOptions.execMode; // the execution mode
+ public static boolean STATISTICS = DMLOptions.defaultOptions.stats; // whether to print statistics
+ public static int STATISTICS_COUNT = DMLOptions.defaultOptions.statsCount; // statistics maximum heavy hitter count
+ public static boolean ENABLE_DEBUG_MODE = DMLOptions.defaultOptions.debug; // debug mode
+ public static ExplainType EXPLAIN = DMLOptions.defaultOptions.explainType; // explain type
+ public static String DML_FILE_PATH_ANTLR_PARSER = DMLOptions.defaultOptions.filePath; // filename of dml/pydml script
+
/**
* Global variable indicating the script type (DML or PYDML). Can be used
* for DML/PYDML-specific tasks, such as outputting booleans in the correct
* case (TRUE/FALSE for DML and True/False for PYDML).
*/
- public static ScriptType SCRIPT_TYPE = ScriptType.DML;
-
- public static boolean USE_ACCELERATOR = false;
- public static boolean FORCE_ACCELERATOR = false;
-
- // flag that indicates whether or not to suppress any prints to stdout
- public static boolean _suppressPrint2Stdout = false;
-
- public static String _uuid = IDHandler.createDistributedUniqueID();
+ public static ScriptType SCRIPT_TYPE = DMLOptions.defaultOptions.scriptType;
+ public static boolean USE_ACCELERATOR = DMLOptions.defaultOptions.gpu;
+ public static boolean FORCE_ACCELERATOR = DMLOptions.defaultOptions.forceGPU;
+
+
+ public static boolean _suppressPrint2Stdout = false; // flag that indicates whether or not to suppress any prints to stdout
+ public static boolean USE_LOCAL_SPARK_CONFIG = false; //set default local spark configuration - used for local testing
public static boolean _activeAM = false;
-
+
+ public static String _uuid = IDHandler.createDistributedUniqueID();
private static final Log LOG = LogFactory.getLog(DMLScript.class.getName());
- public static String USAGE =
- "Usage is " + DMLScript.class.getCanonicalName() + " -f <filename>"
- //+ " (-exec <mode>)?" + " (-explain <type>)?" + " (-stats)?" + " (-clean)?" + " (-config=<config_filename>)?
- + " [-options] ([-args | -nvargs] <args-list>)? \n"
- + " -f: <filename> will be interpreted as a filename path (if <filename> is prefixed\n"
- + " with hdfs or gpfs it is read from DFS, otherwise from local file system)\n"
- //undocumented feature in beta 08/2014 release
- //+ " -s: <filename> will be interpreted as a DML script string \n"
- + " -python: (optional) parses Python-like DML\n"
- + " -debug: (optional) run in debug mode\n"
- + " -gpu: <flags> (optional) use acceleration whenever possible. Current version only supports CUDA.\n"
- + " Supported <flags> for this mode is force=(true|false)\n"
- // Later add optional flags to indicate optimizations turned on or off. Currently they are turned off.
- //+ " -debug: <flags> (optional) run in debug mode\n"
- //+ " Optional <flags> that is supported for this mode is optimize=(on|off)\n"
- + " -exec: <mode> (optional) execution mode (hadoop, singlenode, [hybrid], hybrid_spark)\n"
- + " -explain: <type> (optional) explain plan (hops, [runtime], recompile_hops, recompile_runtime)\n"
- + " -stats: <count> (optional) monitor and report caching/recompilation statistics, default heavy hitter count is 10\n"
- + " -clean: (optional) cleanup all SystemML working directories (FS, DFS).\n"
- + " All other flags are ignored in this mode. \n"
- + " -config: (optional) use config file <config_filename> (default: use parameter\n"
- + " values in default SystemML-config.xml config file; if <config_filename> is\n"
- + " prefixed with hdfs or gpfs it is read from DFS, otherwise from local file system)\n"
- + " -args: (optional) parameterize DML script with contents of [args list], ALL args\n"
- + " after -args flag, each argument must be an unnamed-argument, where 1st value\n"
- + " after -args will replace $1 in DML script, 2nd value will replace $2, etc.\n"
- + " -nvargs: (optional) parameterize DML script with contents of [args list], ALL args\n"
- + " after -nvargs flag, each argument must be be named-argument of form argName=argValue,\n"
- + " where value will replace $argName in DML script, argName must be a valid DML variable\n"
- + " name (start with letter, contain only letters, numbers, or underscores).\n"
- + " <args-list>: (optional) args to DML script \n"
- + " -? | -help: (optional) show this help message \n";
-
-
///////////////////////////////
// public external interface
////////
@@ -195,21 +215,19 @@ public class DMLScript
public static boolean isActiveAM(){
return _activeAM;
}
-
-
+
/**
- * Default DML script invocation (e.g., via 'hadoop jar SystemML.jar -f Test.dml')
- *
+ *
* @param args command-line arguments
* @throws IOException if an IOException occurs
* @throws DMLException if a DMLException occurs
*/
- public static void main(String[] args)
+ public static void main(String[] args)
throws IOException, DMLException
{
Configuration conf = new Configuration(ConfigurationManager.getCachedJobConf());
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
-
+
try {
DMLScript.executeScript(conf, otherArgs);
} catch (ParseException pe) {
@@ -218,7 +236,199 @@ public class DMLScript
// In case of DMLScriptException, simply print the error message.
System.err.println(e.getMessage());
}
- }
+ }
+
+ /**
+ * Parses command line arguments to create a {@link DMLOptions} instance with the correct options
+ * @param args arguments from the command line
+ * @param options an {@link Options} instance containing the options that need to be parsed
+ * @return an instance of {@link Options} that contain the correct {@link Option}s.
+ * @throws org.apache.commons.cli.ParseException if there is an incorrect option specified in the CLI
+ */
+ public static DMLOptions parseCLArguments(String[] args, Options options) throws org.apache.commons.cli.ParseException {
+
+ CommandLineParser clParser = new PosixParser();
+ CommandLine line = clParser.parse(options, args);
+
+ DMLOptions dmlOptions = new DMLOptions();
+ dmlOptions.help = line.hasOption("help");
+ dmlOptions.scriptType = line.hasOption("python") ? ScriptType.PYDML : ScriptType.DML;
+ dmlOptions.debug = line.hasOption("debug");
+ dmlOptions.gpu = line.hasOption("gpu");
+ if (dmlOptions.gpu) {
+ String force = line.getOptionValue("gpu");
+ if (force != null) {
+ if (force.equalsIgnoreCase("force")) {
+ dmlOptions.forceGPU = true;
+ } else {
+ throw new org.apache.commons.cli.ParseException("Invalid argument specified for -gpu option");
+ }
+ }
+ }
+ if (line.hasOption("exec")){
+ String execMode = line.getOptionValue("exec");
+ if (execMode != null){
+ if (execMode.equalsIgnoreCase("hadoop")) dmlOptions.execMode = RUNTIME_PLATFORM.HADOOP;
+ else if (execMode.equalsIgnoreCase("singlenode")) dmlOptions.execMode = RUNTIME_PLATFORM.SINGLE_NODE;
+ else if (execMode.equalsIgnoreCase("hybrid")) dmlOptions.execMode = RUNTIME_PLATFORM.HYBRID;
+ else if (execMode.equalsIgnoreCase("hybrid_spark")) dmlOptions.execMode = RUNTIME_PLATFORM.HYBRID_SPARK;
+ else if (execMode.equalsIgnoreCase("spark")) dmlOptions.execMode = RUNTIME_PLATFORM.SPARK;
+ else throw new org.apache.commons.cli.ParseException("Invalid argument specified for -exec option, must be one of [hadoop, singlenode, hybrid, hybrid_spark, spark]");
+ }
+ }
+ if (line.hasOption("explain")) {
+ dmlOptions.explainType = ExplainType.RUNTIME;
+ String explainType = line.getOptionValue("explain");
+ if (explainType != null){
+ if (explainType.equalsIgnoreCase("hops")) dmlOptions.explainType = ExplainType.HOPS;
+ else if (explainType.equalsIgnoreCase("runtime")) dmlOptions.explainType = ExplainType.RUNTIME;
+ else if (explainType.equalsIgnoreCase("recompile_hops")) dmlOptions.explainType = ExplainType.RECOMPILE_HOPS;
+ else if (explainType.equalsIgnoreCase("recompile_runtime")) dmlOptions.explainType = ExplainType.RECOMPILE_RUNTIME;
+ else throw new org.apache.commons.cli.ParseException("Invalid argument specified for -hops option, must be one of [hops, runtime, recompile_hops, recompile_runtime]");
+ }
+ }
+ dmlOptions.stats = line.hasOption("stats");
+ if (dmlOptions.stats){
+ String statsCount = line.getOptionValue("stats");
+ if (statsCount != null) {
+ try {
+ dmlOptions.statsCount = Integer.parseInt(statsCount);
+ } catch (NumberFormatException e) {
+ throw new org.apache.commons.cli.ParseException("Invalid argument specified for -stats option, must be a valid integer");
+ }
+ }
+ }
+
+ dmlOptions.clean = line.hasOption("clean");
+
+ if (line.hasOption("config")){
+ dmlOptions.configFile = line.getOptionValue("config");
+ }
+
+ if (line.hasOption("f")){
+ dmlOptions.filePath = line.getOptionValue("f");
+ }
+
+ if (line.hasOption("s")){
+ dmlOptions.script = line.getOptionValue("s");
+ }
+
+ // Positional arguments map is created as ("$1", "a"), ("$2", 123), ....
+ if (line.hasOption("args")){
+ String[] argValues = line.getOptionValues("args");
+ for (int k=0; k<argValues.length; k++){
+ String str = argValues[k];
+ if (!str.isEmpty()) {
+ dmlOptions.argVals.put("$" + (k+1), str);
+ }
+ }
+ }
+
+ // Named arguments map is created as ("$K, 123), ("$X", "X.csv"), ....
+ if (line.hasOption("nvargs")){
+ String varNameRegex = "^[a-zA-Z]([a-zA-Z0-9_])*$";
+ String[] nvargValues = line.getOptionValues("nvargs");
+ for (String str : nvargValues){
+ if (!str.isEmpty()){
+ String[] kv = str.split("=");
+ if (kv.length != 2){
+ throw new org.apache.commons.cli.ParseException("Invalid argument specified for -nvargs option, must be a list of space separated K=V pairs, where K is a valid name of a variable in the DML/PyDML program");
+ }
+ if (!kv[0].matches(varNameRegex)) {
+ throw new org.apache.commons.cli.ParseException("Invalid argument specified for -nvargs option, " + kv[0] + " does not seem like a valid variable name in DML. Valid variable names in DML start with upper-case or lower-case letter, and contain only letters, digits, or underscores");
+ }
+ dmlOptions.argVals.put("$" + kv[0], kv[1]);
+ }
+ }
+ }
+
+ return dmlOptions;
+
+ }
+
+ /**
+ * Creates an {@link Options} instance for the command line parameters
+ * As of SystemML 0.13, Apache Commons CLI 1.2 is transitively in the classpath
+ * However the most recent version of Apache Commons CLI is 1.4
+ * Creating CLI options is done using Static methods. This obviously makes it
+ * thread unsafe. Instead of {@link OptionBuilder}, CLI 1.4 uses Option.Builder which
+ * has non-static methods.
+ * @return an appropriate instance of {@link Options}
+ */
+ @SuppressWarnings("static-access")
+ public static Options createCLIOptions() {
+ Options options = new Options();
+ Option nvargsOpt = OptionBuilder.withArgName("key=value")
+ .withDescription("parameterizes DML script with named parameters of the form <key=value>; <key> should be a valid identifier in DML/PyDML")
+ .hasArgs()
+ .create("nvargs");
+ Option argsOpt = OptionBuilder.withArgName("argN")
+ .withDescription("specifies positional parameters; first value will replace $1 in DML program; $2 will replace 2nd and so on")
+ .hasArgs()
+ .create("args");
+ Option configOpt = OptionBuilder.withArgName("filename")
+ .withDescription("uses a given configuration file (can be on local/hdfs/gpfs; default values in SystemML-config.xml")
+ .hasArg()
+ .create("config");
+ Option cleanOpt = OptionBuilder.withDescription("cleans up all SystemML working directories (FS, DFS); all other flags are ignored in this mode. \n")
+ .create("clean");
+ Option statsOpt = OptionBuilder.withArgName("count")
+ .withDescription("monitors and reports caching/recompilation statistics; heavy hitter <count> is 10 unless overridden; default off")
+ .hasOptionalArg()
+ .create("stats");
+ Option explainOpt = OptionBuilder.withArgName("level")
+ .withDescription("explains plan levels; can be 'hops' / 'runtime'[default] / 'recompile_hops' / 'recompile_runtime'")
+ .hasOptionalArg()
+ .create("explain");
+ Option execOpt = OptionBuilder.withArgName("mode")
+ .withDescription("sets execution mode; can be 'hadoop' / 'singlenode' / 'hybrid'[default] / 'hybrid_spark' / 'spark'")
+ .hasArg()
+ .create("exec");
+ Option gpuOpt = OptionBuilder.withArgName("force")
+ .withDescription("uses CUDA instructions when reasonable; set <force> option to skip conservative memory estimates and use GPU wherever possible; default off")
+ .hasOptionalArg()
+ .create("gpu");
+ Option debugOpt = OptionBuilder.withDescription("runs in debug mode; default off")
+ .create("debug");
+ Option pythonOpt = OptionBuilder.withDescription("parses Python-like DML")
+ .create("python");
+ Option fileOpt = OptionBuilder.withArgName("filename")
+ .withDescription("specifies dml/pydml file to execute; path can be local/hdfs/gpfs (prefixed with appropriate URI)")
+ .isRequired()
+ .hasArg()
+ .create("f");
+ Option scriptOpt = OptionBuilder.withArgName("script_contents")
+ .withDescription("specified script string to execute directly")
+ .isRequired()
+ .hasArg()
+ .create("s");
+ Option helpOpt = OptionBuilder.withDescription("shows usage message")
+ .create("help");
+
+ OptionGroup fileOrScriptOpt = new OptionGroup();
+ // Either a clean(-clean), a file(-f), a script(-s) or help(-help) needs to be specified
+ fileOrScriptOpt.addOption(scriptOpt);
+ fileOrScriptOpt.addOption(fileOpt);
+ fileOrScriptOpt.addOption(cleanOpt);
+ fileOrScriptOpt.addOption(helpOpt);
+ fileOrScriptOpt.setRequired(true);
+
+ OptionGroup argsOrNVArgsOpt = new OptionGroup();
+ argsOrNVArgsOpt.addOption(nvargsOpt).addOption(argsOpt); // Either -args or -nvargs
+
+ options.addOption(configOpt);
+ options.addOption(cleanOpt);
+ options.addOption(statsOpt);
+ options.addOption(explainOpt);
+ options.addOption(execOpt);
+ options.addOption(gpuOpt);
+ options.addOption(debugOpt);
+ options.addOption(pythonOpt);
+ options.addOptionGroup(fileOrScriptOpt);
+ options.addOptionGroup(argsOrNVArgsOpt);
+ options.addOption(helpOpt);
+ return options;
+ }
/**
* Single entry point for all public invocation alternatives (e.g.,
@@ -233,127 +443,86 @@ public class DMLScript
public static boolean executeScript( Configuration conf, String[] args )
throws DMLException
{
- //Step 1: parse arguments
- //check for help
- if( args.length==0 || (args.length==1 && (args[0].equalsIgnoreCase("-help")|| args[0].equalsIgnoreCase("-?"))) ){
- System.err.println( USAGE );
- return true;
- }
-
- //check for clean
- else if( args.length==1 && args[0].equalsIgnoreCase("-clean") ){
- cleanSystemMLWorkspace();
- return true;
- }
-
- //check number of args - print usage if incorrect
- if( args.length < 2 ){
- System.err.println( "ERROR: Unrecognized invocation arguments." );
- System.err.println( USAGE );
- return false;
- }
-
- //check script arg - print usage if incorrect
- if (!(args[0].equals("-f") || args[0].equals("-s"))){
- System.err.println("ERROR: First argument must be either -f or -s");
- System.err.println( USAGE );
- return false;
- }
-
//parse arguments and set execution properties
- RUNTIME_PLATFORM oldrtplatform = rtplatform; //keep old rtplatform
- ExplainType oldexplain = EXPLAIN; //keep old explain
-
- // Reset global flags to avoid errors in test suite
- ENABLE_DEBUG_MODE = false;
-
- boolean parsePyDML = false;
+ RUNTIME_PLATFORM oldrtplatform = rtplatform; //keep old rtplatform
+ ExplainType oldexplain = EXPLAIN; //keep old explain
+
+ Options options = createCLIOptions();
try
{
- String fnameOptConfig = null; //optional config filename
- String[] scriptArgs = null; //optional script arguments
- boolean namedScriptArgs = false;
-
- for( int i=2; i<args.length; i++ )
- {
- if( args[i].equalsIgnoreCase("-explain") ) {
- EXPLAIN = ExplainType.RUNTIME;
- if( args.length > (i+1) && !args[i+1].startsWith("-") )
- EXPLAIN = Explain.parseExplainType(args[++i]);
- }
- else if( args[i].equalsIgnoreCase("-stats") ) {
- STATISTICS = true;
- if (args.length > (i + 1) && !args[i + 1].startsWith("-"))
- STATISTICS_COUNT = Integer.parseInt(args[++i]);
- }
- else if ( args[i].equalsIgnoreCase("-exec")) {
- rtplatform = parseRuntimePlatform(args[++i]);
- if( rtplatform==null )
- return false;
- }
- else if (args[i].startsWith("-config=")) // legacy
- fnameOptConfig = args[i].substring(8).replaceAll("\"", "");
- else if (args[i].equalsIgnoreCase("-config"))
- fnameOptConfig = args[++i];
- else if( args[i].equalsIgnoreCase("-debug") ) {
- ENABLE_DEBUG_MODE = true;
- }
- else if( args[i].equalsIgnoreCase("-gpu") ) {
- USE_ACCELERATOR = true;
- if( args.length > (i+1) && !args[i+1].startsWith("-") ) {
- String flag = args[++i];
- if(flag.startsWith("force=")) {
- String [] flagOptions = flag.split("=");
- if(flagOptions.length == 2)
- FORCE_ACCELERATOR = Boolean.parseBoolean(flagOptions[1]);
- else
- throw new DMLRuntimeException("Unsupported \"force\" option for -gpu:" + flag);
- }
- else {
- throw new DMLRuntimeException("Unsupported flag for -gpu:" + flag);
- }
- }
- GPUContext.getGPUContext(); // creates the singleton GPU context object. Return value ignored.
- }
- else if( args[i].equalsIgnoreCase("-python") ) {
- parsePyDML = true;
- }
- else if (args[i].startsWith("-args") || args[i].startsWith("-nvargs")) {
- namedScriptArgs = args[i].startsWith("-nvargs"); i++;
- scriptArgs = new String[args.length - i];
- System.arraycopy(args, i, scriptArgs, 0, scriptArgs.length);
- break;
- }
- else{
- System.err.println("ERROR: Unknown argument: " + args[i]);
- return false;
- }
+ DMLOptions dmlOptions = parseCLArguments(args, options);
+
+ // String[] scriptArgs = null; //optional script arguments
+ // boolean namedScriptArgs = false;
+
+ STATISTICS = dmlOptions.stats;
+ STATISTICS_COUNT = dmlOptions.statsCount;
+ USE_ACCELERATOR = dmlOptions.gpu;
+ FORCE_ACCELERATOR = dmlOptions.forceGPU;
+ EXPLAIN = dmlOptions.explainType;
+ ENABLE_DEBUG_MODE = dmlOptions.debug;
+ SCRIPT_TYPE = dmlOptions.scriptType;
+ rtplatform = dmlOptions.execMode;
+
+ String fnameOptConfig = dmlOptions.configFile;
+ boolean isFile = dmlOptions.filePath != null;
+ String fileOrScript = isFile ? dmlOptions.filePath : dmlOptions.script;
+
+ boolean help = dmlOptions.help;
+
+ if (help) {
+ HelpFormatter formatter = new HelpFormatter();
+ formatter.printHelp( "systemml", options );
+ return true;
}
-
+
+ if (USE_ACCELERATOR){
+ GPUContext.getGPUContext();
+ }
+
+ if (dmlOptions.clean) {
+ cleanSystemMLWorkspace();
+ return true;
+ }
+
//set log level
if (!ENABLE_DEBUG_MODE)
setLoggingProperties( conf );
//Step 2: prepare script invocation
- if (StringUtils.endsWithIgnoreCase(args[1], ".pydml")) {
- parsePyDML = true;
+ if (isFile && StringUtils.endsWithIgnoreCase(fileOrScript, ".pydml")) {
+ SCRIPT_TYPE = ScriptType.PYDML;
}
- String dmlScriptStr = readDMLScript(args[0], args[1]);
- Map<String, String> argVals = createArgumentsMap(namedScriptArgs, scriptArgs);
-
- DML_FILE_PATH_ANTLR_PARSER = args[1];
+
+ String dmlScriptStr = readDMLScript(isFile, fileOrScript);
+ Map<String, String> argVals = dmlOptions.argVals;
+
+ DML_FILE_PATH_ANTLR_PARSER = dmlOptions.filePath;
//Step 3: invoke dml script
- printInvocationInfo(args[1], fnameOptConfig, argVals);
+ printInvocationInfo(fileOrScript, fnameOptConfig, argVals);
if (ENABLE_DEBUG_MODE) {
// inner try loop is just to isolate the debug exception, which will allow to manage the bugs from debugger v/s runtime
- launchDebugger(dmlScriptStr, fnameOptConfig, argVals, parsePyDML);
+ launchDebugger(dmlScriptStr, fnameOptConfig, argVals, SCRIPT_TYPE);
}
else {
- execute(dmlScriptStr, fnameOptConfig, argVals, args, parsePyDML);
+ execute(dmlScriptStr, fnameOptConfig, argVals, args, SCRIPT_TYPE);
}
}
+ catch(AlreadySelectedException e)
+ {
+ System.err.println("Mutually exclusive options were selected. " + e.getMessage());
+ HelpFormatter formatter = new HelpFormatter();
+ formatter.printHelp( "systemml", options );
+ return false;
+ }
+ catch(org.apache.commons.cli.ParseException e)
+ {
+ System.err.println(e.getMessage());
+ HelpFormatter formatter = new HelpFormatter();
+ formatter.printHelp( "systemml", options );
+ }
catch (ParseException pe) {
throw pe;
}
@@ -380,6 +549,17 @@ public class DMLScript
// private internal utils (argument parsing)
////////
+ @Deprecated()
+ /**
+ * Creates an argument map appropriate for consumption by the backend
+ * The only method using this is the legacy {@link MLContext} api.
+ * Once that is removed, this function should be removed as well.
+ * This method uses a fragile position based argument for -args & -nvargs
+ * @param hasNamedArgs true for named arguments, false for positional arguments
+ * @param args in "k=v" format for named arguments and "v" for positional arguments
+ * @return a map containing either ($K,V) or ($1,V) for named and positional arguments respectively
+ * @throws LanguageException when a named argument is an invalid identifier for DML/PyDML
+ */
protected static Map<String,String> createArgumentsMap(boolean hasNamedArgs, String[] args)
throws LanguageException
{
@@ -399,7 +579,7 @@ public class DMLScript
arg.equalsIgnoreCase("-stats") ||
arg.equalsIgnoreCase("-exec") ||
arg.equalsIgnoreCase("-debug") ||
- arg.startsWith("-config="))
+ arg.startsWith("-config"))
{
throw new LanguageException("-args or -nvargs must be the final argument for DMLScript!");
}
@@ -432,17 +612,26 @@ public class DMLScript
return argMap;
}
-
- protected static String readDMLScript( String argname, String script )
+
+
+ /**
+ * Reads the DML/PyDML script into a String
+ * @param isFile Whether the string argument is a path to a file or the script itself
+ * @param scriptOrFilename script or filename
+ * @return a string representation of the script
+ * @throws IOException if error
+ * @throws LanguageException if error
+ */
+ protected static String readDMLScript( boolean isFile, String scriptOrFilename )
throws IOException, LanguageException
{
- boolean fromFile = argname.equals("-f");
String dmlScriptStr;
- if( fromFile )
+ if( isFile )
{
+ String fileName = scriptOrFilename;
//read DML script from file
- if(script == null)
+ if(fileName == null)
throw new LanguageException("DML script path was not specified!");
StringBuilder sb = new StringBuilder();
@@ -450,21 +639,21 @@ public class DMLScript
try
{
//read from hdfs or gpfs file system
- if( script.startsWith("hdfs:")
- || script.startsWith("gpfs:") )
+ if( fileName.startsWith("hdfs:")
+ || fileName.startsWith("gpfs:") )
{
- if( !LocalFileUtils.validateExternalFilename(script, true) )
+ if( !LocalFileUtils.validateExternalFilename(fileName, true) )
throw new LanguageException("Invalid (non-trustworthy) hdfs filename.");
FileSystem fs = FileSystem.get(ConfigurationManager.getCachedJobConf());
- Path scriptPath = new Path(script);
+ Path scriptPath = new Path(fileName);
in = new BufferedReader(new InputStreamReader(fs.open(scriptPath)));
}
// from local file system
else
{
- if( !LocalFileUtils.validateExternalFilename(script, false) )
+ if( !LocalFileUtils.validateExternalFilename(fileName, false) )
throw new LanguageException("Invalid (non-trustworthy) local filename.");
- in = new BufferedReader(new FileReader(script));
+ in = new BufferedReader(new FileReader(fileName));
}
//core script reading
@@ -488,11 +677,12 @@ public class DMLScript
}
else
{
+ String scriptString = scriptOrFilename;
//parse given script string
- if(script == null)
+ if(scriptString == null)
throw new LanguageException("DML script was not specified!");
- InputStream is = new ByteArrayInputStream(script.getBytes());
+ InputStream is = new ByteArrayInputStream(scriptString.getBytes());
Scanner scan = new Scanner(is);
dmlScriptStr = scan.useDelimiter("\\A").next();
scan.close();
@@ -500,26 +690,7 @@ public class DMLScript
return dmlScriptStr;
}
-
- private static RUNTIME_PLATFORM parseRuntimePlatform( String platform )
- {
- RUNTIME_PLATFORM lrtplatform = null;
-
- if ( platform.equalsIgnoreCase("hadoop"))
- lrtplatform = RUNTIME_PLATFORM.HADOOP;
- else if ( platform.equalsIgnoreCase("singlenode"))
- lrtplatform = RUNTIME_PLATFORM.SINGLE_NODE;
- else if ( platform.equalsIgnoreCase("hybrid"))
- lrtplatform = RUNTIME_PLATFORM.HYBRID;
- else if ( platform.equalsIgnoreCase("spark"))
- lrtplatform = RUNTIME_PLATFORM.SPARK;
- else if ( platform.equalsIgnoreCase("hybrid_spark"))
- lrtplatform = RUNTIME_PLATFORM.HYBRID_SPARK;
- else
- System.err.println("ERROR: Unknown runtime platform: " + platform);
-
- return lrtplatform;
- }
+
private static void setLoggingProperties( Configuration conf )
{
@@ -552,7 +723,7 @@ public class DMLScript
* @param fnameOptConfig configuration file
* @param argVals map of argument values
* @param allArgs arguments
- * @param parsePyDML true if PYDML, false if DML
+ * @param scriptType type of script (DML or PyDML)
* @throws ParseException if ParseException occurs
* @throws IOException if IOException occurs
* @throws DMLRuntimeException if DMLRuntimeException occurs
@@ -560,10 +731,10 @@ public class DMLScript
* @throws HopsException if HopsException occurs
* @throws LopsException if LopsException occurs
*/
- private static void execute(String dmlScriptStr, String fnameOptConfig, Map<String,String> argVals, String[] allArgs, boolean parsePyDML)
+ private static void execute(String dmlScriptStr, String fnameOptConfig, Map<String,String> argVals, String[] allArgs, ScriptType scriptType)
throws ParseException, IOException, DMLRuntimeException, LanguageException, HopsException, LopsException
{
- SCRIPT_TYPE = parsePyDML ? ScriptType.PYDML : ScriptType.DML;
+ SCRIPT_TYPE = scriptType;
//print basic time and environment info
printStartExecInfo( dmlScriptStr );
@@ -582,7 +753,7 @@ public class DMLScript
//Step 3: parse dml script
Statistics.startCompileTimer();
- ParserWrapper parser = ParserFactory.createParser(parsePyDML);
+ ParserWrapper parser = ParserFactory.createParser(scriptType);
DMLProgram prog = parser.parse(DML_FILE_PATH_ANTLR_PARSER, dmlScriptStr, argVals);
//Step 4: construct HOP DAGs (incl LVA, validate, and setup)
@@ -700,7 +871,7 @@ public class DMLScript
* @param dmlScriptStr DML script contents (including new lines)
* @param fnameOptConfig Full path of configuration file for SystemML
* @param argVals Key-value pairs defining arguments of DML script
- * @param parsePyDML true if PYDML, false if DML
+ * @param scriptType type of script (DML or PyDML)
* @throws ParseException if ParseException occurs
* @throws IOException if IOException occurs
* @throws DMLRuntimeException if DMLRuntimeException occurs
@@ -709,7 +880,7 @@ public class DMLScript
* @throws HopsException if HopsException occurs
* @throws LopsException if LopsException occurs
*/
- private static void launchDebugger(String dmlScriptStr, String fnameOptConfig, Map<String,String> argVals, boolean parsePyDML)
+ private static void launchDebugger(String dmlScriptStr, String fnameOptConfig, Map<String,String> argVals, ScriptType scriptType)
throws ParseException, IOException, DMLRuntimeException, DMLDebuggerException, LanguageException, HopsException, LopsException
{
DMLDebuggerProgramInfo dbprog = new DMLDebuggerProgramInfo();
@@ -719,7 +890,8 @@ public class DMLScript
ConfigurationManager.setGlobalConfig(conf);
//Step 2: parse dml script
- ParserWrapper parser = ParserFactory.createParser(parsePyDML);
+
+ ParserWrapper parser = ParserFactory.createParser(scriptType);
DMLProgram prog = parser.parse(DML_FILE_PATH_ANTLR_PARSER, dmlScriptStr, argVals);
//Step 3: construct HOP DAGs (incl LVA and validate)