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Posted to commits@systemml.apache.org by na...@apache.org on 2017/04/21 21:53:46 UTC
incubator-systemml git commit: Use Apache Commons CLI to parse
command line arguments in DMLScript
Repository: incubator-systemml
Updated Branches:
refs/heads/gh-pages c4918f5b6 -> c5ff65305
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/c5ff6530
Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/c5ff6530
Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml/diff/c5ff6530
Branch: refs/heads/gh-pages
Commit: c5ff65305a5baeec20aec25bd6fa9ab73b1e1990
Parents: c4918f5
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
----------------------------------------------------------------------
algorithms-classification.md | 44 +++++++++++++++----------------
algorithms-clustering.md | 14 +++++-----
algorithms-descriptive-statistics.md | 14 +++++-----
algorithms-matrix-factorization.md | 18 ++++++-------
algorithms-regression.md | 36 ++++++++++++-------------
algorithms-survival-analysis.md | 16 +++++------
hadoop-batch-mode.md | 16 +++++------
spark-batch-mode.md | 4 +--
standalone-guide.md | 2 +-
troubleshooting-guide.md | 2 +-
10 files changed, 83 insertions(+), 83 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/c5ff6530/algorithms-classification.md
----------------------------------------------------------------------
diff --git a/algorithms-classification.md b/algorithms-classification.md
index b029e0a..ed56c34 100644
--- a/algorithms-classification.md
+++ b/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/c5ff6530/algorithms-clustering.md
----------------------------------------------------------------------
diff --git a/algorithms-clustering.md b/algorithms-clustering.md
index 023a7f5..0c91fa1 100644
--- a/algorithms-clustering.md
+++ b/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/c5ff6530/algorithms-descriptive-statistics.md
----------------------------------------------------------------------
diff --git a/algorithms-descriptive-statistics.md b/algorithms-descriptive-statistics.md
index 1ddf01a..f45ffae 100644
--- a/algorithms-descriptive-statistics.md
+++ b/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/c5ff6530/algorithms-matrix-factorization.md
----------------------------------------------------------------------
diff --git a/algorithms-matrix-factorization.md b/algorithms-matrix-factorization.md
index 51eb614..9af8c19 100644
--- a/algorithms-matrix-factorization.md
+++ b/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/c5ff6530/algorithms-regression.md
----------------------------------------------------------------------
diff --git a/algorithms-regression.md b/algorithms-regression.md
index 31f7ec2..494693c 100644
--- a/algorithms-regression.md
+++ b/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/c5ff6530/algorithms-survival-analysis.md
----------------------------------------------------------------------
diff --git a/algorithms-survival-analysis.md b/algorithms-survival-analysis.md
index a5e641e..239ab08 100644
--- a/algorithms-survival-analysis.md
+++ b/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/c5ff6530/hadoop-batch-mode.md
----------------------------------------------------------------------
diff --git a/hadoop-batch-mode.md b/hadoop-batch-mode.md
index ddc1c1f..3af7c0c 100644
--- a/hadoop-batch-mode.md
+++ b/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/c5ff6530/spark-batch-mode.md
----------------------------------------------------------------------
diff --git a/spark-batch-mode.md b/spark-batch-mode.md
index c199b1f..39bcd3e 100644
--- a/spark-batch-mode.md
+++ b/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/c5ff6530/standalone-guide.md
----------------------------------------------------------------------
diff --git a/standalone-guide.md b/standalone-guide.md
index 2c2092d..586e56e 100644
--- a/standalone-guide.md
+++ b/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/c5ff6530/troubleshooting-guide.md
----------------------------------------------------------------------
diff --git a/troubleshooting-guide.md b/troubleshooting-guide.md
index 629bcf5..4731f51 100644
--- a/troubleshooting-guide.md
+++ b/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.