Repository: spark Updated Branches: refs/heads/master 0fb982555 -> aa305dcaf
[SPARK-11964][DOCS][ML] Add in Pipeline Import/Export Documentation Adding in Pipeline Import and Export Documentation. Author: anabranch <wac.chamb...@gmail.com> Author: Bill Chambers <wchamb...@ischool.berkeley.edu> Closes #10179 from anabranch/master. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/aa305dca Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/aa305dca Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/aa305dca Branch: refs/heads/master Commit: aa305dcaf5b4148aba9e669e081d0b9235f50857 Parents: 0fb9825 Author: anabranch <wac.chamb...@gmail.com> Authored: Fri Dec 11 12:55:56 2015 -0800 Committer: Joseph K. Bradley <jos...@databricks.com> Committed: Fri Dec 11 12:55:56 2015 -0800 ---------------------------------------------------------------------- docs/ml-guide.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/aa305dca/docs/ml-guide.md ---------------------------------------------------------------------- diff --git a/docs/ml-guide.md b/docs/ml-guide.md index 5c96c2b..44a316a 100644 --- a/docs/ml-guide.md +++ b/docs/ml-guide.md @@ -192,6 +192,10 @@ Parameters belong to specific instances of `Estimator`s and `Transformer`s. For example, if we have two `LogisticRegression` instances `lr1` and `lr2`, then we can build a `ParamMap` with both `maxIter` parameters specified: `ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)`. This is useful if there are two algorithms with the `maxIter` parameter in a `Pipeline`. +## Saving and Loading Pipelines + +Often times it is worth it to save a model or a pipeline to disk for later use. In Spark 1.6, a model import/export functionality was added to the Pipeline API. Most basic transformers are supported as well as some of the more basic ML models. Please refer to the algorithm's API documentation to see if saving and loading is supported. + # Code examples This section gives code examples illustrating the functionality discussed above. @@ -455,6 +459,15 @@ val pipeline = new Pipeline() // Fit the pipeline to training documents. val model = pipeline.fit(training) +// now we can optionally save the fitted pipeline to disk +model.save("/tmp/spark-logistic-regression-model") + +// we can also save this unfit pipeline to disk +pipeline.save("/tmp/unfit-lr-model") + +// and load it back in during production +val sameModel = Pipeline.load("/tmp/spark-logistic-regression-model") + // Prepare test documents, which are unlabeled (id, text) tuples. val test = sqlContext.createDataFrame(Seq( (4L, "spark i j k"), --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
