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Simple and Distributed Machine Learning
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Saving lgb model and loading in local Python #738

Open allard-jeff opened 5 years ago

allard-jeff commented 5 years ago

I am using mmlspark_2.11:0.18.1 (unable to access the newest version: see https://github.com/Azure/mmlspark/issues/715)

I am trying to save the actual python based lightgbm model to then load into local python (to apply Shap values). How to do this?

If I run the following: ....

model = pipelineModel.fit(train)
model.stages[2].saveNativeModel("/home/hadoop/mymodel.mod")

It doesn't throw an error but nothing is actually written.

Would this - if it worked - produce a model object that can be read into python like:? bst = lgb.Booster(model_file='/home/hadoop/mymodel2.mod')

imatiach-msft commented 5 years ago

@allard-jeff sorry about the trouble you are having. I believe this path would go to either dbfs, hdfs or wasb (specifically for azure, azure blob storage). To make it go to a local disk file I think you would need to specify file:/home/hadoop/mymodel.mod. saveNativeModel should work and output the file. Note it would only be on the driver node in that path in that case (not on the worker nodes).

allard-jeff commented 5 years ago

@imatiach-msft Unfortunately,model.stages[2].saveNativeModel("file:/home/hadoop/mymodel.mod") fails with error:

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-18-6f08d3fdb92e> in <module>
----> 1 model.stages[2].saveNativeModel("file:/home/hadoop/mymodel.mod")
      2 #mod2=LightGBMClassifier.load("/home/hadoop/mymodel2.mod")
      3 #import lightgbm as lgb
      4 #import shap

/mnt/tmp/spark-9845ff34-d981-4622-84c8-ba3942f50a89/userFiles-5604f34b-853e-4672-9d69-6f40989275ee/com.microsoft.ml.spark_mmlspark_2.11-0.18.1.jar/mmlspark/lightgbm/LightGBMClassifier.py in saveNativeModel(self, filename, overwrite)
     30         Save the booster as string format to a local or WASB remote location.
     31         """
---> 32         self._java_obj.saveNativeModel(filename, overwrite)
     33 
     34     @staticmethod

/usr/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py in __call__(self, *args)
   1255         answer = self.gateway_client.send_command(command)
   1256         return_value = get_return_value(
-> 1257             answer, self.gateway_client, self.target_id, self.name)
   1258 
   1259         for temp_arg in temp_args:

/usr/lib/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
     61     def deco(*a, **kw):
     62         try:
---> 63             return f(*a, **kw)
     64         except py4j.protocol.Py4JJavaError as e:
     65             s = e.java_exception.toString()

/usr/lib/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling o283.saveNativeModel.
: org.apache.spark.SparkException: Job aborted.
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:198)
    at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:159)
    at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
    at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)
    at org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:156)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
    at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
    at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
    at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
    at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
    at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
    at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
    at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:676)
    at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:285)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:271)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:229)
    at org.apache.spark.sql.DataFrameWriter.text(DataFrameWriter.scala:614)
    at com.microsoft.ml.spark.lightgbm.LightGBMBooster.saveNativeModel(LightGBMBooster.scala:112)
    at com.microsoft.ml.spark.lightgbm.LightGBMClassificationModel.saveNativeModel(LightGBMClassifier.scala:174)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 94.0 failed 4 times, most recent failure: Lost task 0.3 in stage 94.0 (TID 134, ip-172-31-15-37.ec2.internal, executor 8): java.io.IOException: Mkdirs failed to create file:/home/hadoop/mymodel.mod/_temporary/0/_temporary/attempt_20191115124257_0094_m_000000_134 (exists=false, cwd=file:/mnt1/yarn/usercache/hadoop/appcache/application_1573819907406_0001/container_1573819907406_0001_01_000010)
    at org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:456)
    at org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:441)
    at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:932)
    at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:913)
    at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:810)
    at org.apache.spark.sql.execution.datasources.CodecStreams$.createOutputStream(CodecStreams.scala:81)
    at org.apache.spark.sql.execution.datasources.text.TextOutputWriter.<init>(TextFileFormat.scala:151)
    at org.apache.spark.sql.execution.datasources.text.TextFileFormat$$anon$1.newInstance(TextFileFormat.scala:84)
    at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.newOutputWriter(FileFormatDataWriter.scala:120)
    at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.<init>(FileFormatDataWriter.scala:108)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:236)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:170)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:121)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:2041)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2029)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2028)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2028)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:966)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2262)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2211)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2200)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:777)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:167)
    ... 35 more
Caused by: java.io.IOException: Mkdirs failed to create file:/home/hadoop/mymodel.mod/_temporary/0/_temporary/attempt_20191115124257_0094_m_000000_134 (exists=false, cwd=file:/mnt1/yarn/usercache/hadoop/appcache/application_1573819907406_0001/container_1573819907406_0001_01_000010)
    at org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:456)
    at org.apache.hadoop.fs.ChecksumFileSystem.create(ChecksumFileSystem.java:441)
    at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:932)
    at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:913)
    at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:810)
    at org.apache.spark.sql.execution.datasources.CodecStreams$.createOutputStream(CodecStreams.scala:81)
    at org.apache.spark.sql.execution.datasources.text.TextOutputWriter.<init>(TextFileFormat.scala:151)
    at org.apache.spark.sql.execution.datasources.text.TextFileFormat$$anon$1.newInstance(TextFileFormat.scala:84)
    at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.newOutputWriter(FileFormatDataWriter.scala:120)
    at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.<init>(FileFormatDataWriter.scala:108)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:236)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:170)
    at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:121)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    ... 1 more
allard-jeff commented 5 years ago

What I have found is that what is possible is to save the model to a (in this case AWS S3) remote location. I am then able to load in the model to local python via lgb.Booster(model_file=)

imatiach-msft commented 5 years ago

@allard-jeff the file:/ prefix should supposedly work if you are trying to save it locally. I can't tell for sure, but based on the error above I wonder if there might be some permissions issue?

Caused by: java.io.IOException: Mkdirs failed to create file:/home/hadoop/mymodel.mod/_temporary/0/_temporary/attempt_20191115124257_0094_m_000000_134 (exists=false, cwd=file:/mnt1/yarn/usercache/hadoop/appcache/application_1573819907406_0001/container_1573819907406_0001_01_000010)
ODemidenko commented 2 years ago

Is there any progress? Is it possible to train lgbm model with SynapseML and to serve through a standard single-node LGBM implementation?