-
Notifications
You must be signed in to change notification settings - Fork 391
/
OpWorkflowRunner.scala
459 lines (417 loc) · 18.8 KB
/
OpWorkflowRunner.scala
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
/*
* Copyright (c) 2017, Salesforce.com, Inc.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* * Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* * Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* * Neither the name of the copyright holder nor the names of its
* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
package com.salesforce.op
import java.io.File
import java.nio.file.Paths
import com.github.fommil.netlib.{BLAS, LAPACK}
import com.salesforce.op.evaluators.{EvaluationMetrics, OpEvaluatorBase}
import com.salesforce.op.features.OPFeature
import com.salesforce.op.readers.{Reader, StreamingReader}
import com.salesforce.op.utils.date.DateTimeUtils
import com.salesforce.op.utils.json.{EnumEntrySerializer, JsonLike, JsonUtils}
import com.salesforce.op.utils.spark.RichRDD._
import com.salesforce.op.utils.spark.{AppMetrics, OpSparkListener}
import com.salesforce.op.utils.version.VersionInfo
import enumeratum._
import org.apache.hadoop.io.compress.CompressionCodec
import org.apache.hadoop.mapred.JobConf
import org.apache.spark.scheduler.{SparkListener, SparkListenerApplicationEnd}
import org.apache.spark.sql.SparkSession
import org.apache.spark.streaming.StreamingContext
import org.slf4j.LoggerFactory
import scala.collection.mutable.ArrayBuffer
import scala.reflect.runtime.universe.WeakTypeTag
import scala.util.{Failure, Success, Try}
/**
* A class for running an TransmogrifAI Workflow.
* Provides methods to train, score, evaluate and computeUpTo for TransmogrifAI Workflow.
*
* @param workflow the workflow that you want to run (Note: the workflow should have the resultFeatures set)
* @param trainingReader reader to use to load up data for training
* @param scoringReader reader to use to load up data for scoring
* @param evaluationReader reader to use to load up data for evaluation
* @param streamingScoreReader reader to use to load up data for streaming score
* @param evaluator evaluator that you wish to use to evaluate the results of your workflow on a test dataset
* @param scoringEvaluator optional scoring evaluator that you wish to use when scoring
* @param featureToComputeUpTo feature to generate data upto if calling the 'Features' run type
*/
class OpWorkflowRunner
(
val workflow: OpWorkflow,
val trainingReader: Reader[_],
val scoringReader: Reader[_],
val evaluationReader: Option[Reader[_]] = None,
val streamingScoreReader: Option[StreamingReader[_]] = None,
val evaluator: Option[OpEvaluatorBase[_ <: EvaluationMetrics]] = None,
val scoringEvaluator: Option[OpEvaluatorBase[_ <: EvaluationMetrics]] = None,
val featureToComputeUpTo: Option[OPFeature] = None
) extends Serializable {
/**
* OpWorkflowRunner ctor
*
* @param workflow the workflow that you want to run
* (Note: the workflow should have the resultFeatures set)
* @param trainingReader reader to use to load up data for training
* @param scoringReader reader to use to load up data for scoring
* @param evaluationReader reader to use to load up data for evaluation
* @param evaluator evaluator that you wish to use to evaluate
* the results of your workflow on a test dataset
* @param scoringEvaluator optional scoring evaluator that you wish to use when scoring
* @param featureToComputeUpTo feature to generate data upto if calling the 'Features' run type
* @return OpWorkflowRunner
*/
@deprecated("Use alternative ctor", "3.2.3")
def this(
workflow: OpWorkflow,
trainingReader: Reader[_],
scoringReader: Reader[_],
evaluationReader: Option[Reader[_]],
evaluator: OpEvaluatorBase[_ <: EvaluationMetrics],
scoringEvaluator: Option[OpEvaluatorBase[_ <: EvaluationMetrics]],
featureToComputeUpTo: OPFeature
) = this(
workflow = workflow, trainingReader = trainingReader, scoringReader = scoringReader,
evaluationReader = evaluationReader, streamingScoreReader = None, evaluator = Option(evaluator),
scoringEvaluator = scoringEvaluator, featureToComputeUpTo = Option(featureToComputeUpTo)
)
/**
* OpWorkflowRunner ctor
*
* @param workflow the workflow that you want to run
* (Note: the workflow should have the resultFeatures set)
* @param trainingReader reader to use to load up data for training
* @param scoringReader reader to use to load up data for scoring
* @param evaluator evaluator that you wish to use to evaluate
* the results of your workflow on a test dataset
* @param featureToComputeUpTo feature to generate data upto if calling the 'Features' run type
* @return OpWorkflowRunner
*/
@deprecated("Use alternative ctor", "3.2.3")
def this(
workflow: OpWorkflow,
trainingReader: Reader[_],
scoringReader: Reader[_],
evaluator: OpEvaluatorBase[_ <: EvaluationMetrics],
featureToComputeUpTo: OPFeature
) = this(
workflow = workflow, trainingReader = trainingReader, scoringReader = scoringReader, evaluationReader = None,
streamingScoreReader = None, evaluator = Option(evaluator), scoringEvaluator = None,
featureToComputeUpTo = Option(featureToComputeUpTo)
)
@transient protected lazy val log = LoggerFactory.getLogger(classOf[OpWorkflowRunner])
// Handles collecting the metrics on app completion
private val onAppEndHandlers = ArrayBuffer.empty[AppMetrics => Unit]
/**
* Add handle to collect the app metrics when the application ends
* @param h a handle to collect the app metrics when the application ends
*/
final def addApplicationEndHandler(h: AppMetrics => Unit): this.type = {
onAppEndHandlers += h
this
}
private def onApplicationEnd(metrics: AppMetrics): Unit = {
onAppEndHandlers.foreach(h =>
Try(h(metrics)).recover { case e => log.error("Failed to handle application end event", e) }
)
}
/**
* This method is called to train your workflow
*
* @param params parameters injected at runtime
* @param spark spark context which runs the workflow
* @return TrainResult
*/
protected def train(params: OpParams)(implicit spark: SparkSession): OpWorkflowRunnerResult = {
val workflowModel = workflow.setReader(trainingReader).train()
workflowModel.save(params.modelLocation.get)
val modelSummary = workflowModel.summary()
for {
location <- params.metricsLocation
metrics = spark.sparkContext.parallelize(Seq(modelSummary), 1)
jobConf = {
val conf = new JobConf(spark.sparkContext.hadoopConfiguration)
conf.set("mapred.output.compress", params.metricsCompress.getOrElse(false).toString)
conf
}
metricCodecClass = params.metricsCodec.map(Class.forName(_).asInstanceOf[Class[_ <: CompressionCodec]])
} metrics.saveAsTextFile(location, metricCodecClass, jobConf)
new TrainResult(modelSummary)
}
/**
* This method can be used to generate features from part of your workflow for exploration outside the app
*
* @param params parameters injected at runtime
* @param spark spark context which runs the workflow
* @return FeaturesResult
*/
protected def computeFeatures(params: OpParams)(implicit spark: SparkSession): OpWorkflowRunnerResult = {
require(featureToComputeUpTo.isDefined,
"The computeFeatures method requires featureToComputeUpTo to be specified")
workflow.setReader(trainingReader).computeDataUpTo(featureToComputeUpTo.get, params.writeLocation.get)
new FeaturesResult()
}
/**
* This method is used to load up a previously trained workflow and use it to score a new dataset
*
* @param params parameters injected at runtime
* @param spark spark context which runs the workflow
* @return ScoreResult
*/
protected def score(params: OpParams)(implicit spark: SparkSession): OpWorkflowRunnerResult = {
val workflowModel =
workflow.loadModel(params.modelLocation.get)
.setReader(scoringReader)
.setParameters(params)
val metrics = scoringEvaluator match {
case None =>
workflowModel.score(path = params.writeLocation)
None
case Some(e) =>
val (_, metrcs) = workflowModel.scoreAndEvaluate(
evaluator = e, path = params.writeLocation, metricsPath = params.metricsLocation
)
Option(metrcs)
}
new ScoreResult(metrics)
}
/**
* This method is used to load up a previously trained workflow and use it to stream scores to a write location
*
* @param params parameters injected at runtime
* @param spark spark context which runs the workflow
* @param streaming spark streaming context which runs the workflow
* @return StreamingScoreResult
*/
protected def streamingScore(params: OpParams)
(implicit spark: SparkSession, streaming: StreamingContext): OpWorkflowRunnerResult = {
require(streamingScoreReader.isDefined,
"The streamingScore method requires an streaming score reader to be specified")
// Prepare write path
def writePath(timeInMs: Long) = Some(Paths.get(params.writeLocation.get, timeInMs.toString).toString)
// Load the model to score with and prepare the scoring function
val workflowModel = workflow.loadModel(params.modelLocation.get).setParameters(params)
val scoreFn: Option[String] => _ = workflowModel.scoreFn(
keepRawFeatures = false, keepIntermediateFeatures = false, persistScores = false
)
// Get the streaming score reader and create input stream
val reader = streamingScoreReader.get.asInstanceOf[StreamingReader[Any]]
val inputStream = reader.stream(params)
inputStream.foreachRDD(rdd => {
// Only score non empty datasets
if (!rdd.isEmpty()) {
val start = DateTimeUtils.now().getMillis
log.info("Scoring a records batch")
// Set input rdd for the workflow to score
workflowModel.setInputRDD[Any](rdd, reader.key)(reader.wtt.asInstanceOf[WeakTypeTag[Any]])
val path = writePath(start) // Prepare write path
scoreFn(path) // Score & save it
log.info("Scored a records batch in {}ms. Saved scores to {}", DateTimeUtils.now().getMillis - start, path)
}
})
new StreamingScoreResult()
}
/**
* This method is used to call the evaluator on a test set of data scored by the trained workflow
*
* @param params parameters injected at runtime
* @param spark spark context which runs the workflow
* @return EvaluateResult
*/
protected def evaluate(params: OpParams)(implicit spark: SparkSession): OpWorkflowRunnerResult = {
require(evaluationReader.isDefined, "The evaluate method requires an evaluation reader to be specified")
require(evaluator.isDefined, "The evaluate method requires an evaluator to be specified")
val workflowModel =
workflow.loadModel(params.modelLocation.get)
.setReader(evaluationReader.get)
.setParameters(params)
val metrics = workflowModel.evaluate(
evaluator = evaluator.get, metricsPath = params.metricsLocation, scoresPath = params.writeLocation
)
new EvaluateResult(metrics)
}
/**
* Run using a specified runner config
*
* @param runType workflow run type
* @param opParams parameters injected at runtime
* @param spark spark context which runs the workflow
* @param streaming spark streaming context which runs the workflow
* @return runner result
*/
def run(runType: OpWorkflowRunType, opParams: OpParams)
(implicit spark: SparkSession, streaming: StreamingContext): OpWorkflowRunnerResult = {
log.info("OP version info:\n{}", VersionInfo().toJson())
log.info("Assuming OP params:\n{}", opParams)
workflow.setParameters(opParams)
log.info("BLAS implementation provided by: {}", BLAS.getInstance().getClass.getName)
log.info("LAPACK implementation provided by: {}", LAPACK.getInstance().getClass.getName)
val listener = sparkListener(runType, opParams)
spark.sparkContext.addSparkListener(listener)
val result = runType match {
case OpWorkflowRunType.Train => train(opParams)
case OpWorkflowRunType.Score => score(opParams)
case OpWorkflowRunType.Features => computeFeatures(opParams)
case OpWorkflowRunType.Evaluate => evaluate(opParams)
case OpWorkflowRunType.StreamingScore =>
val res = streamingScore(opParams)
val timeoutInMs = opParams.awaitTerminationTimeoutSecs.map(_ * 1000L).getOrElse(-1L)
try {
streaming.start()
streaming.awaitTerminationOrTimeout(timeoutInMs)
} catch { case e: Exception => log.error("Streaming context error: ", e) }
res
}
log.info(result.toString)
result
}
private def sparkListener(
runType: OpWorkflowRunType,
opParams: OpParams
)(implicit spark: SparkSession): SparkListener = {
val collectStageMetrics = opParams.collectStageMetrics.exists {
case true if runType == OpWorkflowRunType.StreamingScore =>
log.warn("Stage metrics collection is not available in streaming context")
false
case v => v
}
new OpSparkListener(
appName = spark.sparkContext.appName,
appId = spark.sparkContext.applicationId,
runType = runType.toString,
customTagName = opParams.customTagName,
customTagValue = opParams.customTagValue,
logStageMetrics = opParams.logStageMetrics.getOrElse(false),
collectStageMetrics = collectStageMetrics
) {
override def onApplicationEnd(applicationEnd: SparkListenerApplicationEnd): Unit = {
super.onApplicationEnd(applicationEnd)
val m = super.metrics
log.info("Total run time: {}", m.appDurationPretty)
OpWorkflowRunner.this.onApplicationEnd(m)
}
}
}
}
sealed trait OpWorkflowRunType extends EnumEntry with Serializable
object OpWorkflowRunType extends Enum[OpWorkflowRunType] {
val values = findValues
case object Train extends OpWorkflowRunType
case object Score extends OpWorkflowRunType
case object StreamingScore extends OpWorkflowRunType
case object Features extends OpWorkflowRunType
case object Evaluate extends OpWorkflowRunType
}
/**
* OpWorkflowRunner configuration container
*
* @param runType workflow run type
* @param paramLocation workflow file params location
* @param defaultParams default params to use in case the file params is missing
* @param readLocations read locations
* @param writeLocation write location
* @param modelLocation model location
* @param metricsLocation metrics location
*/
case class OpWorkflowRunnerConfig
(
runType: OpWorkflowRunType = null,
paramLocation: Option[String] = None,
defaultParams: OpParams = new OpParams(),
readLocations: Map[String, String] = Map.empty,
writeLocation: Option[String] = None,
modelLocation: Option[String] = None,
metricsLocation: Option[String] = None
) extends JsonLike {
override def toJson(pretty: Boolean = true): String = {
val serdes = EnumEntrySerializer.jackson[OpWorkflowRunType](OpWorkflowRunType)
JsonUtils.toJsonString(this, pretty = pretty, serdes = Seq(serdes))
}
/**
* Convert the runner config into OpParams
*
* @return OpParams
*/
def toOpParams: Try[OpParams] = Try {
// Load params from paramLocation if specified
val params = paramLocation match {
case None => defaultParams
case Some(pl) =>
OpParams.fromFile(new File(pl)) match {
case Failure(e) => throw new IllegalArgumentException(s"Failed to parse OP params from $pl", e)
case Success(p) => p
}
}
// Command line arguments take precedence over params file values
params.withValues(
readLocations = readLocations,
writeLocation = writeLocation,
modelLocation = modelLocation,
metricsLocation = metricsLocation
)
}
/**
* Validate the config
*
* @return either error or op params
*/
def validate: Either[String, OpParams] =
this.toOpParams match {
case Failure(e) => Left(e.toString)
case Success(opParams) => runType match {
case OpWorkflowRunType.Train if opParams.modelLocation.isEmpty =>
Left("Must provide location to store model when training")
case OpWorkflowRunType.Score if opParams.modelLocation.isEmpty || opParams.writeLocation.isEmpty =>
Left("Must provide locations to read model and write data when scoring")
case OpWorkflowRunType.StreamingScore if opParams.modelLocation.isEmpty || opParams.writeLocation.isEmpty =>
Left("Must provide locations to read model and write data when streaming score")
case OpWorkflowRunType.Features if opParams.writeLocation.isEmpty =>
Left("Must provide location to write data when generating features")
case OpWorkflowRunType.Evaluate if opParams.modelLocation.isEmpty || opParams.metricsLocation.isEmpty =>
Left("Must provide locations to read model and write metrics when evaluating")
case _ => Right(opParams)
}
}
}
trait OpWorkflowRunnerResult extends Serializable
class TrainResult(val modelSummary: String) extends OpWorkflowRunnerResult {
override def toString: String = s"Train result: $modelSummary"
}
class ScoreResult(val metrics: Option[EvaluationMetrics]) extends OpWorkflowRunnerResult {
override def toString: String = s"Score result: ${metrics.getOrElse("{}")}"
}
class StreamingScoreResult() extends OpWorkflowRunnerResult {
override def toString: String = "Streaming Score result: {}"
}
class FeaturesResult() extends OpWorkflowRunnerResult {
override def toString: String = "Features result: {}"
}
class EvaluateResult(val metrics: EvaluationMetrics) extends OpWorkflowRunnerResult {
override def toString: String = s"Evaluation result: $metrics"
}