Can anyone tell what is libraryDependencies for TFNerDLGraphBuilder() for Spark with Scala? It gives me error, Cannot resolve symbol TFNerDLGraphBuilder
I see it works for notebook as given below
https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Public/4.NERDL_Training.ipynb
TensorFlow graphs in Spark NLP are built using TF python api. As far as I know, the java version for creating the Conv1D/BiLSTM/CRC graph is not included.
So, you need to create it first following the instructions in:
https://nlp.johnsnowlabs.com/docs/en/training#tensorflow-graphs
That will create a pb TensorFlow file that you have to include in the NerDLApproach annotator. For example:
val nerTagger = new NerDLApproach()
.setInputCols("sentence", "token", "embeddings")
.setOutputCol("ner")
.setLabelColumn("label")
.setMaxEpochs(100)
.setRandomSeed(0)
.setPo(0.03f)
.setLr(0.2f)
.setDropout(0.5f)
.setBatchSize(100)
.setVerbose(Verbose.Epochs)
.setGraphFolder(TfGrpahPath)
Note that you have to include the embedding annotation first and that the training process will be executed in the driver. It is not distributed as it could be with BigDL.
Related
Title says it all really, I'm trying to find the latest version of a delta table but because im testing locally I dont have access to data-bricks.
Tried googling but not much luck im afraid.
If you configure SparkSession correctly as described in the documentation, then you can run SQL commands as well. But you can also access history using the Python or Scala APIs (see docs), like this:
from delta.tables import *
deltaTable = DeltaTable.forPath(spark, pathToTable)
fullHistoryDF = deltaTable.history()
So, I made an Scala Application to run in Spark, and created the Uber Jar using sbt> assembly.
The file I load is a lookup needed by the application, thus the idea is to package it together. It works fine from within InteliJ using the path "src/main/resources/lookup01.csv"
I am developing in Windows, testing locally, to after deploy it to a remote test server.
But when I call spark-submit on the Windows machine, I get the error :
"org.apache.spark.sql.AnalysisException: Path does not exist: file:/H:/dev/Spark/spark-2.4.3-bin-hadoop2.7/bin/src/main/resources/"
Seems it tries to find the file in the sparkhome location instead of from inside the JAr file.
How could I express the Path so it works looking the file from within the JAR package?
Example code of the way I load the Dataframe. After loading it I transform it into other structures like Maps.
val v_lookup = sparkSession.read.option( "header", true ).csv( "src/main/resources/lookup01.csv")
What I would like to achieve is getting as way to express the path so it works in every environment I try to run the JAR, ideally working also from within InteliJ while developing.
Edit: scala version is 2.11.12
Update:
Seems that to get a hand in the file inside the JAR, I have to read it as a stream, the bellow code worked, but I cant figure out a secure way to extract the headers of the file such as SparkSession.read.option has.
val fileStream = scala.io.Source.getClass.getResourceAsStream("/lookup01.csv")
val inputDF = sparkSession.sparkContext.makeRDD(scala.io.Source.fromInputStream(fileStream).getLines().toList).toDF
When the makeRDD is applied, I get the RDD and then can convert it to a dataframe, but it seems I lost the ability tu use the option from "read" that parsed out the headers as the schema.
Any way around it when using makeRDD ?
Other problem with this is that seems that I will have to manually parse the lines into columns.
You have to get the correct path from classPath
Considering that your file is under src/main/resources:
val path = getClass.getResource("/lookup01.csv")
val v_lookup = sparkSession.read.option( "header", true ).csv(path)
So, it all points to that after the file is inside JAR, it can only be accessed as a inputstream to read the chunk of data from within the compressed file.
I arrived at a solution, even though its not pretty it does what I need, that is to read a csv file, take the 2 first columns and make it into a dataframe and after load it inside a key-value structure (in this case i created a case class to hold these pairs).
I am considering migrating these lookups to a HOCON file, that may make the process less convoluted to load these lookups
import sparkSession.implicits._
val fileStream = scala.io.Source.getClass.getResourceAsStream("/lookup01.csv")
val input = sparkSession.sparkContext.makeRDD(scala.io.Source.fromInputStream(fileStream).getLines().toList).toDF()
val myRdd = input.map {
line =>
val col = utils.Utils.splitCSVString(line.getString(0))
KeyValue(col(0), col(1))
}
val myDF = myRdd.rdd.map(x => (x.key, x.value)).collectAsMap()
fileStream.close()
In Spark MLlib, BisectingKMeansModel in pyspark have no save/load function.
why?
How to save or load the BisectingKMeans Model with Python to HDFS ?
It may be your spark version. For bisecting k_means is recommended to have above 2.1.0.
You can find a complete example here on the class pyspark.ml.clustering.BisectingKMeans, hope it helps:
https://spark.apache.org/docs/2.1.0/api/python/pyspark.ml.html#pyspark.ml.clustering.BisectingKMeans%20featuresCol=%22features%22,%20predictionCol=%22prediction%22
The last part of the example code include a model save/load:
model_path = temp_path + "/bkm_model"
model.save(model_path)
model2 = BisectingKMeansModel.load(model_path)
It works for hdfs as well, but make sure that temp_path/bkm_model folder does not exist before saving the model or it will give you an error:
(java.io.IOException: Path <temp_path>/bkm_model already exists)
I am trying to load google's Pre-trained vectors 'GoogleNews-vectors-negative300.bin.gz' Google-word2vec into spark.
I converted the bin file to txt and created a smaller chunk for testing that I called 'vectors.txt'. I tried loading it as the following:
val sparkSession = SparkSession.builder
.master("local[*]")
.appName("Word2VecExample")
.getOrCreate()
val model2= Word2VecModel.load(sparkSession.sparkContext, "src/main/resources/vectors.txt")
val synonyms = model2.findSynonyms("the", 5)
for((synonym, cosineSimilarity) <- synonyms) {
println(s"$synonym $cosineSimilarity")
}
and to my surprise I am faced with the following error:
Exception in thread "main" org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: file:/home/elievex/Repository/ARCANA/src/main/resources/vectors.txt/metadata
I'm not sure where did the 'metadata' after 'vectors.txt' came from.
I am using Spark, Scala and Scala IDE for Eclipse.
What am I doing wrong? is there a different way to load a pre-trained model in spark? Would appreciate any tips.
How exactly did you get vector.txt? If you read JavaDoc for Word2VecModel.save you may see that:
This saves: - human-readable (JSON) model metadata to path/metadata/ - Parquet formatted data to path/data/
The model may be loaded using Loader.load.
So what you need is model in Parquet format which is standard for Spark ML models.
Unfortunately load from Google's native format has not been implemented yet (see SPARK-9484).
I'm using Fluent library to fire a request to Natural Language Classifier service so as to 'train' the data.
Documentation says following parameters to be passed:
name=training_data; type=file; description=training data
name=training_meta_data; type=file; description=meta data to identify language etc
Below is my code sample:
File trainingCSVFile = new File("path to training file");
Request request=Request.Post(<bluemix service url>).
bodyFile(trainingCSVFile, ContentType.TEXT_PLAIN).
bodyString("{\"language\":\"en\",\"name\":\"PaymentDataClassifier\"}", ContentType.APPLICATION_JSON);
How ever am getting internal server error which plausibly due to my request format. Can any one help me how to pass the above mentioned parameters using Fluent library on priority?
I'm going to assume that you are using Java and suggest you to use the Java SDK. You can find examples to use not only Natural language Classifier but all the Watson services + Alchemy services.
Installation
Download the jar
or use Maven
<dependency>
<groupId>com.ibm.watson.developer_cloud</groupId>
<artifactId>java-sdk</artifactId>
<version>2.10.0</version>
</dependency>
or use Gradle
'com.ibm.watson.developer_cloud:java-sdk:2.10.0'
The code snippet to create a classifier is:
NaturalLanguageClassifier service = new NaturalLanguageClassifier();
service.setUsernameAndPassword("<username>", "<password>");
File trainingData = new File("/path/to/csv/file.csv");
Classifier classifier = service.createClassifier("PaymentDataClassifier", "en", trainingData);
System.out.println(classifier);
The training duration will depend on your data but once it's trained you can do:
Classification classification = service.classify(classifier.getId(), "Is it sunny?");
System.out.println(classification);
Feel free to open an issue in the GitHub repo if you have problems