![Numerical dataflow with Spark
df = sqlContext.createDataFrame(…)
x = tf.placeholder(tf.int32, name=“x”)
y = tf.placeholder(tf.int32, name=“y”)
output = tf.add(x, 3 * y, name=“z”)
output_df = tfs.map_rows(output, df)
output_df.collect()
df: DataFrame[x: int, y: int]
output_df:
DataFrame[x: int, y: int, z: int]
x:
int32
y:
int32
mul 3
z](https://image.slidesharecdn.com/2016-06-03sparkmeetuptensorframes-160608003855/85/Spark-Meetup-TensorFrames-17-320.jpg)
varidx = 0
while(idx < N) {
val z_k = points(idx)
dis(idx) = - (x - z_k) * (x - z_k) / ( 2 * b * b)
idx += 1
}
val minDis = dis.min
varexpSum = 0.0
idx = 0
while(idx < N) {
expSum += math.exp(dis(idx) - minDis)
idx += 1
}
minDis - math.log(b * N) + math.log(expSum)
}
val scoreUDF = sqlContext.udf.register("scoreUDF", score _)
sql("select sum(scoreUDF(sample)) from samples").collect()](https://image.slidesharecdn.com/2016-06-03sparkmeetuptensorframes-160608003855/85/Spark-Meetup-TensorFrames-24-320.jpg)
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Spark Meetup TensorFrames
- 1. TensorFrames: Google Tensorflow on Apache Spark Tim Hunter Spark Summit 2016 - Meetup
- 2. How familiar are you with Spark? 1. What is Apache Spark? 2. I have used Spark 3. I am using Spark in production or I contribute to its development 2
- 3. How familiar are you with TensorFlow? 1. What is TensorFlow? 2. I have heard about it 3. I am training my own neural networks 3
- 4. Founded by the team who created ApacheSpark Offers a hosted service: - Apache Spark in the cloud - Notebooks - Clustermanagement - Productionenvironment About Databricks 4
- 5. Software engineerat Databricks Apache Spark contributor Ph.D. UC Berkeleyin Machine Learning (and Spark user since Spark 0.2) About me 5
- 6. Outline • Numerical computing with Apache Spark • Using GPUs with Spark and TensorFlow • Performance details • The future 6
- 7. Numerical computing for Data Science • Queries are data-heavy • However algorithmsare computation-heavy • They operate on simple data types: integers,floats, doubles, vectors, matrices 7
- 8. The case for speed • Numerical bottlenecksare good targets for optimization • Let data scientists get faster results • Faster turnaroundfor experimentations • How can we run these numerical algorithmsfaster? 8
- 9. Evolution of computing power 9 Failure is not an option: it is a fact When you can afford your dedicated chip GPGPU Scale out Scale up
- 10. Evolution of computing power 10 NLTK Theano Today’s talk: Spark + TensorFlow
- 11. Evolution of computing power • Processorspeedcannotkeep up with memory and network improvements • Accessto the processoris the new bottleneck • ProjectTungstenin Spark: leverage the processor’s heuristicsfor executingcode and fetching memory • Does not accountfor the fact that the problem is numerical 11
- 12. Asynchronous vs. synchronous • Asynchronousalgorithms performupdates concurrently • Spark is synchronousmodel, deep learningframeworks usuallyasynchronous • A large number of ML computations are synchronous • Even deep learningmay benefit from synchronousupdates 12
- 13. Outline • Numerical computing with Apache Spark • Using GPUs with Spark and TensorFlow • Performance details • The future 13
- 14. GPGPUs 14 • Graphics Processing Units for General Purpose computations 6000 Theoretical peak throughput GPU CPU Theoretical peak bandwidth GPU CPU
- 15. • Library for writing “machine intelligence”algorithms • Very popular for deep learning and neural networks • Can also be used for general purpose numerical computations • Interface in C++ and Python 15 Google TensorFlow
- 16. Numerical dataflow with Tensorflow 16 x = tf.placeholder(tf.int32, name=“x”) y = tf.placeholder(tf.int32, name=“y”) output = tf.add(x, 3 * y, name=“z”) session = tf.Session() output_value = session.run(output, {x: 3, y: 5}) x: int32 y: int32 mul 3 z
- 17. Numerical dataflow with Spark df = sqlContext.createDataFrame(…) x = tf.placeholder(tf.int32, name=“x”) y = tf.placeholder(tf.int32, name=“y”) output = tf.add(x, 3 * y, name=“z”) output_df = tfs.map_rows(output, df) output_df.collect() df: DataFrame[x: int, y: int] output_df: DataFrame[x: int, y: int, z: int] x: int32 y: int32 mul 3 z
- 18. Outline • Numerical computing with Apache Spark • Using GPUs with Spark and TensorFlow • Performance details • The future 18
- 19. 19 It is a communication problem Spark worker process Worker python process C++ buffer Python pickle Tungsten binary format Python pickle Java object
- 20. 20 TensorFrames: native embedding of TensorFlow Spark worker process C++ buffer Tungsten binary format Java object
- 21. • Estimation of distribution from samples • Non-parametric • Unknown bandwidth parameter • Can be evaluatedwith goodness of fit An example: kernel density scoring 21
- 22. • In practice, compute: with: • In a nutshell:a complexnumerical function An example: kernel density scoring 22
- 23. 23 Speedup 0 60 120 180 Scala UDF Scala UDF (optimized) TensorFrames TensorFrames + GPU Run time (sec) def score(x: Double): Double = { val dis = points.map { z_k => - (x - z_k) * (x - z_k) / ( 2 * b * b) } val minDis = dis.min val exps = dis.map(d => math.exp(d - minDis)) minDis - math.log(b * N) + math.log(exps.sum) } val scoreUDF = sqlContext.udf.register("scoreUDF", score _) sql("select sum(scoreUDF(sample)) from samples").collect()
- 24. 24 Speedup 0 60 120 180 Scala UDF Scala UDF (optimized) TensorFrames TensorFrames + GPU Run time (sec) def score(x: Double): Double = { val dis = new Array[Double](N) varidx = 0 while(idx < N) { val z_k = points(idx) dis(idx) = - (x - z_k) * (x - z_k) / ( 2 * b * b) idx += 1 } val minDis = dis.min varexpSum = 0.0 idx = 0 while(idx < N) { expSum += math.exp(dis(idx) - minDis) idx += 1 } minDis - math.log(b * N) + math.log(expSum) } val scoreUDF = sqlContext.udf.register("scoreUDF", score _) sql("select sum(scoreUDF(sample)) from samples").collect()
- 25. 25 Speedup 0 60 120 180 Scala UDF Scala UDF (optimized) TensorFrames TensorFrames + GPU Run time (sec) def cost_fun(block, bandwidth): distances = - square(constant(X) - sample) / (2 * b * b) m = reduce_max(distances, 0) x = log(reduce_sum(exp(distances - m), 0)) return identity(x + m - log(b * N), name="score”) sample = tfs.block(df, "sample") score = cost_fun(sample, bandwidth=0.5) df.agg(sum(tfs.map_blocks(score, df))).collect()
- 26. 26 Speedup 0 60 120 180 Scala UDF Scala UDF (optimized) TensorFrames TensorFrames + GPU Run time (sec) def cost_fun(block, bandwidth): distances = - square(constant(X) - sample) / (2 * b * b) m = reduce_max(distances, 0) x = log(reduce_sum(exp(distances - m), 0)) return identity(x + m - log(b * N), name="score”) with device("/gpu"): sample = tfs.block(df, "sample") score = cost_fun(sample, bandwidth=0.5) df.agg(sum(tfs.map_blocks(score, df))).collect()
- 29. Outline • Numerical computing with Apache Spark • Using GPUs with Spark and TensorFlow • Performance details • The future 29
- 31. The future • Integrationwith Tungsten: • Direct memory copy • Columnar storage • Betterintegration with MLlib data types • GPU instances in Databricks: Official support coming this summer 31
- 32. Recap • Spark: an efficient framework for running computations on thousands of computers • TensorFlow: high-performance numerical framework • Get the best of both with TensorFrames: • Simple API for distributed numerical computing • Can leveragethe hardwareof the cluster 32
- 33. Try these demos yourself • TensorFrames source code and documentation: github.com/tjhunter/tensorframes spark-packages.org/package/tjhunter/tensorframes • Demo available lateron Databricks • The official TensorFlow website: www.tensorflow.org • More questions and attending the Spark summit? We will hold office hours at the Databricks booth. 33
- 34. Thank you.