Tools for Making Machine Learning more Reactive

Tools for Making
Machine Learning
more Reactive
Jeff Smith
@jeffksmithjr
jeffsmith.tech

Languages
● Scala
● Erlang/Elixir
● Python

Akka
● Scala & Java
● Actor model
● Concurrency
● Distribution
● Supervision

Model Microservice in Akka HTTP
case class Prediction(id: Long, timestamp: Long, value: Double)
trait Protocols extends DefaultJsonProtocol {
implicit val predictionFormat = jsonFormat3(Prediction)
}

def model(features: Map[Char, Double]) = {
val coefficients = ('a' to 'z').zip(1 to 26).toMap
val predictionValue = features.map {
case (identifier, value) =>
coefficients.getOrElse(identifier, 0) * value
}.sum / features.size
Prediction(Random.nextLong(), System.currentTimeMillis(), predictionValue)
}
def parseFeatures(features: String): Map[String, Double] = {
features.parseJson.convertTo[Map[Char, Double]]
}
def predict(features: String): Prediction = {
model(parseFeatures(features))
}

trait Service extends Protocols {
implicit val system: ActorSystem
implicit def executor: ExecutionContextExecutor
implicit val materializer: Materializer
val logger: LoggingAdapter
val routes = {
logRequestResult("model-service") {
pathPrefix("predict") {
(get & path(Segment)) {
features: String =>
complete {
ToResponseMarshallable(predict(features))}}}}}}

MLlib Model Serialization
{
"class": "org.apache.spark.ml.PipelineModel",
"timestamp": 1467653845388,
"sparkVersion": "2.0.0-preview",
"uid": "pipeline_6b4fb08e8fb0",
"paramMap": {
"stageUids": ["quantileDiscretizer_d3996173db25",
"vecAssembler_2bdcd79fe1cf",
"logreg_9d559ca7e208"]
}
}

MLlib Model Serialization
{ "class": "org.apache.spark.ml.classification.LogisticRegressionModel",
"timestamp": 1467653845650,
"sparkVersion": "2.0.0-preview",
"uid": "logreg_9d559ca7e208",
"paramMap": {
"threshold": 0.5,
"elasticNetParam": 0.0,
"fitIntercept": true,
"tol": 1.0E-6,
"regParam": 0.0,
"maxIter": 5,
"standardization": true,
"featuresCol": "features",
"rawPredictionCol": "rawPrediction",
"predictionCol": "prediction",
"probabilityCol": "probability",
"labelCol": "label"}}

Containerizing with sbt-docker
dockerfile in docker := {
val jarFile: File = sbt.Keys.`package`.in(Compile, packageBin).value
val classpath = (managedClasspath in Compile).value
val mainClass = "com.reactivemachinelearning.PredictiveService"
val jarTarget = s"/app/${jarFile.getName}"
val classpathString = classpath.files.map("/app/" + _.getName)
.mkString(":") + ":" + jarTarget
new Dockerfile {
from("java")
add(classpath.files, "/app/")
add(jarFile, jarTarget)
entryPoint("java", "-cp", classpathString, mainClass)
}
}

Containerizing with sbt-docker
FROM java
ADD 0/spark-core_2.11-2.0.0-preview.jar 1/avro-mapred-1.7.7-hadoop2.jar ...
ADD 166/chapter-7_2.11-1.0.jar /app/chapter-7_2.11-1.0.jar
ENTRYPOINT ["java", "-cp", "/app/spark-core_2.11-2.0.0-preview.jar ...
"com.reactivemachinelearning.PredictiveService"]

Model Server in http4s
object Models {
val modelA = HttpService {
case GET -> Root / "a" / inputData =>
val response = true
Ok(s"Model A predicted $response.")
}
val modelB = HttpService {
case GET -> Root / "b" / inputData =>
val response = false
Ok(s"Model B predicted $response.")
}
}

object Client {
val client = PooledHttp1Client()
private def call(model: String, input: String) = {
val target = Uri.fromString(s"http://localhost:8080/models/$model/$input").toOption.get
client.expect[String](target)
}
def callA(input: String) = call("a", input)
def callB(input: String) = call("b", input)
}

def splitTraffic(data: String) = {
data.hashCode % 10 match {
case x if x < 4 => Client.callA(data)
case _ => Client.callB(data)
}
}

object ModelServer extends ServerApp {
val apiService = HttpService {
case GET -> Root / "predict" / inputData =>
val response = splitTraffic(inputData).run
Ok(response)
}
override def server(args: List[String]): Task[Server] = {
BlazeBuilder
.bindLocal(8080)
.mountService(apiService, "/api")
.mountService(Models.modelA, "/models")
.mountService(Models.modelB, "/models")
.start
}
}

import scala.util.Random
val modelC = HttpService {
case GET -> Root / "c" / inputData => {
val workingOk = Random.nextBoolean()
val response = true
if (workingOk) {
Ok(s"Model C predicted $response.")
} else {
BadRequest("Model C failed to predict.")
}
}
}

private def call(model: String, input: String): Task[Response] = {
val target = Uri.fromString(
s"http://localhost:8080/models/$model/$input"
).toOption.get
client(target)
}
def callC(input: String) = call("c", input)

def splitTraffic(data: String) = {
data.hashCode % 10 match {
case x if x < 4 => Client.callA(data)
case x if x < 6 => Client.callB(data)
case _ => Client.callC(data)
}
}
val apiService = HttpService {
case GET -> Root / "predict" / inputData =>
val response = splitTraffic(inputData).run
response match {
case r: Response if r.status == Ok => Response(Ok).withBody(r.bodyAsText)
case r => Response(BadRequest).withBody(r.bodyAsText)
}
}

private def call(model: String, input: String) = {
val target = Uri.fromString(s"http://localhost:8080/models/$model/$input").toOption.get
client(target).retry(Seq(1 second), {_ => true})
}

Circuit Breakers in Erlang/Elixir
def update(user_data) do
{user_id, _} = user_data
case verify_fuse(user_id) do
:ok ->
write_to_db(user_data)
|> parse_db_response(user_id)
:sorry ->
IO.puts "This user can't be updated now. ¯_(ツ)_/¯"
:sorry
end
end

defp verify_fuse(user_id) do
case :fuse.ask(user_id, :sync) do
{:error, :not_found} ->
IO.puts "Installing"
install_fuse(user_id)
:ok
:blown ->
:sorry
_ -> :ok
end
end

defp install_fuse(user_id) do
:fuse.install(user_id, {{:standard, 3, 60000}, {:reset, 900000}})
end

defp write_to_db({_user_id, _data}) do
Enum.random([{:ok, ""}, {:error, "The DB dropped the ball. ಠ_ಠ"}])
end

defp parse_db_response({:ok, _}, _user_id) do
:ok
end
defp parse_db_response({:error, message}, user_id) do
:fuse.melt(user_id)
IO.puts "Error encountered: #{message}.nMelting the fuse!"
:sorry
end

Circuit Breakers in Akka
class DangerousActor extends Actor with ActorLogging {
import context.dispatcher
val breaker =
new CircuitBreaker(
context.system.scheduler,
maxFailures = 5,
callTimeout = 10.seconds,
resetTimeout = 1.minute).onOpen(notifyMeOnOpen())
def notifyMeOnOpen(): Unit =
log.warning("My CircuitBreaker is now open, and will not close for one minute")
}

Circuit Breakers in Lagom
def descriptor: Descriptor = {
import Service._
named("hello").withCalls(
namedCall("hi", this.sayHi),
namedCall("hiAgain", this.hiAgain)
.withCircuitBreaker(CircuitBreaker.identifiedBy("hello2"))
)
}

Evolution
def evolve(nets, generations) do
evolve(nets, generations, &decrement/1)
end
def evolve(nets, generations, count_function) when generations > 0 do
Task.Supervisor.async(GN.TaskSupervisor, fn ->
IO.puts("Generations remaining: #{generations}")
learn_generation(nets)
|> select()
|> evolve(count_function.(generations), count_function)
end)
end
def evolve(nets, _generations, _count_function) do
nets
end

Evolution
def learn_generation(nets) do
clean_nets = strip_empties(nets)
tasks =
Task.Supervisor.async_stream_nolink(
GN.TaskSupervisor,
clean_nets,
&start_and_spawn(&1),
timeout: GN.Parameters.get(__MODULE__, :timeout)
)
generation = for {status, net} <- tasks, status == :ok, do: net
IO.puts(inspect(generation))
generation
end

Evolution
def spawn_offspring(seed_layers, mutation_rate @mutation_rate) do
duplicate(seed_layers, mutation_rate)
|> remove(mutation_rate)
|> Enum.map(&mutate(&1, mutation_rate))
end

Evolution
def select(pid __MODULE__, nets) do
cutoffs = cutoffs(nets)
for net <- nets do
complexity = length(net.layers)
level = Enum.min(
[Enum.find_index(cutoffs, &(&1 >= complexity)) + 1,
complexity_levels()])
net_acc = net.test_acc
elite_acc = Map.get(get(level), :test_acc)
if is_nil(elite_acc) or net_acc > elite_acc do
put(pid, level, net)
end
end

Evolution
iex(1)> GN.Selection.get_all()
%{
1 => %GN.Network{
id: "0c2020ad-8944-4f2c-80bd-1d92c9d26535",
layers: [
dense: [64, :softrelu],
batch_norm: [],
activation: [:relu],
dropout: [0.5],
dense: [63, :relu]
],
test_acc: 0.8553
},
2 => %GN.Network{
id: "58229333-a05d-4371-8f23-e8e55c37a2ec",
layers: [
dense: [64, :relu],

Continual Learning
iex(2)> GN.Example.infinite_example()
%Task{
owner: #PID<0.171.0>,
pid: #PID<0.213.0>,
ref: #Reference<0.1968944036.911736833.180535>
}
Generations remaining: infinity

Interactive Evolution
iex(3)> GN.Selection.get_all() |> Map.get(2) |> GN.Library.put()
iex(4)> GN.Library.get("02b2a947-f888-4abf-b2a5-5df25668b0ee") |> GN.Selection.put_unevaluated()

Galápagos Nǎo Tech Stack
● Elixir
● Apache MXNet/Gluon
● Python
● Export/ErlPort
● Docker
● Microsoft Cognitive Toolkit*
● ONNX support*
* coming soon

ONNX
● Open interchange format
● Focused on inference
● Broad and growing support

ONNX Format
message AttributeProto {
enum AttributeType {
UNDEFINED = 0;
FLOAT = 1;
INT = 2;
STRING = 3;
TENSOR = 4;
GRAPH = 5;
FLOATS = 6;
INTS = 7;
STRINGS = 8;
TENSORS = 9;
GRAPHS = 10;
}

ONNXS
iex(1)> {:ok, mnist_data} = File.read "./test/examples/mnist.onnx"
{:ok,
<<8, 3, 18, 4, 67, 78, 84, 75, 26, 3, 50, 46, 52, 40, 1, 58, 227, 206, 1, 10,
199, 80, 18, 12, 80, 97, 114, 97, 109, 101, 116, 101, 114, 49, 57, 51, 26,
12, 80, 97, 114, 97, 109, 101, 116, 101, 114, 49, ...>>}

ONNXS
iex(2)> mnist_struct = Onnx.ModelProto.decode(mnist_data)
%Onnx.ModelProto{
doc_string: nil,
domain: nil,
graph: %Onnx.GraphProto{
doc_string: nil,
initializer: [],
input: [
%Onnx.ValueInfoProto{
doc_string: nil,
name: "Input3",
type: %Onnx.TypeProto{
value: {:tensor_type,
%Onnx.TypeProto.Tensor{
elem_type: 1,
shape: %Onnx.TensorShapeProto
...

ONNXS
iex(3)> mnist_updated = %{mnist_struct | model_version: 2}

Model publishing and
serving are hard
problems.

Not all reactive
technologies look like
Erlang.

Reactive patterns are
useful across
toolchains.

Hard problems require
diverse sets of
solutions.

Open standards foster
rich ecosystems.

Reactive Machine Learning
Use the code
rmlsnymu
for 40% off the book!

Tools for Making Machine Learning more Reactive

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