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# Deploy a Workflow with Composer

WARNING:

Composer is an experimental component in FastScore v1.7, and features may be subject to change.

In this example, we’ll use FastScore Composer to deploy a multi-model workflow. We’ll re-use the LSTM model from the TensorFlow example, and add some extra model stages to do pre- and post-processing of the data for this model. We’ll be using a mixture of Python 3 and R for our models.

FastScore Composer is a tool for building and deploying analytic workflows in FastScore. Recall that the LSTM model predicts a CPI-normalized, adjusted S&P closing price based on the previous 30 days’ closing prices. By using Composer, we’ll take in the raw S&P closing prices and consumer price index (CPI) data, produce normalized input for the LSTM model, and then transform the model’s output back to actual S&P 500 prices. This analytic workflow is depicted below:

Analytic Workflow

The daily S&P 500 closing prices since June 1, 2007, as well as the corresponding CPI data, can be obtained here.

## The Preprocessor

The preprocessor model accepts data from two different sources (S&P 500 closing prices, and CPI), and uses that data to produce the transformed inputs that the TensorFlow LSTM model needs to make its predictions. Recall that this transformation consists of two steps:

1. The S&P 500 closing price is divided by the CPI, and then
2. The predicted linear trend is subtracted from this number.

Mathematically, we can write this as:

where $s(t)$ is the S&P 500 closing price on day $t$, $c(t)$ the CPI on day $t$, $m$ and $b$ are the slope and intercept of the linear trend described in the TensorFlow LSTM tutorial, and $\tilde{s}$ is the input to the LSTM model.

Let’s implement our preprocessing step using R. First, we’ll define two functions:

rescale <- function(close, cpi){
close/cpi
}

reg <- function(date){
slope*date + intercept
}


These functions will make our feature transformation expression a little cleaner. If date, sp500, and cpi represent the date, S&P 500 closing price, and CPI, respectively, then calculating the adjusted input is easy:

rescale(sp500, cpi) - reg(date)


To prepare this preprocessor for FastScore, we’ll need to write an R script that can accept asynchronous, heterogeneous inputs from two input streams. Fortunately, FastScore provides abstractions for multiple streams that make this easy. When defining an action function, just include the slot argument in the function signature, and then FastScore will automatically supply the slot number (a numeric identifier of the input stream) along with the input data:

action <- function(data, slot) {
[...]
}


Because our model may receive S&P 500 and CPI data asynchronously, but our calculation depends on having both the S&P 500 price and CPI available in order to perform the calculation, let’s create two lists to keep track of the data received thus far from each of these sources. This can be done in the begin function:

begin <- function(){
slope <<- 0.0002319547958991928
intercept <<- 0.4380634632578033

cpis <<- list()
sp500s <<- list()
}


(Note that we’ve also initialized the slope and intercept global variables here).

In our action function, we’ll first append items to these lists depending on what slot they came in on, and then pop them from the list when both lists have elements available:

action <- function(data, slot){

if(slot == 0){ # SP500 input
count <- length(sp500s)
sp500s[[count + 1]] <<- data
}

if(slot == 2){ # CPI input
count <- length(cpis)
cpis[[count + 1]] <<- data
}

while(length(sp500s) > 0 && length(cpis) > 0){
sp500 <- sp500s[[1]]
sp500s[[1]] <<- NULL # pop from the front of the list
cpi <- cpis[[1]]
cpis[[1]] <<- NULL # pop from the front of the list

[...] # Do something
}
}


Thus far we haven’t given much thought to the schemata used by this preprocessor. Let’s take a moment to address that now. In this example, let’s assume the CPI data looks like JSON-encoded records with two fields (date and value). Some example inputs might be:

{"Date": 20968.0, "CPI": 207.234}
{"Date": 20971.0, "CPI": 207.244}
{"Date": 20972.0, "CPI": 207.254}
{"Date": 20973.0, "CPI": 207.2642}


FastScore uses an Avro schema system to enforce strong typing on models’ inputs and outputs. The Avro schema for this data is:

{
"name":"cpi",
"type":"record",
"fields":[
{"name": "Date", "type":"double"},
{"name": "CPI", "type": "double"}
]
}


The S&P 500 inputs will similarly be JSON records:

{"Date": 20968.0, "Close": 1536.339966}
{"Date": 20971.0, "Close": 1539.180054}
{"Date": 20972.0, "Close": 1530.949951}
{"Date": 20973.0, "Close": 1517.380005}


with Avro schema:

{
"name":"sp500",
"type":"record",
"fields":[
{"name": "Date", "type":"double"},
{"name": "Close", "type": "double"}
]
}


When deserializing data into R objects, FastScore encodes these records as lists with named indices. So, for example, the element

{"Date": 20968.0, "CPI": 207.234}


becomes to the R object

list("Date"=20968.0, "CPI"=207.234)


In FastScore R, the emitTo function can be used to direct output to a particular slot in the engine. So, filling out the action function above, we have:

action <- function(data, slot){
if(slot == 0){ # SP500 input
count <- length(sp500s)
sp500s[[count + 1]] <<- data
}
if(slot == 2){ # CPI input
count <- length(cpis)
cpis[[count + 1]] <<- data
}

while(length(sp500s) > 0 && length(cpis) > 0){
sp500 <- sp500s[[1]]
sp500s[[1]] <<- NULL # pop from the front of the list
cpi <- cpis[[1]]
cpis[[1]] <<- NULL

date <- sp500[['Date']] # Assume inputs in both streams are ordered
lin_reg = reg(date)
adjusted_price <- rescale(sp500[['Close']], cpi[['CPI']]) - lin_reg

lin_reg_plus_one = reg(date + 1) # what to remove from the output

}
}


Putting it all together, here is our FastScore-ready preprocessor R script:

preprocessor.R

# fastscore.schema.0: sp500
# fastscore.schema.2: cpi
# fastscore.schema.1: double

begin <- function(){
slope <<- 0.0002319547958991928
intercept <<- 0.4380634632578033

cpis <<- list()
sp500s <<- list()
}

action <- function(data, slot){
if(slot == 0){ # SP500 input
count <- length(sp500s)
sp500s[[count + 1]] <<- data
}
if(slot == 2){ # CPI input
count <- length(cpis)
cpis[[count + 1]] <<- data
}

while(length(sp500s) > 0 && length(cpis) > 0){
sp500 <- sp500s[[1]]
sp500s[[1]] <<- NULL # pop from the front of the list
cpi <- cpis[[1]]
cpis[[1]] <<- NULL

date <- sp500[['Date']] # Assume inputs in both streams are ordered
lin_reg = reg(date)
adjusted_price <- rescale(sp500[['Close']], cpi[['CPI']]) - lin_reg

lin_reg_plus_one = reg(date + 1) # what to remove from the output

}
}

rescale <- function(close, cpi){
close/cpi
}

reg <- function(date){
slope*date + intercept
}


## The Postprocessor

Just as we had to apply some data transformations on the inputs to the TensorFlow model using a preprocessor script, we also have to reverse these transformations to extract actual S&P 500 price predictions from the TensorFlow model. Let’s write the postprocessor script in Python.

The postprocessor model will take in the “adjustment” output produced by the preprocessor model (which includes the CPI and linear regression data) along with the predictions produced by the LSTM model. To turn these predictions into S&P 500 closing prices, we need to add back the linear contribution, and then multiply the result by the CPI. Denoting the “adjustment” record obtained from the preprocessor’s output by adjust, and the LSTM prediction by pred, the dollar-valued S&P 500 closing price prediction is

lr = adjust['LR']

return cpi*(pred + lr)


However, we’re not done yet—our postprocessing script has two additional considerations:

1. We need to make sure that we’re applying our adjustments to the LSTM predictions for the right date.
2. The LSTM model will not produce any output for the first 30 inputs received, while the preprocessor model produces output for every input. (Recall that the LSTM model predicts the next day’s closing price based on the previous 30 days’ closing prices.)

To address the first issue, we can use the same buffering technique we used in the preprocessor model. For the second issue, we can simply ignore the first 30 adjustments produced by the preprocessor model. In total, the postprocessing script is:

postprocessor.py

# fastscore.schema.0: double
# fastscore.schema.1: double

def begin():
predictions = []
input_count = 0
input_threshold = 30

def action(data, slot):
input_count += 1

if slot == 0:
predictions.append(data)
if slot == 2 and input_count >= input_threshold:

while len(adjustments) > 0 and len(predictions) > 0:
pred = predictions[0]
predictions = predictions[1:]

yield cpi*(pred + lr)


## Setting up Composer

FastScore Composer is an experimental tool for streamlining the creation and deployment of large, complex analytic workflows. In this example, we have a comparatively simple workflow, which serves as a good introduction to Composer’s functionality.

If you’re not interested in manually creating and deploying Composer, feel free to skip ahead to the next section—you can download an automation script to perform all configuration here.

It’s easy to deploy FastScore Composer using Docker Compose or Docker Swarm. For this example, we’ll use Swarm. Docker Swarm uses the same YAML definition files as Docker Compose, so you can re-use the example Docker Compose file from the Getting Started Guide or download all the files needed for this step here.

Composer consists of three microservices: Designer (a web GUI), Composer (the core component), and Conductor (for interactions with the container orchestration layer). To define these services, add the following service definitions to the Compose file:

conductor:
image: fastscore/conductor-docker:dev
ports:
- "8080:8080"
volumes:
- "/var/run/docker.sock:/var/run/docker.sock"
networks:
- fsnet
environment:
MODE: swarm
NETWORK: fastscore_fsnet

composer:
image: fastscore/composer:dev
depends_on:
- proxy
- connect
- conductor
ports:
- "8010:8010"
networks:
- fsnet
environment:
CONDUCTOR_HOST: https://conductor:8080
PROXY: https://proxy:8000
MODE: Kafka
KAFKA_SERVERS: kafka:9092

designer:
image: fastscore/designer:dev
ports:
- "8012:8012"
networks:
- fsnet


Note that we are deploying all of our services in a custom named network (“fsnet”), and that Conductor requires access to the Docker socket on the host machine ( this is needed to interact with and spawn other containers). Additionally, in place of the FastScore Dashboard, we’ll use the lightweight “frontman” proxy:

proxy:
image: fastscore/frontman:dev
ports:
- "8000:8000"
environment:
CONNECT_PREFIX: https://connect:8001
networks:
- fsnet


With the docker-compose YAML file updated, let’s deploy the services using Docker Swarm:

docker swarm init
docker stack deploy -c docker-compose.yaml fastscore


Once deployed, configure the fleet and add all of the assets to Model Manage. For example, using the CLI:

fastscore connect https://localhost:8000 # fleet proxy
fastscore config set config.yaml
fastscore fleet -wait

fastscore model add -type:python3 tf_sp500_lstm tf_sp500_lstm.py



No special configuration commands are necessary for Composer—all relevant settings can be controlled via container environment variables.

## Creating the Workflow in Designer

With the FastScore fleet started and Composer and Designer ready to run, open your browser and navigate to the Designer URL (for example, https://localhost:8012). You may have to add a browser security exception for Designer because, like other components in the FastScore fleet, Designer uses self-signed certificates by default.

Upon accessing Designer, you should see the following display:

The Designer interface consists of a canvas and three menu icons (in the corners of the screen). First, we’ll use the upper-right gear icon to set some configuration options. Click on this icon, and enter the URL of the FastScore fleet proxy relative to your browser:

For example, if you can access Designer at https://localhost:8012, and you are using the supplied Docker Compose file, the proxy prefix is https://localhost:8000/api/1/service.

If you have not entered a valid proxy prefix, or Designer is otherwise unable to connect to the rest of the fleet, an error message will be displayed.

After configuring Designer, let’s start building our workflow. Click on the menu icon in the upper-left corner to display a list of all available stream and model assets for this project.

If you mouse over an asset’s name, an edit icon will appear. This displays the asset’s source code, and, for models, lets you set other execution options (such as the model environment).

The TensorFlow LSTM model requires a model environment that contains TensorFlow. Fortunately, in the TensorFlow tutorial, we have already created a custom FastScore engine image with tag localrepo/engine:tensorflow. Set the model to use this environment, and click the save icon to save changes.

With our assets configured and Designer connected to Composer and the rest of the FastScore fleet, we’re ready to start making our analytic workflow. To build a workflow, just drag and drop assets onto the canvas. Click the “+” button on a workflow node to increase the number of possible connections for that node, and drag a line between two nodes to connect them. Our workflow should look something like this:

Note that we have attached REST streams to the inputs of the preprocessor model and the output of the postprocessor model—this exposes our Workflow as an on-demand RESTful service.

Once the workflow is designed, click on the wrench icon to either save the workflow, or deploy it. Workflows are saved as human-readable YAML documents that describe the connections between each component. For example, the YAML corresponding to the workflow just created is:

---
Assets:
Models:
- Name: tf_sp500_lstm
Environment: localrepo/engine:tensorflow
- Name: preprocessor
- Name: postprocessor
Streams:
- Name: rest

Workflow:
preprocessor:
Inputs:
0: rest
2: rest
Outputs:
1: tf_sp500_lstm
3: postprocessor

tf_sp500_lstm:
Inputs:
0: preprocessor
Outputs:
1: postprocessor

postprocessor:
Inputs:
0: tf_sp500_lstm
2: preprocessor
Outputs:
1: rest



Once a workflow YAML has been created, it can be saved and loaded later into Designer, or deployed directly to Composer via Composer’s REST API.

For now, let’s click the “build” button, give our workflow a name, and deploy it.

## Deploy and execute the Workflow

Upon clicking “build” in Designer, the workflow will be passed to Composer, FastScore engines will be spawned to handle any models in the workflow, and Composer will deploy models to the spawned engines and create internal stream connections to handle inter-engine data streams. (By default, Kafka streams are created, but other formats are also supported.)

Deployment may take a minute or two as new engine containers are created and configured. Once the models are deployed, there are a number of different ways of delivering data to the workflow.

One of the easiest is using the FastScore CLI:

To target the CLI at a specific engine, use the fastscore use command. For example:

fastscore use engine-2


Then, to determine the model active on this engine, enter the command

fastscore model inspect


This information can also be obtained from the Dashboard. For the purposes of this demo, let’s assume that the preprocessor model is deployed on engine-3, and the postprocessor model is depoyed on engine-1. Let’s first watch the outputs from engine-1: in a terminal window, enter the commands

fastscore use engine-1
fastscore model output -c


This will print any REST output produced by the postprocessor model directly to the command line.

Next, in another terminal window, enter the command

fastscore use engine-3


to switch the context back to engine-3. We’ll use the fastscore model input command to supply data to the preprocessor model. Use the fastscore model input 0 command to enter data in input slot 0 (recall that this is the S&P 500 closing prices). Let’s enter in the first 35 days of closing prices:

head -35 close_prices.jsons | fastscore model input 0


And then, the first 35 days of CPI data:

head -35 cpi.jsons | fastcore model input 2


Note that we’re piping the inputs to slot 2 of the model, since this is the CPI data.

With luck, you’ll see the model’s predictions for days 31 through 35 printed to the first terminal window:

\$ fastscore model output -c
1554.22092567
1552.505235
1553.07839698
1549.9272212
1557.06023473


For comparison, the observed S&P 500 closing prices for days 31 through 35 are 1549.52, 1549.37, 1546.17, 1553.08, and 1534.10, so our model’s predictions were accurate to within 2% of the actual closing prices. Not bad!