nerd

README

nerd

Welcome to the nerd repo! This service offers machine learning capabilities through a simple API, thus allowing other services to be smarter without requiring a huge effort.

Index

• Quick Start
• Requirements
• Deployment
• Use
  • Collectors
    • File
  • Metrics Updates
  • Manual Training
  • Evaluating An Input
  • Listing Available Entities
  • Health
• Testing

Quick Start

If you just want to try nerd out and see what it can do, here is a quick guide for running a test setup with containers:

1. Start a Zookeeper instance (nerd currently requires Kafka which in turn requires Zookeeper):

   docker run -d --restart=unless-stopped \
     --log-driver json-file \
     -p 2181:2181 \
     --name zookeeper zookeeper:3.6.2
   

2. Start a Kafka broker:

   docker run -d --restart=unless-stopped \
     --log-driver json-file \
     -p 7203:7203 -p 7204:7204 -p 9092:9092 \
     -e "KAFKA_LISTENERS=PLAINTEXT://:9092" \
     -e "KAFKA_ADVERTISED_LISTENERS=PLAINTEXT://host.docker.internal:9092" \
     -e "KAFKA_DEFAULT_REPLICATION_FACTOR=1" \
     -e "KAFKA_DELETE_TOPIC_ENABLE=true" \
     -e "KAFKA_ZOOKEEPER_CONNECT=host.docker.internal:2181" \
     -e "KAFKA_BROKER_ID=1" \
     -e "KAFKA_HEAP_OPTS=-Xmx4G -Xms4G" \
     -e "ZOOKEEPER_SESSION_TIMEOUT_MS=30000" \
     --name kafka wurstmeister/kafka:2.12-2.1.1
   

   If running on linux, you might need to include the following in the docker run command (anywhere between docker run and the image)(same for the next steps):

     --add-host host.docker.internal:host-gateway
   

3. Start a nerd instance:

   Here we're going to run nerd using the filesystem to store the neural net's parameters and the series' points, if you'd like a more performant setup, refer to the "Requirements" section for instructions on how to setup Redis and Elasticsearch instead.

   docker run -d --restart=unless-stopped -m 64m \
     --log-opt max-size=5m --log-driver=json-file \
     -p 5400:5400 \
     -e "LOG_LEVEL=INFO" \
     -e "SD_KAFKA=host.docker.internal:9092" \
     --name nerd qvantel/nerd:0.3.0
   

4. Check that everything is up and running by going to http://localhost:5400 with your browser (if you see a welcome message, everything is good)

   Not seeing anything? You can check the nerd logs with docker logs --tail 100 nerd to see if there are any errors

5. Train a network to detect forged banknotes:
   1. Download the dataset from the UCI ML repo here

      Credit: Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

   2. Shuffle the points:

      sort -R -o shuffled-dataset.txt data_banknote_authentication.txt
      

   3. Load the test data using the built-in file collector:

      If you want, you can add variance,skewness,curtosis,entropy,class to the beginning of shuffled-dataset.txt and use the -headers variable to properly label the values, otherwise names will be auto-generated

      docker run -it --rm \
       -v $PWD/shuffled-dataset.txt:/opt/docker/dataset \
       --entrypoint=/opt/docker/fcollect qvantel/nerd:0.3.0 \
         -batch 50 \
         -in 4 \
         -margin 0.4999999 \
         -producer "kafka" \
         -sep "," \
         -series "banknote-forgery-detection" \
         -targets "host.docker.internal:9092" \
         dataset
      

   4. Send a training request:

      If you opted to add the headers in the previous step, use ["variance","skewness","curtosis","entropy"] as inputs and ["class"] as the output instead of the values bellow

      curl -XPOST -H "Content-Type: application/json" --data @- \
          localhost:5400/api/v1/nets <<EOF
      {
          "errMargin": 0.4999999,
          "inputs": ["value-0", "value-1", "value-2", "value-3"],
          "outputs": ["value-4"],
          "required": 1372,
          "seriesID": "banknote-forgery-detection"
      }
      EOF
      

   5. Check out the resulting net by going to http://localhost:5400/api/v1/nets
6. Use the network:
   1. With an authentic note (the output should be closer to 0 than 1)

      NET=banknote-forgery-detection-8921e4a37dabacc06fec3318e908d9fe4eb75b46-7804b6fc74b5c0a74cc0820420fa0edf6b1a117c-mlp
      ENDPOINT=localhost:5400/api/v1/nets/$NET/evaluate
      
      curl -XPOST -H"Content-Type: application/json" --data @- \
          $ENDPOINT <<EOF
      {
          "value-0": 3.2403,
          "value-1": -3.7082,
          "value-2": 5.2804,
          "value-3": 0.41291
      }
      EOF
      

   2. With a forged note (the output should be closer to 1 than 0)

      curl -XPOST -H"Content-Type: application/json" --data @- \
          $ENDPOINT <<EOF
      {
          "value-0": -1.4377,
          "value-1": -1.432,
          "value-2": 2.1144,
          "value-3": 0.42067
      }
      EOF
      

   Curious about how to programmatically get the id? It can be calculated like so: seriesID + "-" + sha1(inputs) + "-" + sha1(outputs) + "-" + netType (try it out on go playground here)

Requirements

This service has the following dependencies:

Kafka

Even though metrics updates can be sent through the rest API, it's better to use a service like Kafka (maybe nats in the future) to decouple that interaction and benefit from built-in load balancing. When producing metrics updates, the series ID should be used by the partitioning strategy so that we have a smaller chance of triggering training for the same series twice.

For testing, the Zookeeper and Kafka docker run commands from the "Quick Start" section can be used.

A Network Parameter Store

Currently, Redis (and the filesystem but that should only be used for testing).

When using Redis with Sentinel, the ML_STORE_PARAMS variable should be used (instead of SD_REDIS) like so:

  -e 'ML_STORE_PARAMS={"group": "<master>", "URLs": "<sen1-host>:<sen1-port>,...,<senN-host>:<senN-port>"}'

Where group contains the master group name and URLs the comma-separated list of Sentinel instance host:port pairs.

For testing, the following command can be used to start up a Redis replica:

docker run -d \
  --log-driver json-file \
  -p 6379:6379 \
  --name redis redis:6.0.9-alpine

A Point Store

Currently, Elasticsearch (and the filesystem but that should only be used for testing).

If Elasticsearch is used:

• The action.auto_create_index setting must be set to .watches,.triggered_watches,.watcher-history-* otherwise it will create non optimal mappings increasing the storage impact.

• Given how index refreshing works, the automatic training request for a series that gets a high number of metrics updates in a very short period of time (less than a second)(possible when the lag is momentarily high for example) might not get issued. To avoid this, it's recommended to include multiple points per update with a lower frequency rather than sending one update per point as it is extracted.

For testing, it is possible to get a working Elasticsearch instance quickly with the following command:

docker run -d \
  --log-driver json-file \
  -p 9200:9200 -p 9300:9300 \
  -e "discovery.type=single-node" \
  -e "action.auto_create_index=.watches,.triggered_watches,.watcher-history-*" \
  --name elasticsearch elasticsearch:7.10.1

Deployment

For a simple deployment, the following command can be used to start up a nerd instance that'll use Redis and Elasticsearch (changing the ip:ports for those of the corresponding services in your setup):

docker run -d --restart=unless-stopped -m 64m \
  --log-opt max-size=5m --log-driver=json-file \
  -p 5400:5400 \
  -e "LOG_LEVEL=INFO" \
  -e "SD_ELASTICSEARCH=http://host.docker.internal:9200" \
  -e "SERIES_STORE_TYPE=elasticsearch" \
  -e "SD_KAFKA=host.docker.internal:9092" \
  -e "SD_REDIS=host.docker.internal:6379" \
  -e "ML_STORE_TYPE=redis" \
  --name nerd qvantel/nerd:0.3.0

You can find all available tags here

The following environment variables are available:

Variable	Required	Default	Description
LOG_LEVEL	NO	INFO	Application/root log level, supported values are TRACE, DEBUG, INFO, WARNING and ERROR
MARATHON_APP_DOCKER_IMAGE	NO	qvantel/nerd:$VERSION?	Included in the artifact_id field of log messages, gets filled in automatically when ran through Marathon
SERVICE_NAME	NO	nerd	Included in the service_name field of the log messages
SERVICE_5400_NAME	NO	$SERVICE_NAME	Included in the service_name field of the log messages. If set, overrides whatever is defined in $SERVICE_NAME
ML_ACT_FUNC	NO	bipolar-sigmoid	Activation function for the neural nets. Currently, only bipolar-sigmoid is supported
ML_ALPHA	NO	0.05	Learning rate for the neural nets, a low number (<= 0.1) is recommended. In the future, nerd will automatically test a few values and choose the one with the highest accuracy
ML_HLAYERS	NO	1	Number of hidden layers for MLP nets. Some series might benefit from more than one. In the future, nerd will automatically test a few values and choose the one with the highest accuracy
ML_NET	NO	mlp	Default net type to use. Currently, only multilayer perceptron nets are supported (mlp)
ML_MAX_EPOCH	NO	1000	Maximum number of times the net should iterate over the training set if the tolerance is never met
ML_STORE_TYPE	NO*	file	Storage adapter that should be used for keeping network parameters. Currently supported values are file (for testing) and redis
ML_STORE_PARAMS	NO	{"Path": "."}	Settings for the net params storage adapter
SD_REDIS	NO		Redis replica host:port. Serves as a shortcut for filling in $ML_STORE_PARAMS when selecting the redis adapter
ML_TEST_SET	NO	0.4	Fraction of the patterns provided to the training function that should be put aside for testing the accuracy of the net after training (0.4 is usually a good value)
ML_TOLERANCE	NO	0.1	Mean squared error change rate at which the training should stop to avoid overfitting
SERIES_FAIL_LIMIT	NO	5	Number of subsequent processing failures in the consumer service at which the instance should crash
SD_KAFKA	YES		Comma separated list of Kafka broker host:port pairs
SERIES_KAFKA_GROUP	NO	nerd	Consumer group ID that the instance should use
SERIES_KAFKA_TOPIC	NO	nerd-events	Topic from which metrics updates will be consumed
SERIES_STORE_TYPE	NO*	file	Storage adapter that should be used for storing time series. Currently supported values are file (for testing) and elasticsearch
SERIES_STORE_PARAMS	NO	{"Path": "."}	Settings for the time series storage adapter
SERIES_STORE_PASS	NO	""	Password for the selected series store (if applicable)
SERIES_STORE_USER	NO	""	User for the selected series store (if applicable)
SD_ELASTICSEARCH	NO		Elasticsearch protocol://host:port. Serves as a shortcut for filling in $SERIES_STORE_PARAMS when selecting the elasticsearch adapter

* While not strictly required for operation, the default value should be overridden for anything other than testing and even then, not all testing should be done with those values

Use

Once the service has been deployed, it is possible to interact with it either through Kafka or the rest API.

Collectors

These are lightweight components that can be used to import data from other services into nerd. To facilitate their development, nerd exposes the github.com/qvantel/nerd/api/types and github.com/qvantel/nerd/pkg/producer modules which include the types used by the rest and Kafka interfaces as well as ready-made methods for producing messages to them.

File

At the time of writing, the only public collector is the one built into this project under the fcollect command, which imports datasets from plain text files. It can be accessed from the container (as seen in the "Quick Start" section) by changing the entrypoint to /opt/docker/fcollect like so (anything placed after the image will be passed to fcollect as an argument):

docker run -it --rm \
  -v $PWD/shuffled-dataset.txt:/opt/docker/dataset \
  --entrypoint=/opt/docker/fcollect \
  --name fcollect qvantel/nerd:0.3.0 -series "demo" -targets "http://host.docker.internal:5400" dataset

Where the -series and -targets flags as well as the path to the dataset (full or relative to /opt/docker inside the container) are required. Additionally, the following flags can be used to change the behaviour of the tool:

Flag	Type	Default	Description
-batch	int	10	Maximum number of points to bundle in a single metrics update
-headers	bool	false	If true, the first line will be used to name the values
-in	int	1	Number of inputs, counted left to right, all others will be considered outputs
-margin	float	0	Maximum difference between a prediction and the expected value for it to still be considered correct
-producer	string	"rest"	What producer to use. Supported values are rest and kafka
-sep	string	" "	String sequence that denotes the end of one field and the start of the next
-series	string	N/A	ID of the series that these points belong to
-stage	string	"test"	Category of the data, production for real world patterns, test for anything else
-targets	string	N/A	Comma separated list of protocol://host:port for nerd instances when using rest, host:port of Kafka brokers when using kafka
-timeout	duration	15s	Maximum time to wait for the production of a message
-topic	string	"nerd-events"	Where to produce the messages when using kafka

Metrics Updates

Metrics updates can be ingested through either the $SERIES_KAFKA_TOPIC topic in Kafka or the /api/v1/series/process endpoint. In both cases the message must conform to the Cloud Events v1 specification where the metadata fields should be filled in as follows:

Field	Value
datacontenttype	"application/json"
dataschema	"github.com/qvantel/nerd/api/types/"
id	(a unique string identifier for this event)
source	(name of the service that generated the event)
specversion	(cloud events spec version, should be "1.0")
subject	(the entity that we are reporting about, it can be an environment name for example)
type	"com.qvantel.nerd.metricsupdate"

Additionally, the data fields should be filled in like so:

Field	Considerations
data.seriesID	Should conform to [a-z][a-z0-9\._\-]+ and reference what that data can be used to predict. For example, if it's generic enough to predict storage impact in any env that uses that product stack, it should contain the stack version but not the env
data.errMargin	Maximum difference between the expected and produced result to still be considered correct during testing. Currently, this margin will be applied to all outputs of networks generated automatically
data.labels	Should include any labels that might be useful for filtering later. Note that subject and data.stage will be copied here automatically
data.points	All points for the same series ID must contain the same attributes (doesn't matter if they are noted as inputs or outputs although within the same metrics update they do have to all be categorized in the same way)
data.stage	Must be either production for production grade data (usually that which originates from real world usage) or test (for anything else). The message will not be processed if this field doesn't have a valid value

Example:

{
    "data": {
        "seriesID": "heart-of-gold-lightbulb-usage",
        "errMargin": 0.1,
        "labels": {
            "captain": "Zaphod Beeblebrox"
        },
        "points": [
            {
                "inputs": {
                    "humans": 2,
                    "robots": 1,
                    "aliens": 2
                },
                "outputs": {
                    "lightbulbs-on": 1500
                },
                "timestamp": 777808800
            }
        ],
        "stage": "test"
    },
    "datacontenttype": "application/json",
    "dataschema": "github.com/qvantel/nerd/api/types/",
    "id": "1",
    "source": "test-script",
    "specversion": "1.0",
    "subject": "heart-of-gold",
    "type": "com.qvantel.nerd.metricsupdate"
}

Manual Training

Even though the service will automatically schedule training when it has enough points of a series, it is still possible to manually trigger training from any preexisting series. To do this, just post a training request to the /api/v1/nets endpoint like so (where $URL contains the address of the nerd service):

curl -XPOST -H"Content-Type: application/json" --data @- \
    $URL/api/v1/nets <<EOF
{
    "errMargin": 0.4999999,
    "inputs": ["value-0", "value-1", "value-2", "value-3", "value-4", "value-5", "value-6", "value-7", "value-8"],
    "outputs": ["value-9", "value-10"],
    "required": 699,
    "seriesID": "testloadtestset"
}
EOF

Where, the fields contain the following information:

Field	Description
errMargin	Maximum difference between the expected and produced result to still be considered correct during testing
inputs	Which of the series values should be used as inputs
outputs	Which of the series values should be used as outputs
required	Number of points from the series that should be used to train and test
seriesID	ID of the series that should be used for training

Evaluating An Input

Once a net has been trained, it can be exploited through the /api/v1/nets/{id}/evaluate endpoint like so (where $URL contains the address of the nerd service and $ID the ID of the network):

NOTE: The network ID is different from that of the series it comes from, as multiple nets could be (and are) created from a single series. You can obtain one from another though, like this: seriesID-sha1(inputs)-sha1(outputs)-netType where inputs is the alphabetically ordered concatenation of the inputs and the same goes for outputs (when it's an automatically created net, you will get one per output, so typically you will just need to hash one output).

curl -XPOST -H"Content-Type: application/json" --data @- \
    $URL/api/v1/nets/$ID/evaluate <<EOF
{
    "value-0": 5000,
    "value-1": 0.07,
    "value-2": 0.1,
    "value-3": 100,
    "value-4": 1,
    "value-5": 0.1,
    "value-6": 0.1,
    "value-7": 0.1,
    "value-8": 1000
}
EOF

Sample response:

{"value-9":0.16796547}

Listing Available Entities

• Nets:
  • Endpoint: /api/v1/nets
  • Method: GET
  • Params:
    • offset: Offset to fetch, 0 by default
    • limit: How many networks to fetch, the service might return more in some cases, 10 by default, 50 maximum
  • Returns: A types.PagedRes object and a 200 if successful, a types.SimpleRes object and a 400 or 500 if not (depending on the error)
  • Sample response:

{
    "last": true,
    "next": 0,
    "results": [
        {
            "accuracy": 0.953405,
            "averages": {
                "value-0": 4461.905,
                "value-1": 0.032476295,
                "value-10": 0.35714287,
                "value-2": 0.032452486,
                "value-3": 29.095238,
                "value-4": 0.6552371,
                "value-5": 0.036119178,
                "value-6": 0.034857206,
                "value-7": 0.028881064,
                "value-8": 1664.2858,
                "value-9": 0.64285713
            },
            "deviations": {
                "value-0": 2755.941,
                "value-1": 0.03142687,
                "value-10": 0.47972888,
                "value-2": 0.030137872,
                "value-3": 29.314167,
                "value-4": 0.45890477,
                "value-5": 0.036751248,
                "value-6": 0.02476096,
                "value-7": 0.031203939,
                "value-8": 1788.0956,
                "value-9": 0.47972888
            },
            "errMargin": 0.5,
            "id": "testloadtestset-4821586b0eb056ef9bc6913b1e19b800a4b6c563-6b25e7592c075a7c638b2aabda1cc1c71442f4cb-mlp",
            "inputs": [
                "value-0",
                "value-1",
                "value-2",
                "value-3",
                "value-4",
                "value-5",
                "value-6",
                "value-7",
                "value-8"
            ],
            "outputs": [
                "value-9"
            ],
            "type": "mlp"
        },
        {
            "accuracy": 0.94623655,
            "averages": {
                "value-0": 4390.476,
                "value-1": 0.031095339,
                "value-10": 0.34761906,
                "value-2": 0.031690583,
                "value-3": 28.428572,
                "value-4": 0.6271415,
                "value-5": 0.035738226,
                "value-6": 0.03452388,
                "value-7": 0.028071541,
                "value-8": 1604.762,
                "value-9": 0.65238094
            },
            "deviations": {
                "value-0": 2791.8901,
                "value-1": 0.030394992,
                "value-10": 0.4767823,
                "value-2": 0.029760389,
                "value-3": 29.065111,
                "value-4": 0.43468502,
                "value-5": 0.03649372,
                "value-6": 0.02479531,
                "value-7": 0.030412318,
                "value-8": 1741.2527,
                "value-9": 0.4767823
            },
            "errMargin": 0.5,
            "id": "testloadtestset-4821586b0eb056ef9bc6913b1e19b800a4b6c563-025d516f7acf54b430a00be752f9e4c4e2d1eee7-mlp",
            "inputs": [
                "value-0",
                "value-1",
                "value-2",
                "value-3",
                "value-4",
                "value-5",
                "value-6",
                "value-7",
                "value-8"
            ],
            "outputs": [
                "value-10"
            ],
            "type": "mlp"
        }
    ]
}

• Series:
  • Endpoint: /api/v1/series
  • Method: GET
  • Returns: An array of types.BriefSeries objects and a 200 if successful, a types.SimpleRes object and a 500 if not
  • Sample response:

[
    {
        "name": "testloadtestset",
        "count": 699
    }
]

• Points:
  • Endpoint: /api/v1/series/{id}/points
  • Method: GET
  • Params:
    • limit: How many points to fetch, 10 by default, 500 maximum
  • Returns: An array of pointstores.Point objects and a 200 if successful, a types.SimpleRes object and a 404 or 500 if not (depending on the error)
  • Sample response:

[
    {
        "@timestamp": 777809498,
        "value-0": 1000,
        "value-1": 0.01,
        "value-10": 0,
        "value-2": 0.01,
        "value-3": 10,
        "value-4": 0.4,
        "value-5": 0.01,
        "value-6": 0.02,
        "value-7": 0.01,
        "value-8": 1000,
        "value-9": 1
    },
    {
        "@timestamp": 777809497,
        "value-0": 5000,
        "value-1": 0.07,
        "value-10": 1,
        "value-2": 0.1,
        "value-3": 100,
        "value-4": 1,
        "value-5": 0.1,
        "value-6": 0.1,
        "value-7": 0.1,
        "value-8": 1000,
        "value-9": 0
    }
]

Health

• Startup probe: /api/v1/health/startup

Testing

Run unit tests locally:

NOTE: If an Elasticsearch instance is running at http://localhost:9200 when the tests are run, they will load a test series into it

• Using Go (fastest):

go test -cover ./...

• Using containers (slower but doesn't require to have go installed)

./unit-test.sh