管理エージェントのインストールと設定¶

tar -zxf datarobot_mlops_package-*.tar.gz
cd datarobot_mlops_package-*/
cd tools/bosun_docker/
make build 

tar -zxf datarobot_mlops_package-*.tar.gz
cd datarobot_mlops_package-*/
cd tools/bosun_docker/
make run 

Generate MLOps Management-Agent configuration file.
Enter DataRobot App URL (e.g. https://app.datarobot.com): <https://<MLOPS_HOST>>
Enter DataRobot API Token: <MLOPS_API_TOKEN>
Enter DataRobot Prediction Environment ID: <MLOPS_PREDICTION_ENVIRONMENT_ID> 

cd datarobot_mlops_package-*/
docker run -it \
-v conf/mlops.bosun.conf.yaml:/opt/datarobot/mlops/bosun/conf/mlops.bosun.conf.yaml \
-v conf/plugin.k8s.conf.yaml:/opt/datarobot/mlops/bosun/conf/plugin.k8s.conf.yaml \
datarobot/mlops-management-agent 

mkdir /opt/management-agent-demo
cd /opt/management-agent-demo
python3 -m venv .venv
source .venv/bin/activate
tar -zxf datarobot_mlops_package-*.tar.gz
cd datarobot_mlops_package-*/
pip install lib/datarobot_mlops-*-py2.py3-none-any.whl
pip install lib/datarobot_mlops_connected_client-*-py3-none-any.whl
pip install lib/datarobot_bosun-*-py3-none-any.whl 

<your-chosen-editor> ./conf/mlops.bosun.conf.yaml 

./bin/start-bosun.sh 

# This file contains configuration for the Management Agent
# Items marked "Required" must be set. Other settings can use the defaults set below.

# Required. URL to the DataRobot MLOps service.
mlopsUrl: "https://<MLOPS_HOST>"

# Required. DataRobot API token.
apiToken: "<MLOPS_API_TOKEN>"

# When true, verify SSL certificates when connecting to DR app. When false, SSL verification will not be
# performed. It is highly recommended to keep this config variable as true.
verifySSL: true

# Whether to run management agent as the workload coordinator. The default value is true.
isCoordinator: true

# Whether to run management agent as worker. The default value is true.
isWorker: true

# When true, start a REST server. This will provide several API endpoints (worker health check enables)
serverMode: false

# The port to use for the above REST server
serverPort: "12345"

# The url where to reach REST server, will be use by external configuration services
serverAddress: "http://localhost"

# Specify the configuration service. This is 'internal' by default and the
# workload coordinator and worker are expected to run in the same JVM.
# When run in high availability mode, the configuration needs to be provided by
# a service such as Consul.
configurationService:
  tag: "tag"
  type: "internal"
  connectionDetail: ""

# Path to write Bosun stats
statsPath: "/tmp/management-agent-stats.json"

# HTTP client timeout in milliseconds (30sec timeout).
httpTimeout: 30000

# Number of times the agent will retry sending a request to the MLOps service after it receives a failure.
httpRetry: 3

# Number of active workers to process management agent commands
numActionWorkers: 2

# Timeout in seconds processing active commands, eg. launch, stop, replaceModel
actionWorkerTimeoutSec: 300

# Timeout in seconds for requesting status of PE and the deployment
statusWorkerTimeoutSec: 300

# How often (in seconds) status worker should update DR MLOps about the status of PE and deployments
statusUpdateIntervalSec: 120

# How often (in seconds) to poll MLOps service for new deployment / PE Actions
mlopsPollIntervalSec: 60
# Optional: Plugins directory in which all required plugin jars can be found.
# If you are only using external commands to run plugin actions then there is
# no need to use this option.
# pluginsDir: "../plugins/"

# Model Connector configuration
modelConnector:
  type: "native"
  # Scratch place to work on, default "/tmp"
  scratchDir: "/tmp"
  # Config file for private / secret configuration, management agent will not read this file, just
  # forward the filename in configuration, optional
  secretsConfigFile: "/tmp/secrets.conf"
  # Python command that implements model connector.
  # mcrunner is installed as part the bosun python package. You should either
  # set your PATH to include the location of mcrunner, or provide the full path.
  command: "<BOSUN_VENV_PATH>/bin/mcrunner"

# prediction environments this service will monitor
predictionEnvironments:
# This Prediction Environment ID matches the one in DR MLOps service
- id: "<MLOPS_PREDICTION_ENVIRONMENT_ID>"
    type: "ExternalCommand"
    platform: "os"
    # Enable monitoring for this plugin, so that the MLOps information
    # (viz, url and token) can be forwarded to plugin, default: False
    #
    enableMonitoring: true

    # Provide the command to run the plugin:

    # You can either fix PATH to point to where bosun-plugin-runner is located, or
    # you can provide the full path below.
    # The filesystem plugin used in the example below if one of the built in plugins provided
    # by the bosun-plugin-runner
    command: "<BOSUN_VENV_PATH>/bin/bosun-plugin-runner --plugin filesystem --private-config <CONF_PATH>/plugin.filesystem.conf.yaml"

    # The following example will run the docker plugin
    # (one of the built in plugins provided by bosun-plugin runner)
    # command: "<BOSUN_VENV_PATH>/bin/bosun-plugin-runner --plugin docker --private-config <CONF_PATH>/plugin.docker.conf.yaml"

    # The following example will run the kubernetes plugin
    # (one of the built in plugins provided by bosun-plugin runner)
    # WARNING: this plugin is currently considered ALPHA maturity; please consult your account representative if you
    # are interested in trying it.
    # command: "<BOSUN_VENV_PATH>/bin/bosun-plugin-runner --plugin k8s --private-config <CONF_PATH>/plugin.k8s.conf.yaml"

    # If your plugin was installed as a python module (using pip), you can provide the name
    # of the module that contains the plugin class. For example --plugin sample_plugin.my_plugin
    # command: "<BOSUN_VENV_PATH>/bin/bosun-plugin-runner --plugin sample_plugin.my_plugin --private-config <CONF_PATH>/my_config.yaml"

    # If your plugin is in a directory, you can provide the name of the plugin as the path to the
    # file that contains your plugin. For example:  --plugin sample_plugin/my_plugin.py
    # command: "<BOSUN_VENV_PATH>/bin/bosun-plugin-runner  --plugin sample_plugin/my_plugin.py --private-config <CONF_PATH>/my_config.yaml"

    # Note: you can control the plugin logging via the --log-config option of bosun-plugin-runner 

make build -C tools/bosun_docker