監視エージェントのインストールと設定¶

tar -xvf datarobot_mlops_package-*.tar.gz 

cd datarobot_mlops_package-*;
<your-favorite-editor> ./conf/mlops.agent.conf.yaml 

# This file contains configuration for the MLOps agent

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

# DataRobot API token
apiToken: "<MLOPS_API_TOKEN>"

# Execute the agent once, then exit
runOnce: false

# When dryrun mode is true, do not report the metrics to MLOps service
dryRun: false

# When verifySSL is true, SSL certification validation will be performed when
# connecting to MLOps DataRobot. When verifySSL is false, these checks are skipped.
# Note: It is highly recommended to keep this config variable as true.
verifySSL: true

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

# Prediction Environment served by this agent.
# Events and errors not specific to a single deployment are reported against this Prediction Environment.
# predictionEnvironmentId: "<PE_ID_FROM_DATAROBOT_UI>"

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

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

# Number of concurrent http request, default=1 -> synchronous mode; > 1 -> asynchronous
httpConcurrentRequest: 10

# Number of HTTP Connections to establish with the MLOps service, Default: 1
numMLOpsConnections: 1

# Comment out and configure the lines below for the spooler type(s) you are using.
# Note: The spooler configuration must match that used by the MLOps library.
# Note: The filesystem spooler directory must be an absolute path to the "/tmp/ta" directory.
# Note: Spoolers must be set up before using them.
#       - For the filesystem spooler, create the directory that will be used.
#       - For the SQS spooler, create the queue.
#       - For the PubSub spooler, create the project and topic.
#       - For the Kafka spooler, create the topic.
channelConfigs:
- type: "FS_SPOOL"
    details: {name: "filesystem", directory: "<path_to_spooler_directory>/tmp/ta"}
#  - type: "SQS_SPOOL"
#    details: {name: "sqs", queueUrl: "your SQS queue URL", queueName: "<your AWS SQS queue name>"}
#  - type: "RABBITMQ_SPOOL"
#    details: {name: "rabbit",  queueName: <your rabbitmq queue name>,  queueUrl: "amqp://<ip address>",
#              caCertificatePath: "<path_to_ca_certificate>",
#              certificatePath: "<path_to_client_certificate>",
#              keyfilePath: "<path_to_key_file>"}

#  - type: "PUBSUB_SPOOL"
#    details: {name: "pubsub", projectId: <your project ID>, topicName: <your topic name>, subscriptionName: <your sub name>}
#  - type: "KAFKA_SPOOL"
#    details: {name: "kafka", topicName: "<your topic name>", bootstrapServers: "<ip address 1>,<ip address 2>,…"}

# The number of threads that the agent will launch to process data records.
agentThreadPoolSize: 4

# The maximum number of records each thread will process per fetchNewDataFreq interval.
agentMaxRecordsTask: 100

# Maximum number of records to aggregate before sending to DataRobot MLOps
agentMaxAggregatedRecords: 500

# A timeout for pending records before aggregating and submitting
agentPendingRecordsTimeoutMs: 5000 

make build -C tools/agent_docker