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Global Configuration

This covers configurations that are used globally and as part of startup for DJL Serving.

Command line parameters

User can use the following parameters to start djl-serving, those parameters will override default behavior:

djl-serving -h

usage: djl-serving [OPTIONS]
 -f,--config-file <CONFIG-FILE>    Path to the configuration properties file.
 -h,--help                         Print this help.
 -m,--models <MODELS>              Models to be loaded at startup.
 -s,--model-store <MODELS-STORE>   Model store location where models can be loaded.

Details about the models, model-store, and workflows can be found in the equivalent configuration properties.

config.properties file

DJL Serving use a config.properties file to store configurations.

Configure listening port

DJL Serving only allows localhost access by default.

  • inference_address: inference API binding address, default: http://127.0.0.1:8080
  • management_address: management API binding address, default: http://127.0.0.1:8081

Here are a couple of examples:

# bind inference API to all network interfaces with SSL enabled
inference_address=https://0.0.0.0:8443

# bind inference API to private network interfaces
inference_address=https://172.16.1.10:8443

Configure initial models and workflows

Model Store

The model_store config property can be used to define a directory where each file/folder in it is a model to be loaded. It will then attempt to load all of them by default. Here is an example:

model_store=build/models

Load Models

The load_models config property can be used to define a list of models (or workflows) to be loaded. The list should be defined as a comma separated list of urls to load models from.

Each model can be defined either as a URL directly or optionally with prepended endpoint data like [EndpointData]=modelUrl. The endpoint is a list of data items separated by commas. The possible variations are:

  • [modelName]
  • [modelName:version]
  • [modelName:version:engine]
  • [modelName:version:engine:deviceNames]

The version can be an arbitrary string. The engines uses the standard DJL Engine names.

Possible deviceNames strings include * for all devices and a ; separated list of device names following the format defined in DJL Device.fromName. If no device is specified, it will use the DJL default device (usually GPU if available else CPU).

load_models=djl://ai.djl.zoo/mlp,[mlp:v1:PyTorch:*]=https://resources.djl.ai/test-models/mlp.zip

Workflows

Use the load_models config property to define initial workflows that should be loaded on startup.

load_models=https://resources.djl.ai/test-models/basic-serving-workflow.json

View the workflow documentation to see more information about workflows and their configuration format.

Enable SSL

For users who want to enable HTTPs, you can change inference_address or management_addrss protocol from http to https, for example: inference_addrss=https://127.0.0.1. This will make DJL Serving listen on localhost 443 port to accepting https request.

User also must provide certificate and private keys to enable SSL. DJL Serving support two ways to configure SSL:

  1. Use keystore

    • keystore: Keystore file location, if multiple private key entry in the keystore, first one will be picked.
    • keystore_pass: keystore password, key password (if applicable) MUST be the same as keystore password.
    • keystore_type: type of keystore, default: PKCS12
  2. Use private-key/certificate files

    • private_key_file: private key file location, support both PKCS8 and OpenSSL private key.
    • certificate_file: X509 certificate chain file location.

Self-signed certificate example

This is a quick example to enable SSL with self-signed certificate

User java keytool to create keystore
keytool -genkey -keyalg RSA -alias djl -keystore keystore.p12 -storepass changeit -storetype PKCS12 -validity 3600 -keysize 2048 -dname "CN=www.MY_DOMSON.com, OU=Cloud Service, O=model server, L=Palo Alto, ST=California, C=US"

Config following property in config.properties:

inference_address=https://127.0.0.1:8443
management_address=https://127.0.0.1:8444
keystore=keystore.p12
keystore_pass=changeit
keystore_type=PKCS12
User OpenSSL to create private key and certificate
# generate a private key with the correct length
openssl genrsa -out private-key.pem 2048

# generate corresponding public key
openssl rsa -in private-key.pem -pubout -out public-key.pem

# create a self-signed certificate
openssl req -new -x509 -key private-key.pem -out cert.pem -days 360

# convert pem to pfx/p12 keystore
openssl pkcs12 -export -inkey private-key.pem -in cert.pem -out keystore.p12

Config following property in config.properties:

inference_address=https://127.0.0.1:8443
management_address=https://127.0.0.1:8444
keystore=keystore.p12
keystore_pass=changeit
keystore_type=PKCS12

Environment variables

User can set environment variables to change DJL Serving behavior, following is a list of system environment variables that user can be set for DJL Serving:

Key Type Description
JAVA_HOME env var JDK home path
MODEL_SERVER_HOME env var DJLServing home directory, default: Installation directory (e.g. /usr/local/Cellar/djl-serving/0.27.0/)
DEFAULT_JVM_OPTS env var default: -Dlog4j.configurationFile=${APP_HOME}/conf/log4j2.xml
Override default JVM startup options and system properties.
JAVA_OPTS env var default: -Xms1g -Xmx1g -XX:+ExitOnOutOfMemoryError
Add extra JVM options.
SERVING_OPTS env var default: N/A
Add serving related JVM options.
Some of DJL configuration can only be configured by JVM system properties, user has to set DEFAULT_JVM_OPTS environment variable to configure them.
- -Dai.djl.pytorch.num_interop_threads=2, this will override interop threads for PyTorch
- -Dai.djl.pytorch.num_threads=2, this will override OMP_NUM_THREADS for PyTorch
- -Dai.djl.logging.level=debug change DJL loggging level

Note: Above system environment variable has higher priority than the default value in the container.

Global Model Server settings

Global settings are configured at model server level. Change to these settings usually requires restart model server to take effect.

Most of the model server specific configuration can be configured in conf/config.properties file. You can find the configuration keys here: ConfigManager.java

Each configuration key can also be overridden by environment variable with SERVING_ prefix, for example:

export SERVING_JOB_QUEUE_SIZE=1000 # This will override JOB_QUEUE_SIZE in the config

Similar to system environment variable, global environment variable has higher priority than config.properties.

Per model settings

There two type of model settings, options and arguments. options are used at model loading time, arguments are used by Translator for pre/post processing.

You can provide extra settings with environment variable, prefix with OPTION_ for options and ARGS_ for arguments. See: model configurations for more details.

Note: per model environment will NOT override values in serving.properties.

Appendix

How to configure logging

Option 1: enable debug log:

export SERVING_OPTS="-Dai.djl.logging.level=debug"

Option 2: use your log4j2.xml

export DEFAULT_JVM_OPTS="-Dlog4j.configurationFile=/MY_CONF/log4j2.xml

DJLServing provides a few built-in log4j2-XXX.xml files in DJLServing containers. Use the following environment variable to print HTTP access log to console:

export DEFAULT_JVM_OPTS="-Dlog4j.configurationFile=/usr/local/djl-serving-0.27.0/conf/log4j2-access.xml

Use the following environment variable to print both access log, server metrics and model metrics to console:

export DEFAULT_JVM_OPTS="-Dlog4j.configurationFile=/usr/local/djl-serving-0.27.0/conf/log4j2-console.xml