TorchServe is meant to be easy to use. The default settings form TorchServe should be sufficient for most use cases. If you want to customize TorchServe, the configuration options described in this topic are available.
There are three ways to configure TorchServe. In order of priority, the are:
Command line arguments
For example, the value of an environment variable overrides both command line arguments and a property in the configuration file. The value of a command line argument overrides a value in the configuration file.
You can change TorchServe behavior by setting the following environment variables:
Note: Environment variables have higher priority than command line or config.properties. The value of an environment variable overrides other property values.
Command line parameters¶
Customize TorchServe behavior by using the following command line arguments when you call
–ts-config TorchServe loads the specified configuration file if
TS_CONFIG_FILEenvironment variable is not set
–model-store Overrides the
model_storeproperty in config.properties file
–models Overrides the
load_modelsproperty in config.properties
–log-config Overrides the default log4j.properties
–foreground Runs TorchServe in the foreground. If this option is disabled, TorchServe runs in the background
For more detailed information about
torchserve command line options, see Serve Models with TorchServe.
TorchServe uses a
config.properties file to store configurations. TorchServe uses following, in order of priority, to locate this
TS_CONFIG_FILEenvironment variable is set, TorchServe loads the configuration from the path specified by the environment variable.
--ts-configparameter is passed to
torchserve, TorchServe loads the configuration from the path specified by the parameter.
If there is a
config.propertiesin the folder where you call
torchserve, TorchServe loads the
config.propertiesfile from the current working directory.
If none of the above is specified, TorchServe loads a built-in configuration with default values.
Customize JVM options¶
To control TorchServe frontend memory footprint, configure the vmargs property in the
default: N/A, use JVM default options
Adjust JVM options to fit your memory requirement.
Load models at startup¶
You can configure TorchServe to load models during startup by setting the
The following values are valid:
standalone: default: N/A, No models are loaded at start up.
all: Load all models present in
model1.mar, model2.mar: Load models in the specified MAR files from
model1=model1.mar, model2=model2.mar: Load models with the specified names and MAR files from
standalone: default: N/A, Loading models from the local disk is disabled.
pathname: The model store location is specified by the value of
load_models properties are overridden by command line parameters, if specified.
Configure TorchServe listening address and port¶
TorchServe doesn’t support authentication natively. To avoid unauthorized access, TorchServe only allows localhost access by default. The inference API is listening on port 8080. The management API is listening on port 8081. Both expect HTTP requests. These are the default ports. See Enable SSL to configure HTTPS.
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
metrics_address: metrics API binding address. Default: http://127.0.0.1:8082
To run predictions on models on a public IP address, specify the IP address as
0.0.0.0. To run predictions on models on a specific IP address, specify the IP address and port.
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://172.16.1.10:8080
To enable HTTPs, you can change
metrics_address protocol from http to https. For example:
The default is port 443, but you can make TorchServe listen on whatever port you set to accept https requests.
For example, to receive https traffic on port 8443, you would use:
You must also provide a certificate and private key to enable SSL. TorchServe supports two ways to configure SSL:
Use a keystore:
keystore: the keystore file location. If multiple private key entries exist in the keystore, the first one will be used.
keystore_pass: the keystore password. The password (if applicable) MUST be the same as keystore password.
keystore_type: the type of keystore. Default: PKCS12.
Use private-key/certificate files:
private_key_file: the private key file location. Supports both PKCS8 and OpenSSL private keys.
certificate_file: the X509 certificate chain file location.
Option 1: Use a keystore; generate a keystore with Java’s keytool. Note the
storepass argument expects you to create your own password.
keytool -genkey -keyalg RSA -alias ts -keystore keystore.p12 -storepass changeit -storetype PKCS12 -validity 3600 -keysize 2048 -dname "CN=www.MY_TS.com, OU=Cloud Service, O=model server, L=Palo Alto, ST=California, C=US"
Configure the following properties in config.properties:
inference_address=https://127.0.0.1:8443 management_address=https://127.0.0.1:8444 metrics_address=https://127.0.0.1:8445 keystore=keystore.p12 keystore_pass=changeit keystore_type=PKCS12
Option 2: Use private-key/certificate files; generate your self signed cert and key with OpenSSL:
openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout mykey.key -out mycert.pem
Config following property in config.properties:
inference_address=https://127.0.0.1:8443 management_address=https://127.0.0.1:8444 metrics_address=https://127.0.0.1:8445 private_key_file=mykey.key certificate_file=mycert.pem
Configure Cross-Origin Resource Sharing (CORS)¶
CORS is a mechanism that uses additional HTTP headers to tell a browser to let a web application running at one origin (domain) have permission to access selected resources from a server at a different origin.
CORS is disabled by default. Configure following properties in config.properties file to enable CORS:
# cors_allowed_origin is required to enable CORS, use '*' or your domain name cors_allowed_origin=https://yourdomain.com # required if you want to use preflight request cors_allowed_methods=GET, POST, PUT, OPTIONS # required if the request has an Access-Control-Request-Headers header cors_allowed_headers=X-Custom-Header
Prefer direct buffer¶
Configuration parameter prefer_direct_buffer controls if the model server will be using direct memory specified by -XX:MaxDirectMemorySize. This parameter is for model server only and doesn’t affect other packages’ usage of direct memory buffer. Default: false
Allow model specific custom python packages.¶
Custom models/handlers may depend on different python packages which are not installed by-default as a part of
TorchServe setup. User can supply a requirements.txt file containing the required list of python packages to be installed by
TorchServe for seamless model serving.
install_py_dep_per_model controls if the model server will install the python packages using the
requirements file supplied with the model archive. Default: false
User can also supply custom python packages in zip or tar.gz format using the
--extra-files flag while creating the model-archive and make an entry of the file name in the
Restrict backend worker to access environment variables¶
Environment variables might contain sensitive information, like AWS credentials. Backend workers execute an arbitrary model’s custom code,
which might expose a security risk. TorchServe provides a
blacklist_env_vars property that allows you to restrict which environment variables can be accessed by backend workers.
blacklist_env_vars: a regular expression to filter out environment variable names. Default: all environment variables are visible to backend workers.
Limit GPU usage¶
By default, TorchServe uses all available GPUs for inference. Use
number_of_gpu to limit the usage of GPUs.
number_of_gpu: Maximum number of GPUs that TorchServe can use for inference. Default: all available GPUs in system.
Enable metrics api¶
enable_metrics_api: Enable or disable metric apis i.e. it can be either
false. Default: true (Enabled)
metrics_format: Use this to specify metric report format . At present, the only supported and default value for this is
prometheus' This is used in conjunction withenable_meterics_api` option above.
Most of the following properties are designed for performance tuning. Adjusting these numbers will impact scalability and throughput.
enable_envvars_config: Enable configuring TorchServe through environment variables. When this option is set to “true”, all the static configurations of TorchServe can come through environment variables as well. Default: false
number_of_netty_threads: Number frontend netty thread. This specifies the numer of threads in the child EventLoopGroup of the frontend netty server. This group provides EventLoops for processing Netty Channel events (namely inference and management requests) from accepted connections. Default: number of logical processors available to the JVM.
netty_client_threads: Number of backend netty thread. This specifies the number of threads in the WorkerThread EventLoopGroup which writes inference responses to the frontend. Default: number of logical processors available to the JVM.
default_workers_per_model: Number of workers to create for each model that loaded at startup time. Default: available GPUs in system or number of logical processors available to the JVM.
job_queue_size: Number inference jobs that frontend will queue before backend can serve. Default: 100.
async_logging: Enable asynchronous logging for higher throughput, log output may be delayed if this is enabled. Default: false.
default_response_timeout: Timeout, in seconds, used for model’s backend workers before they are deemed unresponsive and rebooted. Default: 120 seconds.
unregister_model_timeout: Timeout, in seconds, used when handling an unregister model request when cleaning a process before it is deemed unresponsive and an error response is sent. Default: 120 seconds.
decode_input_request: Configuration to let backend workers to decode requests, when the content type is known. If this is set to “true”, backend workers do “Bytearray to JSON object” conversion when the content type is “application/json” and the backend workers convert “Bytearray to utf-8 string” when the Content-Type of the request is set to “text*”. Default: true
initial_worker_port: This is the initial port number for auto assigning port to worker process.
model_store: Path of model store directory.
model_server_home: Torchserve home directory.
max_request_size: The maximum allowable request size that the Torchserve accepts, in bytes. Default: 6553500
max_response_size: The maximum allowable response size that the Torchserve sends, in bytes. Default: 6553500
allowed_urls: Comma separated regex of allowed source URL(s) from where models can be registered. Default: “file://.|http(s)?://.” (all URLs and local file system) eg : To allow base URLs “https://s3.amazonaws.com/” and “https://torchserve.pytorch.org/” use following regex string
All the above config properties can be set using environment variable as follows.
enable_envvars_configto true in config.properties
export environment variable for property as
eg: to set inference_address property run cmd