CONFIGURATION.MD

Configuration

All 0Mind configuration can be described in 2 json files:

configs/logger.json
configs/model_pool_config.json

logger.json

Operates with standart Python logger options, which you can find in official documentation.

model_pool_config.json

It is the default configuration file, but you can specify any using command line option --config_file of the model_pool.py

Sample configuration is:

{
    "id": 1,
    "host": "127.0.0.1",
    "port": 5885,
    "tasks": [
        {
           "id": 1,
           "model_type": "caffe2",
           "model_file": "ML/models/bvlc_alexnet.caffe2",
           "input_filters": {
               "data": [
                   "i_img_file_to_caffe2.ImageFileCaffe2Filter"
               ]
           },
           "output_filters": {
               "default": ["io_argmax.ArgMaxFilter"]
           }
        }
    ],
    "debug": true,
    "model_types": ["caffe2"]
}

Compulsory configuration attributes are:

id, host, port, tasks

id - (integer) unique model pool identifier

host - (string) you can specify desirable existing localy network interface

port - (integer) unique service identifier for this host

tasks - (list of objects) list of tasks for this model pool

All other configuration parameters are not necessary and can be skipped.

Tasks

It is a list of model pool tasks. This list can be empty. In other cases you should specify required task attributes:

id - (string) unique model identifier

model_type - (string) framework identifier (see ML/adapters/base_model.BaseModel.get_package_name())

model_file - (string) full path to model file with name and extension

input_filters - (dictionary) can be empty or should contain input names with filter list, that must be applied on input data in specified order

output_filters - (dictionary) can be empty or should contain output names with filter list, that must be applied on output data in specified order

A lot of auxilary task params are possible. See FRAMEWORKS SECTION.

Auxilary config params

model_types - (list of strings) you can restrict the model loading in this pool only with specified model types

debug - (bool) it is in charge for web server debug mode, that returns detailed info when error is occurs. It's useful in development process