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Sensei Machine Learning API guide appendix

Last update: Fri Jan 06 2023 00:00:00 GMT+0000 (Coordinated Universal Time)

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The following sections provide reference information for various features of the Sensei Machine Learning API.

Query parameters for asset retrieval query

The Sensei Machine Learning API provides support for query parameters with retrieving assets. Available query parameters and their usages are described in the following table:

Query parameter
Description
Default value
start
Indicates the starting index for pagination.
start=0
limit
Indicates the maximum number of results to return.
limit=25
orderby
Indicates the properties to use for sorting in priority order. Include a dash (-) before a property name to sort in descending order, otherwise results are sorted in ascending order.
orderby=created
property
Indicates the comparison expression that an object must satisfy in order to be returned.
property=deleted==false
NOTE
When combining multiple query parameters, they must be separated by ampersands (&).

Python CPU and GPU configurations cpu-gpu-config

Python Engines have the ability to choose between either a CPU or a GPU for its training or scoring purposes, and is defined on an MLInstance as a task specification (tasks.specification).

The following is an example configuration that specifies using a CPU for training and a GPU for scoring:

[
    {
        "name": "train",
        "parameters": [
            {
                "key": "training parameter",
                "value": "parameter value"
            }
        ],
        "specification": {
            "type": "ContainerTaskSpec",
            "cpus": "1"
        }
    },
    {
        "name": "score",
        "parameters": [
            {
                "key": "scoring parameter",
                "value": "parameter value"
            }
        ],
        "specification": {
            "type": "ContainerTaskSpec",
            "gpus": "1"
        }
    }
]
NOTE
The values of cpus and gpus does not signify the number of CPUs or GPUs, but rather the number of physical machines. These values are permissibly "1" and will throw an exception otherwise.

PySpark and Spark resource configurations resource-config

Spark Engines have the ability to modify computational resources for training and scoring purposes. These resources are described in the following table:

Resource
Description
Type
driverMemory
Memory for driver in megabytes
int
driverCores
Number of cores used by driver
int
executorMemory
Memory for executor in megabytes
int
executorCores
Number of cores used by executor
int
numExecutors
Number of executors
int

Resources can be specified on an MLInstance as either (A) individual training or scoring parameters, or (B) within an additional specifications object (specification). For example, the following resource configurations are the same for both training and scoring:

[
    {
        "name": "train",
        "parameters": [
            {
                "key": "driverMemory",
                "value": "2048"
            },
            {
                "key": "driverCores",
                "value": "1"
            },
            {
                "key": "executorMemory",
                "value": "2048"
            },
            {
                "key": "executorCores",
                "value": "2"
            },
            {
                "key": "numExecutors",
                "value": "3"
            }
        ]
    },
    {
        "name": "score",
        "parameters": [
            {
                "key": "scoring parameter",
                "value": "parameter value"
            }
        ],
        "specification": {
            "type": "SparkTaskSpec",
            "name": "Spark Task name",
            "className": "Class name",
            "driverMemoryInMB": 2048,
            "driverCores": 1,
            "executorMemoryInMB": 2048,
            "executorCores": 2,
            "numExecutors": 3
        }
    }
]
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