Service#

Some Ansys products are exposed as services that permit remote execution using technologies like REST or gRPC. These services are typically exposed in a manner where the API has already been abstracted because not all methods can be exposed through a remote API. Here, the abstraction of the service is as crucial as in the case of the desktop API. In this case, remote API calls should be identical if the service is local or remote, with the only difference being that local calls are faster to execute.

Consider the following code examples. The left-hand side shows the amount of work to start, establish a connection to, and submit an input file to MAPDL using auto-generated gRPC interface files. For more information, see pyansys-protos-generator. The right-hand side shows the same workflow but uses PyMAPDL.

Using the gRPC Auto-generated Interface	Using the PyMAPDL Library
import grpc from ansys.mapdl import mapdl_pb2 as pb_types from ansys.mapdl import mapdl_pb2_grpc as mapdl_grpc from ansys.mapdl import kernel_pb2 as anskernel from ansys.client.launcher.client import Launcher # start MAPDL sm = Launcher() job = sm.create_job_by_name("mapdl-212") service_name = f"grpc-{job.name}" mapdl_service = sm.get_service(name=service_name) # create a gRPC channel channel_str = '%s:%d' % (mapdl_service.host, mapdl_service.port) channel = grpc.insecure_channel(channel_str) stub = mapdl_grpc.MapdlServiceStub(channel) # send an input file request request = pb_types.InputRequest(filename='ds.dat') response = stub.InputFileS(request) # additional postprocessing to parse response	from ansys.mapdl import core as pymapdl # start mapdl and read the input file mapdl = pymapdl.launch_mapdl() output = mapdl.input('ds.dat')

Using the gRPC Auto-generated Interface

Using the PyMAPDL Library

import grpc

from ansys.mapdl import mapdl_pb2 as pb_types
from ansys.mapdl import mapdl_pb2_grpc as mapdl_grpc
from ansys.mapdl import kernel_pb2 as anskernel
from ansys.client.launcher.client import Launcher

# start MAPDL
sm = Launcher()
job = sm.create_job_by_name("mapdl-212")
service_name = f"grpc-{job.name}"
mapdl_service = sm.get_service(name=service_name)

# create a gRPC channel
channel_str = '%s:%d' % (mapdl_service.host,
                         mapdl_service.port)
channel = grpc.insecure_channel(channel_str)
stub = mapdl_grpc.MapdlServiceStub(channel)

# send an input file request
request = pb_types.InputRequest(filename='ds.dat')
response = stub.InputFileS(request)
# additional postprocessing to parse response

from ansys.mapdl import core as pymapdl

# start mapdl and read the input file
mapdl = pymapdl.launch_mapdl()
output = mapdl.input('ds.dat')

The approach on the right has a number of advantages, including:

Readability due to the abstraction of the service startup
Short package names
Simplified interface for starting MAPDL
Full documentation strings for all classes, methods, and functions

To properly abstract a service, you must have the option to either launch the service and connect to it locally if the software exists on your machines or connect to a remote instance of the service. One way to do this is to include a function to launch the service.

This example includes the launch_mapdl function, which brokers a connection with the Mapdl class:

>>> from ansys.mapdl.core import Mapdl
>>> mapdl = Mapdl(ip=whatever_ip, port=whatever_port)
>>> print(mapdl)

Product:             Ansys Mechanical Enterprise
MAPDL Version:       21.2
ansys.mapdl Version: 0.59.dev0

This straightforward approach connects to a local or remote instance of MAPDL using gRPC by instantiating an instance of the Mapdl class. At this point, because the assumption is that MAPDL is always remote, it’s possible to issue commands to MAPDL, including those requiring file transfer like Mapdl.input.