Create a custom workflow on distributed processes#

This example shows how to read and postprocess distributed files on distributed processes. After remote postprocessing, results are merged on the local process. This example creates different operator sequences directly on different servers. These operators are then connected together so that you don’t have to care that they are on remote processes.

digraph foo {
    graph [pad="0", nodesep="0.3", ranksep="0.3"]
    node [shape=box, style=filled, fillcolor="#ffcc00", margin="0"];
    rankdir=LR;
    splines=line;

    subgraph cluster_1 {
        ds01 [label="data_src", shape=box, style=filled, fillcolor=cadetblue2];

        ds01 -> stress1 [style=dashed];

        label="Server 1";
        style=filled;
        fillcolor=lightgrey;
    }

    subgraph cluster_2 {
        ds02 [label="data_src", shape=box, style=filled, fillcolor=cadetblue2];

        ds02 -> stress2 [style=dashed];
        stress2 -> mul;

        label="Server 2";
        style=filled;
        fillcolor=lightgrey;
    }

    stress1 -> "merge";
    mul -> "merge";
}

Import the dpf-core module and its examples files.

from ansys.dpf import core as dpf
from ansys.dpf.core import examples
from ansys.dpf.core import operators as ops

Configure the servers. To make it easier, this example starts local servers. However, you can connect to any existing servers on your network.

global_server = dpf.start_local_server(
    as_global=True, config=dpf.AvailableServerConfigs.InProcessServer
)

remote_servers = [
    dpf.start_local_server(
        as_global=False, config=dpf.AvailableServerConfigs.GrpcServer),
    dpf.start_local_server(
        as_global=False, config=dpf.AvailableServerConfigs.GrpcServer),
]

Send files to the temporary directory if they are not in shared memory.

files = examples.download_distributed_files()
server_file_paths = [dpf.upload_file_in_tmp_folder(files[0], server=remote_servers[0]),
                     dpf.upload_file_in_tmp_folder(files[1], server=remote_servers[1])]

Create the first operator chain.

remote_operators = []

stress1 = ops.result.stress(server=remote_servers[0])
remote_operators.append(stress1)
ds = dpf.DataSources(server_file_paths[0], server=remote_servers[0])
stress1.inputs.data_sources(ds)

Create the second operator chain.

stress2 = ops.result.stress(server=remote_servers[1])
mul = stress2 * 2.0
remote_operators.append(mul)
ds = dpf.DataSources(server_file_paths[1], server=remote_servers[1])
stress2.inputs.data_sources(ds)

Create the local merge operator.

merge = ops.utility.merge_fields_containers()

Connect the operator chains together and get the output.

nodal = ops.averaging.to_nodal_fc(merge)

merge.connect(0, remote_operators[0], 0)
merge.connect(1, remote_operators[1], 0)

fc = nodal.get_output(0, dpf.types.fields_container)
print(fc[0])
fc[0].meshed_region.plot(fc[0])
01 distributed workflows on remote
DPF stress_7491.964387Hz Field
  Location: Nodal
  Unit: Pa
  432 entities
  Data:6 components and 432 elementary data

Total running time of the script: ( 0 minutes 2.126 seconds)

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