GitLocker: The Coding Marketplace

Description:

autodice 1.0.3.dev2

AutoDiCE Project.
The source for this AutoDiCE project is available here.

How to use the Partition
### Prepare AlexNet Model and Computing nodes
### mapping.json Template (AlexNet):
{"lenovo_cpu0": ["conv1_1", "conv1_2", "norm1_1", "pool1_1", "conv2_1", "conv2_2", "norm2_1", "pool2_1", "conv3_1", "conv3_2", "conv4_1", "conv4_2", "conv5_1", "conv5_2", "pool5_1", "OC2_DUMMY_0", "fc6_1", "fc6_2"], "lenovo_cpu1": ["fc6_3", "fc7_1", "fc7_2", "fc7_3", "fc8_1", "prob_1"]}

### Quick Start.
import os,sys

from partition.interface import *

if __name__ == '__main__':

output_dirs= './models'
if not os.path.exists(output_dirs):
# Create a new directory because it does not exist
os.makedirs(output_dirs)
print("The output directory %s is created!" % (output_dirs))

origin_model = "bvlcalexnet-9.onnx"
### Can be downloaded from:
# https://github.com/onnx/models/blob/main/vision/classification/alexnet/model/bvlcalexnet-9.onnx
input_model = format_onnx(origin_model)
model = onnx.load(input_model)
# resourceid = { 1:'lenovo_cpu0', 2:'lenovo_cpu1'}
platforms = ['lenovo']

### How the layers in alexnet model distribute over two computing nodes???
random_map = {
"lenovo_cpu0":
["conv1_1", "conv1_2", "norm1_1", "pool1_1", "conv2_1",
"conv2_2", "norm2_1", "pool2_1", "conv3_1", "conv3_2",
"conv4_1", "conv4_2", "conv5_1", "conv5_2", "pool5_1",
"OC2_DUMMY_0", "fc6_1", "fc6_2"],
"lenovo_cpu1":
["fc6_3", "fc7_1", "fc7_2", "fc7_3", "fc8_1", "prob_1"]
}

InputSpecs = Interface(model=input_model, mappings=random_map, platforms=platforms)
#cppname, NodesList, ComputingNodes
### Generate Cpp Files for Multinode-Inference.
GenerateCode = EngineCode(
CppName = "./models/multinode",
Platforms = InputSpecs.platforms,
NodesList = InputSpecs.nodes,
ComputingNodes = InputSpecs.computingnodes,
ValueInfos = InputSpecs.value_map,
Inputs = InputSpecs.inputs,
Outputs = InputSpecs.outputs,
Benchmark = False)

# ...