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[AUTOTVM][DOCS] Add a link to the defining network description of auto-tuning tutorial #4023

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Sep 29, 2019
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[AUTOTVM][DOCS] Add a link to the defining network description of auto-tuning tutorial #4023

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[AUTOTVM][DOCS] Add a link to autoTVM tutorial to direct the details …
cchung100m Sep 27, 2019
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[AUTOTVM][DOCS] Add a link to autoTVM tutorial to direct the details …
cchung100m Sep 28, 2019
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6 changes: 6 additions & 0 deletions tutorials/autotvm/tune_relay_x86.py
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Expand Up @@ -39,9 +39,12 @@
# First we need to define the network in relay frontend API.
# We can load some pre-defined network from :code:`relay.testing`.
# We can also load models from MXNet, ONNX and TensorFlow.
# Alternatively, we can `build a Graph Convolutional Network (GCN) with Relay
# <https://docs.tvm.ai/tutorials/frontend/build_gcn.html#building-a-graph-convolutional-network>`
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Please use internal restructured text anchors for refering to something inside the doc

#
# In this tutorial, we choose resnet-18 as tuning example.


def get_network(name, batch_size):
"""Get the symbol definition and random weight of a network"""
input_shape = (batch_size, 3, 224, 224)
Expand Down Expand Up @@ -73,6 +76,7 @@ def get_network(name, batch_size):

return mod, params, input_shape, output_shape


# Replace "llvm" with the correct target of your CPU.
# For example, for AWS EC2 c5 instance with Intel Xeon
# Platinum 8000 series, the target should be "llvm -mcpu=skylake-avx512".
Expand Down Expand Up @@ -121,6 +125,7 @@ def get_network(name, batch_size):
),
}


# You can skip the implementation of this function for this tutorial.
def tune_kernels(tasks,
measure_option,
Expand Down Expand Up @@ -165,6 +170,7 @@ def tune_kernels(tasks,
autotvm.callback.progress_bar(n_trial, prefix=prefix),
autotvm.callback.log_to_file(log_filename)])


# Use graph tuner to achieve graph level optimal schedules
# Set use_DP=False if it takes too long to finish.
def tune_graph(graph, dshape, records, opt_sch_file, use_DP=True):
Expand Down