Skip to content

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

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Sep 29, 2019
Merged

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

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
da45a66
[AUTOTVM][DOCS] Add a link to autoTVM tutorial to direct the details …
cchung100m Sep 27, 2019
8e86e33
[AUTOTVM][DOCS] Add a link to autoTVM tutorial to direct the details …
cchung100m Sep 28, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
7 changes: 6 additions & 1 deletion tutorials/autotvm/tune_relay_x86.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -37,11 +37,13 @@
# Define network
# --------------
# First we need to define the network in relay frontend API.
# We can load some pre-defined network from :code:`relay.testing`.
# We can either load some pre-defined network from :code:`relay.testing`
# or building :any:`relay.testing.resnet` with relay.
# We can also load models from MXNet, ONNX and TensorFlow.
#
# 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 +75,7 @@ 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 +124,7 @@ 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 +169,7 @@ 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