merrymercy commented on a change in pull request #6512:
URL: https://github.com/apache/incubator-tvm/pull/6512#discussion_r491318913
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File path: tutorials/auto_scheduler/tune_matmul_x86.py
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@@ -155,19 +169,22 @@ def resume_search(task, log_file):
sch, args = auto_scheduler.auto_schedule(task, search_policy,
tuning_options=tune_option)
-# resume_search(task, "matmul.json")
+#resume_search(task, "matmul.json")
######################################################################
# .. note::
# We cannot run the line above because of the conflict between
# python's multiprocessing and tvm's thread pool.
-# After running a tvm generated binary (L112), the python's multiprocessing
-# library will hang forever.
-# You have to make sure that you don't run any tvm generated binaries before
-# calling ansor's search. To run the L156 above, you should comment out
L112-114.
+# After running a tvm generated binary the python's multiprocessing library
+# will hang forever. You have to make sure that you don't run any tvm
+# generated binaries before calling auot-scheduler's search.
+# To run the function above, you should comment out all code in
+# "Check correctness and evaluate performance" section.
#
# You should be careful about this problem in your applications.
# There are other workarounds for this problem.
# For example, you can start a new thread/process (with the builtin python
library
# threading or multiprocessing) and run the tvm binaries in the new
thread/process.
# This provides an isolation and avoids the conflict in the main
thread/process.
+# You can also use :any:`auto_scheduler.measure.LocalRPCMeasureContext` for
auto-scheduler,
+# as shown in the GPU tutorial (:ref:`auto-scheduler-conv-gpu`).
Review comment:
Actually, I removed `LocalRPCMeasureContext` to make the major part of
the tutorial simpler.
Because resuming a search is a very advanced usage, most people don't need
this part. So we can make it complicated. But all sections above should be as
simple as possible.
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