merrymercy commented on a change in pull request #7167:
URL: https://github.com/apache/tvm/pull/7167#discussion_r548921916
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File path: tutorials/auto_scheduler/tune_network_mali.py
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@@ -339,9 +349,14 @@ def tune_and_evaluate():
# 1. During the tuning, the auto-scheduler needs to compile many programs and
# extract feature from them. This part is CPU-intensive,
# so a high-performance CPU with many cores is recommended for faster
search.
-# 2. If you have multiple target devices, you can use all of them for
measurements to
-# parallelize the measurements. Check this :ref:`section
<tutorials-autotvm-rpc-tracker>`
+# 2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode
distill --i log.json`
+# to distill the large log file and only save the best useful records.
+# 3. You can resume a search from the previous log file. You just need to
+# add a new argument :code:`load_log_file` when creating the task scheduler
+# in function :code:`run_tuning`. Say,
+# :code:`tuner = auto_scheduler.TaskScheduler(tasks, task_weights,
load_log_file=log_file)`
+# 4. If you have multiple target CPUs, you can use all of them for
measurements to
Review comment:
```suggestion
# 4. If you have multiple target GPUs, you can use all of them for
measurements to
```
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