[GitHub] [incubator-tvm] comaniac commented on a change in pull request #6597: [tvmc][docs] Getting started tutorial for TVMC

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comaniac commented on a change in pull request #6597:
URL: https://github.com/apache/incubator-tvm/pull/6597#discussion_r498324044



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File path: tutorials/get_started/tvmc_command_line_driver.py
##########
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+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Getting Started with TVM command line driver - TVMC
+===================================================
+**Authors**:
+`Leandro Nunes <https://github.com/leandron>`_,
+`Matthew Barrett <https://github.com/mbaret>`_
+
+This tutorial is an introduction to working with TVMC, the TVM command
+line driver.
+
+In this tutorial we are going to use TVMC to perform common tasks with
+TVM via a command line interface.
+"""
+
+######################################################################
+# Using TVMC
+# ----------
+#
+# TVMC is a Python application, part of the TVM Python package.
+# When you install TVM using a Python package, you will get TVMC as
+# command line application ``tvmc``.
+#
+# Alternatively, if you have TVM as a Python module on your
+# ``$PYTHONPATH``,you can access the command line driver functionality
+# via the executable python module, ``python -m tvm.driver.tvmc``.
+#
+# For simplicity, this tutorial will mention TVMC command line using
+# ``tvmc <options>``, but the same results can be obtained with
+# ``python -m tvm.driver.tvmc <options>``.
+#
+# You can check the help page using:
+#
+# .. code-block:: bash
+#
+#   tvmc --help
+#
+#
+# As you can see in the help page, the main features are
+# accessible via the subcommands ``tune``, ``compile`` and ``run``.
+# To read about specific options under a given subcommand, use
+# ``tvmc <subcommand> --help``.
+#
+# By default, TVMC will only display error messages. In case you want
+# to increase verbosity (more messages), you can do that with:
+#
+# .. code-block:: bash
+#
+#   tvmc -vvv <subcommand>
+#
+#
+# In the following sections we will use TVMC to tune, compile and
+# run a model. But first, we need a model.
+#
+
+
+######################################################################
+# Obtaining the model
+# -------------------
+#
+# We are going to use ResNet-50 V2 as an example to experiment with TVMC.
+# The version below is in ONNX format. To download the file, you can use
+# the command below:
+#
+# .. code-block:: bash
+#
+#   wget https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet50-v2-7.onnx
+#
+#
+
+######################################################################
+# .. note:: Supported model formats
+#
+#   TVMC supports models created with Keras, ONNX, TensorFlow, TFLite
+#   and Torch. Use the option``--model-format`` if you need to
+#   explicitly provide the model format you are using. See ``tvmc
+#   compile --help`` for more information.
+#
+
+######################################################################
+# Tuning the model
+# ----------------
+#
+# Once we've downloaded ResNet-50, it is time to run TVM's auto-tuner.
+# Tuning in TVM refers to the process by which a model is optimized
+# to run faster on a given target. This differs from training or
+# fine-tuning in that it does not affect the accuracy of the model,
+# only the runtime performance.
+#
+# As part of the tuning process, TVM will try running many different
+# operator implementation variants to see which perform best. The
+# results of these runs are stored in a tuning records file, which is
+# ultimately the output of the ``tune`` subcommand.
+#
+# In the simplest form, tuning requires you to provide three things:
+#
+# - the target specification of the device you intend to run this model on;
+# - the path to an output file in which the tuning records will be stored, and finally,
+# - a path to the model to be tuned.
+#
+#
+# The example below demonstrates how that works in practice:
+#
+# .. code-block:: bash
+#
+#   tvmc tune \
+#     --target "llvm" \
+#     --output autotuner_records.json \
+#     resnet50-v2-7.onnx
+#
+#
+# Tuning sessions can take a long time, so ``tvmc tune`` offers many options to
+# customize your tuning process, in terms of number of repetitions (``--repeat`` and
+# ``--number``, for example), the tuning algorithm to be use, and so on.
+# Check ``tvmc tune --help`` for more information.
+#
+# As an output of the tuning process above, we obtained the tuning records stored
+# in ``autotuner_records.json``. This file can be used in two ways: as an input
+# to further tuning (via ``tvmc tune --tuning-records``), or as an input to the
+# compiler. The compiler will use the results to generate high performance code
+# for the model on your specified target.
+#
+# So now let's see how we can invoke the compiler using TVMC.
+#
+
+######################################################################
+# Compiling the model
+# -------------------
+#
+# To compile our model using TVM, we use ``tvmc compile``. The output we get from the
+# compilation process is a TAR package that can be used to run our model on the
+# target device.
+#
+# As previously mentioned, we can use tuning records in order to help the compilation
+# process to generate code that performs better for a given platform. To do that
+# we can use ``--tuning-records``. Check ``tvmc compile --help`` for more options.
+#
+# .. code-block:: bash
+#
+#   tvmc compile \
+#     --target "llvm" \

Review comment:
       You're right about the point I mentioned. Just want to let readers know that the target string may affect the performance. You can probably refer to this tutorial section: https://tvm.apache.org/docs/tutorials/autotvm/tune_relay_x86.html#define-network
   
   




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