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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/12/02 09:45:31 UTC
[GitHub] [tvm] leandron commented on a change in pull request #9633: Tvmc python tutorial
leandron commented on a change in pull request #9633:
URL: https://github.com/apache/tvm/pull/9633#discussion_r760895360
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,236 @@
+# 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 Starting using TVMC Python, the simplified tvm API
+==========================================================
+**Authors**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+# Let's import our model into tvmc.
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into tvm's high level graph representation language called relay.
+# This is to have a unified starting point for all models in tvm. The frameworks we currently
+# support are: Keras, Onnx, Tensorflow, TFLite, and Pytorch.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
Review comment:
I think it would be good if we use the same model between the dvmc command line tutorial and the tvmc API tutorial. That is just a suggestion.
Despite using the same or different models, I suggest pointing to a model that a user can download and use that during the tutorial.
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,236 @@
+# 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 Starting using TVMC Python, the simplified tvm API
+==========================================================
+**Authors**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+# Let's import our model into tvmc.
+# Let's import our model into tvmc. This step converts a machine learning model from
Review comment:
```suggestion
# Let's import our model into tvmc. This step converts a machine learning model from
```
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,236 @@
+# 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 Starting using TVMC Python, the simplified tvm API
+==========================================================
+**Authors**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+# Let's import our model into tvmc.
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into tvm's high level graph representation language called relay.
+# This is to have a unified starting point for all models in tvm. The frameworks we currently
+# support are: Keras, Onnx, Tensorflow, TFLite, and Pytorch.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+#
+# If you'd like to see the relay, you can run:
+# ``model.summary()``
+#
+# All frameworks support over writing the input shapes with a shape_dict argument.
+# For most frameworks this is optional but for Pytorch this is necessary.
+#
+# .. code-block:: python
+# ### Step 1: Load shape_dict Style
+# # shape_dict = {'model_input_name1': [1, 3, 224, 224], 'input2': [1, 2, 3, 4], ...} #example format with random numbers
+# # model = tvmc.load(model_path, shape_dict=shape_dict) #Step 1: Load + shape_dict
+#
+# One way to see the model's input/shape_dict is via `netron <https://netron.app/>`_, . After opening the model,
+# click the first node to see the name(s) and shape(s) in the inputs section.
+
+
+################################################################################
+# Step 2: Compile
+# ----------------
+# Now that our model is in relay, our next step is to compile it to a desired
+# hardware to run on. We refer to this hardware as a target. This compilation process
+# translates the model from relay into a lower-level language that the
+# target machine can understand.
+#
+# In order to compile a model a tvm.target string is required.
+# To learn more about tvm.targets and their options look at the `documentation <https://tvm.apache.org/docs/api/python/target.html>`_.
+# Some examples include:
+# 1. cuda (nvidia gpu)
+# 2. llvm (cpu)
+# 3. llvm -mcpu=cascadelake (intel cpu)
+#
+# .. code-block:: python
+# package = tvmc.compile(model, target="llvm") #Step 2: Compile
+#
+# The compilation step returns a package.
+#
+
+################################################################################
+# Step 3: Run
+# -----------
+# The compiled package can now be run on the hardware target. The device
+# input options are: cpu, cuda, cl, metal, and vulkan.
+#
+# .. code-block:: python
+# result = tvmc.run(package, device="cpu") #Step 3: Run
+#
+# And you can print the results:
+# ``print(results)``
+#
+
+################################################################################
+# Step 1.5: Tune [Optional & Recommended]
+# ---------------------------------------
+# Run speed can further be improved by tuning. This optional step uses
+# machine learning to look at each operation within a model (a function) and
+# tries to find a faster way to run it. We do this through a cost model, and
+# bench marking possible schedules.
+#
+# The target is the same as compile.
+#
+# .. code-block:: python
+# tvmc.tune(model, target="llvm") #Step 1.5: Optional Tune
+#
+# The terminal output should look like:
+# [Task 1/13] Current/Best: 82.00/ 106.29 GFLOPS | Progress: (48/769) | 18.56 s
+# [Task 1/13] Current/Best: 54.47/ 113.50 GFLOPS | Progress: (240/769) | 85.36 s
+# .....
+#
+# There may be UserWarnings that can be ignored.
+# This should make the end result faster, but it can take hours to tune.
+#
+
+################################################################################
+# Save and then start the process in the terminal:
+# ------------------------------------------------
+#
+# .. code-block:: python
+# python my_tvmc_script.py
+#
+# Note: Your fans may become very active
+#
+
+################################################################################
+# Example results:
+# ----------------
+#
+# .. code-block:: python
+# Time elapsed for training: 18.99 s
+# Execution time summary:
+# mean (ms) max (ms) min (ms) std (ms)
+# 25.24 26.12 24.89 0.38
+#
+# Output Names:
+# ['output_0']
+#
+
+Additional TVMC Functionalities
+===============================
+
+################################################################################
+# Saving the model
+# ----------------
+#
+# To make things faster for later, after loading the model (Step 1) save the relay version.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+# model.save(model_path)
+#
+
+################################################################################
+# Saving the package
+# ------------------
+#
+# After the model has been compiled (Step 2) the package also is also saveable.
+#
+# .. code-block:: python
+# tvmc.compile(model, target="llvm", package_path="whatever") #Step 2: Compile
+#
+# new_package = tvmc.TVMCPackage(package_path="whatever")
+# result = tvmc.run(new_package) #Step 3: Run
+#
+
+################################################################################
+# Using Autoscheduler
+# -------------------
+# Use the next generation of tvm to enable potentially faster run speed results.
+# The search space of the schedules is automatically generated unlike
+# previously where they needed to be hand written. (Learn more: 1, 2)
+#
+# .. code-block:: python
+# tvmc.tune(model, target="llvm", enable_autoscheduler = True) #Step 1.5: Optional Tune
Review comment:
```suggestion
# tvmc.tune(model, target="llvm", enable_autoscheduler = True)
```
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,236 @@
+# 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 Starting using TVMC Python, the simplified tvm API
+==========================================================
+**Authors**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+# Let's import our model into tvmc.
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into tvm's high level graph representation language called relay.
+# This is to have a unified starting point for all models in tvm. The frameworks we currently
+# support are: Keras, Onnx, Tensorflow, TFLite, and Pytorch.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+#
+# If you'd like to see the relay, you can run:
+# ``model.summary()``
+#
+# All frameworks support over writing the input shapes with a shape_dict argument.
+# For most frameworks this is optional but for Pytorch this is necessary.
+#
+# .. code-block:: python
+# ### Step 1: Load shape_dict Style
+# # shape_dict = {'model_input_name1': [1, 3, 224, 224], 'input2': [1, 2, 3, 4], ...} #example format with random numbers
+# # model = tvmc.load(model_path, shape_dict=shape_dict) #Step 1: Load + shape_dict
+#
+# One way to see the model's input/shape_dict is via `netron <https://netron.app/>`_, . After opening the model,
+# click the first node to see the name(s) and shape(s) in the inputs section.
+
+
+################################################################################
+# Step 2: Compile
+# ----------------
+# Now that our model is in relay, our next step is to compile it to a desired
+# hardware to run on. We refer to this hardware as a target. This compilation process
+# translates the model from relay into a lower-level language that the
+# target machine can understand.
+#
+# In order to compile a model a tvm.target string is required.
+# To learn more about tvm.targets and their options look at the `documentation <https://tvm.apache.org/docs/api/python/target.html>`_.
+# Some examples include:
+# 1. cuda (nvidia gpu)
+# 2. llvm (cpu)
+# 3. llvm -mcpu=cascadelake (intel cpu)
+#
+# .. code-block:: python
+# package = tvmc.compile(model, target="llvm") #Step 2: Compile
+#
+# The compilation step returns a package.
+#
+
+################################################################################
+# Step 3: Run
+# -----------
+# The compiled package can now be run on the hardware target. The device
+# input options are: cpu, cuda, cl, metal, and vulkan.
+#
+# .. code-block:: python
+# result = tvmc.run(package, device="cpu") #Step 3: Run
+#
+# And you can print the results:
+# ``print(results)``
+#
+
+################################################################################
+# Step 1.5: Tune [Optional & Recommended]
+# ---------------------------------------
+# Run speed can further be improved by tuning. This optional step uses
+# machine learning to look at each operation within a model (a function) and
+# tries to find a faster way to run it. We do this through a cost model, and
+# bench marking possible schedules.
+#
+# The target is the same as compile.
+#
+# .. code-block:: python
+# tvmc.tune(model, target="llvm") #Step 1.5: Optional Tune
+#
+# The terminal output should look like:
+# [Task 1/13] Current/Best: 82.00/ 106.29 GFLOPS | Progress: (48/769) | 18.56 s
+# [Task 1/13] Current/Best: 54.47/ 113.50 GFLOPS | Progress: (240/769) | 85.36 s
+# .....
+#
+# There may be UserWarnings that can be ignored.
+# This should make the end result faster, but it can take hours to tune.
+#
+
+################################################################################
+# Save and then start the process in the terminal:
+# ------------------------------------------------
+#
+# .. code-block:: python
+# python my_tvmc_script.py
+#
+# Note: Your fans may become very active
+#
+
+################################################################################
+# Example results:
+# ----------------
+#
+# .. code-block:: python
+# Time elapsed for training: 18.99 s
+# Execution time summary:
+# mean (ms) max (ms) min (ms) std (ms)
+# 25.24 26.12 24.89 0.38
+#
+# Output Names:
+# ['output_0']
+#
+
+Additional TVMC Functionalities
+===============================
+
+################################################################################
+# Saving the model
+# ----------------
+#
+# To make things faster for later, after loading the model (Step 1) save the relay version.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+# model.save(model_path)
+#
+
+################################################################################
+# Saving the package
+# ------------------
+#
+# After the model has been compiled (Step 2) the package also is also saveable.
+#
+# .. code-block:: python
+# tvmc.compile(model, target="llvm", package_path="whatever") #Step 2: Compile
Review comment:
```suggestion
# tvmc.compile(model, target="llvm", package_path="whatever")
```
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,236 @@
+# 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 Starting using TVMC Python, the simplified tvm API
Review comment:
```suggestion
Getting Starting using TVMC Python: a high-level API for TVM
```
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,236 @@
+# 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 Starting using TVMC Python, the simplified tvm API
+==========================================================
+**Authors**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+# Let's import our model into tvmc.
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into tvm's high level graph representation language called relay.
+# This is to have a unified starting point for all models in tvm. The frameworks we currently
+# support are: Keras, Onnx, Tensorflow, TFLite, and Pytorch.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+#
+# If you'd like to see the relay, you can run:
+# ``model.summary()``
+#
+# All frameworks support over writing the input shapes with a shape_dict argument.
+# For most frameworks this is optional but for Pytorch this is necessary.
+#
+# .. code-block:: python
+# ### Step 1: Load shape_dict Style
+# # shape_dict = {'model_input_name1': [1, 3, 224, 224], 'input2': [1, 2, 3, 4], ...} #example format with random numbers
+# # model = tvmc.load(model_path, shape_dict=shape_dict) #Step 1: Load + shape_dict
+#
+# One way to see the model's input/shape_dict is via `netron <https://netron.app/>`_, . After opening the model,
+# click the first node to see the name(s) and shape(s) in the inputs section.
+
+
+################################################################################
+# Step 2: Compile
+# ----------------
+# Now that our model is in relay, our next step is to compile it to a desired
+# hardware to run on. We refer to this hardware as a target. This compilation process
+# translates the model from relay into a lower-level language that the
+# target machine can understand.
+#
+# In order to compile a model a tvm.target string is required.
+# To learn more about tvm.targets and their options look at the `documentation <https://tvm.apache.org/docs/api/python/target.html>`_.
+# Some examples include:
+# 1. cuda (nvidia gpu)
+# 2. llvm (cpu)
+# 3. llvm -mcpu=cascadelake (intel cpu)
+#
+# .. code-block:: python
+# package = tvmc.compile(model, target="llvm") #Step 2: Compile
+#
+# The compilation step returns a package.
+#
+
+################################################################################
+# Step 3: Run
+# -----------
+# The compiled package can now be run on the hardware target. The device
+# input options are: cpu, cuda, cl, metal, and vulkan.
Review comment:
Use upper case where appropriate
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,236 @@
+# 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 Starting using TVMC Python, the simplified tvm API
+==========================================================
+**Authors**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+# Let's import our model into tvmc.
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into tvm's high level graph representation language called relay.
+# This is to have a unified starting point for all models in tvm. The frameworks we currently
+# support are: Keras, Onnx, Tensorflow, TFLite, and Pytorch.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+#
+# If you'd like to see the relay, you can run:
+# ``model.summary()``
+#
+# All frameworks support over writing the input shapes with a shape_dict argument.
+# For most frameworks this is optional but for Pytorch this is necessary.
+#
+# .. code-block:: python
+# ### Step 1: Load shape_dict Style
+# # shape_dict = {'model_input_name1': [1, 3, 224, 224], 'input2': [1, 2, 3, 4], ...} #example format with random numbers
+# # model = tvmc.load(model_path, shape_dict=shape_dict) #Step 1: Load + shape_dict
+#
+# One way to see the model's input/shape_dict is via `netron <https://netron.app/>`_, . After opening the model,
+# click the first node to see the name(s) and shape(s) in the inputs section.
+
+
+################################################################################
+# Step 2: Compile
+# ----------------
+# Now that our model is in relay, our next step is to compile it to a desired
+# hardware to run on. We refer to this hardware as a target. This compilation process
+# translates the model from relay into a lower-level language that the
+# target machine can understand.
+#
+# In order to compile a model a tvm.target string is required.
+# To learn more about tvm.targets and their options look at the `documentation <https://tvm.apache.org/docs/api/python/target.html>`_.
+# Some examples include:
+# 1. cuda (nvidia gpu)
+# 2. llvm (cpu)
+# 3. llvm -mcpu=cascadelake (intel cpu)
+#
+# .. code-block:: python
+# package = tvmc.compile(model, target="llvm") #Step 2: Compile
+#
+# The compilation step returns a package.
+#
+
+################################################################################
+# Step 3: Run
+# -----------
+# The compiled package can now be run on the hardware target. The device
+# input options are: cpu, cuda, cl, metal, and vulkan.
+#
+# .. code-block:: python
+# result = tvmc.run(package, device="cpu") #Step 3: Run
+#
+# And you can print the results:
+# ``print(results)``
+#
+
+################################################################################
+# Step 1.5: Tune [Optional & Recommended]
+# ---------------------------------------
+# Run speed can further be improved by tuning. This optional step uses
+# machine learning to look at each operation within a model (a function) and
+# tries to find a faster way to run it. We do this through a cost model, and
+# bench marking possible schedules.
+#
+# The target is the same as compile.
+#
+# .. code-block:: python
+# tvmc.tune(model, target="llvm") #Step 1.5: Optional Tune
+#
+# The terminal output should look like:
+# [Task 1/13] Current/Best: 82.00/ 106.29 GFLOPS | Progress: (48/769) | 18.56 s
+# [Task 1/13] Current/Best: 54.47/ 113.50 GFLOPS | Progress: (240/769) | 85.36 s
+# .....
+#
+# There may be UserWarnings that can be ignored.
+# This should make the end result faster, but it can take hours to tune.
+#
+
+################################################################################
+# Save and then start the process in the terminal:
+# ------------------------------------------------
+#
+# .. code-block:: python
+# python my_tvmc_script.py
+#
+# Note: Your fans may become very active
+#
+
+################################################################################
+# Example results:
+# ----------------
+#
+# .. code-block:: python
+# Time elapsed for training: 18.99 s
+# Execution time summary:
+# mean (ms) max (ms) min (ms) std (ms)
+# 25.24 26.12 24.89 0.38
+#
+# Output Names:
+# ['output_0']
+#
+
+Additional TVMC Functionalities
+===============================
+
+################################################################################
+# Saving the model
+# ----------------
+#
+# To make things faster for later, after loading the model (Step 1) save the relay version.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+# model.save(model_path)
+#
Review comment:
It would be good to have some information on what file you actually get, as the result of `model.save`.
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,236 @@
+# 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 Starting using TVMC Python, the simplified tvm API
+==========================================================
+**Authors**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+# Let's import our model into tvmc.
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into tvm's high level graph representation language called relay.
Review comment:
I think the use of TVM should be always in all-capitals. Please review all other instances.
```suggestion
# a supported framework into TVM's high level graph representation language called relay.
```
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,236 @@
+# 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 Starting using TVMC Python, the simplified tvm API
+==========================================================
+**Authors**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+# Let's import our model into tvmc.
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into tvm's high level graph representation language called relay.
+# This is to have a unified starting point for all models in tvm. The frameworks we currently
+# support are: Keras, Onnx, Tensorflow, TFLite, and Pytorch.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+#
+# If you'd like to see the relay, you can run:
+# ``model.summary()``
+#
+# All frameworks support over writing the input shapes with a shape_dict argument.
+# For most frameworks this is optional but for Pytorch this is necessary.
+#
+# .. code-block:: python
+# ### Step 1: Load shape_dict Style
+# # shape_dict = {'model_input_name1': [1, 3, 224, 224], 'input2': [1, 2, 3, 4], ...} #example format with random numbers
+# # model = tvmc.load(model_path, shape_dict=shape_dict) #Step 1: Load + shape_dict
+#
+# One way to see the model's input/shape_dict is via `netron <https://netron.app/>`_, . After opening the model,
+# click the first node to see the name(s) and shape(s) in the inputs section.
+
+
+################################################################################
+# Step 2: Compile
+# ----------------
+# Now that our model is in relay, our next step is to compile it to a desired
+# hardware to run on. We refer to this hardware as a target. This compilation process
+# translates the model from relay into a lower-level language that the
+# target machine can understand.
+#
+# In order to compile a model a tvm.target string is required.
+# To learn more about tvm.targets and their options look at the `documentation <https://tvm.apache.org/docs/api/python/target.html>`_.
+# Some examples include:
+# 1. cuda (nvidia gpu)
+# 2. llvm (cpu)
+# 3. llvm -mcpu=cascadelake (intel cpu)
+#
+# .. code-block:: python
+# package = tvmc.compile(model, target="llvm") #Step 2: Compile
+#
+# The compilation step returns a package.
+#
+
+################################################################################
+# Step 3: Run
+# -----------
+# The compiled package can now be run on the hardware target. The device
+# input options are: cpu, cuda, cl, metal, and vulkan.
+#
+# .. code-block:: python
+# result = tvmc.run(package, device="cpu") #Step 3: Run
+#
+# And you can print the results:
+# ``print(results)``
+#
+
+################################################################################
+# Step 1.5: Tune [Optional & Recommended]
+# ---------------------------------------
+# Run speed can further be improved by tuning. This optional step uses
+# machine learning to look at each operation within a model (a function) and
+# tries to find a faster way to run it. We do this through a cost model, and
+# bench marking possible schedules.
Review comment:
```suggestion
# benchmarking possible schedules.
```
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,236 @@
+# 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 Starting using TVMC Python, the simplified tvm API
+==========================================================
+**Authors**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+# Let's import our model into tvmc.
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into tvm's high level graph representation language called relay.
+# This is to have a unified starting point for all models in tvm. The frameworks we currently
+# support are: Keras, Onnx, Tensorflow, TFLite, and Pytorch.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+#
+# If you'd like to see the relay, you can run:
+# ``model.summary()``
+#
+# All frameworks support over writing the input shapes with a shape_dict argument.
+# For most frameworks this is optional but for Pytorch this is necessary.
+#
+# .. code-block:: python
+# ### Step 1: Load shape_dict Style
+# # shape_dict = {'model_input_name1': [1, 3, 224, 224], 'input2': [1, 2, 3, 4], ...} #example format with random numbers
+# # model = tvmc.load(model_path, shape_dict=shape_dict) #Step 1: Load + shape_dict
+#
+# One way to see the model's input/shape_dict is via `netron <https://netron.app/>`_, . After opening the model,
+# click the first node to see the name(s) and shape(s) in the inputs section.
+
+
+################################################################################
+# Step 2: Compile
+# ----------------
+# Now that our model is in relay, our next step is to compile it to a desired
+# hardware to run on. We refer to this hardware as a target. This compilation process
+# translates the model from relay into a lower-level language that the
+# target machine can understand.
+#
+# In order to compile a model a tvm.target string is required.
+# To learn more about tvm.targets and their options look at the `documentation <https://tvm.apache.org/docs/api/python/target.html>`_.
+# Some examples include:
+# 1. cuda (nvidia gpu)
+# 2. llvm (cpu)
+# 3. llvm -mcpu=cascadelake (intel cpu)
+#
+# .. code-block:: python
+# package = tvmc.compile(model, target="llvm") #Step 2: Compile
+#
+# The compilation step returns a package.
+#
+
+################################################################################
+# Step 3: Run
+# -----------
+# The compiled package can now be run on the hardware target. The device
+# input options are: cpu, cuda, cl, metal, and vulkan.
+#
+# .. code-block:: python
+# result = tvmc.run(package, device="cpu") #Step 3: Run
+#
+# And you can print the results:
+# ``print(results)``
+#
+
+################################################################################
+# Step 1.5: Tune [Optional & Recommended]
+# ---------------------------------------
+# Run speed can further be improved by tuning. This optional step uses
+# machine learning to look at each operation within a model (a function) and
+# tries to find a faster way to run it. We do this through a cost model, and
+# bench marking possible schedules.
+#
+# The target is the same as compile.
+#
+# .. code-block:: python
+# tvmc.tune(model, target="llvm") #Step 1.5: Optional Tune
+#
+# The terminal output should look like:
+# [Task 1/13] Current/Best: 82.00/ 106.29 GFLOPS | Progress: (48/769) | 18.56 s
+# [Task 1/13] Current/Best: 54.47/ 113.50 GFLOPS | Progress: (240/769) | 85.36 s
+# .....
+#
+# There may be UserWarnings that can be ignored.
+# This should make the end result faster, but it can take hours to tune.
+#
+
+################################################################################
+# Save and then start the process in the terminal:
+# ------------------------------------------------
+#
+# .. code-block:: python
+# python my_tvmc_script.py
+#
+# Note: Your fans may become very active
+#
+
+################################################################################
+# Example results:
+# ----------------
+#
+# .. code-block:: python
+# Time elapsed for training: 18.99 s
+# Execution time summary:
+# mean (ms) max (ms) min (ms) std (ms)
+# 25.24 26.12 24.89 0.38
+#
+# Output Names:
+# ['output_0']
+#
+
+Additional TVMC Functionalities
+===============================
+
+################################################################################
+# Saving the model
+# ----------------
+#
+# To make things faster for later, after loading the model (Step 1) save the relay version.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+# model.save(model_path)
+#
+
+################################################################################
+# Saving the package
+# ------------------
+#
+# After the model has been compiled (Step 2) the package also is also saveable.
+#
+# .. code-block:: python
+# tvmc.compile(model, target="llvm", package_path="whatever") #Step 2: Compile
+#
+# new_package = tvmc.TVMCPackage(package_path="whatever")
+# result = tvmc.run(new_package) #Step 3: Run
+#
+
+################################################################################
+# Using Autoscheduler
+# -------------------
+# Use the next generation of tvm to enable potentially faster run speed results.
+# The search space of the schedules is automatically generated unlike
+# previously where they needed to be hand written. (Learn more: 1, 2)
+#
+# .. code-block:: python
+# tvmc.tune(model, target="llvm", enable_autoscheduler = True) #Step 1.5: Optional Tune
+#
+
+################################################################################
+# Saving the tuning results
+# -------------------------
+#
+# The tuning results can be saved in a file for later reuse.
+#
+# Method 1:
+# .. code-block:: python
+# log_file = "hello.json"
+#
+# # Run tuning
+# tvmc.tune(model, target="llvm",tuning_records=log_file)
+#
+# ...
+#
+# # Later run tuning and reuse tuning results
+# tvmc.tune(model, target="llvm",tuning_records=log_file)
+#
+# Method 2:
+# .. code-block:: python
+# # Run tuning
+# tuning_records = tvmc.tune(model, target="llvm")
+#
+# ...
+#
+# # Later run tuning and reuse tuning results
+# tvmc.tune(model, target="llvm",tuning_records=tuning_records)
+#
+
+################################################################################
+# Tuning a more complex model:
+# ----------------------------
+# If you notice T's (timeouts) printed, increase the searching time frame:
Review comment:
Please add a bit more context here, perhaps an example of a timeout in the command line where you can point to the timeouts being mentioned here.
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,236 @@
+# 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 Starting using TVMC Python, the simplified tvm API
+==========================================================
+**Authors**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+# Let's import our model into tvmc.
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into tvm's high level graph representation language called relay.
+# This is to have a unified starting point for all models in tvm. The frameworks we currently
+# support are: Keras, Onnx, Tensorflow, TFLite, and Pytorch.
Review comment:
```suggestion
# support are: Keras, ONNX, Tensorflow, TFLite, and PyTorch.
```
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,236 @@
+# 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 Starting using TVMC Python, the simplified tvm API
+==========================================================
+**Authors**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+# Let's import our model into tvmc.
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into tvm's high level graph representation language called relay.
+# This is to have a unified starting point for all models in tvm. The frameworks we currently
+# support are: Keras, Onnx, Tensorflow, TFLite, and Pytorch.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+#
+# If you'd like to see the relay, you can run:
+# ``model.summary()``
+#
+# All frameworks support over writing the input shapes with a shape_dict argument.
+# For most frameworks this is optional but for Pytorch this is necessary.
+#
+# .. code-block:: python
+# ### Step 1: Load shape_dict Style
+# # shape_dict = {'model_input_name1': [1, 3, 224, 224], 'input2': [1, 2, 3, 4], ...} #example format with random numbers
+# # model = tvmc.load(model_path, shape_dict=shape_dict) #Step 1: Load + shape_dict
+#
+# One way to see the model's input/shape_dict is via `netron <https://netron.app/>`_, . After opening the model,
+# click the first node to see the name(s) and shape(s) in the inputs section.
+
+
+################################################################################
+# Step 2: Compile
+# ----------------
+# Now that our model is in relay, our next step is to compile it to a desired
+# hardware to run on. We refer to this hardware as a target. This compilation process
+# translates the model from relay into a lower-level language that the
+# target machine can understand.
+#
+# In order to compile a model a tvm.target string is required.
+# To learn more about tvm.targets and their options look at the `documentation <https://tvm.apache.org/docs/api/python/target.html>`_.
+# Some examples include:
+# 1. cuda (nvidia gpu)
+# 2. llvm (cpu)
+# 3. llvm -mcpu=cascadelake (intel cpu)
+#
+# .. code-block:: python
+# package = tvmc.compile(model, target="llvm") #Step 2: Compile
+#
+# The compilation step returns a package.
+#
+
+################################################################################
+# Step 3: Run
+# -----------
+# The compiled package can now be run on the hardware target. The device
+# input options are: cpu, cuda, cl, metal, and vulkan.
+#
+# .. code-block:: python
+# result = tvmc.run(package, device="cpu") #Step 3: Run
+#
+# And you can print the results:
+# ``print(results)``
+#
+
+################################################################################
+# Step 1.5: Tune [Optional & Recommended]
+# ---------------------------------------
+# Run speed can further be improved by tuning. This optional step uses
+# machine learning to look at each operation within a model (a function) and
+# tries to find a faster way to run it. We do this through a cost model, and
+# bench marking possible schedules.
+#
+# The target is the same as compile.
+#
+# .. code-block:: python
+# tvmc.tune(model, target="llvm") #Step 1.5: Optional Tune
+#
+# The terminal output should look like:
+# [Task 1/13] Current/Best: 82.00/ 106.29 GFLOPS | Progress: (48/769) | 18.56 s
+# [Task 1/13] Current/Best: 54.47/ 113.50 GFLOPS | Progress: (240/769) | 85.36 s
+# .....
+#
+# There may be UserWarnings that can be ignored.
+# This should make the end result faster, but it can take hours to tune.
+#
+
+################################################################################
+# Save and then start the process in the terminal:
+# ------------------------------------------------
+#
+# .. code-block:: python
+# python my_tvmc_script.py
+#
+# Note: Your fans may become very active
+#
+
+################################################################################
+# Example results:
+# ----------------
+#
+# .. code-block:: python
+# Time elapsed for training: 18.99 s
+# Execution time summary:
+# mean (ms) max (ms) min (ms) std (ms)
+# 25.24 26.12 24.89 0.38
+#
+# Output Names:
+# ['output_0']
+#
+
+Additional TVMC Functionalities
+===============================
+
+################################################################################
+# Saving the model
+# ----------------
+#
+# To make things faster for later, after loading the model (Step 1) save the relay version.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+# model.save(model_path)
+#
+
+################################################################################
+# Saving the package
+# ------------------
+#
+# After the model has been compiled (Step 2) the package also is also saveable.
+#
+# .. code-block:: python
+# tvmc.compile(model, target="llvm", package_path="whatever") #Step 2: Compile
+#
+# new_package = tvmc.TVMCPackage(package_path="whatever")
+# result = tvmc.run(new_package) #Step 3: Run
+#
+
+################################################################################
+# Using Autoscheduler
+# -------------------
+# Use the next generation of tvm to enable potentially faster run speed results.
+# The search space of the schedules is automatically generated unlike
+# previously where they needed to be hand written. (Learn more: 1, 2)
+#
+# .. code-block:: python
+# tvmc.tune(model, target="llvm", enable_autoscheduler = True) #Step 1.5: Optional Tune
+#
+
+################################################################################
+# Saving the tuning results
+# -------------------------
+#
+# The tuning results can be saved in a file for later reuse.
+#
+# Method 1:
+# .. code-block:: python
+# log_file = "hello.json"
+#
+# # Run tuning
+# tvmc.tune(model, target="llvm",tuning_records=log_file)
+#
+# ...
+#
+# # Later run tuning and reuse tuning results
+# tvmc.tune(model, target="llvm",tuning_records=log_file)
+#
+# Method 2:
+# .. code-block:: python
+# # Run tuning
+# tuning_records = tvmc.tune(model, target="llvm")
+#
+# ...
+#
+# # Later run tuning and reuse tuning results
+# tvmc.tune(model, target="llvm",tuning_records=tuning_records)
+#
+
+################################################################################
+# Tuning a more complex model:
+# ----------------------------
+# If you notice T's (timeouts) printed, increase the searching time frame:
+#
+# .. code-block:: python
+# tvmc.tune(model,trials=10000,timeout=10,)
+#
+
+################################################################################
+# Compiling a model for a remote device:
+#
+# A remote procedural call is useful when you would like to compile for hardware
Review comment:
```suggestion
# A remote procedural call (RPC) is useful when you would like to compile for hardware
```
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,236 @@
+# 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 Starting using TVMC Python, the simplified tvm API
+==========================================================
+**Authors**:
Review comment:
```suggestion
**Author**:
```
##########
File path: gallery/how_to/use_tvms_python_api/tvmc_python.py
##########
@@ -0,0 +1,236 @@
+# 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 Starting using TVMC Python, the simplified tvm API
+==========================================================
+**Authors**:
+`Jocelyn Shiue <https://github.com/CircleSpin>`_,
+
+Welcome to TVMC Python
+======================
+Hi! Here we explain the scripting tool designed for the complete TVM beginner. 🙂
+
+################################################################################
+# Step 0: Imports
+# ---------------
+#
+# .. code-block:: python
+#
+# from tvm.driver import tvmc
+#
+
+################################################################################
+# Step 1: Load a model
+# --------------------
+# Let's import our model into tvmc.
+# Let's import our model into tvmc. This step converts a machine learning model from
+# a supported framework into tvm's high level graph representation language called relay.
+# This is to have a unified starting point for all models in tvm. The frameworks we currently
+# support are: Keras, Onnx, Tensorflow, TFLite, and Pytorch.
+#
+# .. code-block:: python
+# model = tvmc.load('my_model.onnx') #Step 1: Load
+#
+# If you'd like to see the relay, you can run:
+# ``model.summary()``
+#
+# All frameworks support over writing the input shapes with a shape_dict argument.
+# For most frameworks this is optional but for Pytorch this is necessary.
+#
+# .. code-block:: python
+# ### Step 1: Load shape_dict Style
+# # shape_dict = {'model_input_name1': [1, 3, 224, 224], 'input2': [1, 2, 3, 4], ...} #example format with random numbers
+# # model = tvmc.load(model_path, shape_dict=shape_dict) #Step 1: Load + shape_dict
+#
+# One way to see the model's input/shape_dict is via `netron <https://netron.app/>`_, . After opening the model,
+# click the first node to see the name(s) and shape(s) in the inputs section.
+
+
+################################################################################
+# Step 2: Compile
+# ----------------
+# Now that our model is in relay, our next step is to compile it to a desired
+# hardware to run on. We refer to this hardware as a target. This compilation process
+# translates the model from relay into a lower-level language that the
+# target machine can understand.
+#
+# In order to compile a model a tvm.target string is required.
+# To learn more about tvm.targets and their options look at the `documentation <https://tvm.apache.org/docs/api/python/target.html>`_.
+# Some examples include:
+# 1. cuda (nvidia gpu)
+# 2. llvm (cpu)
+# 3. llvm -mcpu=cascadelake (intel cpu)
Review comment:
A few more nits on lower vs upper case. Please review other instances of these.
```suggestion
# 1. cuda (Nvidia GPU)
# 2. llvm (CPU)
# 3. llvm -mcpu=cascadelake (Intel CPU)
```
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