[GitHub] [incubator-tvm] tqchen commented on a change in pull request #6112: TVMC - a command line driver for TVM

[GitBox <gi...@apache.org>]

tqchen commented on a change in pull request #6112:
URL: https://github.com/apache/incubator-tvm/pull/6112#discussion_r458934696



##########
File path: Jenkinsfile
##########
@@ -167,6 +167,7 @@ stage('Build') {
           sh "${docker_run} ${ci_cpu} ./tests/scripts/task_python_vta_tsim.sh"
           sh "${docker_run} ${ci_cpu} ./tests/scripts/task_golang.sh"
           sh "${docker_run} ${ci_cpu} ./tests/scripts/task_rust.sh"
+          sh "${docker_run} ${ci_cpu} ./tests/scripts/task_python_tvmc.sh"

Review comment:
       NOTE: changes to the Jenkinsfile won't be immediately reflected, for now, set the sh file to a blank file, and then we can turn in on as a followup PR

##########
File path: tvmc/README.md
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@@ -0,0 +1,122 @@
+<!--- 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. -->
+
+# TVMC
+
+```tvmc``` is a tool that provides useful command line invocations to compile,
+run and tune models using TVM graph runtime.
+
+In order to compile and tune, ```tvmc``` takes a model file and parameters as inputs,
+and outputs a TAR file that contains the TVM modules that represent the
+input model, graph and weights, for the required target. Target can be native or
+cross-compiled.
+
+When running a given model, ```tvmc``` expects a compiled model and input tensor values, so
+that it can produce the outputs, when running on the required target, local or remote.
+
+This document presents an overview and a short tutorial about ```tvmc```.
+
+## Installation
+
+```tvmc``` is a Python tool and - provided TVM and dependencies are available - it can be
+installed in various ways.
+
+The recommended way to install ```tvmc``` is via it's ```setuptools``` configuration file,
+located at ```tvm/tvmc/setup.py```. To do that, go to the the TVM directory and run the
+installation command, as described below:
+
+    cd tvm/tvmc
+    python setup.py install
+
+The command above should install everything needed to get started with ```tvmc```, including
+all the the supported frontends.
+
+Once ```tvmc``` is installed, the main entry-point is the ```tvmc``` command line. A set of
+sub-commands are available, to run the specific tasks offered by ```tvmc```: ```tune```,
+```compile``` and ```run```.
+
+The simplest way to get more information about a specific sub-command is ```tvmc <subcommand>
+-- help```.
+
+    tvmc compile --help
+
+##  Usage
+
+Now, let's compile a network and generate a few predictions using ```tvmc```.
+
+As described above, in order to compile a model using ```tvmc```, the first thing we need is
+a model file. For the sake of this example, let's use a MobileNet V1 model, in TFLite format.
+More information about the model is available on
+[this page](https://www.tensorflow.org/lite/guide/hosted_models).
+
+To download and un-compress the ```.tgz``` file (34Mb), so that we can access the TFLite model,
+run the command lines below:
+
+    wget https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz
+    tar xvzf mobilenet_v1_1.0_224_quant.tgz
+
+With these commands, we should be able to provide the MobileNet V1 file (```mobilenet_v1_1.0_224_quant.tflite```)
+to ```tvmc```, and obtain our TVM compiled model as an output. To do that, run the
+following command line:
+
+    tvmc compile -v mobilenet_v1_1.0_224_quant.tflite -o compiled_model.tar
+
+As an output, you will notice a ```compiled_model.tar```, in the same directory.
+
+Now it is time to feed the model with some input, that will generate a prediction using TVM.
+As models are very diverse in terms of input formats and the source of those inputs (images, streams,
+sensors, sound, to name a few), ```tvmc``` supports ```.npz``` (serialized NumPy arrays) as the
+main format for ```tvmc run```. To learn more about the ```.npz``` format, please read the
+[documentation](https://numpy.org/doc/stable/reference/generated/numpy.savez.html) on NumPy website.
+
+MobileNet V1 expects a ```(224, 224, 3)``` input tensor. The Python code snippet below, can be used
+as an example on how to convert a PNG file into a ```.npz``` file in the expected shape.
+The example below uses [PIL](https://pillow.readthedocs.io/en/stable/) and
+[NumPy](https://numpy.org) functions to import the image and generate the expected file.
+
+    from tvm.contrib.download import download_testdata
+    from PIL import Image
+    import numpy as np
+
+    cat_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true'
+    image_path = download_testdata(cat_url, 'imagenet_cat.png', module='data')
+    resized_image = Image.open(image_path).resize((224, 224))
+    image_data = np.asarray(resized_image).astype("float32")
+    image_data = np.expand_dims(image_data, axis=0)
+
+    np.savez("imagenet_cat", input=image_data)

Review comment:
       Let us consider move it to a sub-namespace of the TVM itself, say, 
   
   ```bash
   python -m tvm.driver
   ```
   




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