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