Import pyserial only when needed

Changes from code review

Use official sphinx-gallery repo

Correctly specify version

Import pyserial only when necessary

Author: guberti

apps/microtvm/arduino/template_project/microtvm_api_server.py

@@ -34,7 +34,6 @@
 import re
 
 from packaging import version
-import serial.tools.list_ports
 
 from tvm.micro.project_api import server
 
@@ -485,6 +484,9 @@ def flash(self, options):
         subprocess.run(upload_cmd, check=True)
 
     def open_transport(self, options):
+        import serial
+        import serial.tools.list_ports
+
         # Zephyr example doesn't throw an error in this case
         if self._serial is not None:
             return

apps/microtvm/pyproject.toml

@@ -129,7 +129,7 @@ importer-tflite = ["tflite", "tensorflow", "tensorflow-estimator"]
 autodocsumm = "^0.1"
 black = "^19.10b0"
 sphinx = "^3.0"
-sphinx-gallery = "^0.8"
+sphinx-gallery = { git = "https://github.com/sphinx-gallery/sphinx-gallery.git", branch = "master" }
 sphinx-rtd-theme = "^0.4"
 matplotlib = "^3.2"
 Image = "^1.5"

docker/install/ubuntu_install_sphinx.sh

@@ -29,5 +29,5 @@ pip3 install \
     matplotlib \
     sphinx==4.2.0 \
     sphinx_autodoc_annotation \
-    sphinx-gallery==0.4.0 \
+    "git+https://github.com/sphinx-gallery/sphinx-gallery.git" \
     sphinx_rtd_theme

gallery/how_to/work_with_microtvm/micro_train.py

@@ -36,7 +36,7 @@
 # .. image:: https://raw.githubusercontent.com/guberti/web-data/micro-train-tutorial-data/images/utilities/colab_button.png
 #      :align: center
 #      :target: https://colab.research.google.com/github/guberti/tvm-site/blob/asf-site/docs/_downloads/a7c7ea4b5017ae70db1f51dd8e6dcd82/micro_train.ipynb
-#      :width: 600px
+#      :width: 300px
 #
 # Motivation
 # ----------
@@ -258,16 +258,17 @@
 #
 # Our applications generally don't need perfect accuracy - 90% is good enough. We can thus use the
 # older and smaller MobileNet V1 architecture. But this *still* won't be small enough - by default,
-# MobileNet V1 with 224x224 inputs and depth 1.0 takes ~50 MB to just **store**. To reduce the size
+# MobileNet V1 with 224x224 inputs and alpha 1.0 takes ~50 MB to just **store**. To reduce the size
 # of the model, there are three knobs we can turn. First, we can reduce the size of the input images
-# from 224x224 to 96x96 or 64x64, and Keras makes it easy to do this. We can also reduce the **depth**
-# of the model, from 1.0 to 0.25. And if we were really strapped for space, we could reduce the
+# from 224x224 to 96x96 or 64x64, and Keras makes it easy to do this. We can also reduce the **alpha**
+# of the model, from 1.0 to 0.25, which downscales the width of the network (and the number of
+# filters) by a factor of four. And if we were really strapped for space, we could reduce the
 # number of **channels** by making our model take grayscale images instead of RGB ones.
 #
-# In this tutorial, we will use an RGB 64x64 input image and 0.25 depth scale. This is not quite
+# In this tutorial, we will use an RGB 64x64 input image and alpha 0.25. This is not quite
 # ideal, but it allows the finished model to fit in 192 KB of RAM, while still letting us perform
-# transfer learning using the official Tensorflow source models (if we used depth scale <0.25 or
-# a grayscale input, we wouldn't be able to do this).
+# transfer learning using the official Tensorflow source models (if we used alpha <0.25 or a
+# grayscale input, we wouldn't be able to do this).
 #
 # What is Transfer Learning?
 # ^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -369,14 +370,12 @@
 # the conversion. By default, TFLite keeps the inputs and outputs of our model as floats, so we must
 # explicitly tell it to avoid this behavior.
 
-converter = tf.lite.TFLiteConverter.from_keras_model(model)
-
-
 def representative_dataset():
     for image_batch, label_batch in full_dataset.take(10):
         yield [image_batch]
 
 
+converter = tf.lite.TFLiteConverter.from_keras_model(model)
 converter.optimizations = [tf.lite.Optimize.DEFAULT]
 converter.representative_dataset = representative_dataset
 converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
@@ -431,7 +430,7 @@ def representative_dataset():
 #
 # Generating our project
 # ^^^^^^^^^^^^^^^^^^^^^^
-# Next, we'll compile the model to TVM's MLF (machine learning format) intermediate representation,
+# Next, we'll compile the model to TVM's MLF (model library format) intermediate representation,
 # which consists of C/C++ code and is designed for autotuning. To improve performance, we'll tell
 # TVM that we're compiling for the ``nrf52840`` microprocessor (the one the Nano 33 BLE uses). We'll
 # also tell it to use the C runtime (abbreviated ``crt``) and to use ahead-of-time memory allocation
@@ -563,6 +562,9 @@ def representative_dataset():
 # not throw any compiler errors:
 
 shutil.rmtree(f"{FOLDER}/models/project/build", ignore_errors=True)
+# sphinx_gallery_start_ignore
+arduino_project = MagicMock()
+# sphinx_gallery_end_ignore
 arduino_project.build()
 print("Compilation succeeded!")
 

tests/scripts/ci.py

@@ -260,7 +260,7 @@ def docs(
             "tlcpack-sphinx-addon==0.2.1",
             "synr==0.5.0",
             "image==1.5.33",
-            "git+https://github.com/guberti/sphinx-gallery.git@ipynb-include-bash",
+            "git+https://github.com/sphinx-gallery/sphinx-gallery.git",
             "sphinx-rtd-theme==1.0.0",
             "matplotlib==3.3.4",
             "commonmark==0.9.1",