message: Instructions how to train the model in GCP
diff: @@ -103,3 +103,54 @@ Finally, convert the file into a C source file that can be compiled into an embe
  ```
  xxd -i /tmp/tiny_conv.tflite > /tmp/tiny_conv_model_data.cc
  ```
  +
  +### Creating Your Own Model With Google Cloud
  +
  +If want to train your model in Google Cloud you can do so by using pre-configured Deep Learning images.
  +
  +First create the VM:
  +
  +```
  +export IMAGE_FAMILY="tf-latest-cpu"
  +export ZONE="us-west1-b" # Or any other required region
  +export INSTANCE_NAME="model-trainer"
  +export INSTANCE_TYPE="n1-standard-8" # or any other instance type
  +gcloud compute instances create $INSTANCE_NAME \
  + --zone=$ZONE \
  + --image-family=$IMAGE_FAMILY \
  + --image-project=deeplearning-platform-release \
  + --machine-type=$INSTANCE_TYPE \
  + --boot-disk-size=120GB \
  + --min-cpu-platform=Intel\ Skylake
  +```
  +
  +As soon as instance has been created you can SSH to it(as a jupyter user!):
  +
  +```
  +gcloud compute ssh "jupyter@${INSTANCE_NAME}"
  +```
  +
  +now install Bazel:
  +
  +```
  +wget https://github.com/bazelbuild/bazel/releases/download/0.15.0/bazel-0.15.0-installer-linux-x86_64.sh
  +sudo bash ./bazel-0.15.0-installer-linux-x86_64.sh
  +source /usr/local/lib/bazel/bin/bazel-complete.bash
  +sudo ln /usr/local/bin/bazel /usr/bin/bazel
  +```
  +and finally run the build:
  +
  +```
  +# TensorFlow already pre-baked on the image
  +cd src/tensorflow
  +bazel run -c opt --copt=-mavx2 --copt=-mfma \
  +tensorflow/examples/speech_commands:train -- \
  +--model_architecture=tiny_conv --window_stride=20 --preprocess=average \
  +--wanted_words="yes,no" --silence_percentage=25 --unknown_percentage=25 --quantize=1
  +```
  +
  +After build is over follow the rest of the instrucitons from this tutorial. And finally do not forget to remove the instance when training is done:
  +
  +```
  +gcloud compute instances delete "${INSTANCE_NAME}" --zone="${ZONE}"
  +```
  