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- # Dockerfile for the TPU and TensorFlow Lite Object Detection tutorial
-
- This Docker image automates the setup involved with training
- object detection models on Google Cloud and building the Android TensorFlow Lite
- demo app. We recommend using this container if you decide to work through our
- tutorial on ["Training and serving a real-time mobile object detector in
- 30 minutes with Cloud TPUs"](https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193), though of course it may be useful even if you would
- like to use the Object Detection API outside the context of the tutorial.
-
- A couple words of warning:
-
- 1. Docker containers do not have persistent storage. This means that any changes
- you make to files inside the container will not persist if you restart
- the container. When running through the tutorial,
- **do not close the container**.
- 2. To be able to deploy the [Android app](
- https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/android/app)
- (which you will build at the end of the tutorial),
- you will need to kill any instances of `adb` running on the host machine. You
- can accomplish this by closing all instances of Android Studio, and then
- running `adb kill-server`.
-
- You can install Docker by following the [instructions here](
- https://docs.docker.com/install/).
-
- ## Running The Container
-
- From this directory, build the Dockerfile as follows (this takes a while):
-
- ```
- docker build --tag detect-tf .
- ```
-
- Run the container:
-
- ```
- docker run --rm -it --privileged -p 6006:6006 detect-tf
- ```
-
- When running the container, you will find yourself inside the `/tensorflow`
- directory, which is the path to the TensorFlow [source
- tree](https://github.com/tensorflow/tensorflow).
-
- ## Text Editing
-
- The tutorial also
- requires you to occasionally edit files inside the source tree.
- This Docker images comes with `vim`, `nano`, and `emacs` preinstalled for your
- convenience.
-
- ## What's In This Container
-
- This container is derived from the nightly build of TensorFlow, and contains the
- sources for TensorFlow at `/tensorflow`, as well as the
- [TensorFlow Models](https://github.com/tensorflow/models) which are available at
- `/tensorflow/models` (and contain the Object Detection API as a subdirectory
- at `/tensorflow/models/research/object_detection`).
- The Oxford-IIIT Pets dataset, the COCO pre-trained SSD + MobileNet (v1)
- checkpoint, and example
- trained model are all available in `/tmp` in their respective folders.
-
- This container also has the `gsutil` and `gcloud` utilities, the `bazel` build
- tool, and all dependencies necessary to use the Object Detection API, and
- compile and install the TensorFlow Lite Android demo app.
-
- At various points throughout the tutorial, you may see references to the
- *research directory*. This refers to the `research` folder within the
- models repository, located at
- `/tensorflow/models/resesarch`.
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