Spaces:
Running
Running
| # 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/examples/tree/master/lite/examples/object_detection/android) | |
| (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/research`. | |