This is the Docker configuration which allows you to run and tweak the book's notebooks without installing any dependencies on your machine!
OK, any except docker and docker-compose.
Follow the instructions on Install Docker and Install Docker Compose for your environment if you haven't got docker and docker-compose already.
Some general knowledge about docker infrastructure might be useful (that's an interesting topic on its own) but is not strictly required to just run the notebooks.
The first option is to pull the image from Docker Hub (this will download over 2.3 GB of data):
$ docker pull ageron/handson-ml2Alternatively, you can build the image yourself. This will be slower, but it will ensure the image is up to date, with the latest libraries. For this, assuming you already downloaded this project into the directory /path/to/project/handson-ml2:
$ cd /path/to/project/handson-ml2/docker
$ docker-compose buildThis will take quite a while, but is only required once.
After the process is finished you have a handson-ml2 image, that will be the base for your experiments. You can confirm that by running the following command:
$ docker images
REPOSITORY TAG IMAGE ID CREATED SIZE
handson-ml2 latest 6c4dc2c7c516 2 minutes ago 6.49GBStill assuming you already downloaded this project into the directory /path/to/project/handson-ml2, run the following commands to start the Jupyter server inside the container (it is also named handson-ml2, just like the image):
$ cd /path/to/project/handson-ml2/docker
$ docker-compose upNext, just point your browser to the URL printed on the screen (or go to http://localhost:8888 if you enabled password authentication inside the jupyter_notebook_config.py file, before building the image) and you're ready to play with the book's code!
The server runs in the directory containing the notebooks, and the changes you make from the browser will be persisted there.
You can close the server just by pressing Ctrl-C in terminal window.
If you have make installed on your computer, you can use it as a thin layer to run docker-compose commands. For example, executing make rebuild will actually run docker-compose build --no-cache, which will rebuild the image without using the cache. This ensures that your image is based on the latest version of the continuumio/miniconda3 image which the handson-ml2 image is based on.
If you don't have make (and you don't want to install it), just examine the contents of Makefile to see which docker-compose commands you can run instead.
Run make exec (or docker-compose exec handson-ml2 bash) while the server is running to run an additional bash shell inside the handson-ml2 container. Now you're inside the environment prepared within the image.
One of the useful things that can be done there would be starting TensorBoard (for example with simple tb command, see bashrc file).
Another one may be comparing versions of the notebooks using the nbdiff command if you haven't got nbdime installed locally (it is way better than plain diff for notebooks). See Tools for diffing and merging of Jupyter notebooks for more details.
You can see changes you made relative to the version in git using git diff which is integrated with nbdiff.
You may also try nbd NOTEBOOK_NAME.ipynb command (custom, see bashrc file) to compare one of your notebooks with its checkpointed version.
To be precise, the output will tell you what modifications should be re-played on the manually saved version of the notebook (located in .ipynb_checkpoints subdirectory) to update it to the current i.e. auto-saved version (given as command's argument - located in working directory).
If you're using Linux, and you have a TensorFlow-compatible GPU card (NVidia card with Compute Capability ≥ 3.5) that you would like TensorFlow to use inside the docker container, then you should download and install the latest driver for your card from nvidia.com. You will also need to install NVidia Docker support: if you are using Docker 19.03 or above, you must install the nvidia-container-toolkit package, and for earlier versions, you must install nvidia-docker2.
If you want to pull the prebuilt image from Docker Hub (this will download over 4 GB of data):
$ docker pull ageron/handson-ml2:latest-gpuIf you prefer to build the image yourself, edit docker-compose.yml, replace the line dockerfile: ./docker/Dockerfile with dockerfile: ./docker/Dockerfile.gpu, and then run the following commands (assuming this project is located at /path/to/project/handson-ml2):
$ cd /path/to/project/handson-ml2/docker
$ docker-compose buildTo run the image, it depends. If you have docker-compose version 1.28 or above, that's great! You can simply uncomment the deploy section in docker-compose.yml, and then run:
$ cd /path/to/project/handson-ml2/docker
$ docker-compose up
[...]
or http://127.0.0.1:8888/?token=[...]However, if you have an earlier version of docker-compose, it's simpler to use docker run directly. If you are using Docker 19.03 or above, you can run:
$ cd /path/to/project/handson-ml2
$ docker run --name handson-ml2 --gpus all -p 8888:8888 -p 6006:6006 --log-opt mode=non-blocking --log-opt max-buffer-size=50m -d -v `pwd`:/home/devel/handson-ml2 handson-ml2 /opt/conda/envs/tf2/bin/jupyter notebook --ip='0.0.0.0' --port=8888 --no-browserIf you are using an older version of Docker, then replace --gpus all with --runtime=nvidia.
Then, display the container's logs and point your browser to the URL printed on the screen:
$ docker logs handson-ml2
[I 09:07:10.805 NotebookApp] Writing notebook server cookie secret to /home/devel/.local/share/jupyter/runtime/notebook_cookie_secret
[...]
or http://127.0.0.1:8888/?token=[...]If everything goes well, Jupyter should appear, and if you open a notebook and execute the following code, it should show a GPU device in the list:
import tensorflow as tf
tf.config.list_physical_devices()Lastly, to stop and destroy the container (but not the image), run:
$ docker stop handson-ml2
$ docker rm handson-ml2