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Rolled-back graphviz 

I gave up on enabling DT visualization for the moment, as graphviz integration
stopped working... I can't run dot in the image any more, receiving:

Could not load "/opt/conda/lib/graphviz/libgvplugin_pango.so.6" - file not found

As far as I was able to diagnose thats (with ldd), that's because libiconv.so.2
is not available. But according to what I see in mailgroups that should not be
needed (on debian at least)...
main
ziembla 2017-11-28 09:57:47 +01:00
parent 4fa5beb93a
commit fc355ca6b9
2 changed files with 41 additions and 48 deletions
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14
docker/Dockerfile
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@ -41,26 +41,18 @@ RUN mkdir -p ${HOME}/.jupyter && \
>> ${HOME}/.jupyter/jupyter_notebook_config.py >> ${HOME}/.jupyter/jupyter_notebook_config.py
# INFO: Below - work in progress, nbdime not totally integrated, still: # INFO: Below - work in progress, nbdime not totally integrated, still it enables diffing
# 1. enables diffing notebooks via nbdiff after connecting to container by "make exec" (docker exec) # notebooks via nbdiff after connecting to container by "make exec" (docker exec)
# Use: # Use:
# nbd NOTEBOOK_NAME.ipynb # nbd NOTEBOOK_NAME.ipynb
# to get nbdiff between checkpointed version and current version of the given notebook # to get nbdiff between checkpointed version and current version of the given notebook
# 2. allows decision tree visualization in notebook
# Use:
# from sklearn import tree
# from graphviz import Source
# Source(tree.export_graphviz(tree_clf, out_file=None, feature_names=iris.feature_names[2:]))
USER root USER root
WORKDIR / WORKDIR /
RUN conda install -y -c conda-forge nbdime RUN conda install -y -c conda-forge nbdime
RUN conda install -y -c conda-forge python-graphviz
USER ${username} USER ${username}
WORKDIR ${HOME}/handson-ml WORKDIR ${HOME}/handson-ml
COPY docker/bashrc /tmp/bashrc COPY docker/bashrc /tmp/bashrc
RUN cat /tmp/bashrc >> ${HOME}/.bashrc RUN cat /tmp/bashrc >> ${HOME}/.bashrc
RUN sudo rm -rf /tmp/bashrc RUN sudo rm -rf /tmp/bashrc

75
docker/README.md
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@ -1,37 +1,38 @@
# Hands-on Machine Learning in Docker :-) # Hands-on Machine Learning in Docker :-)
This is the Docker configuration which allows you to run and tweak the book's notebooks without installing any dependencies on your machine! This is the Docker configuration which allows you to run and tweak the book's notebooks without installing any dependencies on your machine!<br/>
OK, any except `docker`. With `docker-compose`. Well, you may also want `make` (but it is only used as thin layer to call a few simple `docker-compose` commands). OK, any except `docker`. With `docker-compose`. Well, you may also want `make` (but it is only used as thin layer to call a few simple `docker-compose` commands).
## Prerequisites ## Prerequisites
As stated, the two things you need is `docker` and `docker-compose`. As stated, the two things you need is `docker` and `docker-compose`.
Follow the instructions on [Install Docker](https://docs.docker.com/engine/installation/) and [Install Docker Compose](https://docs.docker.com/compose/install/) for your environment if you haven't got `docker` already. Follow the instructions on [Install Docker](https://docs.docker.com/engine/installation/) and [Install Docker Compose](https://docs.docker.com/compose/install/) for your environment if you haven't got `docker` 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. 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.
## Usage ## Usage
### Prepare the image (once) ### Prepare the image (once)
Switch to `docker` directory here and run `make build` (or `docker-compose build`) to build your docker image. That may take some time but is only required once. Or perhaps a few times after you tweak something in a `Dockerfile`. Switch to `docker` directory here and run `make build` (or `docker-compose build`) to build your docker image. That may take some time but is only required once. Or perhaps a few times after you tweak something in a `Dockerfile`.
After the process is finished you have a `handson-ml` image, that will be the base for your experiments. You can confirm that looking on results of `docker images` command. After the process is finished you have a `handson-ml` image, that will be the base for your experiments. You can confirm that looking on results of `docker images` command.
### Run the notebooks ### Run the notebooks
Run `make run` (or just `docker-compose up`) to start the jupyter server inside the container (also named `handson-ml`, same as image). Just point your browser to <http://localhost:8888> or the URL printed on the screen and you're ready to play with the book's code! Run `make run` (or just `docker-compose up`) to start the jupyter server inside the container (also named `handson-ml`, same as image). Just point your browser to <http://localhost:8888> (empty password) or the URL printed on the screen 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. 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. You can close the server just by pressing `Ctrl-C` in terminal window.
### Run additional commands in container ### Run additional commands in container
Run `make exec` (or `docker-compose exec handson-ml bash`) while the server is running to run an additional `bash` shell inside the `handson-ml` container. Now you're inside the environment prepared within the image. Run `make exec` (or `docker-compose exec handson-ml bash`) while the server is running to run an additional `bash` shell inside the `handson-ml` container. Now you're inside the environment prepared within the image.
One of the usefull things that can be done there 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]<https://github.com/jupyter/nbdime> for more details. One of the usefull things that can be done there 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](https://github.com/jupyter/nbdime) for more details.
You may also try `nbd NOTEBOOK_NAME.ipynb` command (custom, defined in the Dockerfile) 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)". You may also try `nbd NOTEBOOK_NAME.ipynb` command (custom, see bashrc file) to compare one of your notebooks with its `checkpointed` version.<br/>
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)*.