Recommended base image
As suggested in my comment, you could write a Dockerfile that looks like:
FROM python:3
RUN pip install --no-cache-dir --upgrade pip && \
pip install --no-cache-dir nibabel pydicom matplotlib pillow med2image
# Note: we had to merge the two "pip install" package lists here, otherwise
# the last "pip install" command in the OP may break dependency resolution…
CMD ["cat", "/etc/os-release"]
And the command example above could confirm at runtime (docker build --pull -t test . && docker run --rm -it test) that this image is based on the GNU/Linux distribution "Debian stable".
Generic Dockerfile template
Finally to give a comprehensive answer, note that a good practice regarding Python dependencies consists in specifying them in a declarative way in a dedicated text file (in alphabetical order, to ease review and update) so that for your example, you may want to write the following file:
requirements.txt
matplotlib
med2image
nibabel
pillow
pydicom
and use the following generic
Dockerfile
FROM python:3
WORKDIR /usr/src/app
COPY requirements.txt ./
RUN pip install --no-cache-dir --upgrade pip \
&& pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["python", "./your-daemon-or-script.py"]
To be more precise, this is the approach suggested in the documentation of the Docker official image python, §. How to use this image
Recommended base image
As suggested in my comment, you could write a Dockerfile that looks like:
FROM python:3
RUN pip install --no-cache-dir --upgrade pip && \
pip install --no-cache-dir nibabel pydicom matplotlib pillow med2image
# Note: we had to merge the two "pip install" package lists here, otherwise
# the last "pip install" command in the OP may break dependency resolution…
CMD ["cat", "/etc/os-release"]
And the command example above could confirm at runtime (docker build --pull -t test . && docker run --rm -it test) that this image is based on the GNU/Linux distribution "Debian stable".
Generic Dockerfile template
Finally to give a comprehensive answer, note that a good practice regarding Python dependencies consists in specifying them in a declarative way in a dedicated text file (in alphabetical order, to ease review and update) so that for your example, you may want to write the following file:
requirements.txt
matplotlib
med2image
nibabel
pillow
pydicom
and use the following generic
Dockerfile
FROM python:3
WORKDIR /usr/src/app
COPY requirements.txt ./
RUN pip install --no-cache-dir --upgrade pip \
&& pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["python", "./your-daemon-or-script.py"]
To be more precise, this is the approach suggested in the documentation of the Docker official image python, §. How to use this image
Some of the other answers/comments are suggesting to change your base image but if you want to keep your ubuntu 16.04 you can also simply specify your version of pip/python to use pip3 or pip3.5 like shown below.
FROM ubuntu:16.04
RUN apt-get update && apt-get install -y --no-install-recommends \
python3.5 \
python3-pip \
&& \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
RUN pip3 install nibabel pydicom matplotlib pillow
RUN pip3 install med2image
While T. Arboreus's answer might fix the issues with resolving 'archive.ubuntu.com', I think the last error you're getting says that it doesn't know about the packages php5-mcrypt and python-pip.
Nevertheless, the reduced Dockerfile of you with just these two packages worked for me (using Debian 8.4 and Docker 1.11.0), but I'm not quite sure if that could be the case because my host system is different than yours.
FROM ubuntu:14.04
# Install dependencies
RUN apt-get update && apt-get install -y \
php5-mcrypt \
python-pip
However, according to this answer you should think about installing the python3-pip package instead of the python-pip package when using Python 3.x.
Furthermore, to make the php5-mcrypt package installation working, you might want to add the universe repository like it's shown right here. I had trouble with the add-apt-repository command missing in the Ubuntu Docker image so I installed the package software-properties-common at first to make the command available.
Splitting up the statements and putting apt-get update and apt-get install into one RUN command is also recommended here.
Oh and by the way, you actually don't need the -y flag at apt-get update because there is nothing that has to be confirmed automatically.
Finally:
FROM ubuntu:14.04
# Install dependencies
RUN apt-get update && apt-get install -y \
software-properties-common
RUN add-apt-repository universe
RUN apt-get update && apt-get install -y \
apache2 \
curl \
git \
libapache2-mod-php5 \
php5 \
php5-mcrypt \
php5-mysql \
python3.4 \
python3-pip
Remark: The used versions (e.g. of Ubuntu) might be outdated in the future.
This command worked fine for me:
RUN apt-get -y install python3-pip
Setting up the docker module in Python
Making library pip-installable vs. creating Docker image with the library inside
Pip installing the correct Python packages during cross-compiling
Dockerfile pip install from local directory
Videos
» pip install docker
Hi. Could anyone help explain to me how to actually setup the docker module in python. I have tried so far:
pip install docker
However, after doing this, I try running the basic script:
import docker
client = docker.from_env()
After doing so, I get the following:
Exception has occurred: ModuleNotFoundError
No module named 'docker'
Could anyone please explain to me when I am getting this message. It's really confusing. Apologies if this is very obvious
Hi.
In the company I'm working for, there's a Python library (repo) which all data scientists are currently using. They want to create a Docker image in ECR which will come with the project already inside of it and appended to the `PYTHONPATH` so they can use it inside SageMaker notebooks. My team insists that making it pip-installable is the appropriate way to distribute it.
What pros and cons do you see in each approach?
Note: all the library's dependencies are pip-installable (so no need to install anything via `apt-get`, etc.)
» pip install docker-py