I need to create a Conda environment and install dependencies (Python, R) in this environment.
All libraries - Python and R - are installed well, as far as I see in logs. No errors or warnings.
But it looks like R dependencies from file r_requirements.R are not installed in the same environment (myenvpython).
When I build and use the Docker image, I can use the installed Python libraries in the envirnment, but loading of R libraries fails.
How can I fix it?
FROM conda/miniconda3
COPY code/ci_dependencies.yml /setup/
COPY code/r_requirements.R /setup/
# activate environment
ENV PATH /usr/local/envs/myenvpython/bin:$PATH
RUN apt-get update && \
apt-get -y install sudo
# RUN useradd -m docker && echo "docker:docker" | chpasswd && adduser docker sudo
RUN conda update -n base -c defaults conda && \
conda install python=3.7.5 && \
conda env create -f /setup/ci_dependencies.yml && \
/bin/bash -c "source activate myenvpython" && \
az --version && \
chmod -R 777 /usr/local/envs/myenvpython/lib/python3.7
RUN apt-get install -y libssl-dev libsasl2-dev
RUN Rscript /setup/r_requirements.R
Related
So my scenario is that I'm trying to create a Dockerfile that I can build on my Mac for running Spacy in production. The production server contains a Nvidia GPU with CUDA. To get Spacy to use GPU, I need the lib cupy-cuda117. That lib won't build on my Mac because it can't find the CUDA GPU. So what I'm trying to do is create an image from the Linux server that has the CUDA GPU, that's already pre-build cupy-cuda117 on it. I'll then use that as the parent image for Docker, as all other libs in my requirements.txt will build on my Mac.
My goal at the moment is to build that lib into the server, but I'm not sure the right path forward. Is it sudo pip3 intall cupy-cuda117? Or should I create a venv, and pip3 install cupy-cuda117? Basically my goal is later to add all the other app code and full requirements.txt, and when pip3 install -r requirements.txt is run by Docker, it'll download/build/install everything, but not cupy-cuda117, because hopefully it'll see that it's already been built.
FYI the handling of using GPU on the prod server and CPU on the dev computer i've already got sorted, it's just the building of that one package I'm stuck on. I basically just need it not to try and rebuild on my Mac. Thanks!
FROM "debian:bullseye-20210902-slim" as builder
# install build dependencies
RUN apt-get update -y && apt-get install --no-install-recommends -y build-essential git locales \
&& apt-get clean && rm -f /var/lib/apt/lists/*_*
# Set the locale
RUN sed -i '/en_US.UTF-8/s/^# //g' /etc/locale.gen && locale-gen
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US:en
ENV LC_ALL en_US.UTF-8
WORKDIR "/app"
RUN apt update -y && apt upgrade -y && apt install -y sudo
# Install Python 3.9 reqs
RUN sudo apt install -y --no-install-recommends wget libxml2 libstdc++6 zlib1g-dev libncurses5-dev libgdbm-dev libnss3-dev libssl-dev libsqlite3-dev libreadline-dev libffi-dev curl libbz2-dev
# Install Python 3.9
RUN wget --no-check-certificate https://www.python.org/ftp/python/3.9.1/Python-3.9.1.tgz && \
tar -xf Python-3.9.1.tgz && \
cd Python-3.9.1 && \
./configure --enable-optimizations && \
make -j $(nproc) && \
sudo make altinstall && \
cd .. && \
sudo rm -rf Python-3.9.1 && \
sudo rm -rf Python-3.9.1.tgz
# Install CUDA
RUN wget --no-check-certificate https://developer.download.nvidia.com/compute/cuda/11.7.1/local_installers/cuda_11.7.1_515.65.01_linux.run && \
sudo chmod +x cuda_11.7.1_515.65.01_linux.run && \
sudo ./cuda_11.7.1_515.65.01_linux.run --silent --override --toolkit --samples --toolkitpath=/usr/local/cuda-11.7 --samplespath=/usr/local/cuda --no-opengl-libs && \
sudo ln -s /usr/local/cuda-11.7 /usr/local/cuda && \
sudo rm -rf cuda_11.7.1_515.65.01_linux.run
## Add NVIDIA CUDA to PATH and LD_LIBRARY_PATH ##
RUN echo 'case ":${PATH}:" in\n\
*:"/usr/local/cuda-11.7/lib64":*)\n\
;;\n\
*)\n\
if [ -z "${PATH}" ] ; then\n\
PATH=/usr/local/cuda-11.7/bin\n\
else\n\
PATH=/usr/local/cuda-11.7/bin:$PATH\n\
fi\n\
esac\n\
case ":${LD_LIBRARY_PATH}:" in\n\
*:"/usr/local/cuda-11.7/lib64":*)\n\
;;\n\
*)\n\
if [ -z "${LD_LIBRARY_PATH}" ] ; then\n\
LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64\n\
else\n\
LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64:$LD_LIBRARY_PATH\n\
fi\n\
esac\n\
export PATH LD_LIBRARY_PATH\n\
export GLPATH=/usr/lib/x86_64-linux-gnu\n\
export GLLINK=-L/usr/lib/x86_64-linux-gnu\n\
export DFLT_PATH=/usr/lib\n'\
>> ~/.bashrc
ENV PATH="$PATH:/usr/local/cuda-11.7/bin"
ENV LD_LIBRARY_PATH="/usr/local/cuda-11.7/lib64"
ENV GLPATH="/usr/lib/x86_64-linux-gnu"
ENV GLLINK="-L/usr/lib/x86_64-linux-gnu"
ENV DFLT_PATH="/usr/lib"
RUN python3.9 -m pip install -U wheel setuptools
RUN sudo pip3.9 install torch torchvision torchaudio
RUN sudo pip3.9 install -U 'spacy[cuda117,transformers]'
# set runner ENV
ENV ENV="prod"
CMD ["bash"]
My local Dockerfile is this:
FROM myacct/myimg:latest
ENV ENV=prod
WORKDIR /code
COPY ./requirements.txt /code/requirements.txt
COPY ./requirements /code/requirements
RUN pip3 install --no-cache-dir -r /code/requirements.txt
COPY ./app /code/app
ENV ENV=prod
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80"]
I'm using below docker to use IJava kernel to my jupyter notebook.
FROM ubuntu:18.04
ARG NB_USER="some-user"
ARG NB_UID="1000"
ARG NB_GID="100"
RUN apt-get update || true && \
apt-get install -y sudo && \
useradd -m -s /bin/bash -N -u $NB_UID $NB_USER && \
chmod g+w /etc/passwd && \
echo "${NB_USER} ALL=(ALL) NOPASSWD: ALL" >> /etc/sudoers && \
# Prevent apt-get cache from being persisted to this layer.
rm -rf /var/lib/apt/lists/*
ARG DEBIAN_FRONTEND=noninteractive
RUN apt-get update && \
apt-get install -y locales && \
sed -i -e 's/# en_US.UTF-8 UTF-8/en_US.UTF-8 UTF-8/' /etc/locale.gen && \
dpkg-reconfigure --frontend=noninteractive locales && \
update-locale LANG=en_US.UTF-8
RUN apt-get install -y \
openjdk-11-jdk-headless \
python3-pip git curl unzip
RUN ln -s /usr/bin/python3 /usr/bin/python & \
pip3 install --upgrade pip
RUN pip3 install packaging jupyter ipykernel awscli jaydebeapi
RUN python -m ipykernel install --sys-prefix
# Install Java kernel
RUN mkdir ijava-kernel/ && cd ijava-kernel && curl -LO https://github.com/SpencerPark/IJava/releases/download/v1.3.0/ijava-1.3.0.zip && \
unzip ijava-1.3.0.zip && \
python install.py --sys-prefix && \
rm -rf ijava-kernel/
RUN jupyter kernelspec list
ENV SHELL=/bin/bash
ENV LANG en_US.UTF-8
ENV LC_ALL en_US.UTF-8
ENV JAVA_HOME=/usr/lib/jvm/java-11-openjdk-arm64/
WORKDIR /home/$NB_USER
USER $NB_UID
As soon as I run the docker image, inside the container:
some-user#023f579253ec:~$ jupyter kernelspec list ─╯
Available kernels:
python3 /usr/local/share/jupyter/kernels/python3
java /usr/share/jupyter/kernels/java
some-user#023f579253ec:~$
As well as, the console with kernel java is also installed and working as per README.md
jupyter console --kernel java
In [2]: String helloWorld = "Hello world!"
In [3]: helloWorld
Out[3]: Hello world!
But as soon as I run open the jupyter notebook inside the container, I only see Python3 kernel not the Java. see attached image.
can anyone help me out to add the Java Kernel to Notebook GUI?
This is an open issue on IJava's GitHub. The discussion thread mentions a few Docker Images that address your issue: 1, 2, 3.
I'm trying to make a new image for python dockerfile, but keeps installing Python 3.10 instead of Python 3.8.
My Dockerfile looks like this:
FROM python:3.8.16
COPY requirements.txt requirements.txt
RUN pip install --no-cache-dir -r requirements.txt
## follow installation from xtb-python github:
ENV CONDA_DIR /opt/conda
RUN apt-get update && apt-get install -y \
wget \
&& rm -rf /var/lib/apt/lists/*
RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh \
&& /bin/bash ~/miniconda.sh -b -p /opt/conda
ENV PATH=$CONDA_DIR/bin:$PATH
RUN conda config --add channels conda-forge \
&& conda install -y -c conda-forge ...
I don't know much about conda (but have to use it).
It is Conda messing with the my Python or I did it something wrong?
I'm building Python 3.7.4 (It's a hard requirement for other software) on a base Ubuntu 20.04 image using a Dockerfile. I'm following this guide.
Everything works fine if I run the image and follow the guide, but I want to setup my virtual environment in the Dockerfile and have the pip requirements persistent when running the image.
Here's the relevant part of my Dockerfile:
...
RUN echo =============== Building and Install Python =============== \
&& cd /tmp \
&& wget https://www.python.org/ftp/python/3.7.4/Python-3.7.4.tgz \
&& tar xvf ./Python-3.7.4.tgz \
&& cd Python-3.7.4 \
&& ./configure --enable-optimizations --with-ensurepip=install \
&& make -j 8 \
&& sudo make install
ENV VIRTUAL_ENV=/opt/python-3.7.4
ENV PATH="$VIRTUAL_ENV:$PATH"
COPY "./hourequirements.txt" /usr/local/
RUN echo =============== Setting up Python Virtual Environment =============== \
&& python3 -m venv $VIRTUAL_ENV \
&& source $VIRTUAL_ENV/bin/activate \
&& pip install --upgrade pip \
&& pip install --no-input -r /usr/local/hourequirements.txt
...
The Dockerfile builds without errors, but when I run the image the environment doesn't exist and python 3.7.4 doesn't show any of the installed requirements.
How can I install Python modules in the virtual environment using PIP in the Dockerfile and have them persist when the docker image runs?
Usual find answer just after post.
I changed:
ENV PATH="$VIRTUAL_ENV:$PATH"
to:
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
in Dockerfile and started working correctly.
I'm trying to build a docker image like
FROM ubuntu:latest
RUN apt update && apt upgrade -y && \
apt install -y git wget libsuitesparse-dev gcc g++ swig && \
cd ~ && wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
sh Miniconda3-latest-Linux-x86_64.sh -b && rm Miniconda3-latest-Linux-x86_64.sh && \
PATH=$PATH:~/miniconda3/condabin && \
conda init bash && conda upgrade -y conda && /bin/bash -c "source ~/.bashrc" && \
pip install numpy scipy matplotlib scikit_umfpack
However, /bin/bash -c "source ~/.bashrc" does not work... so I got /bin/sh: 1: pip: not found
How can I build a docker image installing miniconda and python requirements using pip at the same time?
I would recommend using a pre-existing Docker image that already has Anaconda installed. For example, this link has a Docker image endorsed by Anaconda itself. There may be others on Dockerhub that also have Anaconda installed already. In the case you already tried an image with Anaconda and it didn't meet your needs, let me know.