docker-lvmpy VS docker-stacks

Nodes in the SKALE Network are not required to have a specific amount of disk storage. There is a minimum viable quantity required, but not a set amount for a node. Variance in storage can create instability during some network functions. A collective decision was made in the community to fix the problem with software not hardware. A node could have X amount of storage, but is required to have Y amount of storage. Any excess in X over Y is now not utilized. This creates a uniform storage capacity amongst all nodes (assuming minimums are met) while not requiring the validator community to change any hardware specs. All while increasing stability and performance of the network and SKALE Chains.... ie this was a big Win!! You can see the code implemented here: https://github.com/skalenetwork/docker-lvmpy

See https://github.com/jupyter/docker-stacks

I have an IPython notebook running in a docker container in Google Container Engine, the container is based on this image jupyter/all-spark-notebook

Yes sir :) love the jupyter docker stacks: https://github.com/jupyter/docker-stacks

I use an image that allows me to run spark on jupyter notebooks and use files from my local machine to test code. Here is a good one https://github.com/jupyter/docker-stacks/tree/main/pyspark-notebook

Today I will tell you a story behind making Jupyter Docker Stacks able to build aarch64 images without many (almost any) compromises. If you want to read how it is implemented now, you can skip to the last section of this post.

FROM ubuntu:20.04 LABEL maintainer="PhysicsReplicatorAI " ARG NB_USER="coqui_user" \ NB_UID="1000" \ NB_GID="100" USER root ENV DEBIAN_FRONTEND=noninteractive \ TERM=xterm \ TZ=America/New_York # -------------------------------------------------------------------------------- # Update/Install the basics # -------------------------------------------------------------------------------- RUN apt-get update -y && \ apt-get install -y --no-install-recommends \ sudo locales apt-utils g++ libsndfile1-dev && \ apt-get -y clean autoclean && \ apt-get -y autoremove --purge && \ echo "en_US.UTF-8 UTF-8" > /etc/locale.gen && \ locale-gen # -------------------------------------------------------------------------------- # Configure environment # -------------------------------------------------------------------------------- ENV SHELL=/bin/bash \ NB_USER="${NB_USER}" \ NB_UID=${NB_UID} \ NB_GID=${NB_GID} \ LC_ALL=en_US.UTF-8 \ LANG=en_US.UTF-8 \ LANGUAGE=en_US.UTF-8 \ PATH="/home/${NB_USER}/.local/bin:${PATH}" \ HOME="/home/${NB_USER}" # -------------------------------------------------------------------------------- # Create coqui_user # User creation method/pattern obtained from: # https://github.com/jupyter/docker-stacks/blob/master/base-notebook/Dockerfile # -------------------------------------------------------------------------------- COPY fix-permissions /usr/local/bin/fix-permissions RUN chmod a+rx /usr/local/bin/fix-permissions && \ sed -i 's/^#force_color_prompt=yes/force_color_prompt=yes/' /etc/skel/.bashrc && \ echo "auth requisite pam_deny.so" >> /etc/pam.d/su && \ sed -i.bak -e 's/^%admin/#%admin/' /etc/sudoers && \ sed -i.bak -e 's/^%sudo/#%sudo/' /etc/sudoers && \ useradd -l -m -s /bin/bash -N -u "${NB_UID}" "${NB_USER}" && \ chmod g+w /etc/passwd && \ fix-permissions "${HOME}" && \ cd "${HOME}" && \ apt-get install -y --no-install-recommends python3-dev python3-pip && \ apt-get -y autoclean && \ apt-get -y autoremove --purge && \ apt-get -y purge $(dpkg --get-selections | grep deinstall | sed s/deinstall//g) && \ rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* # -------------------------------------------------------------------------------- # Install TTS # -------------------------------------------------------------------------------- USER ${NB_UID} RUN fix-permissions "${HOME}" && \ python3 -m pip install --no-cache-dir --upgrade TTS && \ rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* EXPOSE 5002 ENTRYPOINT ["tts-server"]

Pretty much what title says. I want to build an image on top of the scy-py image with my preferred libraries such as NLTK, spaCy and stanza. Is there a way to do that?

The disadvantages are that everytime I rebuild the entire docker image I have to pray that all the dependencies that I did not list did not change, because if they did, the build fails with a cryptic error message. Also if the list of pip installs gets too long, pip is no longer able to resolve all the dependencies and then the docker image does not build either. So actually not having a requirements.txt might be a disadvantage. Further, I do not like the fact that I have both Miniconda and "regular Python" in one file. This happened, because this Dockerfile evolved from one of the Jupyter Notebook Dockerfiles. My development environment is Jupyter and I wanted my production environment as close as possible to my development environment.