Poetry VS conda

Since this is a coding challenge, I will be using Poetry to help me create the structure of the project, and to facilitate anyone running it. You can check how to install and use Poetry on their website.

For managing dependencies in this project, I used Poetry. Poetry simplifies the package management process and automates much of the heavy lifting involved with dependencies. It relies on the pyproject.toml file, which is now the standard for defining build requirements in modern Python projects.

# Palindrome project Project used to explain my view on a django project architecture ## Tools, libs, etc. Some time related files. Versions on Poetry. - [Python](https://www.python.org/) Programming languange - [django-environ](https://django-environ.readthedocs.io) Manage .envs in Django - [Poetry](https://python-poetry.org/) Python packaging and dependency management - poetry.lock - pyproject.toml - [Django](https://www.djangoproject.com/) Web framework written in Python - [Docker](https://www.docker.com/) Manage containers for dev environment - compose.yaml - compose/dev/Dockerfile - compose/dev/start - .env - [Just](https://just.systems/) encapsulate commands for easier use - justfile ## Dev environment setup 1. Install Just, Docker and Poetry(opcional). 2. Copy .env.example to .env, no need for edtion. 3. Certified that docker is up and running 4. `$ just build` ## Run the server for development 1. Certified that docker is up and running 2. `$ just runserver` You can access on http://0.0.0.0:8000/

Tools like Poetry aim to fix this problem, but vanilla pip can do just fine.

Our first step is to create our development environment, and install all required dependencies for this project. The good thing is that for this example we only need to install flama to have all the necessary tools to implement JWT authentication. We'll be using poetry to manage our dependencies, but you can also use pip if you prefer:

I use Python Poetry as the package management tool, so for this example I asume you have it already. First thing to do is install the dependencies with poetry install. You could also use pip to install them with: pip install pymongo loguru.

# Official Python runtime as a parent image FROM python:3.10 RUN set -xe # Install Poetry RUN curl -sSL https://install.python-poetry.org | python3 - --git https://github.com/python-poetry/poetry.git@master ENV PATH="/root/.local/bin:$PATH" # Confirm poetry version RUN poetry --version # Set up the working directory WORKDIR /app # Copy and install dependencies COPY . /app RUN poetry install EXPOSE 8000 # Start the application CMD ["bash", "-c", "poetry run bash ./prestart.sh && poetry run uvicorn app.main:app --reload --host 0.0.0.0"]

python and/or poetry

To add. Conda has parallelized downloads and is faster. Not as fast as mamba, but faster than previously. pr merged sep 2022 -> https://github.com/conda/conda/pull/11841

Python's venv module is officially recommended for creating virtual environments since Python 3.5 comes packaged with your Python installation. While there still are additional older tools available, such as conda and virtualenv, if you are new to virtual environments, it is best to use venv now.

conda's dependency solver solves a harder problem than pip's. This quote alludes to it "Conda will never be as fast as pip, so long as we're doing real environment solves and pip satisfies itself only for the current operation." (from https://github.com/conda/conda/issues/7239). Thus mamba was created to improve performance and now conda is bringing in that performance boost.

The other challenge I ran into sometimes was that if I was running a lower memory/storage Codespace instance, when I tried to use Conda from the command line to modify environments, the process would be killed after a few seconds. This turns out to be related to some performance issues Conda has that make it consume a lot of memory when trying to work with the conda-forge installation channel. You can always then just increase the size of the Codespace your are working with (just go to your Codespaces list and use the triple dots to change the settings for a Codespace).

It's worth noting that [ana]conda isn't even fully compatible yet with 3.11 (you can use it to create 3.11 environments--and you really should rather than waiting on relying on the system python--but conda itself can only run on 3.10.

It took some time but as great Christmas present Miniconda was finally released with Python 3.10!

The September update broke it.