toml | TOML parser for Golang with reflection | JSON Processing library

I want to install packages from poetry.lock file; using poetry install.

However, the majority of packages throw the exact same error, indicating a shared fundamental problem.

What is causing this? What is the standard fix?

Specification:

• Windows 10,
• Visual Studio Code,
• Python 3.8.10 & Poetry 1.1.11,
• Ubuntu Bash.

Terminal:

• rm poetry.lock
• poetry update
• poetry install

When a project is specified only via pyproject.toml (i.e. no setup.{py,cfg} files), how can it be installed in editable mode via pip (i.e. python -m pip install -e .)?

I tried both setuptools and poetry for the build system, but neither worked:

I am looking at https://github.com/pypa/setuptools_scm

and I read this part https://github.com/pypa/setuptools_scm#version-number-construction

and i quote

Semantic versioning for projects with release branches. The same as guess-next-dev (incrementing the pre-release or micro segment) if on a release branch: a branch whose name (ignoring namespace) parses as a version that matches the most recent tag up to the minor segment. Otherwise if on a non-release branch, increments the minor segment and sets the micro segment to zero, then appends .devN.

How does this work?

Assuming my setup is at this commit https://github.com/simkimsia/test-setup-py/commit/5ebab14b16b63090ad0554ad8f9a77a28b047323

and the same repo, how do i increment the version by branching?

I updated some files on main branch.

Then i did the following

I`m using python poetry(https://python-poetry.org/) for dependency management in my project.

Though when I`m running poetry install, its giving me below error.

According to doc.rust-lang.org

• cargo rustdoc

build[s] a package's documentation, using specified custom flags

• cargo doc

build[s] a package's documentation

What is the difference between the two? From what I understand cargo rustdoc is just like cargo doc, but it allows for more lints—for instance:

Does it make sense to use Conda + Poetry for a Machine Learning project? Allow me to share my (novice) understanding and please correct or enlighten me:

As far as I understand, Conda and Poetry have different purposes but are largely redundant:

• Conda is primarily a environment manager (in fact not necessarily Python), but it can also manage packages and dependencies.
• Poetry is primarily a Python package manager (say, an upgrade of pip), but it can also create and manage Python environments (say, an upgrade of Pyenv).

My idea is to use both and compartmentalize their roles: let Conda be the environment manager and Poetry the package manager. My reasoning is that (it sounds like) Conda is best for managing environments and can be used for compiling and installing non-python packages, especially CUDA drivers (for GPU capability), while Poetry is more powerful than Conda as a Python package manager.

I've managed to make this work fairly easily by using Poetry within a Conda environment. The trick is to not use Poetry to manage the Python environment: I'm not using commands like poetry shell or poetry run, only poetry init, poetry install etc (after activating the Conda environment).

For full disclosure, my environment.yml file (for Conda) looks like this:

I read ton of articles, but still can't figure out what I'm missing. I'm running a django website from virtualenv. Here's my config file. The website address is replaced by , can't use that here.

Config

I am now using this command to generate schema in rust diesel:

I want to run an example via Cargo but I am facing an error: