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Developer tools · #poetry
Context Our team uses Poetry (1.x, recent releases) for several Python services built in CI and packaged into Docker images. We hit friction because Poetry's default places virtual environments in a per-user cache directory, so the environment path differs between local machines, CI runners, and containers. Goal Pick one canonical virtualenv placement policy
Circular Dependency Handling in Poetry 1.5 Poetry 1.5 enforces deterministic dependency resolution using pyproject.toml as the sole configuration source and a poetry.lock file to pin exact versions. The resolver prefers the lockfile over the TOML to maintain parity across environments. In plugin-based projects, the core package may depend on a plugin that, i
Integration between Poetry.lock and pyproject.toml The goal is to revert a project to a known good dependency state after the schema in pyproject.toml has been altered. In practice, this means restoring an older poetry.lock and invoking poetry install to re‑install the exact package hashes recorded in that lockfile. Key constraints: Poetry 1.x and 2.x use di
The goal is to determine whether Poetry's resolver memory consumption during poetry.lock generation differs between Poetry 1.x and 2.x, and whether the resulting lockfile remains deterministic across Python versions. Poetry resolves the complete dependency tree before writing poetry.lock, so projects with many transitive dependencies can exhaust available me
Poetry's virtualenvs.in-project setting places the managed environment inside the project directory, which simplifies container builds and removes dependence on a shared cache path. The tradeoff is that the environment now sits within the source tree, where standard version-control and packaging workflows may treat it as project content unless explicitly exc
docker
python