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Troubleshooting

Permission errors after running Docker

Symptom: After using the Docker container (or VS Code Dev Container), you get Permission denied errors on the host when trying to edit files, run git, or build the package — even though you own the repository.

Cause: The container runs as an internal user (micromamba, UID 57440). When it writes files into the bind-mounted repo directory, those files are owned by UID 57440 on the host. Your host user (e.g. UID 1000) can no longer write them.

Permanent fix (Dev Container users): devcontainer.json sets "updateRemoteUserUID": true, which tells VS Code to remap the container user's UID to match yours at startup. After rebuilding the container once, files Docker creates will be owned by your host user.

If you still hit it (e.g. after docker compose up tests): restore ownership from the repo root:

sudo chown -R $USER:$USER .

For convenience, add a shell alias:

echo "alias fix-texas='sudo chown -R \$USER:\$USER /path/to/TEXAS'" >> ~/.bashrc && source ~/.bashrc

Stan compilation fails with Permission denied on .hpp file

Symptom: 

Internal compiler error:
(Sys_error ".../stan_models/model_name.hpp: Permission denied")

Cause: Stan's compiler (stanc) writes an intermediate .hpp file into the same directory as the .stan source. If that directory is owned by a different user (from a prior Docker run), the current user cannot write it.

Fix: TEXAS (v0.2.0+) compiles all Stan models into ~/.texas/stan_cache/ — a directory always writable by the current user. Upgrade if needed:

pip install --upgrade texas-psm

Override the build directory:

export TEXAS_STAN_BUILD_DIR=/tmp/texas_stan

Stan binary incompatible after switching between Docker and local env

Symptom: 

Stan model 'model_name' was compiled for a different environment (exit code 127).
The old binary has been removed and the model will be recompiled...

This is expected and self-healing. Stan binaries compiled inside Docker (Linux x86_64 ELF) cannot run on macOS, and vice versa. TEXAS detects this automatically, deletes the stale binary, and recompiles for the current environment. No action needed — sampling will proceed after a one-time recompilation.

CmdStan not found

Symptom: RuntimeError: CmdStan not found.

Fix: Install CmdStan for your setup:

# pip install
python -c "import cmdstanpy; cmdstanpy.install_cmdstan()"

# conda (recommended — sets CMDSTAN automatically)
conda install -c conda-forge cmdstan=2.36.0

# point to an existing install
export CMDSTAN=~/.cmdstan/cmdstan-2.36.0

TEXAS searches CMDSTAN env var → conda prefix → ~/.cmdstan/ → cmdstanpy default. Any version ≥ 2.23.0 is accepted.