Installation#

For users#

Installation#

The package works with Python 3.10+ and can be installed from both PyPI and/or conda-forge.

To install the package using the pip package manager, run the following command:

$ pip install blop

To install the package using the conda package manager, run the following command:

$ conda install -c conda-forge blop

Optional Extras#

blop is modular — install only what you need by appending one or more extras:

Extra	Installs	Notes
`blop[ax]`	`ax-platform`, `botorch`, `gpytorch`, `torch`	GPU torch by default; pair with `[cpu]` for CPU-only
`blop[queueserver]`	`bluesky-queueserver-api`	Transport layer only; pair with `[ax]` for `QueueserverAgent`
`blop[cpu]`	(uv index routing)	Routes `torch` to the CPU-only PyTorch index; requires `uv`
`blop[all]`	All backends + `[queueserver]`	No dev tooling; will grow as new backends are added
`blop[dev]`	`blop[all]` + dev tooling	For contributors

PyTorch Acceleration Options#

By default, blop installs PyTorch with GPU support (~7GB). For environments without GPU support, or to reduce installation size, you can install a CPU-only version (~900MB) using uv:

$ pip install uv
$ uv pip install blop[cpu]

This is particularly useful for:

Containerized deployments without GPU access
CI/CD pipelines
Development environments on laptops without NVIDIA GPUs
Edge computing scenarios

Note

The CPU-only installation requires uv, a fast Python package installer. If you prefer to use standard pip, the default installation will include GPU support.

For conda users who want CPU-only PyTorch:

$ conda install -c conda-forge blop pytorch cpuonly -c pytorch

Running the tutorials#

You have the option of running the tutorials in Jupyter Lab locally or in a browser using Binder.

Binder Blop Tutorials

If you are using Pixi (see For developers below), you can do the following for a local Jupyter Lab instance:

$ pixi run start-jupyter

Your third option is to simply convert the tutorials to ipynb format and use whatever you prefer to run them.

$ jupytext --to ipynb docs/source/tutorials/*.md

For developers#

We recommend using Pixi to manage your development environments. Go to https://pixi.sh/latest/installation/ to install it.

If you don’t want to use Pixi, you can view the configuration in the pixi.toml file and create your own based on it.

Static checks#

For linting, formatting, and static code analysis.

$ pixi run check

Run tests#

For running the tests.

$ pixi run unit-tests
$ pixi run test-docs

Build documentation#

For building this documentation.

$ pixi run build-docs