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Supported platform

Public releases of XLOG are supported on Linux x86_64 with an NVIDIA GPU and CUDA Toolkit 13.x:
  • Linux x86_64
  • nvidia-smi sees the GPU
  • nvcc --version works
  • Rust rustc and cargo are available
  • Python 3.8 or newer
  • xlog prob host-readable output requires xlog-cli built with host-io
Run the doctor first after cloning: 
python scripts/xlog_doctor.py

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Source install

git clone https://github.com/BrainyBlaze/xlog.git
cd xlog
python scripts/xlog_doctor.py
cargo build --release

# If you need host-readable probabilistic CLI output (`xlog prob`),
# build the CLI with host I/O enabled.
cargo build --release -p xlog-cli --features host-io
The release binary is ./target/release/xlog. Published artifacts follow tagged releases and may lag the current main branch.

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GitHub release binary install

Download the Linux x86_64 archive from the GitHub Releases page, unpack it, and run the bundled xlog binary from the extracted directory. Public release archives are built with host-io, so xlog prob has host-readable output without a rebuild.

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PyPI install

Install the latest published pyxlog wheel from PyPI: 
pip install pyxlog
pyxlog auto-configures XLOG_CUBIN_DIR from its packaged pyxlog/kernels/ directory when the wheel includes staged CUDA artifacts. If you are running probe scripts, artifact replays, or source-tree experiments outside that packaged layout, export XLOG_CUBIN_DIR yourself before importing pyxlog: 
export XLOG_CUBIN_DIR=/path/to/xlog/crates/pyxlog/python/pyxlog/kernels
For unreleased main branch features, use the local development install below instead of expecting PyPI to match the current main branch.

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crates.io install

Install the latest published CLI crate from crates.io: 
cargo install xlog-cli --features host-io
As with the GitHub and PyPI artifacts, published crate versions follow tagged releases and may lag the current main branch. The Cargo-installed binary embeds portable PTX for all runtime kernels, so it can run without a sidecar kernels/ directory. If a staged kernels/ directory or XLOG_CUBIN_DIR is present, xlog still prefers those filesystem artifacts so release archives and local builds can use architecture-specific cubins first.

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CUDA kernel artifact model

XLOG does not track generated .ptx or .cubin files in git. Kernel artifacts are produced from kernels/*.cu by the Rust build and are resolved at runtime in this order:
  1. XLOG_CUBIN_DIR
  2. a package- or binary-adjacent kernels/ directory
  3. Cargo build output for source-tree builds
  4. embedded portable PTX compiled into the Cargo-installed binary
This means cargo install xlog-cli --features host-io works without a sidecar kernels/ directory, while GitHub release archives and PyPI wheels still ship staged kernel artifacts for faster, architecture-specific startup when available.

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Local Python development install

Install into the exact Python interpreter used by your downstream project. Do not rely on bare maturin develop from the xlog checkout: if this repository has its own .venv, maturin can install into that environment while your project imports a different Python. 
python scripts/install_pyxlog_for_python.py --python /usr/local/bin/python --user
The helper stages CUDA kernels, builds a local wheel for the requested interpreter, installs that wheel with the same interpreter’s pip, and verifies that the installed pyxlog package contains pyxlog/kernels/.

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Next steps