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rumi (python)

Python bindings for Rumi, stateless raster storage for AI4EO. Rumi stores images as (B, Y, X) and time series as (T, B, Y, X).

pip install rumi-eo

Rumi currently supports Linux and macOS and requires Python 3.11 or newer. The format and APIs may change before 1.0. See the project README for examples and the format specification for the wire format.

Reads return NumPy by default. PyTorch, JAX and TensorFlow results use DLPack; framework="dlpack" returns the one-shot producer itself. Compatibility is checked before opening the source, and Rumi never casts a dtype to make it fit.

PyTorch can read straight into a CUDA tensor on Linux or an MPS tensor on macOS:

chip = rumi.read(path, header, framework="torch", device="cuda:0",
                 window=(row, column, height, width))

mps_chip = rumi.read(path, header, framework="torch", device="mps",
                     window=(row, column, height, width))

The header, byte ranges and copy plan stay on the CPU. PFOR and PivCo frames of one- and two-byte samples decode on the device; other frames use CPU staging before GeoZL writes the result to the selected GPU. MPS reads require PyTorch 2.10 or newer. GPU frame planning follows rumi.set_num_threads or RUMI_NUM_THREADS, set before the first read, and defaults to one thread per process. R and Julia device arrays are not implemented yet.

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rumi_eo-0.27.0a4-py3-none-manylinux_2_28_x86_64.whl Python 3 none Linux glibc 2.28+ x86-64 Details for rumi_eo-0.27.0a4-py3-none-manylinux_2_28_x86_64.whl
rumi_eo-0.27.0a4-py3-none-macosx_11_0_arm64.whl Python 3 none macOS 11.0+ ARM64 Details for rumi_eo-0.27.0a4-py3-none-macosx_11_0_arm64.whl

Total release size: 17.6 MB

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