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pylibwholegraph

WholeGraph supports PyTorch and provides a distributed graph and kv store.

cuGraph-PyG can leverage WholeGraph for even greater scalability.

Using an RMM memory resource

WholeGraph can use RMM's current per-device memory resource for distributed and hierarchy tensors stored on the device. Configure RMM after selecting the process's CUDA device, then enable RMM allocation before creating WholeMemory tensors:

import rmm
import pylibwholegraph.torch as wgth

rmm.reinitialize(pool_allocator=True, initial_pool_size=2**30)
wgth.set_rmm_enabled(True)

WholeMemory looks up RMM's current resource when each supported allocation is created, so normal RMM per-device resource configuration applies. Call wgth.set_rmm_enabled(False) to use WholeMemory's default allocator for future allocations.

Chunked, continuous, and NVSHMEM device tensors require specialized CUDA allocation mechanisms. When RMM is enabled, these tensors emit a warning and use their existing allocation path.

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Release files for pylibwholegraph-cu12 26.10.0

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Table of built distributions (wheels) for pylibwholegraph-cu12 26.10.0
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pylibwholegraph_cu12-26.10.0-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl CPython 3.11 abi3 Linux glibc 2.28+ x86-64, Linux glibc 2.17+ x86-64 Details
pylibwholegraph_cu12-26.10.0-cp311-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl CPython 3.11 abi3 Linux glibc 2.17+ ARM64, Linux glibc 2.28+ ARM64 Details

Total release size: 595.5 kB

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