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List branches in readme
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Explicit std::string to bp::object conversion
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Added support for python 3 and NCCL
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Fixed memory leaks in cudnn conv and relu
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Init test network on all GPUs
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Remove missed legacy parallel code
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Multi GPU training from Python can use any solver
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Restore can be invoked on rank > 0
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Docker refresh: simplified & update to 16.04, cuda8, cudnn5, nccl
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- Parallelize batches among GPUs and tree-reduce the gradients - The effective batch size scales with the number of devices - Batch size is multiplied by the number of devices - Split batches between GPUs, and tree-reduce the gradients - Detect machine topology (twin-GPU boards, P2P connectivity) - Track device in syncedmem (thanks @thatguymike) - Insert a callback in the solver for minimal code change - Accept list for gpu flag of caffe tool, e.g. '-gpu 0,1' or '-gpu all'. Run on default GPU if no ID given. - Add multi-GPU solver test - Deterministic architecture for reproducible runs
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thanks to discussion by @thatguymike and @flx42
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- Make sure each solver accesses a different subset of the data - Sequential reading of DB for performance - Prefetch a configurable amount of data to host memory - Distribute data to solvers in round-robin way for determinism
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- Interrupt the thread before waiting on join - Provide a method for looping threads to exit on demand - CHECK if start and stop succeed instead of returning an error
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