Abstract: We have conducted 175 billion parameters, 1024 GPUs large language model training with up to $99.41 \%$ (Pipeline parallel, PP) and $98.95 \%$ (Data parallel, DP) training efficiency in two ...
Abstract: The rise of edge intelligence is driving distributed machine learning toward a new paradigm of edge-collaborative computing. To overcome the severe communication bottleneck in this paradigm, ...
local-global-graph-transformer/ ├── config/ │ ├── defaults.yaml # Edit simulation/training parameters here │ ├── paths.py # Automatic path management (linear/nonlinear) │ └── constants.py # Physical ...
This demo demonstrates a distributed locking mechanism using Apache ZooKeeper to coordinate access between multiple API replicas trying to write to a shared text file. In distributed systems, multiple ...
Moreover, we discuss strategies for metadata selection and human evaluation to ensure the quality and effectiveness of ITDs. By integrating these elements, this tutorial provides a structured ...
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