This is a supplementary code for an anonymous paper submission for NeurIPS'21. The code is divided into several sections, matching the corresponding evaluations in the original paper.
src contains the common DeDLOC code that is required by all other sectionsalbert is the supplementary code for controlled experiments with ALBERT-large on Wikitext103swav is for training SwAV on ILSVRC data, similarly in similar conditions.sahajbert contains the code used to conduct public collaborative experiment for Bengali language.p2p is a step-by-step tutorial that explains decentralized NAT traversal and circuit relaysBefore running any experiments, one must install the library from src section as such:
cd ./src && python setup.py installFor all distributed experiments, the installation procedure must be repeated on every machine that participates in the experiment. We recommend using machines with at least 2 CPU cores, 16GB RAM and, when applicable, a low/mid-tier NVIDIA GPU.
We recommend running albert experiments first: other experiments build on top of that starter and may reqire more careful setup (e.g. for public participation). Furthermore, for this experiment, we provide a script for launching experiments using cloud preemptible GPUs.
2 commits
Jupyter Notebook
82.4%
Python
16.8%
This is a supplementary code for an anonymous paper submission for NeurIPS'21. The code is divided into several sections, matching the corresponding evaluations in the original paper.
src contains the common DeDLOC code that is required by all other sectionsalbert is the supplementary code for controlled experiments with ALBERT-large on Wikitext103swav is for training SwAV on ILSVRC data, similarly in similar conditions.sahajbert contains the code used to conduct public collaborative experiment for Bengali language.p2p is a step-by-step tutorial that explains decentralized NAT traversal and circuit relaysBefore running any experiments, one must install the library from src section as such:
cd ./src && python setup.py installFor all distributed experiments, the installation procedure must be repeated on every machine that participates in the experiment. We recommend using machines with at least 2 CPU cores, 16GB RAM and, when applicable, a low/mid-tier NVIDIA GPU.
We recommend running albert experiments first: other experiments build on top of that starter and may reqire more careful setup (e.g. for public participation). Furthermore, for this experiment, we provide a script for launching experiments using cloud preemptible GPUs.
2 commits
Jupyter Notebook
82.4%
Python
16.8%