Grid Volunteer Computing For A.I.

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!!Distributed Computing, Grid Computing {br} # Berkeley Open Infrastructure for Network Computing - BOINC # Internet Archive, they maintain a library for everyone to access for free. https://warrior.archiveteam.org/ # European Grid Infrastructure (EGI) - a series of projects funded by the European Commission. # OurGrid - https://ourgrid.org/ # Petals, Run large language models at home, BitTorrent‑style - https://petals.dev/, https://huggingface.co/ {br} !!Distributed storage network (DSN) {br} Filesize is 10mb, 10 computers hold 1mb each. Like RAID striping over a wide area network. Running commands over the data rather than downloading the data such as a search request, SQL, regex, awk or to train an LLM. IPFS (InterPlanetary File System): Employs a content-addressed system with erasure coding to distribute files efficiently across a peer-to-peer network. {br} # TensorFlow: TensorFlow provides the tf.distribute API for distributed training. You can choose strategies like MirroredStrategy or MultiWorkerStrategy depending on your setup (single machine with multiple GPUs or multiple machines). # PyTorch: PyTorch offers the torch.distributed package for distributed training across GPUs and machines. Choose between MPI, GLOO, or NCCL backends based on communication needs. # Horovod: This open-source library simplifies distributed training across diverse platforms (CPUs, GPUs, clusters) and works with TensorFlow, PyTorch, and MXNet. # Ray Tune: This tool offers distributed Hyperparameter Tuning, which can optimize your LLM training by running several trials on separate machines. {br} Data parallelization: Divide your training data across machines to distribute the workload and speed up training. Model parallelization: For even larger models, consider splitting the model itself across machines for parallel processing. {br} # https://github.com/search?q=+distributed+storage+system&type=repositories&s=stars&o=desc&p=2 # https://github.com/horovod/horovod
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