我按照以下的文档进行多卡部署了。多卡部署 如果你有多张 GPU,但是每张 GPU 的显存大小都不足以容纳完整的模型,那么可以将模型切分在多张GPU上。首先安装 accelerate: pip install accelerate,然后通过如下方法加载模型: from utils import load_model_on_gpus model =
│ ❱ 33 model = load_model_on_gpus(checkpoint_path="./chatglm-6b-int4-slim") ││ 34 ││ 35 if args.cpu: ││ 36 │ model = model.float() ││ ││ A:\chatglm_webui-main\utils.py:45 in load_model_on_gpus ││ ││ 42 │ else: ││ 43 │ │ from accelerate impor...
J Parallel Distrib Comput 76:3–15Arash Ashari, Naser Sedaghati, John Eisenlohr, and P. Sadayappan. A model-driven blocking strategy for load balanced sparse matrix-vector multiplication on GPUs. J. Parallel Distrib. Comput., 76:3-15, Febru- ary 2015....
Multi-device OpenCL kernel load balancer and pipeliner API for C#. Uses shared-distributed memory model to keep GPUs updated fast while using same kernel on all devices(for simplicity). - tugrul512bit/Cekirdekler