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Source channel @githubtrending · Post #14659 · May 1
#cplusplus#arm#convolution#deep_learning#embedded_devices#llm#machine_learning#ml#mnn#transformer#vulkan#winograd_algorithm MNN is a lightweight and efficient deep learning framework that helps run AI models on mobile devices and other small devices. It supports many types of AI models and can handle tasks like image recognition and language processing quickly and locally on your device. This means you can use AI features without needing to send data to the cloud, which improves privacy and speed. MNN is used in many apps, including those from Alibaba, and supports various platforms like Android and iOS. It also helps reduce the size of AI models, making them faster and more efficient. https://github.com/alibaba/MNN
@dps_build · Post #32 · 03/08/2023, 07:37 AM
Weights & Biases 测试了在 M2Pro Mac Mini 上跑深度学习的训练。比前一代的 M1 Pro 快了不少，Tensorflow 大约有 15% 的增长，Pytorch 大约有18%。 结论是，这一代的 Mac Mini 可以拿来写模型原型，但是要想训练，还是需要 N 卡。 https://wandb.ai/capecape/pytorch-M1Pro/reports/Is-the-New-M2Pro-Mac-Mini-a-Deep-Learning-Workstation---VmlldzozNjI3NDE5 之前 M1 系列芯片的各种测试： https://t.me/tms_ur_way/2404 #ml
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@dps_build · Post #5 · 03/01/2023, 12:31 PM
Unum 使用四块 RTX 3090 显卡就能训练一个比 OpenAI 还要好的 Vision-Language transformer 模型 -- UForm。要知道 OpenAI 用了 1024块 A100 显卡才训练出来。 UForm 不仅各方面表现优于 OpenAI 的 CLIP，而且推算速度也大大优于 CLIP。 对了，Unum 是一家在亚美尼亚的初创公司，目前只有13个人。 https://www.unum.cloud/blog/2023-02-20-efficient-multimodality #ml
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