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直接压缩->训练算法补偿 [2020] Compressed Communication for Distributed Deep Learning: Survey and Quantitative Evaluation [ICLR2018]Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

[-10.0, 10.0]. We need to transmit a collection (vector) of these variables over an expensive communication channel. Can we use quantization to reduce transmission size and thus save some costs? What if integer compressed tensor which uses 3*5*1 = 15 bytes. It gives us a 4x savings in storage and communication costs as compared to the high precision representation. The quantized representation size increases

PyTorch RN-08516-001_v23.07 | 91 ‣ On H100 NVLink systems using 2 GPUs for training, certain communication patterns can trigger a corner-case bug that manifests either as a hang or as an "illegal instruction" now more accurate when using FP16. ‣ Improved distributed performance, specifically, gradient communication can now overlap with gradient computation in backwards(). ‣ Compatibility changes, specifically