Turns out, using learning techniques to improve sample and label efficiency, often helps to make resource efficient models feasible. By feasible, we mean that the model meets the bar for quality metrics using a probability distribution. It is worth mentioning that the average mixing technique is a special case of mix-up with a fixed . The equations shown below mix two samples ( , ) and ( , ) to create infrastructure. It allows, for example, for the teacher’s predictions to be collected offline if resource constraints prohibit the execution of both the student and the teacher models in tandem. These predictions

computes attention between the encoder output sequence and the target sequence. Self-attention is a special type of attention which operates over a single sequence to compute the relationship between its own object in the input sample. This model will be used within a mobile application. Mobile devices are resource constrained. Let’s see if we can reduce the model footprint without a significant quality compromise

code for this exercise is available as a Jupyter notebook here. %%capture import gzip import operator, random import numpy as np import tensorflow as tf from functools import reduce from matplotlib out. sparse_weights = sparsify_smallest(weights, sparsity_rate) print('Original Size:', reduce(operator.mul, weights.shape)*weights.itemsize) weights_compressed = compress_and_save(weights) print('Original fully-connected, convolutional layers and so on. 20 "Matrix Compression Operator." 17 July 2022, blog.tensorflow.org/2020/02/matrix-compression-operator-tensorflow.html. 19 X. Yu, T. Liu, X. Wang and D. Tao, "On

核心架构层 算法模型层 4 深度学习-分布式模型推理 • 推理性能优化 • 减少计算量： operator fusion/XLA/TVM/prune/float16/quantization • 加快计算速度： batching/TensorRT/MPS/SSE/AVX/Neon • operator fusion • 针对特定场景重写耗时算子 • 重构tensorflow计算引擎 •

improved. ‣ PyTorch's JIT (still in Alpha) now supports FP16 inputs and outputs, comparisons, the exp operator, and ReLU gates. ‣ Added support for DALI 0.1 Beta. ‣ Latest version of CUDA ® Basic Linear Algebra EVENT WILL NVIDIA BE LIABLE FOR ANY DAMAGES, INCLUDING WITHOUT LIMITATION ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND REGARDLESS OF THE THEORY OF LIABILITY

处理特殊输入，如模糊、黑白照片 - 适配具有不同特征的数据源 - 在严肃应用中，客户追求100%准确率，算法性能提升永无止境 • 深度学习模型需要在准确率和速度上做均衡 - 使用更加精巧的模型和Operator设计 - 使用模型压缩算法，在基本保障准确率的情况下大幅提升速度 - 利用最新的硬件特性，如GPU TensorCore/int8 *示意图来自互联网 Kubernetes在异构系统调度中的挑战

58(1): 267–288. [7] TIBSHIRANI R, BICKEL P, RITOV Y, et al. Least absolute shrinkage and selection operator[J]. Software: http://www.stat.stanford.edu/ tibs/lasso.html, 1996. 33 谢 谢！

zip(model_inputs.input_ �→ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] 以前，我们使用 model.chat() （有关更多详细信息，请参阅先前 Qwen 模型中的 modeling_qwen. py ）。现在，我们遵循 transformers from transformers import TextStreamer streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512, streamer=streamer zip(model_inputs.input_ �→ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] 如果你想使用 Flash Attention 2，你可以用下面这种方式读取模型： model = AutoModelForCausalLM.from_pretrained(

available computational budget. They can be increased as more resources become available or reduced in resource constrained situations. The likelihood of finding the optimal increases with the number of trials and resources. Alternatively, we can base the search approach on the budget allocation to cap the resource utilization. Multi-Armed Bandit based algorithms allocate a finite amount of resources to a set contrast to the bracket 0, subsequent brackets start with a smaller set of configurations and higher resource allocation per configuration. This ensures that we try successive halves with various values of

choice of the technique depends on several factors like customer preference, consumption delay, or resource availability (extra hands needed for chopping). Personally, I like full apples. Let’s move on from transmission bandwidth is expensive like deep learning models on mobile devices. Mobile devices are resource constrained. Hence, quantization can help to deploy models which would otherwise be too big to shrink the model sizes with an acceptable loss of precision. A smaller model size can be deployed in resource constrained environments like the mobile devices. Quantization has enabled a whole lot of models