rights reserved. Presenter: Haichen Shen, Yao Wang Amazon SageMaker Neo, Deep Engine Science Dynamic Model in TVM AWS AI© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Models with models© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Support dynamic model in TVM ● Support Any-dim in typing ● Use shape function to compute the type at runtime ● Virtual input_name = "data" input_shape = [tvm.relay.Any(), 3, 224, 224] dtype = "float32" block = get_model('resnet50_v1', pretrained=True) mod, params = relay.frontend.from_mxnet(block, shape={input_name:

国外：GPT-4等效智能在过去18个月内价格下降240倍  国内：大模型「亏本」卖，可以「白嫖」大模型API能力 19政企、创业者必读 DeepSeek出现之前的十大预判 之七 多模态越来越重要  由文本生成迈向图像、视频、3D内容与世界模拟  多模态模态在能力变强的同时，规模正在变小 20政企、创业者必读 21 DeepSeek出现之前的十大预判 之八 智能体推动大模型快速落地  能够调用各种工具，具有行动能力 过去如何做蛋白质研究 AlphaFold 1. X射线晶体衍射 2. 核磁共振 3. 冷冻电子显微镜 1. 利用Transformer的预测能力， 2. 直接从蛋白质的氨基酸序列 3. 中预测蛋白质的3D结构 靠肉眼观察，几年才能发现一个复杂蛋 白质结构，半个世纪预测了20多万种 从数年缩短到几分钟，解开了生物学密码 成功预测了地球存在的2亿种蛋白质结构 45政企、创业者必读  DeepS

combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" @atomicreplace macros, and @atomiconce macros.CHAPTER 25. MULTI-THREADING 326 Specific details of the memory model and other details of the design are written in the Julia Atomics Mani- festo, which will later be it possible to run up to N Tasks on M Process, aka M:N Threading. Then a lock acquiring\releasing model for nextidx will be needed, as it is not safe to let multiple processes read-write a resource at

combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" @atomicreplace macros, and @atomiconce macros.CHAPTER 25. MULTI-THREADING 326 Specific details of the memory model and other details of the design are written in the Julia Atomics Mani- festo, which will later be it possible to run up to N Tasks on M Process, aka M:N Threading. Then a lock acquiring\releasing model for nextidx will be needed, as it is not safe to let multiple processes read-write a resource at

combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" @atomicreplace macros, and @atomiconce macros.CHAPTER 25. MULTI-THREADING 326 Specific details of the memory model and other details of the design are written in the Julia Atomics Mani- festo, which will later be it possible to run up to N Tasks on M Process, aka M:N Threading. Then a lock acquiring\releasing model for nextidx will be needed, as it is not safe to let multiple processes read-write a resource at

combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" @atomic, @atomicswap, @atomicreplace macros, and @atomiconce macros. Specific details of the memory model and other details of the design are written in the Julia Atomics Mani- festo, which will later be it possible to run up to N Tasks on M Process, aka M:N Threading. Then a lock acquiring\releasing model for nextidx will be needed, as it is not safe to let multiple processes read-write a resource at the

combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" @atomic, @atomicswap, @atomicreplace macros, and @atomiconce macros. Specific details of the memory model and other details of the design are written in the Julia Atomics Mani- festo, which will later be it possible to run up to N Tasks on M Process, aka M:N Threading. Then a lock acquiring\releasing model for nextidx will be needed, as it is not safe to let multiple processes read-write a resource at the

combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" @atomic, @atomicswap, @atomicreplace macros, and @atomiconce macros. Specific details of the memory model and other details of the design are written in the Julia Atomics Mani- festo, which will later be it possible to run up to N Tasks on M Process, aka M:N Threading. Then a lock acquiring\releasing model for nextidx will be needed, as it is not safe to let multiple processes read-write a resource at the

combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" @atomic, @atomicswap, @atomicreplace macros, and @atomiconce macros. Specific details of the memory model and other details of the design are written in the Julia Atomics Mani- festo, which will later be it possible to run up to N Tasks on M Process, aka M:N Threading. Then a lock acquiring\releasing model for nextidx will be needed, as it is not safe to let multiple processes read-write a resource at the

combinations of argument types, and applied by dis- patching to the most specific matching definition. This model is a good fit for mathematical programming, where it is unnatural for the first argument to "own" @atomic, @atomicswap, @atomicreplace macros, and @atomiconce macros. Specific details of the memory model and other details of the design are written in the Julia Atomics Mani- festo, which will later be it possible to run up to N Tasks on M Process, aka M:N Threading. Then a lock acquiring\releasing model for nextidx will be needed, as it is not safe to let multiple processes read-write a resource at the