app/Contents/MacOS/kicad-cli.| |---|---| ## kicad-cli commands The kicad-cli command has 5 subcommands: fp, pcb, sch, sym, and version. Each subcommand may have its own subcommands and arguments. For example Footprint The fp subcommand exports footprints to another format or upgrades the footprint libraries to the current version of the KiCad footprint file format. ## Footprint export The fp export svg command command exports one or more footprints from the specified library into SVG files. Usage: kicad-cli fp export svg [--help] [--output OUTPUT_FILE] [--layers LAYER_LIST] [--define-var KEY=VALUE] [--theme

使用Python训练和部署低精度模型 (TensorFlow版) 张校捷 2019/9/21 ## 目录 >> 低精度的概念和意义 TensorFlow的FP16模型 >> TensorRT的FP16/Int8模型 总结  FP16: E8M7 (TPU, tf.bfloat16) FP16: E5M10 (GPU, tf.float16) Int8 ## 低精度浮点数的优点 ### 1. 节约内存/显存的使用（FP16为原来的1/2，int8为原来的1/4） 2. 特殊的硬件专门用于低精度浮点数的计算加速（TensorCore） FP16 storage/input storage/input Full precision product Sum with FP32 accumulator Convert to FP32 result  ![Image](/uploads/documents/a

Training/Inference PAI (Platform of AI) Alibaba Cloud Intelligence ## Outline • TensorCore AutoCodeGen in TVM • FP16 Mixed-Precision Training on PAI • INT8 Inference on PAI-Blade ## TensorCore ## AutoCodeGen ## Background A_{2,2} & A_{2,3} \\ A_{3,0} & A_{3,1} & A_{3,2} & A_{3,3} \end{array} $$ FP16 or FP32 FP16 $$ \begin{array}{l|ccc} B_{0,0} & B_{0,1} & B_{0,2} & B_{0,3} \\ \hline B_{1 1} & B_{2,2} & B_{2,3} \\ B_{3,0} & B_{3,1} & B_{3,2} & B_{3,3} \end{array} $$ FP16 $$ \begin{array}{cccc} C_{0,0} & C_{0,1} & C_{0,2} & C_{0,3} \\ C_{1,0} & C_{1

机器学习-第十一章 关联规则 黄海广 副教授 2021年06月 ## 本章目录 01 关联规则概述 02 Apriori 算法 03 FP-Growth 算法 ### 1. 关联规则概述 ## 01 关联规则概述 02 Apriori 算法 03 FP-Growth算法 ### 1. 关联规则概述 ## 关联规则 关联规则（Association Rules）反映一个事物与其他事物之 \frac{Support}{Support(A) \times Support(B)} $ ### 2. Apriori算法 ## 01 关联规则概述 ## 02 Apriori 算法 03 FP-Growth算法 ### 2. Apriori算法 Apriori算法利用频繁项集生成关联规则。它基于频繁项集的子集也必须是频繁项集的概念。 频繁项集是支持值大于阈值（support）的项集。 ### 3. FP-Growth算法 01 关联规则概述 02 Apriori 算法 03 FP-Growth算法 ### 3. FP-Growth算法 FP-growth（Frequent Pattern Growth）算法思想 FP-growth(频繁模式增长)算法是韩家炜老师在2000年提出的关联分析算法，它采取如下分治策略：将提供频繁项集的数据库压缩到一棵频繁模式树（FP-Tree），但仍保留项集关联信息。

library for accelerating Transformer models on NVIDIA GPUs. It includes support for 8-bit floating point (FP8) precision on Hopper GPUs which provides better training and inference performance with lower memory training by adding only three lines of Python to an existing FP32 (default) script. AMP will select an optimal set of operations to cast to FP16. FP16 operations require 2X reduced memory bandwidth (resulting model). Additionally, GEMMs and convolutions with FP16 inputs can run on Tensor Cores, which provide an 8X increase in computational throughput over FP32 arithmetic. APEX AMP is included to support models

ntime/panic.go:1047 +0x5d fp=0x7fffb29cd648 sp=0x7fffb29cd618 pc=0x434a7d runtime.sysMapOS(0xc000400000, 0x6fc400000?) /usr/local/go/src/runtime/mem_linux.go:187 +0x11b fp=0x7fffb29cd690 sp=0x7fffb29cd648 /usr/local/go/src/runtime/mem.go:142 +0x35 fp=0x7fffb29cd6c0 sp=0x7fffb29cd690 pc=0x417955 runtime.(*mheap).grow(0x69dde0, 0x37e11e?) /usr/local/go/src/runtime/mheap.go:1522 +0x245 fp=0x7fffb29cd738 sp=0x7fffb29cd6c0 /usr/local/go/src/runtime/mheap.go:1243 +0x1b7 fp=0x7fffb29cd7d0 sp=0x7fffb29cd738 pc=0x4275f7 runtime.(*mheap).alloc.func1() /usr/local/go/src/runtime/mheap.go:961 +0x65 fp=0x7fffb29cd818 sp=0x7fffb29cd7d0 pc=0x4270a5

incoming requests - Fast model execution with CUDA/HIP graph - Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache - Optimized CUDA kernels vLLM is flexible and easy to use with: - Seamless integration CPU vLLM initially supports basic model inferencing and serving on x86 CPU platform, with data types FP32 and BF16. Table of contents: 1. Requirements 2. Quick start using Dockerfile 3. Build from source Note: - BF16 is the default data type in the current CPU backend (that means the backend will cast FP16 to BF16), and is compatible will all CPUs with AVX512 ISA support. - AVX512_BF16 is an extension

incoming requests - Fast model execution with CUDA/HIP graph - Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache - Optimized CUDA kernels vLLM is flexible and easy to use with: - Seamless integration pattern of users. - VLLM_OPENVINO_CPU_KV_CACHE_PRECISION=u8 to control KV cache precision. By default, FP16 / BF16 is used depending on platform. - VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS=ON to enable U8 weights CPU vLLM initially supports basic model inferencing and serving on x86 CPU platform, with data types FP32 and BF16. Table of contents: 1. Requirements 2. Quick start using Dockerfile 3. Build from source

incoming requests - Fast model execution with CUDA/HIP graph - Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache - Optimized CUDA kernels vLLM is flexible and easy to use with: - Seamless integration pattern of users. - VLLM_OPENVINO_CPU_KV_CACHE_PRECISION=u8 to control KV cache precision. By default, FP16 / BF16 is used depending on platform. - VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS=ON to enable U8 weights CPU vLLM initially supports basic model inferencing and serving on x86 CPU platform, with data types FP32 and BF16. Table of contents: 1. Requirements 2. Quick start using Dockerfile 3. Build from source

incoming requests - Fast model execution with CUDA/HIP graph - Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache - Optimized CUDA kernels vLLM is flexible and easy to use with: - Seamless integration pattern of users. - VLLM_OPENVINO_CPU_KV_CACHE_PRECISION=u8 to control KV cache precision. By default, FP16 / BF16 is used depending on platform. - VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS=ON to enable U8 weights CPU vLLM initially supports basic model inferencing and serving on x86 CPU platform, with data types FP32 and BF16. Table of contents: 1. Requirements 2. Quick start using Dockerfile 3. Build from source