$pip install pytorch-quantization /Processing... ✓Done Start your free trial 5 Releases 2.2.1 Stable version 1year ago Released 2.2.02 years ago 2.1.32 years ago 0.0.1.dev54 years ago 0.0.1.dev44 years ago PyPI on Cloudsmith Getting started with PyPI on Cloudsmith is fast and easy....
由于pytorch-quantization可能依赖于英伟达提供的特定包,因此首先需要安装nvidia-pyindex,这是一个pip源,用于连接英伟达的服务器下载需要的包。 bash pip install nvidia-pyindex 如果上述命令安装失败,您可以尝试手动将nvidia-pyindex源添加到pip源中: bash pip config set global.index-url https://pypi.ngc.nvidia...
这个错误可能是由于您正在尝试从PyPI(Python Package Index)安装"pytorch-quantization"模块,而该模块实际上是托管在NVIDIA Python Package Index上的。因此,您需要先安装"nvidia-pyindex"模块,然后再安装"pytorch-quantization"模块。 您可以使用以下命令先安装"nvidia-pyindex"模块: pip install nvidia-pyindex 然后再使...
1) 模型训练完毕后动态量化:post training dynamic quantization 2) 模型训练完毕后静态量化:post training static quantization 3) 模型训练中开启量化:quantization aware training(QAT) 关于post training dynamic/static quantization的方法,可以参考下面的博客 Pytorch模型量化_凌逆战的博客-CSDN博客blog.csdn.net/qq...
· PyTorch 中的实际量化 https://pytorch.org/blog/quantization-in-practice/ · 面向 PyTorch 的英特尔扩展文档 https://intel.github.io/intel-extension-for-pytorch/1.11/ * 本文仅做翻译处理,实际性能受使用情况、配置和其他因素的差异影响。
https://oneapi-src.github.io/oneDNN/dev_guide_understanding_memory_formats.html · PyTorch 中的实际量化 https://pytorch.org/blog/quantization-in-practice/ · 面向 PyTorch 的英特尔扩展文档 https://intel.github.io/intel-extension-for-pytorch/1.11/...
We are planning to make all functions undertorch.ao.quantization.pt2e.graph_utilsprivate. This update marksget_control_flow_submodulesas a private API. If you have to or want to continue usingget_control_flow_submodules, please make a private call by using_get_control_flow_submodules. ...
If you don't have enough VRAM to quantize your entire model on GPU and you find CPU quantization to be too slow then you can use the device argument like soquantize_(model, Int8WeightOnlyConfig(), device="cuda")which will send and quantize each layer individually to your GPU. ...
├── quantization (量化) ├── utils (具体包括backcompat、bottleneck、data、ffi、hipify和tensorboard) third_party三方模块 谷歌、Facebook、NVIDIA、Intel等开源的第三方库,具体包含请见前文。 分层的视角看待: 1 第一层C10: 最核心的Tensor实现,手机端、服务端都用; ...
这一部分需要安装onnx, github地址:https://github.com/onnx/onnx以及安装一个转换工具onnx_coreML,github地址:https://github.com/onnx/onnx-coreml。里面用到了一个coremltools :https://pypi.org/project/coremltools/,这个tool目前仅支持python2.7环境下使用。