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mjq2020mjq2020
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add audio classify jupyter
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‎demo/audio_classify.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 环境配置\n",
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"1. 下载[edgelab](https://github.com/mjq2020/edgelab)仓库代码\n",
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"2. 运行环境配置脚本\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%cd ~\n",
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"!git clone https://github.com/mjq2020/edgelab\n",
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"%cd edgelab"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!python tools/env_config.py\n",
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"import os\n",
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"os.environ['PYTHONPATH'] = '~/edgelab'\n",
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"!nvidia-smi"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 模型训练\n",
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"1. 运行train.py,设置任务类别、配置文件路径、等"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!~/anaconda3/envs/edgelab/bin/python tools/train.py mmcls configs/audio_classify/ali_classiyf_small_8k_8192.py --cfg-options runner.max_epochs=10"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 导出ONNX\n",
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"1. 使用torch2onnx.py转换刚才完成训练的权重文件\n",
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"2. onnx文件生成位置位于原权重文件相同位置(后缀.onnx)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!~/anaconda3/envs/edgelab/bin/python tools/torch2onnx.py --config configs/audio_classify/ali_classiyf_small_8k_8192.py --checkpoint work_dirs/exp1/latest.pth --task mmcls"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## onnx转ncnn及量化\n",
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"1. 使用export_quantize.py脚本将生成的onnx文件转为ncnn或进行量化\n",
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"2. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!~/anaconda3/envs/edgelab/bin/python tools/export_quantize.py --onnx work_dirs/exp1/latest.onnx --type ncnn"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.8.13 ('mmset')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.15"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "4371667da6ad0a80396cd63c529a9936708aaf3de3d23ccc7c4ed575a0b05f73"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎demo/meter.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 表计回归\n",
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"1. 模型训练\n",
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"2. 导出onnx\n",
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"3. 导出ncnn"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 环境配置\n",
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"1. 下载[edgelab](https://github.com/mjq2020/edgelab)仓库代码\n",
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"2. 运行环境配置脚本\n"
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"1. 下载edgelab仓库代码\n",
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"2. 运行项目环境配置脚本"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 模型训练\n",
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"1. 运行train.py,设置任务类别、配置文件路径、等"
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"## 数据集下载与上传\n",
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"1. 首先下载[表计数据集](https://1drv.ms/u/s!AqG2uRmVUhlShtIhyd_7APHXEhpeXg?e=WwGx5m)\n",
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"2. 将数据集上传至用户根目录"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%unzip ~/meter.zip -d ~ "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 修改配置文件并训练\n",
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"1. 修改config文件中的相配置。\n",
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"2. 运行train.py,设置任务类别、配置文件路径、等"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"!~/anaconda3/envs/edgelab/bin/python tools/train.py mmcls configs/audio_classify/ali_classiyf_small_8k_8192.py --cfg-options runner.max_epochs=10"
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"!~/anaconda3/envs/edgelab/bin/python tools/train.py mmpose configs/pfld/pfld_mv2n_112.py --cfg-options runner.max_epochs=10"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"!~/anaconda3/envs/edgelab/bin/python tools/torch2onnx.py --config configs/audio_classify/ali_classiyf_small_8k_8192.py --checkpoint work_dirs/exp1/latest.pth --task mmcls"
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"!~/anaconda3/envs/edgelab/bin/python tools/torch2onnx.py --config configs/pfld/pfld_mv2n_112.py --checkpoint work_dirs/exp1/latest.pth --task mmcls"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## onnx转ncnn及量化\n",
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"1. 使用export_quantize.py脚本将生成的onnx文件转为ncnn或进行量化\n",
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"2. "
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"1. 使用export_quantize.py脚本将生成的onnx文件转为ncnn或进行量化"
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.15"
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"version": "3.8.13"
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},
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"orig_nbformat": 4,
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"vscode": {

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