207 lines
4.9 KiB
Plaintext
207 lines
4.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "6b269e64-be58-43b5-ad60-0fbd1d37861a",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CUDA is available. Number of GPUs: 1\n",
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"GPU Name: NVIDIA GeForce RTX 3060\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"\n",
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"# Check if CUDA is available\n",
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"if torch.cuda.is_available():\n",
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" print(f\"CUDA is available. Number of GPUs: {torch.cuda.device_count()}\")\n",
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" print(f\"GPU Name: {torch.cuda.get_device_name(0)}\")\n",
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"else:\n",
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" print(\"CUDA is not available. No GPU detected.\")\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": 2,
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"id": "97906ea2-b284-4966-9c11-b8629f053815",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import plotly"
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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": 3,
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"id": "7313a620-a0eb-4207-a12a-90aeee3cd980",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]'"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import sys\n",
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"sys.version"
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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": 4,
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"id": "95d9a2e6-3464-4dbe-9a97-0c2d5eb34193",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"environ{'PATH': '/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin',\n",
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" 'HOSTNAME': '6da2c53b1b4e',\n",
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" 'JUPYTER_ENABLE_LAB': 'yes',\n",
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" 'NVIDIA_VISIBLE_DEVICES': 'all',\n",
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" 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility',\n",
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" 'LD_LIBRARY_PATH': '/usr/local/nvidia/lib:/usr/local/nvidia/lib64',\n",
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" 'PYTORCH_VERSION': '2.2.1',\n",
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" 'HOME': '/root',\n",
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" 'LC_CTYPE': 'C.UTF-8',\n",
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" 'JPY_SESSION_NAME': '/workspace/Testing GPU.ipynb',\n",
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" 'JPY_PARENT_PID': '1',\n",
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" 'PYDEVD_USE_FRAME_EVAL': 'NO',\n",
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" 'TERM': 'xterm-color',\n",
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" 'CLICOLOR': '1',\n",
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" 'FORCE_COLOR': '1',\n",
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" 'CLICOLOR_FORCE': '1',\n",
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" 'PAGER': 'cat',\n",
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" 'GIT_PAGER': 'cat',\n",
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" 'MPLBACKEND': 'module://matplotlib_inline.backend_inline',\n",
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" 'CUDA_MODULE_LOADING': 'LAZY'}"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"import matplotlib\n",
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"os.environ"
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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": 5,
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"id": "eb38de31-fc19-4515-b08d-9cd7607ea958",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "97026f0bacf64f09977d0dd5cff9ed20",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(IntSlider(value=5, description='x', max=10), Output()), _dom_classes=('widget-interact',…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Done\n"
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]
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}
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],
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"source": [
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"import ipywidgets\n",
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"from ipywidgets import interact, IntSlider\n",
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"\n",
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"def f(x):\n",
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" return x\n",
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"\n",
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"interact(f, x=IntSlider(min=0, max=10, step=1, value=5))\n",
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"print(\"Done\")\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": 6,
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"id": "f46e46a7-9b57-44aa-9bc9-dcbcf643bc88",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"jupyter-events==0.10.0\n",
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"jupyter-ydoc==0.2.5\n",
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"jupyter_client==7.4.9\n",
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"jupyter_core==5.7.2\n",
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"jupyter_server==2.14.1\n",
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"jupyter_server_fileid==0.9.2\n",
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"jupyter_server_terminals==0.5.3\n",
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"jupyter_server_ydoc==0.8.0\n",
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"jupyterlab==3.6.7\n",
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"jupyterlab-widgets==1.1.7\n",
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"jupyterlab_pygments==0.3.0\n",
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"jupyterlab_server==2.27.2\n"
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]
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}
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],
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"source": [
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"!pip freeze | grep jupyter"
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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 (ipykernel)",
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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": "3.10.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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