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Merge pull request #20 from CodingBlood/feature/ML
Finish Tutorials on Pytorch before Starting Project #19
2 parents d265d4e + ae2cbbb commit 040e857

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‎.gitignore‎

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.env
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.idea/
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node_modules/
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data/
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ml/artifacts/
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models/
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*.pt
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*.pth
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mlruns/
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mlruns/

ml/Readme.md renamed to ml/.github/workflows/.gitkeep

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‎ml/config/config.yaml‎

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‎ml/dvc.yaml‎

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‎ml/params.yaml‎

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‎ml/requirements.txt‎

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fastapi~=0.133.0
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torch~=2.5.1+cu121
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numpy~=2.2.6
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pandas~=2.3.3
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sympy~=1.13.1
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torchinfo~=1.8.0
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scikit-learn~=1.7.2
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matplotlib~=3.10.8
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kagglehub~=1.0.0

‎ml/research/PytorchDemo/00_Setup_Pytorch.ipynb‎

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{
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"cells": [
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-02-24T17:53:25.651209170Z",
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"start_time": "2026-02-24T17:53:21.331584938Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import torch\n",
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"print(torch.version.cuda)"
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],
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"id": "8effc7e6f58c9b21",
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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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"12.1\n"
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]
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}
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],
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"execution_count": 1
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-02-24T17:53:27.457104418Z",
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"start_time": "2026-02-24T17:53:27.375831476Z"
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}
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},
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"cell_type": "code",
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"source": [
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"x = torch.rand(3,3)\n",
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"x"
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],
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"id": "7724c24d65443f73",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[0.2387, 0.0177, 0.6430],\n",
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" [0.9004, 0.2064, 0.3525],\n",
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" [0.9998, 0.0965, 0.6391]])"
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]
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},
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"execution_count": 2,
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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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"execution_count": 2
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-02-24T17:53:33.290072334Z",
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"start_time": "2026-02-24T17:53:31.135526425Z"
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}
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},
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"cell_type": "code",
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"source": [
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"\n",
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"print(f\"PyTorch version: {torch.__version__}\")\n",
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"print(f\"CUDA available: {torch.cuda.is_available()}\")\n",
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"print(f\"CUDA version: {torch.version.cuda}\")\n",
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"print(f\"cuDNN version: {torch.backends.cudnn.version()}\")\n",
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"print(f\"Device Name: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'None'}\")\n"
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],
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"id": "479d22a19052b601",
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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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"PyTorch version: 2.5.1+cu121\n",
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"CUDA available: True\n",
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"CUDA version: 12.1\n",
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"cuDNN version: 90100\n",
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"Device Name: NVIDIA GeForce RTX 4050 Laptop GPU\n"
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]
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}
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],
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"execution_count": 3
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-02-24T17:53:40.898288652Z",
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"start_time": "2026-02-24T17:53:40.766527442Z"
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}
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},
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"cell_type": "code",
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"source": [
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"print(\"Torch:\", torch.__version__)\n",
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"print(\"CUDA available:\", torch.cuda.is_available())\n",
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"print(\"GPU:\", torch.cuda.get_device_name(0))"
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],
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"id": "742ddba6863aba74",
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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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"Torch: 2.5.1+cu121\n",
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"CUDA available: True\n",
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"GPU: NVIDIA GeForce RTX 4050 Laptop GPU\n"
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]
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}
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],
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"execution_count": 4
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-02-24T17:53:43.745676614Z",
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"start_time": "2026-02-24T17:53:43.645100345Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import sys\n",
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"print(sys.executable)"
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],
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"id": "a1284b92a27174ee",
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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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"/home/kartik-agarwal/Documents/PytorchDemo/.venv/bin/python\n"
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]
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}
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],
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"execution_count": 5
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-02-24T17:53:46.811841062Z",
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"start_time": "2026-02-24T17:53:45.269999324Z"
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}
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},
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"cell_type": "code",
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"source": [
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"\n",
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"import time\n",
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"\n",
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"device = torch.device(\"cuda\")\n",
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"\n",
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"x = torch.randn(10000, 10000).to(device)\n",
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"\n",
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"start = time.time()\n",
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"y = torch.mm(x, x)\n",
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"torch.cuda.synchronize()\n",
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"print(\"Time:\", time.time() - start)\n",
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"\n",
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"\n",
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"# watch GPU spike by \"watch -n 1 nvidia-smi\""
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],
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"id": "876b55fcfa5ae0f1",
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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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"Time: 0.3460721969604492\n"
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]
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}
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],
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"execution_count": 6
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": "",
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"id": "957ec4831c67d3c5"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-02-24T18:59:32.139923224Z",
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"start_time": "2026-02-24T18:59:31.949651746Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# Make sure we're using a NVIDIA GPU\n",
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"if torch.cuda.is_available():\n",
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" gpu_info = !nvidia-smi\n",
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" gpu_info = '\\n'.join(gpu_info)\n",
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" if gpu_info.find(\"failed\") >= 0:\n",
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" print(\"Not connected to a GPU, to leverage the best of PyTorch 2.0, you should connect to a GPU.\")\n",
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"\n",
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" # Get GPU name\n",
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" gpu_name = !nvidia-smi --query-gpu=gpu_name --format=csv\n",
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" gpu_name = gpu_name[1]\n",
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" GPU_NAME = gpu_name.replace(\" \", \"_\") # remove underscores for easier saving\n",
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" print(f'GPU name: {GPU_NAME}')\n",
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"\n",
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" # Get GPU capability score\n",
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" GPU_SCORE = torch.cuda.get_device_capability()\n",
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" print(f\"GPU capability score: {GPU_SCORE}\")\n",
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" if GPU_SCORE >= (8, 0):\n",
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" print(f\"GPU score higher than or equal to (8, 0), PyTorch 2.x speedup features available.\")\n",
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" else:\n",
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" print(f\"GPU score lower than (8, 0), PyTorch 2.x speedup features will be limited (PyTorch 2.x speedups happen most on newer GPUs).\")\n",
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"\n",
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" # Print GPU info\n",
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" print(f\"GPU information:\\n{gpu_info}\")\n",
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"\n",
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"else:\n",
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" print(\"PyTorch couldn't find a GPU, to leverage the best of PyTorch 2.0, you should connect to a GPU.\")"
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],
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"id": "820858027c722588",
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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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"GPU name: NVIDIA_GeForce_RTX_4050_Laptop_GPU\n",
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"GPU capability score: (8, 9)\n",
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"GPU score higher than or equal to (8, 0), PyTorch 2.x speedup features available.\n",
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"GPU information:\n",
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"Wed Feb 25 00:29:31 2026 \n",
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"+-----------------------------------------------------------------------------------------+\n",
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"| NVIDIA-SMI 590.48.01 Driver Version: 590.48.01 CUDA Version: 13.1 |\n",
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"+-----------------------------------------+------------------------+----------------------+\n",
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"| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
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"| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n",
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"| | | MIG M. |\n",
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"|=========================================+========================+======================|\n",
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"| 0 NVIDIA GeForce RTX 4050 ... Off | 00000000:01:00.0 Off | N/A |\n",
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"| N/A 47C P8 1W / 100W | 997MiB / 6141MiB | 0% Default |\n",
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"| | | N/A |\n",
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"+-----------------------------------------+------------------------+----------------------+\n",
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"\n",
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"+-----------------------------------------------------------------------------------------+\n",
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"| Processes: |\n",
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"| GPU GI CI PID Type Process name GPU Memory |\n",
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"| ID ID Usage |\n",
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"|=========================================================================================|\n",
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"| 0 N/A N/A 5445 C .../PytorchDemo/.venv/bin/python 890MiB |\n",
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"| 0 N/A N/A 5945 C .../PytorchDemo/.venv/bin/python 96MiB |\n",
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"+-----------------------------------------------------------------------------------------+\n"
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]
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}
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],
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"execution_count": 7
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-02-24T19:00:17.073846383Z",
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"start_time": "2026-02-24T19:00:17.050400781Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# Check available GPU memory and total GPU memory\n",
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"total_free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info()\n",
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"print(f\"Total free GPU memory: {round(total_free_gpu_memory * 1e-9, 3)} GB\")\n",
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"print(f\"Total GPU memory: {round(total_gpu_memory * 1e-9, 3)} GB\")"
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],
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"id": "ea0d6667eedccfd0",
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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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"Total free GPU memory: 5.009 GB\n",
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"Total GPU memory: 6.053 GB\n"
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]
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}
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],
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"execution_count": 8
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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",
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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": 2
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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": "ipython2",
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"version": "2.7.6"
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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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