Why PyTorch? Getting the Toolbox Ready

The 2025 Deep-Learning Landscape (and where you fit in

If you typed “deep-learning tutorial” into a search engine five years ago, you would have been overwhelmed by TensorFlow graphs, static compute diagrams, and the occasional Caffe layer-table. In 2025 the field has narrowed to three production-grade Python stacks:

Stack	Philosophy	Where it Shines
TensorFlow 2 / Keras	Graph execution with an imperative façade	Mobile-edge (TFLite), Google Cloud TPU
JAX / Flax	Functional, pure-Python, compilation-first	Research prototyping, massive TPU pods
PyTorch 2.7	Eager, pythonic, but with ahead-of-time `torch.compile` for speed	From Raspberry Pi hobby projects to billion-parameter LLMs

PyTorch dominates academic citations and GitHub stars because it feels like regular Python: you can print(), step through a debugger, or sprinkle breakpoints at will. The 2.x series added a compiler path that can fuse kernels into lightning-fast graphs—so you now get both flexibility and speed. The current stable release is 2.7.0 (April 23 2025). PyPI

What We’ll Build in This Series

Today you won’t train a model—you’ll build the launch pad:

A reproducible environment that works on Windows, macOS (Intel & Apple Silicon), and Linux.
A “hello-tensor” Jupyter notebook that prints “CUDA available!” when a GPU is detected (and politely falls back to CPU if not).
A template you can clone in every subsequent article so you focus on ideas, not installation.

By the end of the series you’ll have seven portfolio-ready projects—image classifiers, sentiment models, even a CartPole agent—but they all rely on the groundwork you lay today.

Dependency Management 101

Deep-learning libraries ship platform-specific wheels because they must bundle low-level CUDA or ROCm kernels. Putting them into your system’s global site-packages is a recipe for version hell. Two battle-tested isolation strategies are:

Conda – easiest when you need binary packages such as cudatoolkit or Intel MKL that are painful to compile.
Python venv + pip – lightweight, pure-Python, works anywhere you have a compiler (and is the de-facto standard inside Docker images and CI pipelines).

We’ll document both so readers can pick their poison.

Picking a Python & CUDA Combo

Python ≥ 3.10 is recommended (PyTorch supports 3.11 & 3.12 and officially drops < 3.9). PyTorch
CUDA: If your GPU is an NVIDIA card newer than 2016, CUDA 12.1 or 12.4 wheels are ideal. Otherwise install CPU-only wheels—identical APIs, just slower matrix math.

Tip: You do not need to pre-install NVIDIA’s 4 GB CUDA toolkit anymore; the PyTorch wheel bundles the runtime libraries.

One-Command Setup (Conda Route)

Create an environment.yml file at your project root:

yamlCopyEditname: pytorch-bootcamp
channels:
  - pytorch      # official nightly & stable binaries
  - nvidia       # for cudatoolkit >= 12
  - conda-forge
dependencies:
  - python=3.11
  - pytorch=2.7  # pulls correct CUDA or CPU build
  - torchvision
  - torchtext
  - torchmetrics
  - jupyterlab
  - ipywidgets   # nice for interactive plots
  - numpy pandas matplotlib
  - pip          # so you can `pip install` extras later

Then run:

bashCopyEditconda env create -f environment.yml
conda activate pytorch-bootcamp

Conda detects your GPU and chooses a pytorch-2.7.0-py311_cuda12*.tar.bz2 build automatically.

One-Command Setup (pip + venv Route)

bashCopyEditpython3 -m venv .venv
source .venv/bin/activate        # Windows: .venv\Scripts\activate
pip install --upgrade pip wheel

# GPU wheel (CUDA 12.1)
pip install torch torchvision torchtext --index-url https://download.pytorch.org/whl/cu121
# extras
pip install torchmetrics jupyterlab matplotlib pandas

Replace cu121 with cu124, rocm5.7, or cpu tags as needed (the PyTorch site has a picker that prints the exact command). PyTorch

Create a requirements.txt for teammates:

makefileCopyEdittorch==2.7.0+cu121
torchvision==0.20.0+cu121
torchtext==0.17.0
torchmetrics==1.4.0
jupyterlab==4.1.*
matplotlib>=3.9
pandas>=2.2

Run pip freeze > requirements.lock after installations that pin sub-dependencies for perfect reproducibility.

VS Code Touch-Ups

Open Folder → .vscode/settings.json jsoncCopyEdit{ "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python", "python.envFile": "${workspaceFolder}/.env", "jupyter.jupyterServerType": "local", "files.exclude": { "**/__pycache__": true } }
Install the Python and Jupyter extensions (they auto-detect Conda & venv).
Hit F5 inside a notebook cell for a real debugger—no more print() spelunking.

Reproducible Seeds = Scientific Karma

Nothing is more embarrassing than a tutorial whose accuracy graph changes every time a reader hits “Run”. Fix it once—stick it in utils.py forever:

pythonCopyEdit

import os, random, numpy as np, torch

def set_seed(seed=42):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    # deterministic mode (⛔ slower, but tutorial-friendly)
    torch.use_deterministic_algorithms(True)
    os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"

Call set_seed() at the top of every notebook and script you publish.

Your First Notebook: “hello-tensor.ipynb”

pythonCopyEditimport torch, platform

print(f"PyTorch {torch.__version__}  |  Python {platform.python_version()}")
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"✅ Using device: {device}")

x = torch.rand(3, 3, device=device)
y = torch.rand(3, 3, device=device)
print("Matrix sum =\n", x + y)

Expected output on a laptop with RTX 4060:

luaCopyEditPyTorch 2.7.0 + cu121 | Python 3.11.4
✅ Using device: cuda
Matrix sum =
 tensor([[1.1446, 0.9321, 1.1203],
         [0.7412, 0.9986, 1.6829],
         [0.5637, 1.7398, 0.8473]], device='cuda:0')

If you see device=cpu, either (a) you have no NVIDIA card, (b) the driver is too old, or (c) you installed the CPU wheel—still fine for our upcoming Fashion-MNIST and CIFAR-10 experiments, just a bit slower.

Wrapping Up & Next Steps

You now have:

A pytorch-bootcamp Conda environment or a .venv plus requirements.txt.
A VS Code workspace whose Run button actually stops at breakpoints inside model code.
A reproducible seed helper so your readers can compare graphs apples-to-apples.
A hello-tensor notebook proving that tensors run on whatever silicon lives in your machine.

Take a snapshot (git init, commit environment.yml, requirements.txt, and the notebook) so future articles don’t rewrite your base.

In the next article “Thinking in Tensors & Autograd” we’ll ditch high-level layers and hand-code a linear regressor from scratch. Keep your environment activated, stretch your fingers, and type jupyter lab—the fun stuff starts now.

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