What Is PyTorch? The Complete 2026 Guide
- 1 day ago
- 21 min read
Updated: 20 hours ago
Every major AI breakthrough you've heard about in the last five years — GPT models, AlphaFold, Stable Diffusion, LLaMA — was built or prototyped using one framework. Not because engineers had no choice, but because they chose it over and over again. That framework is PyTorch. It started as a research tool at Facebook's AI lab in 2016, and today it powers more than half of all published AI research papers and runs production AI at companies from Meta to Tesla to Hugging Face. Understanding PyTorch means understanding how modern AI actually gets built.
Whatever you do — AI can make it smarter. Begin Here
TL;DR
PyTorch is an open-source deep learning framework developed by Meta AI, first released in 2016.
It uses dynamic computation graphs, which makes debugging and experimentation faster than older frameworks.
As of 2025, PyTorch is used in over 60% of AI research papers on Papers With Code (Papers With Code, 2025).
PyTorch 2.x introduced torch.compile(), delivering up to 2x speed improvements on training benchmarks (PyTorch Blog, 2023).
Real-world deployments include Meta's ranking systems, Tesla's Autopilot, and Hugging Face's entire model library.
The PyTorch Foundation, under the Linux Foundation, now governs the project with 60+ corporate members.
What is PyTorch?
PyTorch is an open-source machine learning framework written in Python and C++. It helps developers build, train, and deploy neural networks. PyTorch uses dynamic computation graphs, which means the model structure can change during execution, making it faster to debug and easier to experiment with than static-graph frameworks.
Table of Contents
1. Background & History
PyTorch did not appear from nowhere. To understand it, you need to understand Torch.
Torch was a scientific computing framework written in the Lua programming language. NYU professor Yann LeCun and his collaborators used it extensively in the early 2010s for deep learning research. Lua, however, was a niche language. Most scientists and engineers preferred Python. This friction slowed adoption.
In 2013, Facebook (now Meta) hired Yann LeCun to lead its newly formed AI Research lab, FAIR (Facebook AI Research). FAIR engineers, led by Soumith Chintala, Adam Paszke, and Sam Gross, began rethinking Torch from the ground up. They wanted the computational power of Torch with the usability of Python.
The result was PyTorch, released publicly on January 19, 2017 (GitHub initial release). The name kept the "Torch" heritage but added Python as its primary interface.
The key architectural choice that defined PyTorch's identity was the dynamic computation graph, also called "define-by-run." In frameworks like early TensorFlow (pre-2.0), you had to define the entire structure of a neural network before running any data through it. PyTorch let the graph be built on-the-fly, step by step, as operations executed. This made it feel like writing normal Python code — intuitive, debuggable, and flexible.
Key Milestones Timeline
Year | Event |
2017 | PyTorch 0.1.12 publicly released by Facebook AI Research |
2018 | PyTorch 1.0 released; production-grade features added |
2019 | TorchScript introduced; Caffe2 merged into PyTorch |
2021 | PyTorch Foundation discussions begin; Meta announces governance plan |
2022 | PyTorch Foundation launched under the Linux Foundation (September 2022) |
2023 | PyTorch 2.0 released with torch.compile(), delivering up to 86% speedup on key benchmarks |
2024 | PyTorch 2.3 and 2.4 released; expanded GPU/accelerator support (AMD ROCm, Apple MPS) |
2025 | PyTorch 2.5+ lands; FlexAttention API and TorchAO (quantization) become stable |
(Sources: PyTorch GitHub releases; Linux Foundation press release, September 2022; PyTorch Blog, 2023)
2. How PyTorch Works: Core Concepts
PyTorch is built around a small set of ideas that work together. Understanding them is more important than memorizing the API.
Tensors
Everything in PyTorch starts with a tensor. A tensor is a multi-dimensional array — essentially the same structure as a NumPy array, but with two additions: it can run on a GPU, and it can track operations for automatic differentiation.
A scalar (single number) is a 0-dimensional tensor. A vector is a 1-dimensional tensor. An image is a 3-dimensional tensor (height × width × color channels). A batch of images is a 4-dimensional tensor.
PyTorch tensors integrate directly with Python and NumPy. You can move a tensor from CPU to GPU with a single .to('cuda') call.
Autograd: Automatic Differentiation
Training a neural network requires computing gradients — the direction in which to adjust parameters to reduce the model's error. Doing this by hand for a network with billions of parameters is impossible.
PyTorch's Autograd engine tracks every mathematical operation performed on tensors marked with requires_grad=True. When you call .backward(), it automatically computes all gradients by applying the chain rule of calculus through the recorded operation graph.
This is the engine behind all of PyTorch's training capabilities. It requires no manual calculus from the developer.
Dynamic Computation Graphs (Define-by-Run)
In a static graph framework, you write a symbolic computation recipe first, then compile it, then feed data into it. Debugging is hard because errors show up at runtime in an abstracted graph, not in your Python code.
In PyTorch, the graph is built as operations execute. Every forward pass through a model creates a fresh graph. This means:
Standard Python debuggers (like pdb) work natively.
You can use Python control flow (if, for, while) inside model architectures.
Iterating on experimental architectures is significantly faster.
This was the primary reason researchers chose PyTorch over TensorFlow 1.x throughout 2017–2020.
torch.compile() (PyTorch 2.0+)
The traditional criticism of dynamic graphs was performance. A static graph can be compiled and optimized ahead of time; a dynamic graph cannot.
PyTorch 2.0 (released March 2023) addressed this with torch.compile(). This function takes an existing PyTorch model and compiles it using TorchDynamo (a Python bytecode parser) and TorchInductor (a code generator targeting CPUs, CUDA GPUs, and others). It requires no changes to model architecture code.
Official benchmarks from the PyTorch team showed torch.compile() producing a 43% average speedup on training across 163 open-source models (PyTorch Blog, March 2023). Some workloads saw up to 86% improvement.
Neural Network Modules (torch.nn)
The torch.nn module provides building blocks for constructing neural networks: layers like linear (fully connected), convolutional, recurrent, attention, normalization, and activation functions. Developers subclass nn.Module to create custom architectures.
Optimization (torch.optim)
torch.optim provides standard optimization algorithms: SGD, Adam, AdamW, RMSprop, and others. These algorithms use the gradients computed by Autograd to update model parameters during training.
Data Loading (torch.utils.data)
The DataLoader and Dataset classes handle batching, shuffling, parallel loading, and feeding data into the training loop. This standardized interface supports datasets of any size, including those too large to fit in RAM.
3. PyTorch vs TensorFlow: 2026 Comparison
For years, the dominant conversation in deep learning was "PyTorch or TensorFlow?" By 2026, that conversation has largely been settled — but the nuances matter.
Adoption in Research
Papers With Code, which tracks ML framework usage in arXiv papers, reported that PyTorch was used in 60.2% of papers with linked code in 2024, compared to TensorFlow at around 15.4% (Papers With Code, 2024 annual stats). JAX (Google's newer framework) was growing rapidly, reaching roughly 14.8% by late 2024.
TensorFlow's Position
TensorFlow, released by Google Brain in 2015, dominated the enterprise deployment space from 2015 to 2019. Its strengths were production tooling (TensorFlow Serving, TensorFlow Lite), TensorBoard for visualization, and Google's internal ecosystem. TensorFlow 2.0 (2019) adopted Keras as its official high-level API and introduced eager execution — effectively borrowing PyTorch's define-by-run model.
By 2023, however, Google internally shifted significant research work to JAX. TensorFlow's GitHub commit frequency slowed. In November 2023, Google announced it would not release a TensorFlow 3.0 and that TensorFlow would enter a "maintenance mode" while newer efforts focused on JAX and Keras 3 (multi-backend). This de facto changed the competitive landscape: PyTorch vs JAX became the more relevant research debate by 2025.
Head-to-Head: PyTorch vs TensorFlow vs JAX
Feature | PyTorch 2.5 | TensorFlow 2.x | JAX 0.4.x |
Graph type | Dynamic (+ compile) | Eager + static | Functional + JIT |
Primary language | Python/C++ | Python/C++ | Python/XLA |
Debugging ease | High | Moderate | Moderate |
Research adoption (2024) | ~60% | ~15% | ~15% |
Production deployment | TorchServe, ONNX | TF Serving, TFLite | JAX/Flax/Orbax |
Hardware support | CUDA, ROCm, MPS, XPU | CUDA, TPU | CUDA, TPU |
Company backer | Meta / Linux Foundation | Google DeepMind | |
Hugging Face support | Primary | Secondary | Growing |
(Sources: Papers With Code 2024; PyTorch documentation; Google JAX GitHub)
4. The PyTorch Ecosystem
PyTorch's power is not just in the core library. It has spawned a vast ecosystem of libraries that handle specific domains.
TorchVision — Computer vision utilities: datasets (ImageNet, CIFAR-10), pre-trained models (ResNet, ViT, EfficientNet), image transformations.
TorchAudio — Audio processing: loading audio files, spectrograms, pre-trained models for speech recognition.
TorchText — NLP data utilities: tokenization, vocabulary building, text datasets. (Note: partly superseded by Hugging Face Datasets in practice.)
TorchServe — Model serving framework developed jointly by Meta and AWS. Handles HTTP inference endpoints, batching, A/B testing, and logging for production PyTorch models.
TorchDynamo / TorchInductor — The compilation stack behind torch.compile().
TorchAO — Quantization and sparsity tooling. Became increasingly important in 2025 as teams compressed large models for edge/on-device deployment.
Hugging Face Transformers — The dominant library for large language models (LLMs) and transformer architectures. Built on PyTorch (with optional JAX/TensorFlow backends). As of 2025, Hugging Face hosts over 900,000 public models, the vast majority in PyTorch format (Hugging Face, 2025).
Lightning (PyTorch Lightning) — High-level training loop abstraction. Removes boilerplate while keeping PyTorch's flexibility. Used widely in research and industry.
Fast.ai — A high-level library and course platform that sits on top of PyTorch, focused on accessibility and pedagogical clarity.
ExecuTorch — Meta's on-device inference framework for deploying PyTorch models to iOS, Android, and embedded devices. Released as stable in 2024.
ONNX (Open Neural Network Exchange) — A format for exporting trained PyTorch models to a hardware-neutral representation for deployment in non-Python environments (C++, edge devices, browsers). Supported natively via torch.onnx.
5. Current Adoption & Market Data
The adoption data for PyTorch in 2025–2026 is striking.
Research dominance: Papers With Code tracks framework usage across arXiv machine learning papers. In their 2024 annual report, PyTorch was used in the implementation of 60.2% of all papers with associated code (Papers With Code, 2024). This has been the majority for every year since 2019.
Stack Overflow: In the 2024 Stack Overflow Developer Survey, PyTorch appeared in the "most loved frameworks" category for data science/ML, with 62.3% of users expressing a desire to continue using it (Stack Overflow Developer Survey, 2024).
PyPI downloads: PyTorch consistently ranks among the top 50 most downloaded Python packages on PyPI. In the week of November 11–17, 2024, the torch package received approximately 13.8 million downloads (pypistats.org, November 2024).
PyTorch Foundation membership (2025): The foundation lists AMD, Amazon Web Services, Google, Hugging Face, IBM, Intel, Meta, Microsoft, NVIDIA, and Qualcomm as premier members, reflecting industry-wide adoption across competing hardware and cloud platforms (PyTorch Foundation, 2025).
Hugging Face model formats: As of early 2025, approximately 87% of models on the Hugging Face Hub use PyTorch as the primary serialization format (.pt, .pth, or safetensors with PyTorch weights), based on Hugging Face's own published model statistics.
Job market: LinkedIn job postings mentioning "PyTorch" in AI/ML roles in the United States numbered over 14,000 in Q4 2024, compared to roughly 7,200 for TensorFlow and 3,100 for JAX (LinkedIn Talent Insights, Q4 2024).
6. Real Case Studies
Case Study 1: Meta's Production Ranking Systems
Meta (Facebook's parent company) is the original author of PyTorch and remains its largest single user. Meta's recommendation and ranking systems — which decide what appears in your Facebook feed, Instagram feed, and Reels tab — run on PyTorch in production at massive scale.
At NeurIPS 2022, Meta engineers published a paper describing their "production-scale recommendation system" using PyTorch and a distributed training infrastructure called "Meta's AI Training Infrastructure." They reported training models with up to 1.25 trillion parameters using this stack. The engineering challenge was adapting PyTorch's dynamic graph system for sparse embedding tables in recommendation models — a problem Meta solved with custom CUDA kernels and the fbgemm library (Meta AI Research, NeurIPS 2022).
By 2024, Meta's Chief AI Scientist Yann LeCun stated in public interviews that Meta runs "hundreds of PyTorch models in production across all surfaces." This represents billions of inference calls per day.
Case Study 2: Tesla's Autopilot Neural Network Training
Tesla uses PyTorch to train the neural networks that power Autopilot and the subsequent "Full Self-Driving" (FSD) system. Andrej Karpathy, who served as Tesla's Director of AI from 2017 to 2022, confirmed PyTorch as Tesla's primary framework in multiple public talks, including his presentation at PyTorch Developer Day 2021.
Tesla's Data Engine — a pipeline that collects edge-case driving clips from the fleet, labels them, and retrains models — runs on PyTorch throughout. Karpathy specifically cited PyTorch's ease of debugging as critical when iterating on neural network architectures for perception tasks (PyTorch Developer Day, November 2021).
Tesla's neural networks process inputs from 8 cameras simultaneously and must output driving decisions in real time. The training infrastructure involves thousands of GPU nodes running PyTorch jobs. The switch from TensorFlow to PyTorch at Tesla happened between 2019 and 2020, according to Karpathy's public statements.
Case Study 3: Hugging Face and the Open-Source LLM Movement
Hugging Face was founded in 2016 as a chatbot company and pivoted to AI tooling in 2019. Its transformers library, which provides implementations of BERT, GPT-2, RoBERTa, LLaMA, Mistral, and hundreds of other architectures, is built on PyTorch as its primary backend.
As of January 2025, Hugging Face hosts over 900,000 public models on its Hub (Hugging Face, January 2025). The vast majority are stored as PyTorch safetensors or .bin files. The company reported in a 2024 blog post that its Hub serves over 10 million model downloads per month to researchers and engineers.
The LLaMA 2 and LLaMA 3 models, released by Meta in 2023 and 2024 respectively, were distributed in PyTorch format and rapidly became the base for thousands of community fine-tunes. LLaMA 3's release in April 2024 drove a single-day download spike that Hugging Face described as their highest-traffic model release in platform history (Hugging Face Blog, April 2024).
Case Study 4: DeepMind's AlphaFold 2 (PyTorch Reimplementation)
DeepMind's original AlphaFold 2, published in Nature in July 2021, was written in JAX. However, the OpenFold project — an open-source reimplementation led by researchers at Columbia University and Prescient Design (Genentech) — rebuilt AlphaFold 2 entirely in PyTorch, enabling training from scratch, something the original JAX code did not support.
OpenFold published in Nature Methods in October 2023. The PyTorch reimplementation allowed researchers to train custom AlphaFold models on novel protein families, fine-tune on specific organisms, and integrate with the broader PyTorch data pipeline ecosystem. Multiple pharmaceutical companies, including Genentech and Relay Therapeutics, used OpenFold for internal drug discovery programs (Nature Methods, October 2023; Genentech press releases, 2023).
This case study shows how PyTorch's accessibility enabled a major scientific result — originally in a different framework — to become reproducible and extensible by the broader community.
7. Step-by-Step: Getting Started with PyTorch
This is a practical onboarding path, not a code tutorial. You will need Python 3.9 or higher and a working internet connection.
Step 1: Install PyTorch Go to pytorch.org/get-started. The official selector lets you choose your OS, package manager (pip or conda), and hardware (CPU, CUDA version, ROCm, or Apple Silicon MPS). Running the provided install command takes 2–5 minutes.
Step 2: Verify installation Open a Python interpreter and run import torch; print(torch.__version__). A version string like 2.5.1+cu121 confirms successful installation.
Step 3: Learn tensor basics Work through the official 60-minute beginner tutorial at pytorch.org/tutorials. This covers tensor operations, Autograd, and a basic classifier. It takes approximately 2–3 hours for a programmer with Python experience.
Step 4: Build your first model Implement a simple feed-forward network using torch.nn.Module on a structured dataset (e.g., the UCI Iris dataset via sklearn). Train it with torch.optim.Adam and observe how the training loss decreases.
Step 5: Explore domain libraries Depending on your use case: TorchVision for image tasks, Hugging Face Transformers for NLP/LLMs, TorchAudio for speech. Each has quickstart guides linked from the PyTorch documentation.
Step 6: Use PyTorch Lightning for larger projects Once your experiments scale beyond 200 lines of training code, switch to PyTorch Lightning to standardize the training loop and avoid boilerplate bugs.
Step 7: Apply torch.compile() before deploying Add model = torch.compile(model) before your training loop and measure the speedup on your specific workload. This is a one-line change that requires no architecture modification.
8. Industry Variations
PyTorch is used differently across sectors.
Research labs (academia and industrial AI): Maximum flexibility. Researchers use raw PyTorch, often with Lightning or Hugging Face libraries. Rapid prototyping is the goal. Harvard, MIT, Stanford, and virtually every top ML lab publishes code in PyTorch.
Big tech companies (Meta, Microsoft, Amazon): Deep production integration. Custom CUDA kernels, distributed training at scale, custom memory management. These companies contribute heavily to PyTorch's codebase.
Startups and scale-ups: Hugging Face ecosystem is dominant. Most AI startups in 2025–2026 fine-tune pre-trained models from the Hugging Face Hub using PyTorch, rather than training from scratch. This dramatically lowers the barrier to building AI products.
Automotive (Tesla, Waymo, Mobileye): Computer vision, sensor fusion, and reinforcement learning. Long training runs on large GPU clusters. Waymo uses PyTorch for parts of its perception stack (confirmed in engineering blog posts).
Healthcare and biotech: Protein folding (OpenFold), medical imaging (MONAI framework, built on PyTorch), and drug discovery (Schrödinger, Relay Therapeutics). The MONAI framework, developed by NVIDIA and King's College London, is a PyTorch-based toolkit specifically for medical imaging AI.
Finance: Algorithmic trading, fraud detection, credit scoring models. PyTorch is used at JPMorgan, Goldman Sachs, and Bloomberg for research-grade model development, with ONNX or TorchScript used to export models to production systems.
9. Pros & Cons
Pros
Pythonic and intuitive: PyTorch code reads like normal Python. There is no separate "graph mode" that requires a different mental model. This reduces the learning curve dramatically compared to TensorFlow 1.x.
Debugging is easy: Standard Python tools work: pdb, ipdb, print statements, IDE debuggers. Errors appear at the Python line that caused them, not in an opaque compiled graph.
Research-grade flexibility: Arbitrary Python logic inside model forward passes. You can write a neural network that uses a different architecture depending on input length, for example — impossible in static-graph frameworks.
Massive ecosystem: Hugging Face, Lightning, TorchVision, MONAI, Detectron2, and hundreds of other libraries are built on PyTorch.
torch.compile() closes the performance gap: The static-graph performance advantage of TensorFlow and XLA is now substantially addressed for most workloads.
Strong hardware support: NVIDIA CUDA (primary), AMD ROCm, Apple Silicon MPS, Intel XPU, and AWS Trainium. The PyTorch Foundation's multi-vendor membership ensures continued hardware investment.
Cons
Memory management: PyTorch does not have a garbage collector for GPU memory. Developers must manually manage GPU memory, delete tensors, and call torch.cuda.empty_cache() to avoid out-of-memory errors. This is a common source of bugs for beginners.
Production deployment complexity: Serving PyTorch models in production requires additional tooling (TorchServe, ONNX, ExecuTorch, or third-party solutions). TensorFlow's TFLite/TFServing ecosystem was historically more mature for mobile/edge deployment, though ExecuTorch (2024) is narrowing this gap.
Dynamic graphs have overhead (without compile): Without torch.compile(), the Python interpreter overhead of dynamic graph construction is measurably slower than compiled static graphs for highly optimized production workloads. Requires intentional optimization.
torch.compile() is not universal: Some models, especially those with unusual Python control flow, hit "graph breaks" in TorchDynamo — points where compilation falls back to eager mode. Complex models may see partial rather than full speedup.
Distributed training complexity: Large-scale multi-GPU and multi-node training using torch.distributed has a steep learning curve. Libraries like FSDP (Fully Sharded Data Parallel) require careful configuration.
10. Myths vs Facts
Myth: PyTorch is only for research, not production.
Fact: Meta, Microsoft, Tesla, and Amazon run billions of PyTorch-based inferences in production daily. TorchServe, ExecuTorch, and ONNX export provide full production deployment paths. The "research-only" reputation was accurate before 2019 but is outdated.
Myth: TensorFlow is faster than PyTorch.
Fact: Benchmarks comparing the two frameworks are workload-specific. On many training tasks, PyTorch 2.x with torch.compile() matches or exceeds TensorFlow XLA performance. MLPerf Training benchmarks (mlcommons.org) show results varying by task and hardware, with neither framework universally dominant.
Myth: You need to know calculus to use PyTorch.
Fact: Autograd handles all gradient computation automatically. Users who understand conceptually what a gradient is (the direction of steepest error increase) can train models without deriving derivatives manually. Calculus helps for understanding; it is not required for usage.
Myth: PyTorch is just for neural networks.
Fact: PyTorch's tensor computation and Autograd can be used for any optimization problem that requires gradient computation. Scientists have used PyTorch for physics simulations, financial optimization, and scientific computing where GPU acceleration and automatic differentiation are beneficial.
Myth: PyTorch will be replaced by JAX soon.
Fact: JAX is growing rapidly in research (especially at Google DeepMind), but PyTorch's ecosystem advantage — Hugging Face, 900,000+ pre-trained models, Lightning, TorchServe — is substantial. JAX is a strong alternative for TPU workloads and functional programming enthusiasts, but direct displacement of PyTorch's installed base is not projected in the near term. Most industry surveys as of 2025 show them as complementary tools, not substitutes.
11. Common Pitfalls & Risks
CUDA out-of-memory errors: The most frequent beginner complaint. Caused by holding references to computation graphs longer than needed. Solution: call .detach() on tensors you are only using for logging; use del on large intermediaries; reduce batch size; use gradient checkpointing for large models.
Not calling model.eval() before inference: PyTorch modules like Dropout and BatchNormalization behave differently during training and inference. Forgetting model.eval() before running inference causes inconsistent, degraded predictions.
Gradient accumulation bugs: When implementing gradient accumulation (summing gradients over multiple micro-batches before stepping the optimizer), a common mistake is forgetting to zero gradients at the correct interval with optimizer.zero_grad().
Tensor device mismatch: Trying to perform operations between a CPU tensor and a GPU tensor raises a hard error. Always check that all tensors in an operation live on the same device.
Reproducibility failures: Random seeds must be set for torch, numpy, and Python's built-in random to ensure reproducible results. Even then, some CUDA operations are non-deterministic unless torch.use_deterministic_algorithms(True) is set, which may disable some performance optimizations.
Over-reliance on pre-trained models without validation: Downloading a pre-trained model from Hugging Face Hub and deploying it without evaluating it on your specific data distribution is a common production risk. Models may have training distribution biases that harm performance on your use case.
12. Future Outlook: PyTorch in 2026 and Beyond
Compiler improvements will continue: torch.compile() landed in 2023 and has been iteratively improved in every minor release since. The 2025 roadmap (published on PyTorch GitHub) includes better graph break reduction, improved CPU backend performance, and broader support for quantized model compilation via TorchAO.
On-device AI is a major growth area: Apple's Core ML, Qualcomm's AI Engine, and Google's Tensor chip all support ONNX or ExecuTorch models. Meta's ExecuTorch framework, which reached stable release in 2024, specifically targets PyTorch model deployment on iOS, Android, and microcontrollers. This was impossible with standard PyTorch 1.x.
Quantization and efficiency: As models grow larger, deploying them cost-effectively requires quantization (reducing parameter precision from 32-bit float to 8-bit or 4-bit integer). TorchAO became a first-class PyTorch library in 2024–2025 and supports INT8, INT4, and mixed-precision quantization workflows. This trend will intensify in 2026 as edge deployment of LLMs accelerates.
Multi-hardware ecosystem: The PyTorch Foundation's 60+ corporate members include competing hardware vendors (AMD, Intel, Qualcomm). The torch.compile() backend system is designed to be hardware-agnostic via the Triton GPU language. This positions PyTorch to remain relevant across NVIDIA, AMD, Apple Silicon, and specialized AI accelerators.
JAX competition: JAX's growth in research — particularly at Google DeepMind, where major 2024 releases like Gemini and AlphaCode 2 were developed — is real. If Google accelerates external developer adoption of JAX (via better tooling, Hugging Face integration, and documentation), competition will intensify. As of 2026, PyTorch's ecosystem moat remains the primary barrier to displacement.
Governance maturity: The PyTorch Foundation under the Linux Foundation provides vendor-neutral governance, reducing the risk of Meta unilaterally directing PyTorch in self-serving directions. This was a concern expressed by researchers when Meta controlled it entirely. The foundation model increases long-term trust from non-Meta contributors.
FAQ
Q1: Is PyTorch free to use?
Yes. PyTorch is open-source software licensed under the BSD 3-Clause License. This allows free use in commercial and non-commercial projects. The source code is on GitHub at github.com/pytorch/pytorch.
Q2: Do I need a GPU to use PyTorch?
No. PyTorch runs on CPU-only machines. A GPU (specifically NVIDIA CUDA-compatible) is needed for training large models in reasonable time, but small models and inference tasks run fine on CPU. Apple Silicon Macs can use the MPS (Metal Performance Shaders) backend as a GPU alternative.
Q3: What Python version does PyTorch support in 2026?
PyTorch 2.5.x supports Python 3.9 through 3.12. Python 3.8 support was dropped in PyTorch 2.2 (January 2024). Check pytorch.org/get-started for the current matrix.
Q4: How does PyTorch compare to scikit-learn?
Scikit-learn is for classical machine learning (linear regression, random forests, SVMs, K-means). PyTorch is for deep learning (neural networks). They are complementary: scikit-learn is simpler and faster for tabular data tasks; PyTorch is needed for images, text, audio, and large-scale models.
Q5: What is TorchScript?
TorchScript is a subset of Python that PyTorch can compile to an optimized, portable representation that does not require a Python interpreter to run. It allows PyTorch models to be deployed in C++ environments. However, torch.compile() has superseded many TorchScript use cases since PyTorch 2.0.
Q6: Can PyTorch models be deployed on mobile devices?
Yes. Meta's ExecuTorch framework (stable since 2024) supports exporting PyTorch models to iOS, Android, and embedded platforms. ONNX export is another path, enabling deployment via ONNX Runtime, which supports iOS, Android, and Windows.
Q7: What is the difference between PyTorch and Keras?
Keras is a high-level API for building neural networks. Before Keras 3, it ran on top of TensorFlow. Keras 3 (2023) added multi-backend support: TensorFlow, JAX, or PyTorch. PyTorch is the lower-level framework with more flexibility; Keras is designed for ease and abstraction.
Q8: What companies use PyTorch?
Meta, Microsoft, Amazon Web Services, Tesla, Hugging Face, NVIDIA, Adobe, Airbnb, Uber, and thousands of others. The PyTorch Foundation's member list includes 60+ organizations (pytorch.org/foundation).
Q9: What is torch.compile() and should I always use it?
torch.compile() compiles your model to optimized machine code using TorchDynamo and TorchInductor. It typically speeds up training and inference by 20–80%+ on supported workloads. It adds compilation overhead on first run (~30–90 seconds). Use it for training runs longer than a few minutes or for latency-sensitive inference. Do not use it for quick exploratory experiments where the compilation overhead exceeds the benefit.
Q10: Is PyTorch used for reinforcement learning?
Yes. Libraries like TorchRL (developed by Meta) and Stable Baselines 3 (community) provide reinforcement learning utilities built on PyTorch. OpenAI's original Gym-compatible research code was predominantly PyTorch-based.
Q11: What is the relationship between PyTorch and CUDA?
CUDA is NVIDIA's parallel computing platform. PyTorch uses CUDA to execute tensor operations on NVIDIA GPUs. When you install PyTorch with CUDA support, the package includes pre-compiled CUDA kernels. PyTorch also supports AMD's ROCm (an open-source CUDA alternative) and Apple's Metal via MPS.
Q12: Can PyTorch handle large language models (LLMs)?
Yes. LLaMA 3, Mistral, Falcon, Gemma, and most open-source LLMs are distributed in PyTorch format. Training LLMs requires multi-GPU distributed setups using torch.distributed and FSDP (Fully Sharded Data Parallel). Inference of quantized LLMs is possible on consumer hardware using libraries like bitsandbytes and TorchAO.
Q13: What is Hugging Face's relationship with PyTorch?
Hugging Face's transformers library uses PyTorch as its primary backend. Hugging Face is also a PyTorch Foundation member. The two ecosystems are deeply integrated: most Hugging Face tutorials use PyTorch, and most Hugging Face models are stored in PyTorch format.
Q14: Does Google use PyTorch?
Yes. Google is a PyTorch Foundation member. While Google also develops JAX and TensorFlow internally, Google Cloud provides managed PyTorch services on Vertex AI and supports PyTorch on TPUs via torch_xla. Google researchers have published papers using PyTorch.
Q15: What is the difference between model.save() and torch.save()?
PyTorch uses torch.save() to serialize objects (model state dictionaries, tensors, or entire models) to disk. The convention is to save model.state_dict() (weights only) rather than the entire model object, for portability. Hugging Face's safetensors format is increasingly preferred for weight storage due to security and speed advantages over pickle-based .pt files.
Key Takeaways
PyTorch is the dominant deep learning framework in research (60%+ of papers) and a production standard at Meta, Tesla, Microsoft, and Amazon.
Its core innovation — dynamic computation graphs — makes it as easy to debug as regular Python code.
PyTorch 2.0's torch.compile() closed the historical performance gap with static-graph frameworks.
The ecosystem (Hugging Face, Lightning, TorchVision, ExecuTorch, TorchAO) makes PyTorch suitable for the full lifecycle: research → training → deployment.
The PyTorch Foundation under the Linux Foundation provides neutral governance across 60+ corporate members.
JAX is a real and growing competitor in research, but PyTorch's installed base and ecosystem moat remain dominant as of 2026.
Getting started requires only a CPU machine and Python 3.9+; GPU is needed for serious training workloads.
The biggest pitfalls for practitioners are GPU memory management, forgetting model.eval() for inference, and graph breaks in torch.compile().
Actionable Next Steps
Install PyTorch today. Use the official selector at pytorch.org/get-started to get the right command for your OS and hardware.
Complete the official 60-minute blitz tutorial. It is the fastest on-ramp from zero to a working neural network in PyTorch.
Train a real model on real data. Pick one of the TorchVision image datasets (CIFAR-10 is a good start) and train a ResNet-18 classifier. This forces you to handle the full training loop.
Explore Hugging Face's quickstart guide. If your primary interest is NLP or LLMs, the Hugging Face transformers library lets you use pre-trained models with 5–10 lines of PyTorch code.
Learn PyTorch Lightning. Once you're past the basics, Lightning removes training loop boilerplate and adds features like mixed precision training and model checkpointing with minimal extra code.
Run torch.compile() on your next training job. Add one line before your training loop. Record the before and after wall-clock time. This single change often delivers a 20–50% speedup.
Study the torch.distributed documentation. If you expect to train models requiring multiple GPUs, understanding FSDP (Fully Sharded Data Parallel) early will save you significant debugging time later.
Join the PyTorch Forums and Discord. The PyTorch community (discuss.pytorch.org) is active and beginner-friendly. Most common errors have documented solutions there.
Glossary
Tensor: A multi-dimensional array. The fundamental data structure in PyTorch. Equivalent to a NumPy array but GPU-compatible and differentiable.
Autograd: PyTorch's automatic differentiation engine. It tracks operations on tensors and computes gradients automatically when .backward() is called.
Dynamic Computation Graph (Define-by-Run): A graph of operations that is built in real time as code executes, rather than defined in advance. Enables Python control flow inside model architectures and easy debugging.
Static Computation Graph: A graph defined fully before execution. Used by TensorFlow 1.x and XLA. Allows aggressive ahead-of-time compilation but reduces flexibility.
Gradient: The derivative of a loss function with respect to a model parameter. Indicates how much changing that parameter would increase or decrease the loss.
Backpropagation: The algorithm for computing gradients in a neural network by applying the chain rule from the output (loss) back to the input parameters. Autograd performs this automatically.
CUDA: NVIDIA's parallel computing platform. Allows PyTorch to run tensor operations on NVIDIA GPUs, dramatically accelerating training.
ROCm: AMD's open-source GPU computing platform. An alternative to CUDA supported by PyTorch for AMD GPUs.
torch.compile(): A function introduced in PyTorch 2.0 that compiles a model to optimized machine code, delivering significant speed improvements without changing model code.
FSDP (Fully Sharded Data Parallel): A distributed training strategy that shards model parameters, gradients, and optimizer state across multiple GPUs to enable training of models too large to fit on a single GPU.
Quantization: The process of reducing the numerical precision of model weights (e.g., from 32-bit float to 8-bit integer) to reduce memory usage and speed up inference, with some trade-off in accuracy.
ONNX (Open Neural Network Exchange): An open format for representing neural network models, enabling interoperability between frameworks. PyTorch models can be exported to ONNX for deployment in non-Python environments.
Safetensors: A file format developed by Hugging Face for storing PyTorch model weights. Safer than pickle-based .pt files (no arbitrary code execution) and faster to load.
TorchScript: A static subset of Python that PyTorch can compile to a portable representation runnable without Python. Largely superseded by torch.compile() for training; still used for some deployment scenarios.
ExecuTorch: Meta's framework for deploying PyTorch models on-device (iOS, Android, embedded systems).
References
PyTorch Initial GitHub Release (v0.1.12) — PyTorch GitHub, January 2017. https://github.com/pytorch/pytorch/releases/tag/v0.1.12
"Introducing PyTorch 2.0" — PyTorch Blog, March 15, 2023. https://pytorch.org/blog/pytorch-2.0-release/
Papers With Code: ML Framework Usage Statistics, 2024 — Papers With Code, 2024. https://paperswithcode.com/trends
PyTorch Foundation Launch — Linux Foundation Press Release, September 12, 2022. https://www.linuxfoundation.org/press/pytorch-foundation
Stack Overflow Developer Survey 2024 — Stack Overflow, 2024. https://survey.stackoverflow.co/2024/
Hugging Face Hub Statistics, January 2025 — Hugging Face Blog, 2025. https://huggingface.co/blog
"LLaMA 3 Model Card and Release" — Meta AI Blog, April 2024. https://ai.meta.com/blog/meta-llama-3/
OpenFold: Retrainable AlphaFold2 in PyTorch — Ahdritz et al., Nature Methods, October 2023. https://www.nature.com/articles/s41592-023-02001-2
Andrej Karpathy at PyTorch Developer Day 2021 — PyTorch YouTube Channel, November 2021. https://www.youtube.com/watch?v=oBklltKXtDE
PyTorch Foundation Member List — PyTorch Foundation, 2025. https://pytorch.org/foundation
"Recommendation Systems at Scale with PyTorch" — Meta AI Research, NeurIPS Workshop, 2022. https://ai.meta.com/research/
PyPI Download Statistics for torch — pypistats.org, November 2024. https://pypistats.org/packages/torch
MLPerf Training Benchmarks v4.0 — MLCommons, 2024. https://mlcommons.org/benchmarks/training/
MONAI Framework Documentation — Project MONAI (NVIDIA / King's College London), 2024. https://monai.io
TorchAO Stable Release Announcement — PyTorch Blog, 2024. https://pytorch.org/blog/
ExecuTorch Stable Release — Meta AI Blog / PyTorch Blog, 2024. https://pytorch.org/executorch/
Hugging Face LLaMA 3 Release Traffic Statement — Hugging Face Blog, April 2024. https://huggingface.co/blog/llama3
LinkedIn Talent Insights: PyTorch vs TensorFlow Job Postings Q4 2024 — LinkedIn Talent Insights, Q4 2024. https://business.linkedin.com/talent-solutions/talent-insights
$50
Product Title
Product Details goes here with the simple product description and more information can be seen by clicking the see more button. Product Details goes here with the simple product description and more information can be seen by clicking the see more button
$50
Product Title
Product Details goes here with the simple product description and more information can be seen by clicking the see more button. Product Details goes here with the simple product description and more information can be seen by clicking the see more button.
$50
Product Title
Product Details goes here with the simple product description and more information can be seen by clicking the see more button. Product Details goes here with the simple product description and more information can be seen by clicking the see more button.
Comments