pytorch vs tensorflow
Two of the top projects in ML frameworks and libraries, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
pytorch
Python tensor library with GPU support and tape-based autograd for deep learning
tensorflow
Tensor and neural network framework with Python and C++ APIs
Verdict
pytorch ranks higher, 89 to 80. Of the 4 score parts we measure for both, pytorch leads on 3, tensorflow on 1.
- Adoptiontensorflow leads by 21 points, 99 to 78: a wide gap.
- Freshnesspytorch leads by 3 points, 100 to 97: close to even.
- Maintenancepytorch leads by 19 points, 89 to 70: a wide gap.
- Agent-readypytorch leads by 85 points, 85 to 0: a wide gap.
Pick pytorch if you are
ML researchers and engineers building and training neural networks in Python
- Dynamic graphs execute line by line, so debuggers and stack traces point at user code
- Backends for NVIDIA CUDA, AMD ROCm and Intel GPU, plus CPU-only builds
Pick tensorflow if you are
ML engineers and researchers building and deploying neural networks
- Stable Python and C++ APIs
- CUDA GPU support via pip on Ubuntu and Windows; separate tensorflow-cpu package
| What we compare | pytorch | tensorflow |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 78, popular | 99, widely used |
| Freshness | 100, active | 97, active |
| Maintenance | 89, healthy | 70, fair |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 85, ready | 0, none |
| Facts from GitHub and the README | ||
| Stars | 104.8k | 200.9k |
| License | custom license (read the license) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Mar 2026 |
| Language | Python | C++ |
| Docker | Yes | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Not stated | Not stated |
pytorch
PyTorch is a Python package that provides NumPy-like tensor computation on CPU or GPU, plus a reverse-mode autograd system for building neural networks imperatively. It ships torch.nn, torch.multiprocessing and a DataLoader, and can be extended in Python or C/C++. GPU backends covered in the README are NVIDIA CUDA, AMD ROCm (Linux only) and Intel GPU.
Who it is for: ML researchers and engineers building and training neural networks in Python
Strengths
- Dynamic graphs execute line by line, so debuggers and stack traces point at user code
- Backends for NVIDIA CUDA, AMD ROCm and Intel GPU, plus CPU-only builds
- Layers can be written in Python or via a C++ extension API
- Pre-built pip/conda binaries, Docker images and Jetson wheels are available
Weaknesses
- Building from source needs a C++20 compiler, 10 GB disk and 30-60 minutes
- ROCm support is Linux only; Windows distributed support is described as a prototype
- Source build with CUDA requires matching cuDNN 9.0+, NVTX and compiler versions
- Low-level framework only; no model serving or application layer included
- GPU optional
- Docker
- Needs Python 3.10+, C++20 compiler (source builds), CUDA/cuDNN, ROCm or Intel GPU stack (optional), Intel MKL
- Models: torch.nn modules, CUDA, ROCm, Intel XPU, CPU
tensorflow
TensorFlow is a machine learning framework for building, training and deploying models, with stable Python and C++ APIs and non-guaranteed APIs for other languages. It installs via pip, with CUDA GPU support on Ubuntu and Windows and a smaller CPU-only package. Related tooling includes TensorBoard and LiteRT for Android.
Who it is for: ML engineers and researchers building and deploying neural networks
Strengths
- Stable Python and C++ APIs
- CUDA GPU support via pip on Ubuntu and Windows; separate tensorflow-cpu package
- Device plugins for DirectX and macOS Metal
- Surrounding tooling: TensorBoard, LiteRT for Android, official models and tutorials
Weaknesses
- APIs for languages other than Python and C++ have no backward compatibility guarantee
- Built-in GPU support is CUDA only; other devices need separate plugins
- Linux XLA build artifacts listed as TBA
- RAM and VRAM requirements not stated in the README
- GPU optional
- Needs Python, CUDA (for GPU)
- Models: LiteRT (Android)
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