The Best PyTorch Alternatives in 2026

Deep learning in Rust, with shape checked tensors and neural networks.

DyNet provides tools for building dynamic neural networks and deep learning models.

einops provides readable tensor operations for PyTorch, JAX, TensorFlow, and other frameworks.

DEAP provides a Python framework for prototyping evolutionary computation algorithms and testing new ideas.

Hyperopt supports distributed asynchronous hyperparameter optimization in Python.

Sinabs supports fast training of spiking neural networks with PyTorch and inference on neuromorphic hardware.

PyTorch Ignite provides a high level library for flexibly training and evaluating neural networks.

Catniff provides JavaScript developers with a Torch-like deep learning framework.

Kubeflow provides a machine learning toolkit for deploying workflows on Kubernetes.

Build machine learning applications with Delta, an open-source framework implemented in Rust.

MLOps framework to package, deploy, monitor and manage thousands of production machine learning models.

Caffe provides an open framework for building and running deep learning models.

The cONNXr project provides a pure C runtime for ONNX models intended for embedded devices.

The Deep Diamond library provides fast tensor and neural network computations in Clojure using native CPU and GPU routines.

Infer.NET runs Bayesian inference in graphical models for a range of machine learning problems.

Espresso AI uses machine learning to optimize Snowflake and Databricks data warehouses and reduce query and infrastructure costs.

Cortex provides cloud infrastructure for deploying, managing, and scaling machine learning models in production.

Mosec serves machine learning models with dynamic batching and CPU or GPU pipelines for inference workloads.

MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.

The colibri-core NLP tool and library works with n-grams, skipgrams, and other linguistic patterns.

Gymnasium provides a standard API for single agent reinforcement learning environments and related utilities.

CLIP-as-service provides scalable image and sentence embeddings, reasoning, and ranking with CLIP.

Tensorforce provides a TensorFlow library for applied reinforcement learning tasks.

Extensible Toolkit for Finetuning and Inference of Large Foundation Models.