The Best dfdx Alternatives in 2026

Burn provides a flexible, efficient, and portable tensor library for building deep learning applications.

Tensors and Dynamic neural networks in Python with strong GPU acceleration.

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

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

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

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

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

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

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

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.

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

oneDNN provides a library of optimized deep neural network operations for oneAPI.

The Darknet neural network framework is written in C and CUDA and supports CPU and GPU computation.

Candle provides a minimalist Rust framework for machine learning model development and execution tasks.

N2D2 provides an open source CAD framework for deep neural network simulation and application building.

This repository contains machine checkable security models for Clawdbot, including regression cases checked with TLC.

List of awesome compiler projects and papers for tensor computation and deep learning.

dstack provides an open framework for confidential AI computing and deployments.

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

Build neural networks and support machine learning research with Sonnet, a library based on TensorFlow.

Fiber presents a simple approach to distributed computing for AI workloads.

Rockpool supports training spiking neural networks with PyTorch and JAX and deploying them to neuromorphic hardware.