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Usage

Quick Start Guide
Lazy Evaluation
Unified Memory
Indexing Arrays
Saving and Loading Arrays
Function Transforms
Compilation
Conversion to NumPy and Other Frameworks
Distributed Communication
Using Streams
Exporting Functions

Examples

Linear Regression
Multi-Layer Perceptron
LLM inference

Python API Reference

Array
Data Types
Devices and Streams
Export Functions
Operations
Random
Transforms
Fast
FFT
Linear Algebra
Metal
CUDA
- mlx.core.cuda.is_available
Memory Management
Neural Networks
Optimizers
Distributed Communication
Tree Utils

C++ API Reference

Operations

Further Reading

Custom Extensions in MLX
Metal Debugger
Custom Metal Kernels
Using MLX in C++

.rst

CUDA

CUDA#

is_available()

Check if the CUDA back-end is available.

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By MLX Contributors

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