{"library":"mlx","title":"MLX - Machine Learning for Apple Silicon","description":"MLX is an open-source machine learning framework optimized for Apple silicon, providing a familiar NumPy-like API for array operations and a PyTorch-like API for neural networks. It leverages Apple's Metal Performance Shaders (MPS) for high-performance computation. The library is under active and rapid development, with frequent patch and minor releases, currently at version 0.31.1.","language":"python","status":"active","last_verified":"Fri May 15","install":{"commands":["pip install mlx"],"cli":{"name":"mlx","version":"sh: 1: mlx: not found"}},"imports":["import mlx.core as mx","import mlx.nn as nn","import mlx.optimizers as optim"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import mlx.core as mx\n\n# Create a simple MLX array\na = mx.array([1.0, 2.0, 3.0])\nb = mx.array([4.0, 5.0, 6.0])\n\n# Perform an element-wise operation\nc = a + b\n\n# MLX uses lazy evaluation. To materialize the result, call .eval()\nprint(f\"Result before evaluation: {c}\")\nc.eval()\nprint(f\"Result after evaluation: {c}\")\n\n# Or convert to a standard Python type (which also triggers evaluation)\nprint(f\"Result as NumPy array: {c.numpy()}\")\nprint(f\"Result as Python list: {c.tolist()}\")","lang":"python","description":"This quickstart demonstrates creating MLX arrays and performing a basic element-wise operation. It highlights MLX's lazy evaluation model, where computations are built into a graph and executed only when explicitly requested via `.eval()` or conversion to a standard Python type.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":{"tag":null,"tag_description":null,"last_tested":"2026-05-15","installed_version":"0.29.3","pypi_latest":"0.31.2","is_stale":true,"summary":{"python_range":"3.10–3.9","success_rate":50,"avg_install_s":1.8,"avg_import_s":null,"wheel_type":"wheel"},"results":[{"runtime":"python:3.10-alpine","python_version":"3.10","os_libc":"alpine (musl)","variant":"mlx","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.10-slim","python_version":"3.10","os_libc":"slim (glibc)","variant":"mlx","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"broken","install_time_s":1.8,"import_time_s":null,"mem_mb":null,"disk_size":"21M"},{"runtime":"python:3.11-alpine","python_version":"3.11","os_libc":"alpine (musl)","variant":"mlx","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.11-slim","python_version":"3.11","os_libc":"slim (glibc)","variant":"mlx","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"broken","install_time_s":1.8,"import_time_s":null,"mem_mb":null,"disk_size":"23M"},{"runtime":"python:3.12-alpine","python_version":"3.12","os_libc":"alpine (musl)","variant":"mlx","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.12-slim","python_version":"3.12","os_libc":"slim (glibc)","variant":"mlx","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"broken","install_time_s":1.6,"import_time_s":null,"mem_mb":null,"disk_size":"15M"},{"runtime":"python:3.13-alpine","python_version":"3.13","os_libc":"alpine (musl)","variant":"mlx","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.13-slim","python_version":"3.13","os_libc":"slim (glibc)","variant":"mlx","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"broken","install_time_s":1.6,"import_time_s":null,"mem_mb":null,"disk_size":"14M"},{"runtime":"python:3.9-alpine","python_version":"3.9","os_libc":"alpine (musl)","variant":"mlx","exit_code":1,"wheel_type":null,"failure_reason":"build_error","import_side_effects":null,"install_time_s":null,"import_time_s":null,"mem_mb":null,"disk_size":null},{"runtime":"python:3.9-slim","python_version":"3.9","os_libc":"slim (glibc)","variant":"mlx","exit_code":0,"wheel_type":"wheel","failure_reason":null,"import_side_effects":"broken","install_time_s":2.1,"import_time_s":null,"mem_mb":null,"disk_size":"20M"}]}}