Skip to main content

A python library to run metal compute kernels on macOS

Project description

metalcompute for Python

Build status

A python library to run metal compute kernels on macOS >= 11

Installations

Install latest stable release from PyPI:

> python3 -m pip install metalcompute

Install latest unstable version from Github:

> python3 -m pip install git+https://github.com/baldand/py-metal-compute.git

Install locally from source:

> python3 -m pip install .

Basic test

Example execution from M1-based Mac running macOS 12:

> python3 tests/basic.py
Calculating sin of 1234567 values
Expected value: 0.9805107116699219 Received value: 0.9807852506637573
Metal compute took: 0.0040209293365478516 s
Reference compute took: 0.1068720817565918 s

Interface

import metalcompute as mc

devices = mc.get_devices()
# Get list of available Metal devices

dev = mc.Device() 
# Call before use. Will open default Metal device
# or to pick a specific device:
# mc.Device(device_index)

program = """
#include <metal_stdlib>
using namespace metal;

kernel void test(const device float *in [[ buffer(0) ]],
                device float  *out [[ buffer(1) ]],
                uint id [[ thread_position_in_grid ]]) {
    out[id] = sin(in[id]);
}
"""
function_name = "test"

kernel_fn = dev.kernel(program).function(function_name)
# Will raise exception with details if metal kernel has errors

buf_0 = array('f',[1.0,3.14159]) # Any python buffer object
buf_n = dev.buffer(out_size) 
# Allocate metal buffers for input and output (must be compatible with kernel)
# Input buffers can be dev.buffer or python buffers (will be copied)
# Output buffers must be dev.buffer
# Buffer objects support python buffer protocol
# Can be modified or read using e.g. memoryview, numpy.frombuffer

kernel_fn(kernel_call_count, buf_0, ..., buf_n)
# Run the kernel once with supplied input data, 
# filling supplied output data
# Specify number of kernel calls
# Will block until data available

handle = kernel_fn(kernel_call_count, buf_0, ..., buf_n)
# Run the kernel once, 
# Specify number of kernel calls
# Supply all needed buffers
# Will return immediately, before kernel runs, 
# allowing additional kernels to be queued
# Do not modify or read buffers until kernel completed!

del handle
# Block until previously queued kernel has completed

Examples

Measure TFLOPS of GPU

> metalcompute-measure
Using device: Apple M1 (unified memory=True)
Running compute intensive Metal kernel to measure TFLOPS...
Estimated GPU TFLOPS: 2.53236
Running compute intensive Metal kernel to measure data transfer rate...
Data transfer rate: 58.7291 GB/s

Render a 3D image with raymarching

# Usage: metalcompute-raymarch [-width <width>] [-height <height>] [-outname <output image file: PNG, JPG>]

> metalcompute-raymarch.py -width 1024 -height 1024 -outname raymarch.jpg
Render took 0.0119569s

Raymarched spheres scene

Mandelbrot set

# Usage: metalcompute-mandelbrot [-width <width>] [-height <height>] [-outname <output image file: PNG, JPG>]

> metalcompute-mandelbrot
Rendering mandelbrot set using Metal compute, res:4096x4096, iters:8192
Render took 0.401446s
Writing image to mandelbrot.png
Image encoding took 1.35182s

Mandelbrot set

Livecoding visual kernels in VSCode

There is an example script to allow livecoding of visual metal kernels entirely within VSCode using a localhost http server to render frames.

It also includes syntax error highlighting in the editor.

See livemetal.py

Status

This is a preview version.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

metalcompute-0.2.5.tar.gz (16.5 kB view hashes)

Uploaded source

Built Distributions

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page