Device & Platform Utilities#

用于检测和配置计算平台和设备的函数。

平台检测#

import genesis as gs

# Get platform before init
platform = gs.get_platform()
print(platform)  # "Linux", "macOS", or "Windows"

# After init, access global
gs.init()
print(gs.platform)  # Same result

设备信息#

import genesis as gs

gs.init(backend=gs.gpu)

# Get PyTorch device
device = gs.device
print(device)  # cuda:0, mps:0, cpu, etc.

# Get active backend
backend = gs.backend
print(backend)  # gs.cuda, gs.metal, gs.cpu, etc.

Backend 选择#

自动选择#

# GPU auto-selects based on platform
gs.init(backend=gs.gpu)
# Linux -> CUDA
# macOS -> Metal

手动选择#

# Force specific backend
gs.init(backend=gs.cuda)    # NVIDIA CUDA
gs.init(backend=gs.metal)   # Apple Metal
gs.init(backend=gs.cpu)     # CPU fallback

随机种子#

import genesis as gs

# Set random seed for reproducibility
gs.init(seed=42)

# Or set later
gs.set_random_seed(42)

全局变量#

gs.init() 后，以下变量可用：

Variable

Type

Description

gs.platform

str

平台: “Linux”, “macOS”, “Windows”

gs.device

torch.device

PyTorch 张量设备

gs.backend

gs.backend

活动计算后端

gs.EPS

float

数值 epsilon（例如，1e-15）

类型提示#

# Taichi types
gs.ti_float  # Taichi float type
gs.ti_int    # Taichi int type
gs.ti_vec3   # Taichi 3D vector
gs.ti_mat3   # Taichi 3x3 matrix

# PyTorch types
gs.tc_float  # PyTorch float dtype
gs.tc_int    # PyTorch int dtype

另请参阅#