The code is adapted from https://github.com/Anthony-Tatowicz/docker-ethminer.
Make sure you have 1) Docker and 2) Nvidia runtime installed then you are good to go.
| π Morning 227 commits βββββββββββββββββββββ 28.6% | |
| π Daytime 372 commits βββββββββββββββββββββ 46.9% | |
| π Evening 123 commits βββββββββββββββββββββ 15.5% | |
| π Night 72 commits βββββββββββββββββββββ 9.1% |
The code is adapted from https://github.com/Anthony-Tatowicz/docker-ethminer.
Make sure you have 1) Docker and 2) Nvidia runtime installed then you are good to go.
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torchaudio.functional import lfilter as torch_lfilter | |
| from torch.autograd import Function, gradcheck | |
| class lfilter(Function): | |
| @staticmethod |
| import numpy as np | |
| import networkx as nx | |
| from scipy.spatial import Delaunay | |
| def W(x): | |
| return (x + np.pi) % (2 * np.pi) - np.pi | |
| def mcf_sparse(x, y, psi, capacity=None): | |
| points = np.vstack((x, y)).T | |
| num_points = points.shape[0] |
| import numpy as np | |
| import networkx as nx | |
| def W(x): | |
| return (x + np.pi) % (2 * np.pi) - np.pi | |
| def mcf(x: np.ndarray, capacity=None): | |
| assert x.ndim == 2, "Input x should be a 2d array!" | |
| # construct index for each node |