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ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIClt2EFzdP8qOhRSR2xhLDwqaDdl7Hkwzbz4Yc7Mk4H3 fandi

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ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIJbbRYuF366f0XjqZ8KMJc1K3tQJZg6RVWoPaCFX4qas fandi@tigapilarmandiri.com

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source information https://medium.com/leadkaro/setting-up-pycuda-on-ubuntu-18-04-for-gpu-programming-with-python-830e03fc4b81

sudo apt-get install build-essential binutils gdb

sudo apt-get install freeglut3 freeglut3-dev libxi-dev libxmu-dev

sudo apt install nvidia-cuda-toolkit

sudo apt-get install build-essential python-dev python-setuptools libboost-python-dev libboost-thread-dev -y

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import random
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm, trange
from pathlib import Path
import cv2 as cv

def random_color_image(size=(600,800)):
    r = int(np.random.uniform(0,255))
    g = int(np.random.uniform(0,255))

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# build your model
class StandardMNIST(nn.Module):
    def __init__(self):
        super().__init__()
        # mnist images are (1, 28, 28) (channels, width, height)
        self.layer1 = torch.nn.Linear(28 * 28, 128)
        self.layer2 = torch.nn.Linear(128, 256)
        self.layer3 = torch.nn.Linear(256, 10)
    
    def forward(self, x):

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# build your model
class CustomMNIST(LightningModule):
    def __init__(self):
        super().__init__()
        # mnist images are (1, 28, 28) (channels, width, height)
        self.layer1 = torch.nn.Linear(28 * 28, 128)
        self.layer2 = torch.nn.Linear(128, 256)
        self.layer3 = torch.nn.Linear(256, 10)

    def forward(self, x):

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# import all you need
import os
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, random_split
from torchvision.datasets import MNIST
from torchvision import datasets, transforms
import pytorch_lightning as pl

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# build your model
class CustomMNIST(LightningModule):
    def __init__(self):
        super().__init__()
        # mnist images are (1, 28, 28) (channels, width, height)
        self.layer1 = torch.nn.Linear(28 * 28, 128)
        self.layer2 = torch.nn.Linear(128, 256)
        self.layer3 = torch.nn.Linear(256, 10)

    def forward(self, x):

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class StandardMNIST(LightningModule):
    def __init__(self):
        super().__init__()
        # mnist images are (1, 28, 28) (channels, width, height)
        self.layer1 = torch.nn.Linear(28 * 28, 128)
        self.layer2 = torch.nn.Linear(128, 256)
        self.layer3 = torch.nn.Linear(256, 10)

    def forward(self, x):
        batch_size, channels, width, height = x.size()

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# Looping Over Collection Forward
colors = ["red", "green", "blue"]

# bad
for i in range(len(colors)):
    print(colors[i])
    
# good
for color in colors:
    print(color)