ARB Security Solutions, LLC.

Multiple GPU Checkpoint Pre SPO Upload

Import Dat Torch!

import os
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
from import DataLoader
from shareplum import Site
from shareplum import Office365

# SharePoint Online credentials and site information
username = 'your_username'
password = 'your_password'
site_url = 'https://your_sharepoint_site_url'
document_library = 'Documents'

# Custom dataset
class CustomDataset(
def __init__(self, data_dir): = [...] # Your custom data loading logic goes here

def __getitem__(self, index):
# Your data retrieval logic goes here
return data

def __len__(self):
return len(

# Custom model
class CustomModel(nn.Module):
def __init__(self):
super(CustomModel, self).__init__()
# Your model architecture goes here

def forward(self, x):
# Your forward pass logic goes here
return output

# Training function
def train_model(model, train_loader, optimizer, criterion, device):
for data, target in train_loader:
data, target =,
output = model(data)
loss = criterion(output, target)

# Main script
if __name__ == '__main__':
# SharePoint Online connection
with Office365(site_url, username=username, password=password) as auth:
site = Site(site_url, auth=auth)
folder = site.Folder(f'{document_library}/DeepLearning')

# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Custom dataset
dataset = CustomDataset(data_dir='path_to_data')
train_loader = DataLoader(dataset, batch_size=64, shuffle=True)

# Custom model
model = CustomModel().to(device)

# Training parameters
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Training loop
for epoch in range(10):
train_model(model, train_loader, optimizer, criterion, device)

# Save model checkpoint
checkpoint_filename = f'checkpoint_epoch_{epoch}.pt', checkpoint_filename)
with open(checkpoint_filename, 'rb') as checkpoint_file:
folder.upload_file(checkpoint_file, os.path.basename(checkpoint_filename))

# Save the trained model
model_filename = '', model_filename)
with open(model_filename, 'rb') as model_file:
folder.upload_file(model_file, os.path.basename(model_filename))

print("Model and checkpoints uploaded to SharePoint Online.")

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