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doc:beam/b26fe48b-ffb9-4219-a7c2-c1ab2278f503outputs = model(inputs) loss = criterion(outputs, targets) loss.backward() optimizer.step() print(f'Epoch [{epoch+1}/10], Loss: {loss.item()}') ``` ### Key Improvements 1. **Data Encryption**: - Implemented a method…
doc:beam/726b2023-3e14-4535-b1b0-ff2ac58bf4c5key = Fernet.generate_key() cipher_suite = Fernet(key) # Define a custom dataset class for our queries class QueryDataset(Dataset): def __init__(self, queries, labels): self.queries = queries self.labels = labels d…
doc:beam/460f970b-e5a9-4221-a69b-6362a6c74450return iv + encrypted_data def generate_key(): # Generate a 256-bit (32-byte) key. return os.urandom(32) # Generate a secure key for AES-256 key = generate_key() # Sample data to encrypt data = b'Hello, World!' # Encrypt the…
doc:beam/901c90f7-6b68-4e8f-bbc8-2d84f939b1f4) key = kdf.derive(password) iv = os.urandom(16) # 128 bits return key, iv # Encrypt data def encrypt_data(key: bytes, iv: bytes, data: bytes) -> bytes: cipher = Cipher(algorithms.AES(key), modes.CBC(iv), backend=defau…
doc:beam/31eb4071-2157-4298-9c43-525858c96bd2# Encrypt the data encryptor = cipher.encryptor() padder = padding.PKCS7(128).padder() padded_data = padder.update(data) + padder.finalize() encrypted_data = encryptor.update(padded_data) + encryptor.finalize() retu…
doc:beam/d71b63cf-6419-4ee8-9e80-10f81d233677return os.urandom(24) # Example usage key = generate_key() store_key(key) data = b"This is some sensitive metadata" encrypted_data = encrypt_data(key, data) print(f"Encrypted data: {encrypted_data.hex()}") decrypted_data = decrypt_da…
doc:beam/75f888ef-9c4b-4ebe-8d95-cab5cf884c4c[Turn 9457] Assistant: Certainly! To refine your code for secure and efficient model fine-tuning, we need to focus on several key areas: 1. **Data Encryption**: Ensure that data is encrypted both in transit and at rest. 2. **Efficient Data…
doc:beam/5337c991-73b0-4e6e-ab32-bb1cc2d8b450with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor: future = executor.submit(train_model, X, y) result = future.result() end_time = time.time() latency = end_time - start_time print(f'…
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