API Reference

This document provides detailed information about all available functions and classes in the AIVM client library.

Functions

Model Management

upload_model(model_path, model_name, model_type)

Uploads a custom model to the AIVM server.

Parameters:

• model_path (str): Path to the model file
• model_name (str): Name to identify the model
• model_type (ModelType): Type of the model (must be one of the supported types)

Example:

import aivm_client as aic

# Upload a custom model directly
aic.upload_model("path/to/model.pth", "MyCustomModel", aic.ModelType.LeNet5)

upload_bert_tiny_model(model_path, model_name)

Convenience function to upload a BertTiny model.

Parameters:

• model_path (str): Path to the model file
• model_name (str): Name to identify the model

Example:

import aivm_client as aic

# Upload a custom BertTiny model
aic.upload_bert_tiny_model("path/to/bert_model.pth", "MyBertModel")

upload_lenet5_model(model_path, model_name)

Convenience function to upload a LeNet5 model.

Parameters:

• model_path (str): Path to the model file
• model_name (str): Name to identify the model

Example:

import aivm_client as aic

# Upload a custom LeNet5 model
aic.upload_lenet5_model("path/to/lenet_model.pth", "MyLeNetModel")

Inference

get_prediction(inputs, model, model_type=None)

Performs secure inference using encrypted inputs.

Parameters:

• inputs (ArithmeticSharedTensor): Encrypted input tensor
• model (str): Name of the model to use
• model_type (ModelType, optional): Type of the model if using raw cryptensor

Returns:

• Prediction result from the model

Raises:

• ValueError: If inputs are not of a supported cryptensor type and no model_type is provided

Example:

import aivm_client as aic
import torch

# For LeNet5
input_tensor = torch.randn(1, 1, 28, 28)
encrypted_input = aic.LeNet5Cryptensor(input_tensor)
result = aic.get_prediction(encrypted_input, "LeNet5MNIST")

# For BertTiny
sentence = "Hello, this is a test message"
tokens = aic.tokenize(sentence)
encrypted_input = aic.BertTinyCryptensor(*tokens)
result = aic.get_prediction(encrypted_input, "BertTinySpam")

get_supported_models()

Retrieves a list of all models available for inference.

Returns:

• list: Names of supported models

Example:

import aivm_client as aic

# Get list of available models
models = aic.get_supported_models()
print(f"Available models: {models}")

Text Processing

tokenize(sentence)

Tokenizes input text for use with BertTiny models.

Parameters:

• sentence (str): Input text to tokenize

Returns:

• tuple: (input_ids, attention_mask) tensors

Example:

import aivm_client as aic

# Tokenize text for BertTiny
sentence = "This is a sample text for sentiment analysis"
tokens = aic.tokenize(sentence)
encrypted_input = aic.BertTinyCryptensor(*tokens)

Classes

BertTinyCryptensor

Specialized cryptensor for BertTiny model inputs.

Inherits from: ArithmeticSharedTensor

Parameters:

• *inputs (torch.Tensor): Two tensors (tokens and attention_mask), each of shape (1, 128)

Example:

import aivm_client as aic

# Create encrypted input for BertTiny
sentence = "Sample text for classification"
tokens = aic.tokenize(sentence)
encrypted_input = aic.BertTinyCryptensor(*tokens)

LeNet5Cryptensor

Specialized cryptensor for LeNet5 model inputs.

Inherits from: ArithmeticSharedTensor

Parameters:

• inputs (torch.Tensor): Input tensor for the LeNet5 model

Example:

import aivm_client as aic
import torch

# Create encrypted input for LeNet5
image = torch.randn(28, 28)  # Sample input image
encrypted_input = aic.LeNet5Cryptensor(image)

Complete Usage Example

Here's a complete example showing how to use the AIVM client for both BertTiny and LeNet5 models:

import aivm_client as aic
import torch

# List available models
models = aic.get_supported_models()
print(f"Available models: {models}")

# Example with LeNet5
image_input = torch.randn(28, 28)
encrypted_image = aic.LeNet5Cryptensor(image_input)
digit_prediction = aic.get_prediction(encrypted_image, "LeNet5MNIST")

# Example with BertTiny
text_input = "This is a sample message for spam detection"
tokens = aic.tokenize(text_input)
encrypted_text = aic.BertTinyCryptensor(*tokens)
spam_prediction = aic.get_prediction(encrypted_text, "BertTinySpam")

# Upload custom models
aic.upload_lenet5_model("path/to/custom_lenet.pth", "MyCustomLeNet")
aic.upload_bert_tiny_model("path/to/custom_bert.pth", "MyCustomBert")