TensorShareClient

The TensorShareClient class is a core component of this project, enabling users to send tensors to a remote server using asynchronous HTTP requests.

It's built on top of the aiohttp library, allowing asynchronous HTTP communication and enhancing the efficiency and speed of tensor sharing.

This class extends the TensorShare ecosystem by providing both synchronous and asynchronous interfaces for sending tensors, ensuring flexibility and adaptability to various use cases and environments.

Note

While it's inherently asynchronous (thanks to the `aiohttp`` library), the class provides a synchronous interface for users who prefer it, especially for testing and non-production use.

However, due to potential performance implications, using the synchronous method in production environments isn't recommended.

Pre-requisites

This module requires the aiohttp library to be installed:

pip install tensorshare[client]

Initialization

To create an instance of TensorShareClient, users need to pass in the server configuration, a timeout duration (defaulted to 10 seconds), and optionally decide if they want to validate server endpoints at initialization.

from tensorshare import TensorShareServer, TensorShareClient

server_config = TensorShareServer.from_dict(
    server_config={"url": "http://localhost:5000"}
)
client = TensorShareClient(server_config, timeout=10, validate_endpoints=True)

Parameters

• server_config (TensorShareServer): The configuration for the remote server.
• timeout (int): Specifies the maximum duration (in seconds) to wait for an HTTP response. Defaults to 10 seconds.
• validate_endpoints (bool): Determines whether the client should validate the server's endpoints upon initialization. Defaults to True (recommended).

Methods

The methods are available in both synchronous and asynchronous forms, with the latter being prefixed with async_. The asynchronous methods are recommended for production use, as they are non-blocking and more efficient.

Note

The synchronous methods use the asynchronous ones under the hood wrapped in an asyncio.run call,

ping_server

The ping_server method sends a GET request to the server's /ping endpoint, which returns a DefaultResponse object with a status_code of 200 if the server is up and running.

SyncAsync

from tensorshare import TensorShareServer, TensorShareClient

server_config = TensorShareServer.from_dict(
    server_config={"url": "http://localhost:5000"}
)
client = TensorShareClient(server_config)

r = client.ping_server()
print(r)
>>> True

import asyncio
from tensorshare import TensorShareServer, TensorShareClient

server_config = TensorShareServer.from_dict(
    server_config={"url": "http://localhost:5000"}
)
client = TensorShareClient(server_config)

async def main():
    r = await client.async_ping_server()
    print(r)

asyncio.run(main())
>>> True

send_tensor

The send_tensor method sends a tensor to the server's /receive_tensor endpoint, which returns the response_model defined in the server's configuration.

SyncAsync

import torch
from tensorshare import TensorShare, TensorShareServer, TensorShareClient

server_config = TensorShareServer.from_dict(
    server_config={"url": "http://localhost:5000"}
)
client = TensorShareClient(server_config)

ts = TensorShare.from_dict({"embeddings": torch.rand(10, 10)})

r = client.send_tensor(ts)

import asyncio
import torch
from tensorshare import TensorShare, TensorShareServer, TensorShareClient

server_config = TensorShareServer.from_dict(
    server_config={"url": "http://localhost:5000"}
)
client = TensorShareClient(server_config)

ts = TensorShare.from_dict({"embeddings": torch.rand(10, 10)})

async def main():
    r = await client.async_send_tensor(ts)

asyncio.run(main())

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Last update: 2023-08-20
Created: 2023-08-20