ollama - 💡(How to fix) Fix Start inferencing on CPU while the model weights are being uploaded to GPU VRAM [1 comments, 2 participants]

import torch
from torch import nn
from queue import Queue

# Initialize the model and device
model = nn.Module()  # Replace with your actual model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Create a queue to handle incoming requests
request_queue = Queue()

def load_model_to_vram(model):
    # Load the model to VRAM
    model.to(device)
    # Trigger CPU inference while loading
    cpu_inference(model)

def cpu_inference(model):
    # Perform inference on the CPU
    model.cpu()
    # Process requests from the queue
    while not request_queue.empty():
        request = request_queue.get()
        # Generate response using CPU inference
        response = model(request)
        # Add response to the queue
        request_queue.put(response)

def switch_to_vram_inference(model):
    # Switch to VRAM inference once the model is loaded
    model.to(device)
    # Process requests from the queue using VRAM inference
    while not request_queue.empty():
        request = request_queue.get()
        # Generate response using VRAM inference
        response = model(request)
        # Add response to the queue
        request_queue.put(response)

# Load the model to VRAM and trigger CPU inference
load_model_to_vram(model)

# Add requests to the queue
request_queue.put("Request 1")
request_queue.put("Request 2")

# Switch to VRAM inference once the model is loaded
switch_to_vram_inference(model)