Develop Local GenAI LLM Application with OpenVINO

 

intel OpenVino framework

OpenVINO can help accelerate the processing of your local LLM (Large Language Model) application generation in several ways.

OpenVINO can significantly aid in developing LLM and Generative AI applications on a local system like a laptop by providing optimized performance and efficient resource usage. Here are some key benefits:

1. Optimized Performance: OpenVINO optimizes models for Intel hardware, improving inference speed and efficiency, which is crucial for running complex LLM and Generative AI models on a laptop.

2. Hardware Acceleration: It leverages CPU, GPU, and other accelerators available on Intel platforms, making the most out of your laptop's hardware capabilities.

3. Ease of Integration: OpenVINO supports popular deep learning frameworks like TensorFlow, PyTorch, and ONNX, allowing seamless integration and conversion of pre-trained models into the OpenVINO format.

4. Edge Deployment: It is designed for edge deployment, making it suitable for running AI applications locally without relying on cloud infrastructure, thus reducing latency and dependency on internet connectivity.

5. Model Optimization: The Model Optimizer in OpenVINO helps in transforming and optimizing pre-trained models into an Intermediate Representation (IR) that can be efficiently executed by the Inference Engine.

6. Pre-trained Models: OpenVINO provides a model zoo with pre-trained models, including those for natural language processing and computer vision, which can be fine-tuned for specific applications.

By using OpenVINO, you can develop and run LLM and Generative AI applications efficiently on your laptop, making it feasible to prototype and experiment with AI models locally.

Optimized Inference: OpenVINO provides an optimized inference engine that can take advantage of various hardware platforms, including CPUs, GPUs, and VPUs. This optimization can lead to faster processing times for your LLM application.

Model Optimization: OpenVINO includes tools to optimize your LLM model for better performance, such as model quantization, pruning, and knowledge distillation. These optimizations can reduce the computational requirements of your model, leading to faster processing times.

Hardware Acceleration: OpenVINO supports various hardware accelerators, including Intel's Deep Learning Boost (DL Boost) and OpenVINO's own hardware accelerator, the Intel Neural Stick. These accelerators can significantly speed up the processing of your LLM application.

Parallel Processing: OpenVINO allows you to take advantage of multi-core processors and parallel processing, which can significantly speed up the processing of your LLM application.

Streamlined Processing: OpenVINO provides a streamlined processing pipeline that can help reduce overhead and improve overall processing efficiency.

To leverage OpenVINO for faster LLM application generation, you can:

Use OpenVINO's Model Optimizer: Optimize your LLM model using OpenVINO's Model Optimizer tool.

Integrate OpenVINO's Inference Engine: Integrate OpenVINO's Inference Engine into your application to take advantage of optimized inference.

Utilize Hardware Accelerators: Use hardware accelerators like Intel's DL Boost or the Intel Neural Stick to accelerate processing.

Parallelize Processing: Use OpenVINO's parallel processing capabilities to take advantage of multi-core processors.

By applying these techniques, you can significantly accelerate the processing of your local LLM application generation using OpenVINO.

OpenVINO is not exclusive to Intel processors, but it's optimized for Intel hardware. You can install OpenVINO on non-Intel processors, including AMD and ARM-based systems. However, the level of optimization and support may vary.

Initially, OpenVINO was designed to take advantage of Intel's hardware features, such as:

Intel CPUs: OpenVINO is optimized for Intel Core and Xeon processors.

Intel Integrated Graphics: OpenVINO supports Intel Integrated Graphics, including Iris and UHD Graphics.

Intel Neural Stick: OpenVINO is optimized for the Intel Neural Stick, a USB-based deep learning accelerator.

However, OpenVINO can still be installed and run on non-Intel processors, including:

AMD CPUs: You can install OpenVINO on AMD-based systems, but you might not get the same level of optimization as on Intel CPUs.

ARM-based systems: OpenVINO can be installed on ARM-based systems, such as those using Raspberry Pi or other ARM-based CPUs.

NVIDIA GPUs: Although OpenVINO is not specifically optimized for NVIDIA GPUs, you can still use OpenVINO on systems with NVIDIA GPUs. However, you might need to use the NVIDIA CUDA toolkit and cuDNN library to leverage GPU acceleration.

To install OpenVINO on a non-Intel processor, ensure you meet the system requirements and follow the installation instructions for your specific platform. You might need to use a compatible backend, such as OpenCV or TensorFlow, to leverage OpenVINO's capabilities.

Keep in mind that while OpenVINO can run on non-Intel processors, the performance and optimization level might vary. If you're unsure about compatibility or performance, you can consult the OpenVINO documentation or seek support from the OpenVINO community.

OpenVINO and CUDA serve similar purposes but are tailored to different hardware platforms and have distinct features:

OpenVINO

1. Target Hardware: Primarily optimized for Intel hardware, including CPUs, integrated GPUs, VPUs (Vision Processing Units), and FPGAs (Field Programmable Gate Arrays).

2. Optimization: Focuses on optimizing inference performance across a wide range of Intel architectures.

3. Ease of Use: Provides easy model conversion from popular deep learning frameworks like TensorFlow, PyTorch, and ONNX.

4. Flexibility: Supports heterogeneous execution, allowing models to run across multiple types of Intel hardware simultaneously.

5. Pre-trained Models: Offers a model zoo with pre-trained models that can be fine-tuned and deployed easily.

6. Edge Deployment: Designed with edge AI applications in mind, making it suitable for running AI workloads on local devices without relying on cloud resources.

CUDA

1. Target Hardware: Optimized for NVIDIA GPUs, including desktop, laptop, server, and specialized AI hardware like the Jetson series.

2. Performance: Leverages the parallel processing capabilities of NVIDIA GPUs to accelerate computation-heavy tasks, including deep learning training and inference.

3. Programming Flexibility: Provides a comprehensive parallel computing platform and programming model that developers can use to write highly optimized code for NVIDIA GPUs.

4. Deep Learning Frameworks: Strong integration with deep learning frameworks like TensorFlow, PyTorch, and MXNet, often with specific GPU optimizations.

5. Training and Inference: Widely used for both training and inference of deep learning models, offering high performance and scalability.

6. Community and Ecosystem: A large developer community and extensive ecosystem of libraries and tools designed to work with CUDA.

Key Differences

1. Hardware Dependency: OpenVINO is tailored for Intel hardware however it can run other CPU as well as I described in details above, while CUDA is specific to NVIDIA GPUs.

2. Optimization Goals: OpenVINO focuses on inference optimization, especially for edge devices, whereas CUDA excels in both training and inference, primarily in environments with NVIDIA GPUs.

3. Deployment: OpenVINO is well-suited for local and edge deployment on a variety of Intel devices, while CUDA is best utilized where high-performance NVIDIA GPUs are available, typically in data centers or high-performance computing setups.

In summary, OpenVINO is ideal for optimizing AI workloads on Intel-based systems, especially for inference on local and edge devices. CUDA, on the other hand, is optimized for high-performance AI tasks on NVIDIA GPUs, suitable for both training and inference in environments where NVIDIA hardware is available.

More details and how to install you can find here

Leveraging CUDA for General Parallel Processing Application

 

Photo by SevenStorm JUHASZIMRUS by pexel

Differences Between CPU-based Multi-threading and Multi-processing

CPU-based Multi-threading:

- Concept: Uses multiple threads within a single process.

- Shared Memory: Threads share the same memory space.

- I/O Bound Tasks: Effective for tasks that spend a lot of time waiting for I/O operations.

- Global Interpreter Lock (GIL): In Python, the GIL can be a limiting factor for CPU-bound tasks since it allows only one thread to execute Python bytecode at a time.

CPU-based Multi-processing:

- Concept: Uses multiple processes, each with its own memory space.

- Separate Memory: Processes do not share memory, leading to more isolation.

- CPU Bound Tasks: Effective for tasks that require significant CPU computation since each process can run on a different CPU core.

- No GIL: Each process has its own Python interpreter and memory space, so the GIL is not an issue.

CUDA with PyTorch:

- Concept: Utilizes the GPU for parallel computation.

- Massive Parallelism: GPUs are designed to handle thousands of threads simultaneously.

- Suitable Tasks: Highly effective for tasks that can be parallelized at a fine-grained level (e.g., matrix operations, deep learning).

- Memory Management: Requires explicit memory management between CPU and GPU.

Here's an example of parallel processing in Python using the concurrent.futures library, which uses CPU:

Python

import concurrent.futures

def some_function(x):

    # Your function here

    return x * x

with concurrent.futures.ProcessPoolExecutor() as executor:

    inputs = [1, 2, 3, 4, 5]

    results = list(executor.map(some_function, inputs))

    print(results)

And here's an example of parallel processing in PyTorch using CUDA:

Python

import torch

def some_function(x):

    # Your function here

    return x * x

inputs = torch.tensor([1, 2, 3, 4, 5]).cuda()

results = torch.zeros_like(inputs)

with torch.no_grad():

    outputs = torch.map(some_function, inputs)

    results.copy_(outputs)

print(results)

Note that in the PyTorch example, we need to move the inputs to the GPU using the .cuda() method, and also create a torch.zeros_like() tensor to store the results. The torch.map() function is used to apply the function to each element of the input tensor in parallel.

Also, you need to make sure that your function some_function is compatible with PyTorch's tensor operations.

You can also use torch.nn.DataParallel to parallelize your model across multiple GPUs.

Python

model = MyModel()

model = torch.nn.DataParallel(model)

Please let me know if you need more information or help with converting your specific code to use CUDA with PyTorch.

Example: Solving a Linear Equation in Parallel

Using Python's `ProcessPoolExecutor`

Here, we solve multiple instances of a simple linear equation `ax + b = 0` in parallel.

```python

import concurrent.futures

import time

def solve_linear_equation(params):

    a, b = params

    time.sleep(1)  # Simulate a time-consuming task

    return -b / a

equations = [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]

start_time = time.time()

# Using ProcessPoolExecutor for parallel processing

with concurrent.futures.ProcessPoolExecutor() as executor:

    results = list(executor.map(solve_linear_equation, equations))

print("Results:", results)

print("Time taken:", time.time() - start_time)

```

Using CUDA with PyTorch

Now, let's perform the same task using CUDA with PyTorch.

```python

import torch

import time

# Check if CUDA is available

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Coefficients for the linear equations

a = torch.tensor([1, 2, 3, 4, 5], device=device, dtype=torch.float32)

b = torch.tensor([2, 3, 4, 5, 6], device=device, dtype=torch.float32)

start_time = time.time()

# Solving the linear equations ax + b = 0 -> x = -b / a

results = -b / a

print("Results:", results.cpu().numpy())  # Move results back to CPU and convert to numpy array

print("Time taken:", time.time() - start_time)

```

Transitioning to CUDA with PyTorch

Current Python Parallel Processing with `ProcessPoolExecutor` or `ThreadPoolExecutor`

Here's an example of parallel processing with `ProcessPoolExecutor`:

```python

import concurrent.futures

def compute(task):

    # Placeholder for a task that takes time

    return task ** 2

tasks = [1, 2, 3, 4, 5]

with concurrent.futures.ProcessPoolExecutor() as executor:

    results = list(executor.map(compute, tasks))

```

Converting to CUDA with PyTorch

1. Identify the Parallelizable Task:

   - Determine which part of the task can benefit from GPU acceleration.

2. Transfer Data to GPU:

   - Move the necessary data to the GPU.

3. Perform GPU Computation:

   - Use PyTorch operations to leverage CUDA.

4. Transfer Results Back to CPU:

   - Move the results back to the CPU if needed.

Example:

```python

import torch

def compute_on_gpu(tasks):

    # Move tasks to GPU

    tasks_tensor = torch.tensor(tasks, device=torch.device("cuda"), dtype=torch.float32)

    # Perform computation on GPU

    results_tensor = tasks_tensor ** 2

    # Move results back to CPU

    return results_tensor.cpu().numpy()

tasks = [1, 2, 3, 4, 5]

results = compute_on_gpu(tasks)

print("Results:", results)

```

CPU-based Multi-threading vs. Parallel Processing with Multi-processing Multi-threading:

Multiple threads share the same memory space and resources Threads are lightweight and fast to create/switch between Suitable for I/O-bound tasks, such as web scraping or database queries

Python's Global Interpreter Lock (GIL) limits true parallelism 

Multi-processing: Multiple processes have separate memory spaces and resources

Processes are heavier and slower to create/switch between Suitable for CPU-bound tasks, such as scientific computing or data processing 

True parallelism is achieved, but with higher overhead

Parallel Processing with CUDA PyTorch

CUDA PyTorch uses the GPU to parallelize computations. Here's an example of parallelizing a linear equation:

y = w * x + b

x is the input tensor (e.g., 1000x1000 matrix)

w is the weight tensor (e.g., 1000x1000 matrix)

b is the bias tensor (e.g., 1000x1 vector)

In CUDA PyTorch, we can parallelize the computation across the GPU's cores:

Python

import torch

x = torch.randn(1000, 1000).cuda()

w = torch.randn(1000, 1000).cuda()

b = torch.randn(1000, 1).cuda()

y = torch.matmul(w, x) + b

This will parallelize the matrix multiplication and addition across the GPU's cores.

Fitting Python's ProcessPoolExecutor or ThreadPoolExecutor to CUDA PyTorch

To parallelize existing Python code using ProcessPoolExecutor or ThreadPoolExecutor with CUDA PyTorch, you can:

Identify the computationally intensive parts of your code. Convert those parts to use PyTorch tensors and operations. Move the tensors to the GPU using .cuda()

Use CUDA PyTorch's parallelization features (e.g., torch.matmul(), torch.sum(), etc.)

For example, if you have a Python function that performs a linear equation:

Python

def linear_equation(x, w, b):

    return np.dot(w, x) + b

You can parallelize it using ProcessPoolExecutor:

Python

with concurrent.futures.ProcessPoolExecutor() as executor:

    inputs = [(x, w, b) for x, w, b in zip(X, W, B)]

    results = list(executor.map(linear_equation, inputs))

To convert this to CUDA PyTorch, you would:

Python

import torch

x = torch.tensor(X).cuda()

w = torch.tensor(W).cuda()

b = torch.tensor(B).cuda()

y = torch.matmul(w, x) + b

This will parallelize the computation across the GPU's cores.

Summary

- CPU-based Multi-threading: Good for I/O-bound tasks, limited by GIL for CPU-bound tasks.

- CPU-based Multi-processing: Better for CPU-bound tasks, no GIL limitation.

- CUDA with PyTorch: Excellent for highly parallel tasks, especially those involving large-scale numerical computations.

NVIDIA CUDA

 

To install NVIDIA CUDA with your GeForce 940MX GPU and Intel Core i7 processor, follow these steps:

1. Verify GPU Compatibility: First, ensure that your GPU (GeForce 940MX) is supported by CUDA. According to the NVIDIA forums, the 940MX is indeed supported¹. You can also check the official NVIDIA specifications for the GeForce 940MX, which confirms its CUDA support².

2. System Requirements: To use CUDA on your system, you’ll need the following installed:

   • A CUDA-capable GPU (which you have)
   • A supported version of Windows (e.g., Windows 10, Windows 11)
   • NVIDIA CUDA Toolkit (available for download from the NVIDIA website³)

3. Download and Install CUDA Toolkit:

   • Visit the NVIDIA CUDA Toolkit download page and select the appropriate version for your system.
   • Follow the installation instructions provided on the page. Make sure to choose the correct version for your operating system.

4. Test the Installation: After installation, verify that CUDA is working correctly:

   • Open a command prompt or terminal.
   • Run the following command to check if CUDA is installed:

     nvcc --version
     

   • If you see version information, CUDA is installed successfully.

Remember that CUDA enables parallel computing on GPUs, allowing you to harness their power for high-performance tasks. Good luck with your CUDA development! 😊

GPU with Tensorflow

 

You might have used GPU for faster processing of your Machine Learning code with Pytorch. However, do you know that you can use that with Tensorflow as well?

Here are the steps on how to enable GPU acceleration for TensorFlow to achieve faster performance:

1. Verify GPU Compatibility:

• Check for CUDA Support: Ensure your GPU has a compute capability of 3.5 or higher (check NVIDIA's website).
• Install CUDA Toolkit and cuDNN: Download and install the appropriate CUDA Toolkit and cuDNN versions compatible with your TensorFlow version and GPU from NVIDIA's website.

2. Install GPU-Enabled TensorFlow:

• Use pip: If you haven't installed TensorFlow yet, use the following command to install the GPU version:
  Bash

  pip install tensorflow-gpu
  

• Upgrade Existing Installation: If you already have TensorFlow installed, upgrade it to the GPU version:
  Bash

  pip install --upgrade tensorflow-gpu
  

3. Verify GPU Detection:

• Run a TensorFlow script: Create a simple TensorFlow script and run it. If it detects your GPU, you'll see a message like "Found GPU at: /device:GPU:0".
• Check in Python: You can also check within Python:
  Python

  import tensorflow as tf
  print(tf.config.list_physical_devices('GPU'))
  

4. Place Operations on GPU:

• Manual Placement: Specify with tf.device('/GPU:0') to place operations on GPU:
  Python

  with tf.device('/GPU:0'):
      # Code to run on GPU
  

• Automatic Placement: TensorFlow often places operations on the GPU automatically if available.

5. Monitor GPU Usage:

• Tools: Use tools like NVIDIA System Management Interface (nvidia-smi) or TensorFlow's profiling tools to monitor GPU usage and memory during training.

Additional Tips:

• TensorFlow Version: Ensure your TensorFlow version is compatible with your CUDA and cuDNN versions.
• Multiple GPUs: If you have multiple GPUs, TensorFlow can utilize them by setting tf.config.set_visible_devices().
• Performance Optimization: Explore techniques like mixed precision training and XLA compilation for further performance gains.

Remember:

• Consult TensorFlow's documentation for the most up-to-date instructions and troubleshooting tips. https://www.tensorflow.org/guide/gpu
• GPU acceleration can significantly improve performance, especially for large models and datasets.