AI Implementation over Personal Computer

Web3 GPU Dock

Overview

This guide provides a comprehensive introduction to installing, making inferences, and fine-tuning AI models locally using a Web3 GPU Dock. It covers the installation of necessary software, setup procedures, and detailed steps for running and fine-tuning AI models. By following this guide, users will be able to leverage their local GPU capabilities to achieve efficient and powerful AI implementations.

Quick Start

Prerequisites

Before we begin, ensure you have the following:

• A computer with a modern processor and at least 8GB of RAM (16GB recommended).

• An internet connection for downloading necessary software and models.

• GPU dock attached to your machine.

Step 1: Installing NVIDIA GPU Driver and CUDA

If you have an NVIDIA GPU, installing the NVIDIA driver and CUDA toolkit will enable you to leverage GPU acceleration for faster model training and inference.

1. Check Your GPU:

   1. Ensure your computer has an NVIDIA GPU. You can check this by going to Device Manager (Windows) or System Information (macOS).

2. Download and Install NVIDIA Driver:

   1. Visit the NVIDIA Driver Downloads page.

   2. Select your GPU model and operating system, then download and install the appropriate driver.

3. Download and Install CUDA Toolkit:

   1. Visit the CUDA Toolkit Download page.

   2. Select your operating system, architecture, and CUDA version (the latest version is recommended).

   3. Follow the installation instructions provided on the website.

4. Verify CUDA Installation:

   1. Open your command prompt (Windows) or terminal (macOS/Linux).

   2. Type nvcc --version and press Enter. You should see the CUDA version information.

Step 2: Installing Python

Python is a popular programming language that we will use to run our models.

1. Download and Install Python:

   1. Visit the official Python website.
   2. Download the latest version (or any LTS version higher than 3.8) of Python for your operating system.

   3. Follow the installation instructions. Ensure you check the option to add Python to your PATH during installation.

2. Verify Python Installation:

   1. Open your command prompt (Windows) or terminal (macOS/Linux).

   2. Type python --version and press Enter. You should see the version of Python you installed.

LLMs - Build Your Local GPT

Welcome to your comprehensive guide to installing, running, and fine-tuning large language models (LLMs) on your local machine using a Web3 GPU Dock. In this guide, we'll focus on GPT-Neo as our example model. You'll learn how to set up the necessary software, configure your environment, and utilize your GPU Dock to make inferences and fine-tune the model for your specific needs. Whether you're new to AI or looking to enhance your capabilities, this guide will provide you with the essential steps to get started with GPT-Neo using your GPU Dock efficiently and effectively.

Step 1: Setting Up the Development Environment with Anaconda and PyCharm

Using Anaconda and PyCharm makes managing your Python environment easier with a user-friendly interface.

Using Anaconda

1. Download and Install Anaconda:

   1. Visit the Anaconda website.
   2. Download the Anaconda installer for your operating system and follow the installation instructions.

2. Create a New Environment:

   1. Open the Anaconda Navigator from your start menu or applications folder.

   2. In the Anaconda Navigator, click on the Environments tab on the left.

   3. Click the Create button.

   4. Name your environment (e.g., llm_env) and select Python version 3.8 or higher. Click Create.

3. Activate the Environment:

   1. In the Environments tab, select the environment you just created and click Open Terminal.

Using PyCharm

1. Download and Install PyCharm:

   1. Visit the PyCharm website.
   2. Download and install the Community edition of PyCharm.

2. Create a New Project:

   1. Open PyCharm and click on New Project.

   2. In the New Project window, select Pure Python.

   3. Choose a location for your project and set the Base Interpreter to the Python interpreter from your Anaconda environment. You can find it under your Anaconda installation directory, inside the envs folder and then your environment folder (e.g., C:\Users\<YourUsername>\Anaconda3\envs\llm_env\python.exe).

   4. Click Create.

Step 2: Installing Necessary Libraries

We'll need several libraries to work with GPT-Neo, including PyTorch and Hugging Face Transformers.

1. Install PyTorch:

   1. In PyCharm, open the terminal within the IDE (you can find it at the bottom of the window).

   2. Visit the PyTorch website and follow the instructions to install PyTorch appropriate for your system. Copy the command provided on the website and paste it into the terminal. For example:
   3. pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

2. Install Transformers Library:

   1. In the same terminal, type the following command and press Enter:

   2. pip install transformers

3. Install Other Dependencies:

   1. If needed, install additional dependencies using pip:

   2. pip install datasets

Step 3: Downloading and Running GPT-Neo Models

Now we'll download and run the GPT-Neo models for inference using the Hugging Face Transformers library.

1. Download Models:

   1. In PyCharm, create a new Python file (right-click on your project folder, select New, then Python File) and name it run_models.py.

   2. Add the following code to download the models:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Download GPT-Neo model 
gpt_neo_model_name = "EleutherAI/gpt-neo-125M" 
gpt_neo_model = AutoModelForCausalLM.from_pretrained(gpt_neo_model_name) 
gpt_neo_tokenizer = AutoTokenizer.from_pretrained(gpt_neo_model_name)

1. Run Models for Inference:

   1. Add the following code to generate text with GPT-Neo:

import torch

# Move model to GPU if available 
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
gpt_neo_model.to(device)  
# Function to generate text
def generate_text(model, tokenizer, prompt, max_length=50):     
    inputs = tokenizer(prompt, return_tensors="pt").to(device)  # Move inputs to GPU     
    attention_mask = inputs['attention_mask']     
    outputs = model.generate(inputs["input_ids"], attention_mask=attention_mask, max_length=max_length, pad_token_id=tokenizer.eos_token_id)     
    return tokenizer.decode(outputs[0], skip_special_tokens=True)  
    
# Generate text with GPT-Neo 
gpt_neo_prompt = "In a distant future"
print("GPT-Neo Output:", generate_text(gpt_neo_model, gpt_neo_tokenizer, gpt_neo_prompt))

1. Run the Script:

   1. In PyCharm, right-click on the run_models.py file and select Run 'run_models'.

Step 4: Training the Models

Training large language models on a local machine can be resource-intensive. We'll demonstrate a simple fine-tuning process on a smaller dataset.

1. Prepare Your Dataset:

   1. Create a text file named train.txt with sample sentences for training.

2. Fine-tune the Model:

   1. Use the following code to fine-tune GPT-Neo:

from datasets import load_dataset
from transformers import Trainer, TrainingArguments, AutoTokenizer, AutoModelForCausalLM
import torch

# Download GPT-Neo model 
gpt_neo_model_name = "EleutherAI/gpt-neo-125M"
gpt_neo_model = AutoModelForCausalLM.from_pretrained(gpt_neo_model_name)
gpt_neo_tokenizer = AutoTokenizer.from_pretrained(gpt_neo_model_name)

# Ensure the tokenizer has a pad token
if gpt_neo_tokenizer.pad_token is None:
    gpt_neo_tokenizer.pad_token = gpt_neo_tokenizer.eos_token

# Load dataset
dataset = load_dataset('text', data_files={'train': 'train.txt'})

# Tokenize dataset
def tokenize_function(examples):
    return gpt_neo_tokenizer(examples["text"], padding="max_length", truncation=True, max_length=512)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Move model to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
gpt_neo_model.to(device)

# Training arguments
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=2,
    num_train_epochs=1,
    weight_decay=0.01,
)

# Trainer
trainer = Trainer(
    model=gpt_neo_model,
    args=training_args,
    train_dataset=tokenized_datasets["train"],
)

# Train the model
trainer.train()

# Save the model 
output_dir = "./trained_model" 
gpt_neo_model.save_pretrained(output_dir) 
gpt_neo_tokenizer.save_pretrained(output_dir)
print(f"Model saved to {output_dir}")

1. Run the Training Script:

   1. In PyCharm, create a new Python file named train_model.py and add the above code.

   2. Right-click on the train_model.py file and select Run 'train_model'.

Image Generation - Become the Artist of Your Own

Welcome to your comprehensive guide to installing, running, and fine-tuning Stable Diffusion models on your local machine using a Web3 GPU Dock. In this guide, we'll focus on Stable Diffusion 2 as our example model. You'll learn how to set up the necessary software, configure your environment, and utilize your GPU Dock to make inferences and fine-tune the model for your specific needs. Whether you're new to AI or looking to enhance your capabilities, this guide will provide you with the essential steps to get started with Stable Diffusion 2 using your GPU Dock efficiently and effectively.

Step 1: Setting Up the Development Environment with Anaconda and PyCharm

Using Anaconda and PyCharm makes managing your Python environment easier with a user-friendly interface.Note: It is common practice to set up separate environments for different AI services to avoid conflicts and ensure proper dependency management. However, you can still use the environment set up in the previous part if you prefer.

Using Anaconda

1. Download and Install Anaconda:

   1. Visit the Anaconda website.

   2. Download the Anaconda installer for your operating system and follow the installation instructions.

2. Create a New Environment:

   1. Open the Anaconda Navigator from your start menu or applications folder.

   2. In the Anaconda Navigator, click on the Environments tab on the left.

   3. Click the Create button.

   4. Name your environment (e.g., stable_diffusion_env) and select Python version 3.8 or higher. Click Create.

3. Activate the Environment:

   1. In the Environments tab, select the environment you just created and click Open Terminal.

Using PyCharm

1. Download and Install PyCharm:

   1. Visit the PyCharm website.
   2. Download and install the Community edition of PyCharm.

2. Create a New Project:

   1. Open PyCharm and click on New Project.

   2. In the New Project window, select Pure Python.

   3. Choose a location for your project and set the Base Interpreter to the Python interpreter from your Anaconda environment. You can find it under your Anaconda installation directory, inside the envs folder and then your environment folder (e.g., C:\Users\<YourUsername>\Anaconda3\envs\stable_diffusion_env\python.exe).

   4. Click Create.

Step 2: Installing Necessary Libraries

We'll need several libraries to work with Stable Diffusion 2, including PyTorch and Hugging Face Transformers.

1. Install PyTorch:

   1. In PyCharm, open the terminal within the IDE (you can find it at the bottom of the window).

   2. Visit the PyTorch website and follow the instructions to install PyTorch appropriate for your system. Copy the command provided on the website and paste it into the terminal. For example:

   3. pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

2. Install Diffusers Library:

   1. In the same terminal, type the following command and press Enter:

   2. pip install diffusers transformers

3. Install Other Dependencies:

   1. If needed, install additional dependencies using pip:

   2. pip install datasets

Step 3: Downloading and Running Stable Diffusion 2 Model

Now we'll download and run the Stable Diffusion 2 model for inference using the Hugging Face Diffusers library.

1. Download Model:

• In PyCharm, create a new Python file (right-click on your project folder, select New, then Python File) and name it download_model.py.

• Add the following code to download the model:

from diffusers import StableDiffusionPipeline

# Load Stable Diffusion 2 model
model_id = "stabilityai/stable-diffusion-2"
pipe = StableDiffusionPipeline.from_pretrained(model_id)
pipe.save_pretrained("stable_diffusion_2")

print("Model downloaded and saved to 'stable_diffusion_2'")

1. Run the Script:

   1. In PyCharm, right-click on the download_model.py file and select Run 'download_model'.

   2. This will download the model and save it to the stable_diffusion_2 directory.

2. Run Inference:

   1. In PyCharm, create a new Python file (right-click on your project folder, select New, then Python File) and name it run_stable_diffusion.py.

   2. Add the following code to load the saved model and run inference:

from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler
import torch

# Load Stable Diffusion 2 model
model_path = "stable_diffusion_2"
pipe = StableDiffusionPipeline.from_pretrained(model_path)

# Move model to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = pipe.to(device)

# Function to generate an image with custom parameters
def generate_image(pipe, prompt, guidance_scale=7.5, num_inference_steps=100, width=768, height=512, sampler=None):
    if sampler:
        pipe.scheduler = sampler.from_config(pipe.scheduler.config)
    image = pipe(prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, width=width, height=height).images[0]
    return image

# Parameters explanation:# guidance_scale: Controls the creativity vs. accuracy of the generated image (higher value = more creative).# num_inference_steps: Number of steps for the image generation process (more steps = higher quality).# width and height: Dimensions of the generated image.# sampler: Optional parameter to use a different sampling method.# Generate an image with all custom parameters
prompt = "A futuristic cityscape"
image = generate_image(pipe, prompt, guidance_scale=7.5, num_inference_steps=20, width=512, height=512, sampler=EulerAncestralDiscreteScheduler)
image.save("output_all_custom.png")
print("Image saved as output_all_custom.png")

# Display the image 
image.show()

1. Run the Script:

   1. In PyCharm, right-click on the run_stable_diffusion.py file and select Run 'run_stable_diffusion'.

2. Explanation of Parameters:

• guidance_scale: Controls the balance between creativity and accuracy. A higher value results in more creative output, while a lower value focuses on accuracy.

• num_inference_steps: The number of steps used during the image generation process. More steps generally lead to higher quality but take longer to compute.

• width and height: The dimensions of the generated image. Adjust these values based on your desired image size.

• sampler: An optional parameter to use a different sampling method. In the example, we used EulerAncestralDiscreteScheduler.

Step 6: Fine-Tuning the Stable Diffusion 2 Model

Fine-tuning large models on a local machine can be resource-intensive. We'll demonstrate a simple fine-tuning process on a smaller dataset.

1. Prepare Your Dataset:

   1. Create a folder named train_data and add your training images and corresponding text prompts.

2. Fine-Tune the Model:

   1. Use the following code to fine-tune Stable Diffusion 2:

from datasets import load_dataset
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler, StableDiffusionTrainingArguments, Trainer
import torch

# Load dataset
dataset = load_dataset('imagefolder', data_dir='train_data')

# Tokenize dataset
def tokenize_function(examples):
    return gpt_neo_tokenizer(examples["text"], padding="max_length", truncation=True, max_length=512)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Training arguments
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=2,
    num_train_epochs=1,
    weight_decay=0.01,
)

# Trainer
trainer = Trainer(
    model=pipe,
    args=training_args,
    train_dataset=tokenized_datasets["train"],
)

# Train the model
trainer.train()

# Save the model 
output_dir = "./trained_model" 
pipe.save_pretrained(output_dir) 

print(f"Model saved to {output_dir}")

1. Run the Training Script:

   1. In PyCharm, create a new Python file named train_stable_diffusion.py and add the above code.

   2. Right-click on the train_stable_diffusion.py file and select Run 'train_stable_diffusion'.