AI startup company - Large model training

Scenario

A startup company focused on generative AI aims to develop a powerful text generation model and provide it as a SaaS service to other businesses.

1. Register and Create Instances:

After registering an account on NiceGPU, the company applied for several computing nodes equipped with high-performance GPUs based on the scale of the model and training duration, forming a Ray cluster.

1. What is NCU?
   • 1 NCU is equivalent to the computational power of one A100 GPU that you can use continuously for 24 hours on our platform.
2. AI startup companies often involve more complex and large-scale AI model training and deployment. Therefore, the demand for computing power is also much greater. Here are some examples of typical scenarios:
   • Large-scale language model (LLM) training:
     • Task: Train large language models similar to GPT-3, capable of generating text, translating, writing code, and more.
     • Required NCU: Hundreds or even thousands of NCUs. LLMs have huge parameter counts and require vast amounts of training data, necessitating significant computing power.
     • Scenario: Develop general-purpose AI assistants, intelligent customer service, content generation tools, etc.
   • Multimodal large model training:
     • Task: Train models capable of handling multiple modalities of data, including text, images, and videos.
     • Required NCU: Hundreds of NCUs. Multimodal models involve various types of data and complex structures, requiring substantial computing resources.
     • Scenario: Develop AI painting tools, video generation tools, virtual humans, etc.
   • Distributed training:
     • Task: Distribute model training tasks across multiple GPUs to accelerate the training process.
     • Required NCU: Depends on the model size and distribution strategy, typically requiring dozens to hundreds of NCUs.
     • Scenario: Train ultra-large scale models, accelerate experimental iterations, etc.
3. Factors affecting NCU demand, aside from model size and data volume, include the following:
   • Model architecture: Different architectures like Transformer and CNN models have varying computational resource requirements.
   • Optimization algorithms: Different optimization algorithms vary in their efficiency in utilizing computational resources.
   • Hardware configuration: GPU models, memory size, and other hardware configurations impact training speed.
   • Distribution strategy: Different distribution strategies such as data parallelism and model parallelism have varying impacts on communication overhead and resource utilization rates.

2. Data and Code Preparation:

• Data preparation: The company collected a massive amount of text data, cleaned and preprocessed it to form a format suitable for model training.
• Code development: The company's AI engineers used PyTorch and the Transformers framework to develop training code for large-scale language models based on the Transformer architecture.

3. Configuring Ray Cluster:

• Resource Allocation: The company allocated CPU, GPU, memory, and other resources from compute nodes to the Ray cluster.
• Distribution Strategy: The company utilized Ray's distributed data parallelism (DDP) and model parallelism (MP) technologies to partition models and data across multiple GPUs, accelerating the training process.
• Fault Tolerance: The company configured Ray's fault tolerance mechanisms to ensure that training tasks can automatically recover in the event of a failure.

4. Starting Training Tasks:

• Task Submission: The company submits training tasks to the Ray cluster, and Ray automatically schedules these tasks, distributing the computational load across various nodes.
• Monitoring Training Process: The company monitors the training progress in real-time through Ray's monitoring panel, including metrics like training loss and perplexity.

  import ray
  from transformers import AutoModelForSeq2SeqLM
  
  # connect to Ray Cluster
  ray.init(address="auto")
  
  # Distributed Data Parallelism
  @ray.remote
  class TrainTask:
      def __init__(self, config, data_loader):
          self.model = AutoModelForSeq2SeqLM.from_config(config)
          self.data_loader = data_loader
  
      def train_epoch(self):
          # Code for training one epoch
          pass
  
  # ... (Remaining code omitted)
  

  

5. Model Saving and Deployment:

• Model Saving: After completing the training, the company saves the trained model to cloud storage.
• Model Deployment: The company uses frameworks such as FastAPI or Flask to deploy the model as a RESTful API, providing services to external users.
• Containerization: The company containerizes the model service and deploys it on a container orchestration platform (such as Kubernetes) to achieve automatic scaling and high availability.

6. Providing SaaS Services:

• API Exposure: The company exposes the model services through an API gateway, allowing other enterprises to call the model via API to generate text.

  # Deployment as API Service:
  from fastapi import FastAPI
  from transformers import pipeline
  
  app = FastAPI()
  generator = pipeline("text-generation", model="saved_model")
  
  @app.post("/generate")
  def generate_text(prompt: str):
      result = generator(prompt, max_length=100)
      return {"generated_text": result[0]["generated_text"]}
  

7. Summary:

By leveraging the shared computing platform and Ray cluster, this AI startup successfully trained a large-scale language model and deployed it as a SaaS service, achieving commercialization.

This case demonstrates the critical role of NiceGPU in AI model training and deployment, providing AI startups with fast, flexible, and scalable solutions.