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Building, Deploying & Fine-Tunning AI Models
Maximize Performance with AI-Tailored NVIDIA A100 and H100 GPUs
Unlock the full potential of your AI workloads with our range of high-performance GPUs. Our offerings include AI-optimized NVIDIA A100 and H100 GPUs integrated into DELTA HGX Baseboards, featuring 8 GPUs interconnected by NVLink. Additionally, we provide L40s and other GPUs available in the PCIe form factor.
Efficient Onboarding and Support Services
Benefit from our onboarding process and expert assistance tailored to your needs. Whether you require support with complex cases or optimization of platform usage, our team is dedicated to reducing your problem-solving time and ensuring a seamless experience.
Explore Our Marketplace for AI-Specific Tools
Discover a wide range of AI-specific tools from leading vendors, including OS images and Kubernetes® apps. Our marketplace provides the perfect workspace for data scientists and ML engineers, offering everything you need to enhance your AI projects.
How to choose GPU for fine-tuning
V100
V100 with NVLink is a good choice for fine-tuning of diffusion and transformer models, e.g. Llama2 and Stable Diffusion, as well as non-transofrmer models like Resnet-50 and Wav2Vec2.
A100
Cost effective for fine-tuning of conventional models.
Great for domains where CNN, RNN models are popular, e. g. computer vision or medical diagnostics.
H100
Best choice if speed is your top priority.
Perfect for bigNLP, LLM, and all models with Transformer architecture.
H200
The world’s most powerful GPU for supercharging AI and HPC workloads coming soon!
Understanding the Difference : Model Training vs. Fine-Tuning
The process of developing machine learning models can be categorized into two main approaches: model training and fine-tuning. While model training involves building a model from scratch, fine-tuning adjusts an existing, pre-trained model to meet specific requirements. Here’s a practical comparison of model fine-tuning versus model training:
| Aspect | Model Training | Model Fine-tuning |
|---|---|---|
| Starting Point | Begins with a blank slate, no prior knowledge | Starts with a pre-trained model |
| Data Requirements | Requires large, diverse datasets | Can work with smaller, specific datasets |
| Time and Resources | Often time-consuming and resource-intensive | More efficient, leverages existing resources |
| Objective | To create a general model capable of learning from data | To adapt a model to perform better on specific tasks |
| Techniques | Involves basic learning algorithms, building layers, setting initial hyperparameters | Involves hyperparameter tuning, regularization, adjusting layers |
| Challenges | Needs extensive data to avoid overfitting, underfitting | Risk of overfitting to new data, maintaining balance in adjustments |
| Metrics | Focuses on overall accuracy, loss metrics | Emphasizes improvement in task-specific performance |
| Best Practices | Requires careful data preprocessing, model selection | Necessitates cautious adjustments, validation of new data |
Techniques for Model Fine-tuning
Hyperparameter Tuning
Fine-tuning involves adjusting the parameters of a model to enhance its performance. For instance, in a neural network, modifying the learning rate or batch size can significantly improve accuracy. A common approach is to use grid or random search methods to identify the optimal hyperparameters for a given classification task.
Transfer Learning
Fine-tuning pre-trained models is a common practice for adapting them to new tasks. For example, you can utilize a model trained on a large image dataset to enhance performance on a smaller, specialized image classification task.
Data Augmentation
Enhance your training dataset by generating modified versions of data points. In image processing, this could involve rotating, flipping, or adding noise to images. By doing so, you can create a more robust model and reduce the risk of overfitting.
Regularization Methods
Techniques such as L1 or L2 regularization effectively prevent overfitting by penalizing complex models. By incorporating a regularization term into the loss function, these techniques help maintain model simplicity, resulting in improved generalization and better performance on unseen data.