Education & Training Services - Further your AI knowledge by signing up to our of our instructor-led courses

• Learn to prepare large-scale, high-quality datasets using NVIDIA NeMo Curator.
• Perform essential data curation tasks: text cleaning, filtering, and PII removal.
• Generate high-quality synthetic Question-Answer pairs to create robust datasets for Supervised Fine-Tuning (SFT).
• Understand the importance of data quality in the LLM development lifecycle.

• Explore multiple LLM evaluation techniques, from simple "eyeballing" to systematic, quantitative methods.
• Evaluate models against industry-standard benchmarks like MMLU.
• Implement LLM-as-a-judge for nuanced, automated evaluation.
• Use the NeMo Evaluator microservice to compare zero-shot vs. few-shot (in-context learning) performance.
• Track and visualize evaluation experiments using MLflow.

• Dive into three key customization techniques: CPT, SFT, and DPO.
• Use Continued Pretraining (CPT) to teach a model new knowledge about a specific domain.
• Apply Supervised Fine-Tuning (SFT) to teach a model new skills, such as solving math problems in a different language.
• Utilize Direct Preference Optimization (DPO) to align a model's conversational style to human preferences (e.g., formal vs. informal, specific dialects).
• Gain hands-on experience with the NeMo framework for all customization tasks.

• Learn to compress and accelerate LLMs for efficient inference.
• Apply Post-Training Quantization (PTQ) to reduce model size and memory usage using TensorRT-LLM, focusing on the FP8 format.
• Use Depth Pruning to reduce model size by removing entire layers.
• Employ Knowledge Distillation to recover performance lost during pruning by training a smaller "student" model to mimic a larger "teacher" model.
• Evaluate the performance vs. accuracy trade-offs of each optimization technique.

• Apply your knowledge in a hands-on coding assessment.
• Use Direct Preference Optimization (DPO) to align a Llama 3.1 8B model to a unique conversational style (Shakespearean English).
• Demonstrate your ability to prepare a preference dataset, run an alignment job with NeMo-RL, and evaluate the final model.
• Earn a certificate of competency by successfully completing the assessment.

• Familiarity with Python programming and Jupyter notebooks.
• Basic understanding of Large Language Models and their applications.
• Conceptual knowledge of deep learning and neural networks.

• Meet the instructor
• Create an account at courses.nvidia.com/join

• Learn the significance of stochastic gradient descent when training on multiple GPUs.
• Understand the issues with sequential single-thread data processing and the theory behind speeding up applications with parallel processing.
• Understand loss function, gradient descent, and stochastic gradient descent (SGD).
• Understand the effect of batch size on accuracy and training time with an eye towards its use on multi-GPU systems.

• Learn to convert single GPU training to multiple GPUs using PyTorch Distributed Data Parallel.
• Understand how DDP coordinates training among multiple GPUs.
• Refactor single-GPU training programs to run on multiple GPUs with DDP.

• Understand and apply key algorithmic considerations to retain accuracy when training on multiple GPUs.
• Understand what might cause accuracy to decrease when parallelizing training on multiple GPUs.
• Learn and understand techniques for maintaining accuracy when scaling training to multiple GPUs.

• Use what you have learned during the workshop: complete the workshop assessment to earn a certificate of competency.

• Review key learnings and wrap up questions.
• Take the workshop survey.

• Discuss your AI projects with the Scan AI data science team
• Make a follow-up appointment

• Meet the instructor.
• Create an account at courses.nvidia.com/join

Learn the essential syntax and concepts to be able to write GPU-enabled C/C++ applications with CUDA:

• Write, compile, and run GPU code.
• Control parallel thread hierarchy.
• Allocate and free memory for the GPU.

Learn the command-line profiler and CUDA-managed memory, focusing on observation-driven application improvements and a deep understanding of managed memory behavior:

• Profile CUDA code with the command-line profiler.
• Go deep on unified memory.
• Optimize unified memory management.

Identify opportunities for improved memory management and instruction-level parallelism:

• Profile CUDA code with NVIDIA Nsight Systems.
• Use concurrent CUDA streams.

• Review key learnings and wrap up questions.
• Complete the assessment to earn a certificate.
• Take the workshop survey.