NVIDIA Data Flywheel
Exploring NVIDIA’s Data Flywheel Principle & NeMo
I don’t often write about NVIDIA’s technology, primarily because their solutions typically demand specialised hardware that isn’t always accessible for me.
Getting their software stack up and running can also be e a technical challenge, requiring a fair bit of expertise to navigate.
That said, NVIDIA’s innovations in AI are hard to ignore, especially when it comes to their data-centric approaches like the data flywheel.
I wanted to get a grasp on the concept of the data flywheel, leveraging NVIDIA’s NeMo framework, and explore the critical components of data delivery, design, and development that make it all work.
Lower down, I’ve included a working notebook demonstrating a very basic proof-of-concept (PoC) for NeMo’s NLP capabilities, so you can see it in action.
The data flywheel is a seamless feedback loop.
It kicks off with collecting data — user clicks, sensor signals, or online searches.
This data trains AI models to spot patterns or make decisions, like recommending a song or navigating a car. When people use these AI-powered tools, they create new data through their actions.
That fresh data flows back into the system, refining the models to be sharper and more precise. With each spin, the flywheel gains speed, making AI better every day.
The Why
It supercharges innovation, letting companies roll out smarter features fast.
It personalises experiences — think spot-on Netflix suggestions or tailored learning apps.
And it scales effortlessly, handling bigger data and tougher tasks with ease.
Real-Life Impact
Picture online shopping: your clicks and purchases fine-tune recommendation algorithms, which suggest better products, sparking more clicks.
Or self-driving cars: every mile driven feeds data to improve navigation, making each trip smoother. Social media? Your likes and shares shape what you see next, keeping you hooked.
Notebook Example
Below is a simple notebook example on how to get NVIDIA NeMo running in a colab notebook.
# Google Colab Notebook for NVIDIA NeMo Text Processing (Sentiment Analysis)
# Cell 1: Setup and Install Dependencies
"""
This notebook demonstrates NVIDIA NeMo's text processing capabilities using a sentiment analysis model.
Run this cell to install dependencies and set up the environment.
"""
!pip install wget
!pip install torch transformers
!pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[nlp]
# Clear Hugging Face cache to avoid corrupted model files
import os
os.system("rm -rf ~/.cache/huggingface")
# Restart runtime to ensure packages are loaded (Colab-specific)
# Note: You may need to manually restart the runtime after this cell
os._exit(00)Now you will need to set your runtime type to GPU
# Cell 2: Import Libraries and Check GPU
"""
Import necessary libraries and verify GPU availability.
"""
import nemo.collections.nlp as nemo_nlp
import torch
import numpy as np
from transformers import BertForSequenceClassification, BertTokenizer
from nemo.utils import logging
# Check if GPU is available
if torch.cuda.is_available():
print(f"GPU is available: {torch.cuda.get_device_name(0)}")
else:
print("No GPU available, running on CPU")# Cell 3: Load Pre-trained Sentiment Analysis Model
"""
Load a pre-trained BERT-based model for sentiment analysis using Hugging Face's transformers.
"""
# List available NeMo models for reference
print("Available NeMo NLP models:")
print(nemo_nlp.models.TextClassificationModel.list_available_models())
try:
# Load Hugging Face BERT model for sequence classification
model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
except Exception as e:
print(f"Error loading model: {e}")
print("Ensure internet connectivity and try again, or check model name.")
raise
# Move model to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()# Cell 4: Define Sample Queries and Preprocess
"""
Define sample text queries and preprocess them for the model.
"""
queries = [
"I absolutely loved the movie, it was fantastic!",
"The service was terrible, I won't go back.",
"The weather today is quite pleasant."
]
def preprocess_queries(queries, tokenizer, max_len=128):
input_ids = []
attention_masks = []
for query in queries:
encoded = tokenizer.encode_plus(
query,
add_special_tokens=True,
max_length=max_len,
padding="max_length",
truncation=True,
return_attention_mask=True,
return_tensors="pt"
)
input_ids.append(encoded["input_ids"])
attention_masks.append(encoded["attention_mask"])
input_ids = torch.cat(input_ids, dim=0).to(device)
attention_masks = torch.cat(attention_masks, dim=0).to(device)
return input_ids, attention_masks
input_ids, attention_masks = preprocess_queries(queries, tokenizer)# Cell 5: Perform Sentiment Analysis
"""
Run inference on the sample queries and display results.
"""
# Get model predictions
with torch.no_grad():
outputs = model(input_ids=input_ids, attention_mask=attention_masks)
logits = outputs.logits
preds = torch.argmax(logits, dim=-1)
probs = torch.softmax(logits, dim=-1)
# Map predictions to labels
label_map = {0: "negative", 1: "positive"} # bert-base-uncased typically uses 0=negative, 1=positive
predictions = [label_map[pred.item()] for pred in preds]
# Display results
print("\nSentiment Analysis Results:")
for query, pred, prob in zip(queries, predictions, probs):
# confidence = prob[pred == "positive"].item() if pred == "positive" else prob[pred == "negative"].item()
confidence = prob[preds[queries.index(query)].item()].item() # Use pred.item() for indexing, then .item() to get the scalar
print(f"Query: {query}")
print(f"Predicted Sentiment: {pred} (Confidence: {confidence:.2f})\n")And the final output…
Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
