Ingero FAQs

Question 1

How does Ingero provide unique insights compared to other GPU profilers?

Accepted Answer

Ingero traces both CUDA Runtime and Driver APIs, revealing hidden kernel launches from libraries like cuBLAS/cuDNN. It also correlates GPU events with host OS activities (CPU, memory) to identify true root causes of GPU stalls and performance issues that other tools miss.

Question 2

What are Ingero's infrastructure requirements and overhead?

Accepted Answer

Ingero is deployed as a single, statically linked Go binary with an embedded SQLite database, requiring no external dependencies like ClickHouse or PostgreSQL. It boasts a very low overhead of less than 2% for production environments.

Question 3

What is Ingero and its primary function for GPU monitoring?

Accepted Answer

Ingero is an eBPF agent that provides causal observability for GPU workloads. It traces from Linux kernel events through CUDA API calls to Python source lines, offering deep insights into performance bottlenecks with <2% overhead and zero code changes.

Question 4

Can Ingero help diagnose issues in PyTorch or other AI/ML frameworks?

Accepted Answer

Absolutely. Ingero maps CUDA operations directly back to specific Python source lines within frameworks like PyTorch, allowing AI/ML engineers to quickly pinpoint the exact code causing GPU performance problems or resource contention.

Question 5

How does Ingero leverage AI assistants for incident investigation?

Accepted Answer

Ingero integrates with AI assistants via the Model Context Protocol (MCP). This enables natural language queries for GPU incident investigation, delivering detailed causal explanations, source code references, and timing statistics directly to the AI assistant.

Ingero

Ingero

Key Features

Use Cases

Key Features

Use Cases