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AI/ML benchmark for local LLM inference and XGBoost training on GPU/CPU
AI/ML benchmark for local LLM inference and XGBoost training on GPU/CPU
AI Coding Tools
A single‑command Python suite that benchmarks GPU/CPU performance on typical AI/ML workloads such as Ollama LLMs and XGBoost, producing an interactive HTML report.
Python
·
Latest v0.6.4 · 3d ago
Security brief →
Features
One‑command execution of comprehensive GPU/CPU AI/ML benchmarks
Supports both Ollama large language model latency/throughput tests and XGBoost training/inference on the HIGGS dataset
Generates an automatically opened Jupyter notebook exported as HTML for immediate results
Optionally uploads encrypted benchmark data to a central Streamlit dashboard for community comparison
No immediate action
v0.6.2
Feature
·
1mo
Resource detection + flags
No immediate action
v0.5.0
Bugfix
·
5mo
pyarrow → fastparquet; XGBoost CUDA disabled
No immediate action
v0.4.0
Feature
·
8mo
Progress bar + integrity check + Mermaid diagram
No immediate action
v0.3.0
Bugfix
·
8mo
Exception handling improvements
No immediate action
v0.2.0
Feature
·
10mo
Notebook generation + cloud sharing
Weekly OSS security release digest.
The CVE patches and breaking changes that affected production tools this week. One email, every Sunday.
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Releases per month
Releases per month, last 12 months.
Cadence
0.3 / wk
Last release
3d
Churn
+346 / −313 lines · 2 files · 4 commits
Tracked
8
Security score
6.5/10
OpenSSF
—
Open CVEs
0
Active maintainer
Community
GitHub stars
18
Open issues
0
Open PRs
0
Stars/wk velocity
0.0
About
Languages
Python
·
Mermaid
View on GitHub
Live demo
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Install & Platforms
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About
Languages
Python
·
Mermaid
View on GitHub
Live demo
{ copied = true; setTimeout(() => copied = false, 2000) })"
class="flex items-center gap-1.5 text-[12px] text-[var(--text-muted)] dark:text-[var(--text-muted)] hover:text-[var(--accent)] dark:hover:text-[var(--accent)] transition-colors"
>
Install & Platforms
Install via
brew
npm
cargo
go
shell-script
binary
Platforms
linux
macos
windows
arm64
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