Browsing: Nvidia

a production-ready pipeline for multi-stream video analytics: hardware-accelerated decoding, tracking, on-screen display, and message brokering, all wired through GStreamer. For standard detection models exported to TensorRT,…

print(“Batch scanning the whole corpus (static-only)…\n”) summary_rows = [] all_findings = [] for skill in SKILLS: res = scan(skill, use_llm=False, output_format=”json”) fnds = findings_of(res) summary_rows.append({ “skill”:…

fig, ax = plt.subplots(2, 2, figsize=(14, 9)) lang_counts.head(12).iloc[::-1].plot.barh(ax=ax[0, 0], color=”#76b900″) ax[0, 0].set_title(“Top 12 languages (sample)”); ax[0, 0].set_xlabel(“files”) df[“ext”].value_counts().head(12).iloc[::-1].plot.barh(ax=ax[0, 1], color=”#5b8def”) ax[0, 1].set_title(“Top 12 file extensions (sample)”);…

print(“\n” + “=” * 90) print(“[5] cuTile kernels are defined only if cuda.tile imports successfully”) print(“=” * 90) if cutile_import_ok: ConstInt = ct.Constant[int] @ct.kernel def cutile_vec_add_direct_kernel(a,…

print(“\n########## 5. ANALYSIS ##########”) import numpy as np, pandas as pd def find_latest_report(): cands = [] for base in [os.path.expanduser(“~/.local/share/garak/garak_runs”), os.path.expanduser(“~/.cache/garak”), “.”]: cands += glob.glob(os.path.join(base, “**”,…

NVIDIA’s Nemotron Speech team has released Nemotron 3.5 ASR. It is a 600M-parameter streaming Automatic Speech Recognition (ASR) model. A single checkpoint transcribes 40 language-locales in…

In production inference deployments, demand fluctuates over time, requiring inference replicas to scale elastically. Cold-starting inference workloads on Kubernetes can take several minutes. During that time,…

NVIDIA has released Nemotron 3 Ultra, the largest model in its Nemotron 3 family. It targets a specific problem: long-running agents that plan, call tools, and…

NVIDIA AI team have released Cosmos 3. It is a family of omnimodal world models for physical AI. The models combine physical reasoning, world generation, and…

print(“\n### SECTION D: end-to-end Transformer (vanilla fp32 vs Apex fused + AMP) ###”) VOCAB, D, NHEAD, LAYERS, SEQ, BATCH, STEPS = 2000, 256, 4, 4, 128,…