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Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference
Hands-on tutorial implements NVIDIA cuML and RAPIDS to GPU-accelerate scikit-learn-style ML workflows with benchmarking, clustering, and inference.
The tutorial demonstrates NVIDIA cuML as a GPU-accelerated machine learning framework, using cuml.accel to speed up unmodified scikit-learn scripts with zero code changes and the native cuML API for CuPy/cuDF interoperability. It benchmarks CPU versus GPU implementations of PCA, K-Means, nearest-neighbor search, logistic regression, random forests, and DBSCAN on datasets up to 200,000 samples with 64 features. It also builds GPU pipelines with UMAP, t-SNE, and HDBSCAN, validates GPU-generated SHAP explanations, uses the FIL library for forest inference, and covers model serialization and GPU/CPU portability.
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NVIDIA Open-Sources OSMO: One YAML Orchestrates Physical AI Training, Simulation, and Robot Testing
NVIDIA open-sourced OSMO, a Kubernetes-native YAML orchestrator running physical-AI training, simulation, and robot testing across mixed GPU tiers.
OSMO (Apache-2.0, latest release 6.3.1) lets teams describe training, simulation, and hardware-in-the-loop pipelines in a single YAML and routes tasks across datacenter GPUs (GB200), workstation RTX hardware, and edge devices like Jetson AGX Thor. It ships Helm charts and containers on NGC, uses the KAI Scheduler with NVLink topology-aware placement, and includes RBAC, OAuth2, and TLS termination. NVIDIA says it is battle-tested on GR00T, Isaac Lab, Isaac Sim, and Isaac ROS, and integrates with Claude Code, OpenAI Codex, and Cursor agents.
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Same Cluster, 33 Points More Utilization: What Changed Was the Order
A Dharma AI blog post on Hugging Face claims GPU cluster utilization rose 33 points after changing job ordering.
A community blog post published on Hugging Face, part of a GPU management series by Dharma AI, discusses improving utilization on the same GPU cluster. According to the title, reordering jobs or tasks was the change that produced roughly 33 additional points of utilization. No article body was available for further detail.
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Akuity gives AI agents operational context to safely ship software
Akuity launches Agentic Control Plane and MCP Server to govern AI agent actions inside production software delivery pipelines.
Akuity introduced an Agentic Control Plane and MCP Server that lets AI agents such as Claude, Codex, and Cursor access deployment and cluster context under existing identity, permission, and audit controls. MLB reported the product identified over 100 degraded applications within hours. Akuity positions it against open-source MCP servers granting raw access with no identity inheritance or policy layer.
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Mirantis collaborates with Equinix to help businesses accelerate cloud-native application deployment
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Confluent for Kubernetes brings cloud-native capabilities to data streams in private infrastructures
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