Results for “kernel-module”

Detect container escape attempts using Falco, seccomp, and auditd, with rules for privileged containers, Docker socket access, and kernel module loading.

Launch, monitor, stop, and debug NeMo-RL recipes on a Kubernetes cluster using the nrl-k8s CLI, supporting ephemeral and long-lived RayCluster modes.

Install npm packages in a Docker sandbox environment with virtiofs-mounted workspaces, avoiding native binary crashes by installing on the local ext4 filesystem and symlinking back.

Integrate Hardware Security Modules (HSMs) using the PKCS#11 interface for cryptographic key management, signing operations, and secure key storage with python-pkcs11, AWS CloudHSM, and YubiHSM2.

Generates production-grade Kubernetes manifests including Deployments with probes and security contexts, Services, Ingress with TLS, HPA, PDB, NetworkPolicy, ConfigMaps, and Secrets, with optional Helm charts, Kustomize overlays, Istio mesh, and ArgoCD GitOps.

Manages Helm charts, releases, repositories, and template rendering for Kubernetes, including installation, upgrades, rollbacks, and debugging.

Provides production-grade Kubernetes YAML patterns and kubectl debugging commands for deploying, managing, and debugging workloads reliably.

Packages and deploys Kubernetes applications with Helm, including charts, templates, and releases.

Kubernetes

Prevent Kubernetes hallucinations by diagnosing and fixing failure modes: insecure workload defaults, resource starvation, network exposure, privilege sprawl, fragile rollouts, and API drift. Use when generating, reviewing, refactoring, or migrating manifests, Helm charts, Kustomize overlays, cluster policies, and platform-specific Kubernetes work for EKS, GKE, AKS, OpenShift, GitOps controllers, or observability stacks.

Kubernetes Architect

Execute código Python na nuvem com contêineres serverless, GPUs e autoscaling. Use ao fazer deploy de modelos de ML, executar jobs de processamento em lote, agendar tarefas compute-intensivas ou servir APIs que exigem aceleração GPU ou scaling dinâmico.

Deploy and serve AI/ML models on Modal's serverless cloud platform with on-demand GPUs, autoscaling containers, persistent storage, and scheduled jobs.

Guides machine learning development with experiment tracking, hyperparameter optimization, model registry, and MLOps pipeline integration.

Generate production-grade Kubernetes manifests — Deployments with probes and security contexts, Services, Ingress with TLS, HPA, PDB, NetworkPolicy, ConfigMaps, Secrets — with optional Helm charts, Kustomize overlays, Istio mesh, and ArgoCD GitOps

Routes reverse engineering, exploitation, penetration testing, malware, mobile, firmware, browser automation, documentation, and security tasks to the appropriate specialist skill. Use when a task spans modules or the correct reverse-skill entrypoint is unclear.

Provides guidance on enabling and optimizing GKE Cluster Autoscaler, including Node Auto Provisioning, troubleshooting scale-up/down issues, and best practices for capacity management.

Author or debug Kubernetes manifests with distinct probes, right-sized resources, autoscaling, and safe reversible rollouts.

Grow firmware on any hardware through HTTP: upload C source, compile on the device, and apply with watchdog rollback.

Captures a baseline snapshot of a Jetson device's module model, L4T version, kernel, OS version, and power mode for performance testing or verification.