> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/nemo-platform/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo-platform/_mcp/server. # v0.5.0 - 2026-09-04 NeMo Platform v0.5.0 focuses on productionizing the agent and customization workflows introduced in the previous releases. The release adds GRPO training with custom reward environments, completes the `rl` backend path for DPO and GRPO in Studio, expands Fabric-backed agent packaging and deployment, improves task-driven agent evaluation, and hardens local, Docker, Kubernetes, and Helm operations for the 0.5 release line. ## Highlights * **GRPO with custom reward environments.** Customizer can run GRPO jobs on the `rl` backend against uploaded NeMo Gym reward environment FileSets, including native Gym layouts, vendored wheel packages, and `verifiers` hub adapters. * **RL customization in Studio.** Studio can create and inspect DPO and GRPO customization jobs, including GRPO reward-environment selection, compute settings, status, and reward-oriented training metrics. * **Fabric 0.2 agent runtime.** Platform-managed agents move to Fabric 0.2.0 fields, with reusable environment and compute specs, environment-aware deployments and execute jobs, and packaged agent images for Docker and Kubernetes. * **Task-driven evaluation improvements.** Agent evaluation has built-in runner reward metrics, clearer local and Harbor runners, richer run bundles, and Intake trace inspection for evaluation authoring. * **Operational hardening.** Jobs and deployments have better wait/watch behavior, Docker GPU cleanup is safer, NIM weight permissions are repaired at startup, and the 0.5 images include dependency and container CVE remediation. ## What's included ### Customizer * GRPO jobs are available through `nemo customization rl submit` with `training.type: "grpo"`. * GRPO trains against a separate environment FileSet and prompt dataset FileSet. The environment package declares its layout in `nemo-environment.yaml`. * Supported environment formats are `native-v1`, `wheels-v1`, and `adapter-wheels-v1`; the converter and validator help package `verifiers` environments and validate environment FileSet structure before upload. * GRPO supports full-weight and LoRA fine-tuning modes, reward and off-policy time-series metrics, generation controls, batching controls, micro-batching, Hugging Face config overrides, and backend-specific policy knobs. * DPO continues on the `rl` backend with NeMo-RL and Ray, with Studio support for DPO job creation and status review. * Customizer docs and coding-agent skills now cover GRPO environment packages, RL hyperparameters, Kubernetes/Ray setup, OpenSandbox, Volcano, and common troubleshooting paths. * Customizer task images include the model SDK and FileSet handling needed by customization jobs and model-entity publication. ### Agents and Fabric * Agents can store reusable `AgentEnvironmentSpec`, `AgentComputeSpec`, and `AgentEnvironment` entities through the API, Python SDK, and CLI. * `nemo agents deploy --environment` can deploy an agent with a stored or inline environment, merging the environment spec into the Fabric config and snapshotting compute and secret references onto the deployment. * Agent execute jobs can use environment and compute specs, so batch execution and service deployment share the same runtime configuration model. * Fabric-backed packaging supports Platform-managed `agent.yaml` packages, staged relative artifacts, custom image entrypoints, and Docker/Kubernetes deployment modes. * Agent gateway errors on the OpenAI-compatible surface now preserve useful upstream messages and codes for OpenAI SDK clients. * Session ownership rules and no-auth session lifecycle access are tightened so agent sessions behave predictably across authenticated and local setups. ### Agent Intent and Optimization * The durable agent-intent document is now `ETHOS.md`. The old `AGENT-SPEC.md` contract is replaced by Ethos terminology across agent creation, analysis, experimentalist, and packaging workflows. * `nemo agents ethos migrate` provides a guided migration for platform-owned legacy spec packages, Filesets, and optimizer profiles. * The Ethos schema records `Purpose & Outcomes`, `Principles`, `Trade-offs`, `Constraints`, `Metric Semantics`, and `Vision`, with explicit migration behavior for older agent-spec packages. * Agent optimization jobs support FileSets and inline configuration, and the optimizer can continue using Intake evidence and Harbor-compatible evaluation runs when validating candidate changes. ### Agent Evaluation * Agent-eval runner metrics such as Harbor reward, Gym reward, phase success, evidence presence, and skill use are built in instead of requiring pickled custom metric bundles. * Local SDK evaluation remains available without platform services, with a runnable quickstart, on-disk run bundles, `summary.json`, `scores.jsonl`, `trials.jsonl`, `tasks.jsonl`, and `report.html`. * The Harbor runner path is clearer: Harbor task suites run through the same `AgentEvaluator` result model and use the built-in Harbor reward metric. * Result summaries expose per-task metric values and task outcomes, making it easier to identify which tasks or trials drove aggregate scores. * Eval Author is now represented as coding-agent skills for Harbor discovery and Intake trace inspection, rather than a standalone CLI command group. ### Studio * Studio adds GRPO customization form support and GRPO job detail panels with reward environment and training-health views. * Studio adds RL/DPO customization job support and removes the older Prompt Tuning form from the navigation. * Agents pages show metrics and open Insight details from an agent detail view. * Data Designer adds transform flows for existing files, with field mapping, format presets, previews, and custom template rows. * Guardrail configuration in Studio now uses explicit rail toggles, starting with self-check, plus configurable rail descriptions, model selection for checks, and self-check generation controls. * Studio can chat with virtual models and has cleaner model filtering for fine-tunable and base-model views. ### Platform, CLI, and Deployment * The Python SDK gains typed clients for additional platform and plugin service areas, with a `NemoClient` compatibility layer for plugin SDK resources. * `nemo jobs watch` and `--watch` on job creation stream job events until a terminal status, with better rendering for terminal task state and Docker task fallback. * Generated CLI reference docs include plugin commands. * Helm deployments can configure controller and API groups, and the docs add OpenSandbox, OpenSandbox with Kata, and Volcano setup guidance. * Docker-backed jobs preserve cancellation intent and release GPU allocations when deleted. * Model-serving startup repairs pulled-weight permissions before launching NIM. * Release images and dependency locks include 0.5 CVE remediation, CPython 3.13.15 backports, and extended distroless Python runtimes. * Pending-deletion workspaces are hidden from the list API. ## Install For a fresh local checkout: ```bash git clone https://github.com/NVIDIA-NeMo/nemo-platform.git cd nemo-platform make bootstrap source .venv/bin/activate nemo setup ``` See [Setup](/documentation/get-started/setup) for prerequisites and provider configuration. For self-managed Kubernetes, start with [Install NeMo Platform Helm Chart](/documentation/kubernetes-deployment/setup/helm/install). GRPO jobs also require Kubernetes/Ray, OpenSandbox-enabled Gym execution, and job-storage PVC configuration; see [GRPO and Reward Environments](/documentation/customizer-reference/grpo-and-reward-environments) and [OpenSandbox](/documentation/kubernetes-deployment/setup/helm/opensandbox). ## Upgrade from v0.4.x From an existing local checkout: ```bash git fetch git checkout main make bootstrap source .venv/bin/activate nemo setup ``` After setup, restart local services before using CLI, SDK, Studio, or plugin workflows against the upgraded checkout. If you have pre-0.5 platform-owned agent intent packages, migrate them from `AGENT-SPEC.md` to `ETHOS.md` before using newer optimization workflows: ```bash nemo agents ethos migrate --name ``` ## Compatibility * Python 3.12-3.13 * macOS and Linux for local CLI, SDK, Studio, and hosted-provider workflows * Linux x86\_64 for local NVIDIA GPU workloads * CUDA 13-capable NVIDIA drivers for local GPU workloads * Self-managed Kubernetes clusters deployed with Helm * Docker for local services, Docker-backed platform jobs, local ClickHouse, Docker Sandboxes, and local model-serving workflows * Kubernetes/Ray runtime for NeMo-RL DPO and GRPO customization jobs * OpenSandbox and a job-storage PVC for sandboxed GRPO reward environments * ClickHouse for Intake trace storage and Experiments rollups * NVIDIA GPU access for local training, model serving, RL training, and GPU-backed synthetic data workflows * Node 22.18.0+ for Studio assets * Platform API and `nemo-platform` Python SDK `0.5.0` ## Current constraints * **Self-managed scope.** v0.5.0 documents local setup and user-managed Kubernetes deployment. It is not a managed hosted-service release. * **Research-preview optimizer.** Analyst and Experimentalist are early agentic workflows. They are useful for guided optimization, but quality and autonomy are still research-preview and require developer review. * **GRPO runtime.** GRPO jobs require Kubernetes/Ray execution and sandboxed Gym configuration. They do not have a local Docker fallback. * **GRPO environment packaging.** `adapter-wheels-v1` packages require network access at job start for the built-in `verifiers_agent` path. Fully offline `wheels-v1` jobs need a complete wheel closure for the training image's Python and node architecture. * **DPO runtime.** Customizer `rl` DPO jobs run on Kubernetes/Ray and do not have a local Docker fallback. DPO remains full-weight only. * **Eval Author migration.** Eval Author discovery is now skill-driven. The old `nemo agents eval-author` CLI group is no longer the supported surface. * **Agent intent migration.** `AGENT-SPEC.md` is legacy. Use `ETHOS.md` for new work and run `nemo agents ethos migrate` for platform-owned legacy packages before relying on optimizer workflows. * **Optuna HPO runtime.** Optuna-based hyperparameter optimization runs locally from a developer workstation. It is not available as a horizontally scaled cluster service in this release. * **Access keys.** Scoped access keys remain optional and must be enabled by the platform administrator. * **Embedded ClickHouse scope.** Helm's embedded ClickHouse is intended for development, evaluation, and non-critical single-node deployments. Use an externally managed ClickHouse for production deployments that require high availability, backups, or larger scale. ## Links * Repository: [https://github.com/NVIDIA-NeMo/nemo-platform](https://github.com/NVIDIA-NeMo/nemo-platform) * Issues: [https://github.com/NVIDIA-NeMo/nemo-platform/issues](https://github.com/NVIDIA-NeMo/nemo-platform/issues) * GRPO and Reward Environments: [https://docs.nvidia.com/nemo-platform/documentation/customizer-reference/grpo-and-reward-environments](https://docs.nvidia.com/nemo-platform/documentation/customizer-reference/grpo-and-reward-environments) * GRPO Environment Packages: [https://docs.nvidia.com/nemo-platform/documentation/customizer-reference/tutorials/grpo-environment-packages](https://docs.nvidia.com/nemo-platform/documentation/customizer-reference/tutorials/grpo-environment-packages) * OpenSandbox setup: [https://docs.nvidia.com/nemo-platform/documentation/kubernetes-deployment/setup/helm/opensandbox](https://docs.nvidia.com/nemo-platform/documentation/kubernetes-deployment/setup/helm/opensandbox) * NeMo Fabric: [https://docs.nvidia.com/nemo/fabric/about-nemo-fabric/overview/](https://docs.nvidia.com/nemo/fabric/about-nemo-fabric/overview/) * NeMo Agent Toolkit: [https://docs.nvidia.com/nemo/agent-toolkit/latest/](https://docs.nvidia.com/nemo/agent-toolkit/latest/)