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213 changes: 153 additions & 60 deletions docs/about/release-notes/current-release.mdx
Original file line number Diff line number Diff line change
@@ -1,56 +1,134 @@
---
title: "v0.3.0"
title: "v0.4.0"
description: ""
---
NeMo Platform v0.3.0 expands the OSS distribution beyond local setup with
self-managed Kubernetes and Helm documentation, while continuing to support
the local-first Python package, CLI, SDK, Studio, and plugin workflows.
NeMo Platform v0.4.0 introduces a research-preview optimization loop for
improving agents from real telemetry. Agent owners can send traces to Intake,
use agents to diagnose recurring issues, create targeted evaluation coverage,
experiment against a local code base, and review validated candidate changes.
The release also moves new agent workflows toward `agent.yaml` packages backed
by [Fabric](https://github.com/NVIDIA/nemo-fabric/) while keeping NAT workflows
available as a legacy path.

## Highlights

- **Self-managed Kubernetes.** The docs now cover Helm-based deployment to
user-managed Kubernetes clusters, including local Kind workflows and
cluster prerequisites.
- **Agent deployment docs.** Agent container deployment guidance covers local
and Kubernetes paths and clarifies how SDK, CLI, and REST callers resolve
default model placeholders.
- **Versioned compatibility.** Requirements and support-matrix pages describe
the 0.3.0 release instead of the previous local-only scope.
- **Generated reference docs.** The configuration reference remains generated
from platform config models and renders as regular Fern MDX.
- **Research-preview agent optimization.** Analyst and Experimentalist
workflows now support the observe, diagnose, experiment, and evaluate loop for
agents under active development.
- **Trace-driven diagnosis.** NeMo Intake ingests OTLP, chat-completions, and
ATIF telemetry, stores traces in ClickHouse, and attaches annotations and
evaluator results so agents and humans can investigate failures from the same
evidence.
- **Validated candidate changes.** Experimentalist can run against a local
agent source tree or git source, evaluate candidates with Harbor, write
optimization artifacts locally, and open a draft PR when a changed
winner is found.
- **Fabric-first agents.** New Platform-managed agents use the
`nemo-agents-spec-v1` `agent.yaml` contract. Fabric-backed agents can run
through supported harnesses and route model traffic through the Inference
Gateway; NAT remains supported for existing workflows.
- **Experiments for review.** NeMo Experiments compares evaluation runs using
cost, latency, token, and evaluator-score rollups computed from Intake
telemetry, with Studio leaderboard and drill-down views behind the Experiments
feature flag.
- **Customizer DPO.** Customizer adds DPO training through the `rl` backend,
powered by NeMo-RL and Ray on Kubernetes, with backend-specific
hyperparameter guidance for Automodel, Unsloth, and RL jobs.

## What's included

### Platform

- Local source-install workflows with `make bootstrap`, `nemo setup`, and
`nemo services run`.
- Self-managed Helm/Kubernetes documentation for users deploying the platform
outside the local developer process.
- Source-install auth and OIDC bootstrap guidance for users who enable RBAC
locally.

### Agents

- NAT-based agent workflow support through `nemo agents`.
- Agent container rendering, building, publishing, and deployment guidance.
- SDK and CLI examples that use the configured default model for agent
registration.

### Models and Inference

- Inference Gateway support for provider registration, virtual models, and
OpenAI-compatible routing.
- Local and provider-backed model workflows for CLI, SDK, Studio, and agents.

### Plugins

- First-party plugin workflows continue from the previous release line,
including Agents, Customizer, Safe Synthesizer, Auditor, Guardrails,
Evaluator, Anonymizer, Data Designer, Switchyard middleware, and
Deployments.
- Plugin docs cover runtime service, CLI, job, controller, inference
middleware, and coding-agent skill surfaces.
### Insight-Driven Optimization

- `nemo agents analyst` scans Intake traces, evaluator scores, and reviewer
annotations to produce evidence-backed Insights.
- `nemo agents experimentalist` turns an Insight, or a dataset-only objective,
into candidate code changes, train/validation evaluations, local artifacts,
and an optional draft PR or MR.
- `optimizer.yaml` provides the shared per-agent profile for the loop, including
the agent name, workspace, agent source, `AGENT-SPEC.md`, datasets, task
template, and experiment configuration.
- `AGENT-SPEC.md` remains the durable Markdown contract for intended agent
behavior and can be consumed by the Analyst and Experimentalist when present.
- Benchmark and proof assets include Terminal-Bench 2.1, Tau3 Airline, Banking,
Retail, and Telecom suites, plus a guided Tau3 example-agent walkthrough.

### Intake and Experiments

- Intake supports OTLP, chat-completions, and ATIF ingest paths for agent
telemetry.
- Traces, spans, sessions, annotations, and evaluator results are queryable by
API and reviewable in Studio.
- Local setup can automatically provision and reuse a local ClickHouse
container for Intake unless an external ClickHouse URL is configured.
- The Helm chart includes an embedded ClickHouse option for development and
non-critical single-node installations, plus configuration for external
ClickHouse in production-oriented deployments.
- Experiments and Evaluations are durable entities whose leaderboard metrics are
computed from retained Intake telemetry.
- The `nemo-experiments-upload` skill helps coding agents publish evaluation
runs and verify rollups.

### Agents and Fabric

- New agents use Platform-managed `agent.yaml` files with the
`nemo-agents-spec-v1` config format.
- NeMo Fabric is the preferred runtime wrapper for new agents, with support for
harness adapters, shared model bindings, skills, MCP servers, tool policy, and
Relay/ATIF/ATOF telemetry configuration.
- `nemo agents create`, `deploy`, `invoke`, `run`, and `package` support the
Fabric-backed agent path.
- Fabric agent artifacts can be staged into Docker and Kubernetes deployments.
- Streaming responses are supported through Fabric-backed agents.
- Legacy NAT workflow YAMLs continue to work for existing NAT-specific
evaluation and deployment workflows.

### Agent Evaluation

- The evaluator SDK adds Fabric runtime support, Docker Compose sandboxing,
native agent-eval aggregation, pass-at-k scoring, typed run metadata, and
task/taskset revisions.
- Agent evaluations can publish results to Intake so Experiments and Insights
can consume the same run telemetry.

### Customizer

- The `rl` customization backend supports full-weight DPO jobs through NeMo-RL.
- DPO schemas expose schedule, optimizer, batch, DPO-specific loss, gradient,
activation-checkpointing, and parallelism controls.
- Customizer skills and references now cover backend-specific job JSON for
Automodel, Unsloth, and RL, including dataset formats, batch sizing,
integrations, and troubleshooting.
- Customizer adds local Optuna HPO workflows for tuning hyperparameters from a
developer workstation.

### Studio

- Studio includes primary surfaces for Agents, Data Designer, Guardrails,
Safe Synthesizer, Intake traces, Optimizer Insights, Experiments, and Jobs,
with Customizer and Anonymizer surfaces available behind their feature flags.
- Guardrails in Studio can create and delete configurations and manage
guardrail test cases through the checks surface.
- Data Designer adds more seed sources, AI-assisted job configuration, preview,
and file transform workflows.
- Safe Synthesizer reports include score-driven gauges.
- Studio can load plugin web bundles through `/apis/plugins` and render them
inside the Studio React tree with trusted bundle URL checks.
- NeMo Studio Copilot adds a packaged agent, chat history, tool-call rendering,
and reasoning display for Studio-assisted workflows.

### Platform, CLI, and Deployment

- OpenShell deployment support adds an opt-in sandboxed backend for local agent
deployments with default-deny egress policy examples.
- Deployment readiness now gates on workload reachability for container-backed
deployments.
- Optional scoped access keys are available for authenticated deployments.
- The CLI adds machine-readable output controls, Intake and Experiments command
exposure, anonymous usage telemetry, and a Telemetry and Privacy reference
page.
- Python 3.12 is now the minimum supported Python version for the source
checkout and `nemo-platform` distribution.
- Auditor adds a blocking submit option and an aggregated artifacts endpoint.

## Install

Expand All @@ -70,7 +148,7 @@ configuration.
For self-managed Kubernetes, start with
[Install NeMo Platform Helm Chart](/documentation/self-managed-deployment/setup/helm/install).

## Upgrade from v0.2.x
## Upgrade from v0.3.x

From an existing local checkout:

Expand All @@ -90,31 +168,46 @@ workflows against the upgraded checkout.
- 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 Docker-backed platform, job, and local model-serving workflows
- Docker for local services, Docker-backed platform jobs, local ClickHouse,
Docker Sandboxes, and local model-serving workflows
- ClickHouse for Intake trace storage and Experiments rollups
- NVIDIA GPU access for local training, model serving, and GPU-backed
synthetic data workflows
- Node 22.18.0+ for Studio assets
- Platform API and `nemo-platform` Python SDK `0.3.0`
- Platform API and `nemo-platform` Python SDK `0.4.0`

## Current constraints

- **Self-managed scope.** v0.3.0 documents local setup and user-managed
Kubernetes deployment. It is not a managed hosted-service release.
- **Docker model serving.** v0.3.0 includes vLLM deployment in Docker. Docker
deployment for NIM is not included in this release.
- **Customizer Docker support.** Docker-backed Customizer jobs target GPU
Linux environments. ARM64 images and NeMo Automodel Docker execution are not
part of this release.
- **Auth and RBAC.** Source-install auth and OIDC setups require the bootstrap
IAM seed step described in [Setup](/documentation/get-started). Validate
role bindings for your deployment mode before relying on them for multi-user
access control.
- **Skill Evaluation.** Skill Evaluation workflows are not included in
v0.3.0.
- **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.
- **Local-first issue-driven loop.** The full Insight to Experimentalist loop
runs from a local developer environment against local paths or a git source.
A remote platform can provide Intake and entity APIs, but the issue-driven
optimization agents do not yet run as a horizontally scaled cluster service.
- **Evaluation backend scope.** The Experimentalist loop validates candidates
with Harbor-compatible train and validation datasets. Other evaluator
backends are not the validated path for this loop yet.
- **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.
- **DPO runtime.** Customizer `rl` DPO jobs run on Kubernetes/Ray and do not
have a local Docker fallback. DPO is full-weight only; PEFT/adapter output is
not supported for RL jobs.
- **Experiments feature flag.** The Experiments Studio surface is gated by
`VITE_FF_EXPERIMENT`, which is off by default.
- **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.
- **Python support.** Python 3.11 is no longer part of the supported source
checkout or `nemo-platform` distribution matrix.

## 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)
- 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/)
3 changes: 2 additions & 1 deletion docs/about/release-notes/index.mdx
Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@ description: ""
---
Check out the latest release notes for NeMo Platform.

- [Release 0.3.0](/documentation/reference/release-notes/current-release)
- [Release 0.4.0](/documentation/reference/release-notes/current-release)
- [Release 0.3.0](/documentation/reference/release-notes/release-0-3-0)
- [Release 0.2.0](/documentation/reference/release-notes/release-0-2-0)
- [Release 0.1.0](/documentation/reference/release-notes/release-0-1-0)
120 changes: 120 additions & 0 deletions docs/about/release-notes/release-0-3-0.mdx
Original file line number Diff line number Diff line change
@@ -0,0 +1,120 @@
---
title: "v0.3.0 - 2026-07-28"
description: ""
---
NeMo Platform v0.3.0 expands the OSS distribution beyond local setup with
self-managed Kubernetes and Helm documentation, while continuing to support
the local-first Python package, CLI, SDK, Studio, and plugin workflows.

## Highlights

- **Self-managed Kubernetes.** The docs now cover Helm-based deployment to
user-managed Kubernetes clusters, including local Kind workflows and
cluster prerequisites.
- **Agent deployment docs.** Agent container deployment guidance covers local
and Kubernetes paths and clarifies how SDK, CLI, and REST callers resolve
default model placeholders.
- **Versioned compatibility.** Requirements and support-matrix pages describe
the 0.3.0 release instead of the previous local-only scope.
- **Generated reference docs.** The configuration reference remains generated
from platform config models and renders as regular Fern MDX.

## What's included

### Platform

- Local source-install workflows with `make bootstrap`, `nemo setup`, and
`nemo services run`.
- Self-managed Helm/Kubernetes documentation for users deploying the platform
outside the local developer process.
- Source-install auth and OIDC bootstrap guidance for users who enable RBAC
locally.

### Agents

- NAT-based agent workflow support through `nemo agents`.
- Agent container rendering, building, publishing, and deployment guidance.
- SDK and CLI examples that use the configured default model for agent
registration.

### Models and Inference

- Inference Gateway support for provider registration, virtual models, and
OpenAI-compatible routing.
- Local and provider-backed model workflows for CLI, SDK, Studio, and agents.

### Plugins

- First-party plugin workflows continue from the previous release line,
including Agents, Customizer, Safe Synthesizer, Auditor, Guardrails,
Evaluator, Anonymizer, Data Designer, Switchyard middleware, and
Deployments.
- Plugin docs cover runtime service, CLI, job, controller, inference
middleware, and coding-agent skill surfaces.

## 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) for prerequisites and provider
configuration.

For self-managed Kubernetes, start with
[Install NeMo Platform Helm Chart](/documentation/self-managed-deployment/setup/helm/install).

## Upgrade from v0.2.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.

## 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
- Self-managed Kubernetes clusters deployed with Helm
- Docker for Docker-backed platform, job, and local model-serving workflows
- NVIDIA GPU access for local training, model serving, and GPU-backed
synthetic data workflows
- Node 22.18.0+ for Studio assets
- Platform API and `nemo-platform` Python SDK `0.3.0`

## Current constraints

- **Self-managed scope.** v0.3.0 documents local setup and user-managed
Kubernetes deployment. It is not a managed hosted-service release.
- **Docker model serving.** v0.3.0 includes vLLM deployment in Docker. Docker
deployment for NIM is not included in this release.
- **Customizer Docker support.** Docker-backed Customizer jobs target GPU
Linux environments. ARM64 images and NeMo Automodel Docker execution are not
part of this release.
- **Auth and RBAC.** Source-install auth and OIDC setups require the bootstrap
IAM seed step described in [Setup](/documentation/get-started). Validate
role bindings for your deployment mode before relying on them for multi-user
access control.
- **Skill Evaluation.** Skill Evaluation workflows are not included in
v0.3.0.

## 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)
- NeMo Agent Toolkit: [https://docs.nvidia.com/nemo/agent-toolkit/latest/](https://docs.nvidia.com/nemo/agent-toolkit/latest/)
3 changes: 3 additions & 0 deletions docs/fern/versions/latest.yml
Original file line number Diff line number Diff line change
Expand Up @@ -467,6 +467,9 @@ navigation:
contents:
- page: Current Release
path: ../../about/release-notes/current-release.mdx
- page: v0.3.0
slug: release-0-3-0
path: ../../about/release-notes/release-0-3-0.mdx
- page: v0.2.0
slug: release-0-2-0
path: ../../about/release-notes/release-0-2-0.mdx
Expand Down
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