Fix observability_evaluation_and_profiling example notebook (#1874)

* This notebook was installing the `nvidia-nat-profiling` package which was dropped in v1.5, causing the notebook to install nat v1.3
* Replace the model with a nano model to avoid being rate limited during the eval steps.
* Update `migration-guide.md` to fix profiler installation instructions.
* Replace broken documentation links (unrelated link check errors found in CI)
 
## By Submitting this PR I confirm:
- I am familiar with the [Contributing Guidelines](https://github.com/NVIDIA/NeMo-Agent-Toolkit/blob/develop/docs/source/resources/contributing/index.md).
- We require that all contributors "sign-off" on their commits. This certifies that the contribution is your original work, or you have rights to submit it under the same license, or a compatible license.
  - Any contribution which contains commits that are not Signed-Off will not be accepted.
- When the PR is ready for review, new or existing tests cover these changes.
- When the PR is ready for review, the documentation is up to date with these changes.



## Summary by CodeRabbit

* **Documentation**
  * Updated NeMo Customizer links in the finetuning guide; revised packaging/install guidance to replace the profiling extra with profiler and document the eval/profiler split.

* **Examples**
  * Updated Microservices setup link and cleaned README whitespace/formatting.

* **Notebooks**
  * Switched profiling extra name to profiler and added eval where applicable; updated generated model/config defaults (model selection and token limits) and bumped notebook kernel/Python metadata.

Authors:
  - David Gardner (https://github.com/dagardner-nv)

Approvers:
  - https://github.com/mnajafian-nv
  - Bryan Bednarski (https://github.com/bbednarski9)

URL: #1874

Author: dagardner-nv

docs/source/improve-workflows/finetuning/dpo_with_nemo_customizer.md

@@ -19,7 +19,7 @@ limitations under the License.
 
 # DPO with NeMo Customizer
 
-This guide covers Direct Preference Optimization (DPO) training using the NeMo Agent Toolkit finetuning harness integrated with [NVIDIA NeMo Customizer](https://docs.nvidia.com/nemo/microservices/latest/fine-tune/index.html). 
+This guide covers Direct Preference Optimization (DPO) training using the NeMo Agent Toolkit finetuning harness integrated with [NVIDIA NeMo Customizer](https://docs.nvidia.com/nemo/microservices/latest/customizer/index.html).
 This integration enables preference-based finetuning of large language models using NVIDIA's enterprise-grade training infrastructure.
 
 ## Understanding DPO
@@ -947,4 +947,4 @@ See the example's README for detailed instructions.
 - [Extending the Finetuning Harness](../../extend/custom-components/finetuning.md) - Creating custom components
 - [OpenPipe ART Integration](rl_with_openpipe.md) - Alternative RL training with ART
 - [Custom Evaluators](../../extend/custom-components/custom-evaluator.md) - Creating reward functions
-- [NeMo Customizer Documentation](https://docs.nvidia.com/nemo/microservices/latest/fine-tune/index.html) - Official NeMo Customizer documentation
+- [NeMo Customizer Documentation](https://docs.nvidia.com/nemo/microservices/latest/customizer/index.html) - Official NeMo Customizer documentation

docs/source/resources/migration-guide.md

@@ -133,19 +133,19 @@ NVIDIA NeMo Agent Toolkit 1.2 changed the name and API. Compatibility aliases an
 - All prior meta-packages have been removed.
   - `nvidia-nat-all` (no replacement, though `nvidia-nat[most]` extra does exist)
   - `nvidia-nat-ingestion` (no replacement; examples directly use dependencies)
-  - `nvidia-nat-profiling` (use `nvidia-nat[profiling]`)
+  - `nvidia-nat-profiling` (use `nvidia-nat[profiler]`)
 
 #### Evaluation Package Split
 
-Evaluation and profiling implementations moved out of core into the `nvidia-nat-eval` package.
+Evaluation and profiling implementations moved out of core into the `nvidia-nat-eval` and `nvidia-nat-profiler` packages.
 
 To migrate:
 - Install evaluation support when needed:
   - `pip install "nvidia-nat[eval]"`
   - `pip install nvidia-nat-eval`
 - Install profiling support when needed:
-  - `pip install "nvidia-nat[profiling]"`
-  - `pip install "nvidia-nat-eval[profiling]"`
+  - `pip install "nvidia-nat[profiler]"`
+  - `pip install "nvidia-nat[eval, profiler]"`
 - Treat these commands as eval-owned commands that require `nvidia-nat-eval`: `nat eval`, `nat red-team`, and `nat sizing`.
 
 #### Import Path Changes

examples/finetuning/dpo_tic_tac_toe/README.md

@@ -53,7 +53,7 @@ The model learns to prefer responses similar to the chosen examples while avoidi
 ## Prerequisites
 
 > [!IMPORTANT]
-> This example assumes you are already familiar with the NVIDIA NeMo Microservices platform and have it set up and running. If you're new to NeMo Microservices, please refer to the [NeMo Microservices Setup Guide](https://docs.nvidia.com/nemo/microservices/latest/get-started/setup/index.html) first.
+> This example assumes you are already familiar with the NVIDIA NeMo Microservices platform and have it set up and running. If you're new to NeMo Microservices, please refer to the [NeMo Microservices Setup Guide](https://docs.nvidia.com/nemo/microservices/latest/index.html) first.
 
 ### 1. Python Environment
 
@@ -276,10 +276,10 @@ For non-terminal positions without forced outcomes, the scorer uses these featur
 
 ## Installation
 
-This example is meant to be run using a NeMo Agent Toolkit installation from source. You 
+This example is meant to be run using a NeMo Agent Toolkit installation from source. You
 can follow the [NeMo Agent Toolkit Installation Guide](../../../docs/source/get-started/installation.md) to set up your environment.
 
-Then: 
+Then:
 
 ```bash
 uv pip install -e examples/finetuning/dpo_tic_tac_toe

examples/notebooks/observability_evaluation_and_profiling.ipynb

@@ -572,10 +572,11 @@
    },
    "source": [
     "There are several optional subpackages available for `nvidia-nat`. For this example, we will rely on three subpackages:\n",
+    "* The `eval` subpackage contains components for evaluation.\n",
     "* The `langchain` subpackage contains useful components for integrating and running within [LangChain](https://python.langchain.com/docs/introduction/).\n",
     "* The `llama-index` subpackage contains useful components for integrating and running within [LlamaIndex](https://developers.llamaindex.ai/python/framework/).\n",
     "* The `phoenix` subpackage contains components for integrating with [Phoenix](https://phoenix.arize.com/).\n",
-    "* The `profiling` subpackage contains components common for profiling with NeMo Agent Toolkit."
+    "* The `profiler` subpackage contains components common for profiling with NeMo Agent Toolkit."
    ]
   },
   {
@@ -589,18 +590,20 @@
    "outputs": [],
    "source": [
     "%%bash\n",
+    "uv pip show -q \"nvidia-nat-eval\"\n",
+    "nat_eval_installed=$?\n",
     "uv pip show -q \"nvidia-nat-langchain\"\n",
     "nat_langchain_installed=$?\n",
     "uv pip show -q \"nvidia-nat-llama-index\"\n",
     "nat_llama_index_installed=$?\n",
     "uv pip show -q \"nvidia-nat-phoenix\"\n",
     "nat_phoenix_installed=$?\n",
-    "uv pip show -q \"nvidia-nat-profiling\"\n",
-    "nat_profiling_installed=$?\n",
-    "if [[ ${nat_langchain_installed} -ne 0 || ${nat_llama_index_installed} -ne 0 || ${nat_phoenix_installed} -ne 0 || ${nat_profiling_installed} -ne 0 ]]; then\n",
-    "    uv pip install \"nvidia-nat[langchain,llama-index,phoenix,profiling]\"\n",
+    "uv pip show -q \"nvidia-nat-profiler\"\n",
+    "nat_profiler_installed=$?\n",
+    "if [[ ${nat_eval_installed} -ne 0 || ${nat_langchain_installed} -ne 0 || ${nat_llama_index_installed} -ne 0 || ${nat_phoenix_installed} -ne 0 || ${nat_profiler_installed} -ne 0 ]]; then\n",
+    "    uv pip install \"nvidia-nat[eval,langchain,llama-index,phoenix,profiler]\"\n",
     "else\n",
-    "    echo \"nvidia-nat[langchain,llama-index,phoenix,profiling] is already installed\"\n",
+    "    echo \"nvidia-nat[eval,langchain,llama-index,phoenix,profiler] is already installed\"\n",
     "fi"
    ]
   },
@@ -1101,10 +1104,9 @@
     "llms:\n",
     "  nim_llm:\n",
     "    _type: nim\n",
-    "    model_name: meta/llama-3.3-70b-instruct\n",
+    "    model_name: nvidia/nemotron-3-nano-30b-a3b\n",
     "    temperature: 0.0\n",
-    "    max_tokens: 2048\n",
-    "    context_window: 32768\n",
+    "    max_tokens: 16384\n",
     "    api_key: $NVIDIA_API_KEY\n",
     "\n",
     "embedders:\n",
@@ -1881,6 +1883,11 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -1891,7 +1898,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.9"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,

examples/notebooks/optimize_model_selection.ipynb

@@ -176,12 +176,12 @@
     "%%bash\n",
     "uv pip show -q \"nvidia-nat-langchain\"\n",
     "nat_langchain_installed=$?\n",
-    "uv pip show -q \"nvidia-nat-profiling\"\n",
-    "nat_profiling_installed=$?\n",
-    "if [[ ${nat_langchain_installed} -ne 0 || ${nat_profiling_installed} -ne 0 ]]; then\n",
-    "    uv pip install \"nvidia-nat[langchain,profiling]\"\n",
+    "uv pip show -q \"nvidia-nat-profiler\"\n",
+    "nat_profiler_installed=$?\n",
+    "if [[ ${nat_langchain_installed} -ne 0 || ${nat_profiler_installed} -ne 0 ]]; then\n",
+    "    uv pip install \"nvidia-nat[langchain,profiler]\"\n",
     "else\n",
-    "    echo \"nvidia-nat[langchain,profiling] is already installed\"\n",
+    "    echo \"nvidia-nat[langchain,profiler] is already installed\"\n",
     "fi"
    ]
   },