Add neuronx-cc installation cell before compilation

labs/FineTuning/HuggingFaceExample/01_finetuning/Finetune-Qwen3-1.7B.ipynb

@@ -105,6 +105,13 @@
     "Neuron compilation optimizes the model graph and converts it into a Neuron Executable File Format (NEFF), enabling efficient execution on NeuronCores."
    ]
   },
+  {
+   "cell_type": "code",
+   "source": "!pip install --upgrade neuronx-cc==2.21.* --extra-index-url=https://pip.repos.neuron.amazonaws.com",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -219,4 +226,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

labs/vLLM/Chess/Chess-Deployment.ipynb

@@ -146,6 +146,11 @@
     "MODEL_PATH=PRECOMPILED_MODEL_PATH"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "source": "## Step 4.5: Configure Environment for Testing Your Fine-Tuned Model (Optional)\n\nIf you want to test your own fine-tuned model against Stockfish later in this notebook, you'll need to update the environment configuration file.\n\n**When to do this:**\n- After you've confirmed your fine-tuned model exists at `/home/ubuntu/environment/ml/qwen-chess/compiled_model`\n- Before running the Stockfish games in Step 8\n\n**How to configure:**\n\n1. Open the environment configuration file:\n   ```bash\n   # From the terminal or a new cell:\n   nano /home/ubuntu/environment/vLLM/Chess/assets/env.example\n   ```\n\n2. Update the `VLLM_MODEL` line to point to your compiled model:\n   ```bash\n   # Change this line:\n   VLLM_MODEL=kunhunjon/ChessLM_Qwen3_Trainium_AWS_Format\n   \n   # To this:\n   VLLM_MODEL=/home/ubuntu/environment/ml/qwen-chess/compiled_model\n   ```\n\n3. Save the file (in nano: Ctrl+O, Enter, Ctrl+X)\n\n**Note:** You can skip this step for now if you want to use the pre-compiled model first. You can always come back and update the configuration later to test your own fine-tuned model.",
+   "metadata": {}
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -553,4 +558,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}