chore: update doc (#3857)

# What does this PR do?
follows https://github.com/llamastack/llama-stack/pull/3839

## Test Plan

docs/docs/advanced_apis/post_training.mdx

@@ -51,8 +51,8 @@ device: cpu
 You can access the HuggingFace trainer via the `starter` distribution:
 
 ```bash
-llama stack build --distro starter --image-type venv
+llama stack list-deps starter | xargs -L1 uv pip install
-llama stack run ~/.llama/distributions/starter/starter-run.yaml
+llama stack run starter
 ```
 
 ### Usage Example

docs/docs/building_applications/playground.mdx

@@ -175,8 +175,7 @@ llama-stack-client benchmarks register \
 **1. Start the Llama Stack API Server**
 
 ```bash
-# Build and run a distribution (example: together)
+llama stack list-deps together | xargs -L1 uv pip install
-llama stack build --distro together --image-type venv
 llama stack run together
 ```
 
@@ -209,7 +208,7 @@ The playground works with any Llama Stack distribution. Popular options include:
 <TabItem value="together" label="Together AI">
 
 ```bash
-llama stack build --distro together --image-type venv
+llama stack list-deps together | xargs -L1 uv pip install
 llama stack run together
 ```
 
@@ -222,7 +221,7 @@ llama stack run together
 <TabItem value="ollama" label="Ollama (Local)">
 
 ```bash
-llama stack build --distro ollama --image-type venv
+llama stack list-deps ollama | xargs -L1 uv pip install
 llama stack run ollama
 ```
 
@@ -235,7 +234,7 @@ llama stack run ollama
 <TabItem value="meta-reference" label="Meta Reference">
 
 ```bash
-llama stack build --distro meta-reference --image-type venv
+llama stack list-deps meta-reference | xargs -L1 uv pip install
 llama stack run meta-reference
 ```
 

docs/docs/building_applications/rag.mdx

@@ -20,7 +20,8 @@ RAG enables your applications to reference and recall information from external
 In one terminal, start the Llama Stack server:
 
 ```bash
-uv run llama stack build --distro starter --image-type venv --run
+llama stack list-deps starter | xargs -L1 uv pip install
+llama stack run starter
 ```
 
 ### 2. Connect with OpenAI Client

docs/docs/contributing/new_api_provider.mdx

@@ -67,7 +67,7 @@ def get_base_url(self) -> str:
 
 ## Testing the Provider
 
-Before running tests, you must have required dependencies installed. This depends on the providers or distributions you are testing. For example, if you are testing the `together` distribution, you should install dependencies via `llama stack build --distro together`.
+Before running tests, you must have required dependencies installed. This depends on the providers or distributions you are testing. For example, if you are testing the `together` distribution, install its dependencies with `llama stack list-deps together | xargs -L1 uv pip install`.
 
 ### 1. Integration Testing
 

docs/docs/distributions/importing_as_library.mdx

@@ -12,7 +12,7 @@ This avoids the overhead of setting up a server.
 ```bash
 # setup
 uv pip install llama-stack
-llama stack build --distro starter --image-type venv
+llama stack list-deps starter | xargs -L1 uv pip install
 ```
 
 ```python

docs/docs/distributions/ondevice_distro/android_sdk.md

@@ -59,7 +59,7 @@ Start a Llama Stack server on localhost. Here is an example of how you can do th
 uv venv starter --python 3.12
 source starter/bin/activate  # On Windows: starter\Scripts\activate
 pip install --no-cache llama-stack==0.2.2
-llama stack build --distro starter --image-type venv
+llama stack list-deps starter | xargs -L1 uv pip install
 export FIREWORKS_API_KEY=<SOME_KEY>
 llama stack run starter --port 5050
 ```

docs/docs/distributions/self_hosted_distro/dell.md

@@ -166,10 +166,10 @@ docker run \
 
 ### Via venv
 
-Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
+Install the distribution dependencies before launching:
 
 ```bash
-llama stack build --distro dell --image-type venv
+llama stack list-deps dell | xargs -L1 uv pip install
 INFERENCE_MODEL=$INFERENCE_MODEL \
 DEH_URL=$DEH_URL \
 CHROMA_URL=$CHROMA_URL \

docs/docs/distributions/self_hosted_distro/meta-reference-gpu.md

@@ -81,10 +81,10 @@ docker run \
 
 ### Via venv
 
-Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
+Make sure you have the Llama Stack CLI available.
 
 ```bash
-llama stack build --distro meta-reference-gpu --image-type venv
+llama stack list-deps meta-reference-gpu | xargs -L1 uv pip install
 INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
 llama stack run distributions/meta-reference-gpu/run.yaml \
   --port 8321

docs/docs/distributions/self_hosted_distro/nvidia.md

@@ -136,11 +136,11 @@ docker run \
 
 ### Via venv
 
-If you've set up your local development environment, you can also build the image using your local virtual environment.
+If you've set up your local development environment, you can also install the distribution dependencies using your local virtual environment.
 
 ```bash
 INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct
-llama stack build --distro nvidia --image-type venv
+llama stack list-deps nvidia | xargs -L1 uv pip install
 NVIDIA_API_KEY=$NVIDIA_API_KEY \
 INFERENCE_MODEL=$INFERENCE_MODEL \
 llama stack run ./run.yaml \

docs/docs/providers/external/external-providers-guide.mdx

@@ -240,6 +240,6 @@ additional_pip_packages:
 - sqlalchemy[asyncio]
 ```
 
-No other steps are required other than `llama stack build` and `llama stack run`. The build process will use `module` to install all of the provider dependencies, retrieve the spec, etc.
+No other steps are required beyond installing dependencies with `llama stack list-deps <distro> | xargs -L1 uv pip install` and then running `llama stack run`. The CLI will use `module` to install the provider dependencies, retrieve the spec, etc.
 
 The provider will now be available in Llama Stack with the type `remote::ramalama`.

docs/getting_started.ipynb

@@ -123,7 +123,8 @@
     "  del os.environ[\"UV_SYSTEM_PYTHON\"]\n",
     "\n",
     "# this command installs all the dependencies needed for the llama stack server with the together inference provider\n",
-        "!uv run --with llama-stack llama stack build --distro together\n",
+    "!uv run --with llama-stack llama stack list-deps together | xargs -L1 uv pip install\n",
+    "!uv run --with llama-stack llama stack run together\n",
     "\n",
     "def run_llama_stack_server_background():\n",
     "    log_file = open(\"llama_stack_server.log\", \"w\")\n",

docs/getting_started_llama4.ipynb

@@ -233,7 +233,8 @@
     "  del os.environ[\"UV_SYSTEM_PYTHON\"]\n",
     "\n",
     "# this command installs all the dependencies needed for the llama stack server\n",
-        "!uv run --with llama-stack llama stack build --distro meta-reference-gpu\n",
+    "!uv run --with llama-stack llama stack list-deps meta-reference-gpu | xargs -L1 uv pip install\n",
+    "!uv run --with llama-stack llama stack run meta-reference-gpu\n",
     "\n",
     "def run_llama_stack_server_background():\n",
     "    log_file = open(\"llama_stack_server.log\", \"w\")\n",

docs/getting_started_llama_api.ipynb

@@ -223,7 +223,8 @@
     "  del os.environ[\"UV_SYSTEM_PYTHON\"]\n",
     "\n",
     "# this command installs all the dependencies needed for the llama stack server\n",
-        "!uv run --with llama-stack llama stack build --distro llama_api\n",
+    "!uv run --with llama-stack llama stack list-deps llama_api | xargs -L1 uv pip install\n",
+    "!uv run --with llama-stack llama stack run llama_api\n",
     "\n",
     "def run_llama_stack_server_background():\n",
     "    log_file = open(\"llama_stack_server.log\", \"w\")\n",

docs/notebooks/Alpha_Llama_Stack_Post_Training.ipynb

@@ -2864,7 +2864,7 @@
     }
    ],
    "source": [
-    "!llama stack build --distro experimental-post-training --image-type venv --image-name __system__"
+    "!llama stack list-deps experimental-post-training | xargs -L1 uv pip install"
    ]
   },
   {

docs/notebooks/Llama_Stack_Agent_Workflows.ipynb

@@ -38,7 +38,7 @@
    "source": [
     "# NBVAL_SKIP\n",
     "!pip install -U llama-stack\n",
-    "!UV_SYSTEM_PYTHON=1 llama stack build --distro fireworks --image-type venv"
+    "llama stack list-deps fireworks | xargs -L1 uv pip install\n"
    ]
   },
   {

docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb

@@ -57,7 +57,7 @@
    "outputs": [],
    "source": [
     "# NBVAL_SKIP\n",
-        "!UV_SYSTEM_PYTHON=1 llama stack build --distro together --image-type venv"
+    "!uv run llama stack list-deps together | xargs -L1 uv pip install\n"
    ]
   },
   {

docs/notebooks/crewai/Llama_Stack_CrewAI.ipynb

@@ -136,7 +136,8 @@
     "    \"\"\"Build and run LlamaStack server in one step using --run flag\"\"\"\n",
     "    log_file = open(\"llama_stack_server.log\", \"w\")\n",
     "    process = subprocess.Popen(\n",
-    "        \"uv run --with llama-stack llama stack build --distro starter --image-type venv --run\",\n",
+    "        \"uv run --with llama-stack llama stack list-deps starter | xargs -L1 uv pip install\",\n",
+    "        \"uv run --with llama-stack llama stack run starter\",\n",
     "        shell=True,\n",
     "        stdout=log_file,\n",
     "        stderr=log_file,\n",
@@ -172,7 +173,7 @@
     "\n",
     "def kill_llama_stack_server():\n",
     "    # Kill any existing llama stack server processes using pkill command\n",
-    "    os.system(\"pkill -f llama_stack.core.server.server\")"
+    "    os.system(\"pkill -f llama_stack.core.server.server\")\n"
    ]
   },
   {

docs/notebooks/langchain/Llama_Stack_LangChain.ipynb

@@ -105,7 +105,8 @@
     "    \"\"\"Build and run LlamaStack server in one step using --run flag\"\"\"\n",
     "    log_file = open(\"llama_stack_server.log\", \"w\")\n",
     "    process = subprocess.Popen(\n",
-    "        \"uv run --with llama-stack llama stack build --distro starter --image-type venv --run\",\n",
+    "        \"uv run --with llama-stack llama stack list-deps starter | xargs -L1 uv pip install\",\n",
+    "        \"uv run --with llama-stack llama stack run starter\",\n",
     "        shell=True,\n",
     "        stdout=log_file,\n",
     "        stderr=log_file,\n",

docs/notebooks/nvidia/beginner_e2e/Llama_Stack_NVIDIA_E2E_Flow.ipynb

@@ -92,7 +92,7 @@
    "metadata": {},
    "source": [
     "```bash\n",
-    "LLAMA_STACK_DIR=$(pwd) llama stack build --distro nvidia --image-type venv\n",
+    "uv run --with llama-stack llama stack list-deps nvidia | xargs -L1 uv pip install\n",
     "```"
    ]
   },

docs/notebooks/nvidia/tool_calling/1_data_preparation.ipynb

@@ -81,7 +81,7 @@
    "metadata": {},
    "source": [
     "```bash\n",
-    "LLAMA_STACK_DIR=$(pwd) llama stack build --distro nvidia --image-type venv\n",
+    "uv run --with llama-stack llama stack list-deps nvidia | xargs -L1 uv pip install\n",
     "```"
    ]
   },

docs/quick_start.ipynb

@@ -145,7 +145,7 @@
     "  del os.environ[\"UV_SYSTEM_PYTHON\"]\n",
     "\n",
     "# this command installs all the dependencies needed for the llama stack server with the ollama inference provider\n",
-        "!uv run --with llama-stack llama stack build --distro starter\n",
+    "!uv run --with llama-stack llama stack list-deps starter | xargs -L1 uv pip install\n",
     "\n",
     "def run_llama_stack_server_background():\n",
     "    log_file = open(\"llama_stack_server.log\", \"w\")\n",

docs/src/pages/index.js

@@ -47,11 +47,11 @@ function QuickStart() {
               <pre><code>{`# Install uv and start Ollama
 ollama run llama3.2:3b --keepalive 60m
 
+# Install server dependencies
+uv run --with llama-stack llama stack list-deps starter | xargs -L1 uv pip install
+
 # Run Llama Stack server
-OLLAMA_URL=http://localhost:11434 \\
+OLLAMA_URL=http://localhost:11434 uv run --with llama-stack llama stack run starter
-  uv run --with llama-stack \\
-  llama stack build --distro starter \\
-  --image-type venv --run
 
 # Try the Python SDK
 from llama_stack_client import LlamaStackClient

docs/zero_to_hero_guide/README.md

@@ -78,17 +78,14 @@ If you're looking for more specific topics, we have a [Zero to Hero Guide](#next
 
 ## Build, Configure, and Run Llama Stack
 
-1. **Build the Llama Stack**:
+1. **Install dependencies**:
-   Build the Llama Stack using the `starter` template:
    ```bash
-   uv run --with llama-stack llama stack build --distro starter --image-type venv
+   llama stack list-deps starter | xargs -L1 uv pip install
    ```
-   **Expected Output:**
+
+2. **Start the distribution**:
    ```bash
-   ...
+   llama stack run starter
-   Build Successful!
-   You can find the newly-built template here: ~/.llama/distributions/starter/starter-run.yaml
-   You can run the new Llama Stack Distro via: uv run --with llama-stack llama stack run starter
    ```
 
 3. **Set the ENV variables by exporting them to the terminal**:

llama_stack/distributions/meta-reference-gpu/doc_template.md

@@ -70,10 +70,10 @@ docker run \
 
 ### Via venv
 
-Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
+Make sure you have the Llama Stack CLI available.
 
 ```bash
-llama stack build --distro {{ name }} --image-type venv
+llama stack list-deps meta-reference-gpu | xargs -L1 uv pip install
 INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
 llama stack run distributions/{{ name }}/run.yaml \
   --port 8321

llama_stack/distributions/nvidia/doc_template.md

@@ -126,11 +126,11 @@ docker run \
 
 ### Via venv
 
-If you've set up your local development environment, you can also build the image using your local virtual environment.
+If you've set up your local development environment, you can also install the distribution dependencies using your local virtual environment.
 
 ```bash
 INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct
-llama stack build --distro nvidia --image-type venv
+llama stack list-deps nvidia | xargs -L1 uv pip install
 NVIDIA_API_KEY=$NVIDIA_API_KEY \
 INFERENCE_MODEL=$INFERENCE_MODEL \
 llama stack run ./run.yaml \