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 run ~/.llama/distributions/starter/starter-run.yaml
+llama stack list-deps starter | xargs -L1 uv pip install
+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 build --distro together --image-type venv
+llama stack list-deps together | xargs -L1 uv pip install
 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`.