chore: rename templates to distributions (#3035)

As the title says. Distributions is in, Templates is out.

`llama stack build --template` --> `llama stack build --distro`. For
backward compatibility, the previous option is kept but results in a
warning.

Updated `server.py` to remove the "config_or_template" backward
compatibility since it has been a couple releases since that change.

docs/source/advanced_apis/evaluation_concepts.md

@@ -43,7 +43,7 @@ We have built-in functionality to run the supported open-benckmarks using llama-
 
 Spin up llama stack server with 'open-benchmark' template
 ```
-llama stack run llama_stack/templates/open-benchmark/run.yaml
+llama stack run llama_stack/distributions/open-benchmark/run.yaml
 
 ```
 

docs/source/advanced_apis/post_training/huggingface.md

@@ -23,7 +23,7 @@ To use the HF SFTTrainer in your Llama Stack project, follow these steps:
 You can access the HuggingFace trainer via the `ollama` distribution:
 
 ```bash
-llama stack build --template starter --image-type venv
+llama stack build --distro starter --image-type venv
 llama stack run --image-type venv ~/.llama/distributions/ollama/ollama-run.yaml
 ```
 

docs/source/building_applications/playground/index.md

@@ -97,7 +97,7 @@ To start the Llama Stack Playground, run the following commands:
 1. Start up the Llama Stack API server
 
 ```bash
-llama stack build --template together --image-type venv
+llama stack build --distro together --image-type venv
 llama stack run together
 ```
 

docs/source/contributing/new_api_provider.md

@@ -6,7 +6,7 @@ This guide will walk you through the process of adding a new API provider to Lla
 - Begin by reviewing the [core concepts](../concepts/index.md) of Llama Stack and choose the API your provider belongs to (Inference, Safety, VectorIO, etc.)
 - Determine the provider type ({repopath}`Remote::llama_stack/providers/remote` or {repopath}`Inline::llama_stack/providers/inline`). Remote providers make requests to external services, while inline providers execute implementation locally.
 - Add your provider to the appropriate {repopath}`Registry::llama_stack/providers/registry/`. Specify pip dependencies necessary.
-- Update any distribution {repopath}`Templates::llama_stack/templates/` `build.yaml` and `run.yaml` files if they should include your provider by default. Run {repopath}`./scripts/distro_codegen.py` if necessary. Note that `distro_codegen.py` will fail if the new provider causes any distribution template to attempt to import provider-specific dependencies. This usually means the distribution's `get_distribution_template()` code path should only import any necessary Config or model alias definitions from each provider and not the provider's actual implementation.
+- Update any distribution {repopath}`Templates::llama_stack/distributions/` `build.yaml` and `run.yaml` files if they should include your provider by default. Run {repopath}`./scripts/distro_codegen.py` if necessary. Note that `distro_codegen.py` will fail if the new provider causes any distribution template to attempt to import provider-specific dependencies. This usually means the distribution's `get_distribution_template()` code path should only import any necessary Config or model alias definitions from each provider and not the provider's actual implementation.
 
 
 Here are some example PRs to help you get started:
@@ -52,7 +52,7 @@ 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 --template 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, you should install dependencies via `llama stack build --distro together`.
 
 ### 1. Integration Testing
 

docs/source/distributions/building_distro.md

@@ -141,7 +141,7 @@ You may then pick a template to build your distribution with providers fitted to
 
 For example, to build a distribution with TGI as the inference provider, you can run:
 ```
-$ llama stack build --template starter
+$ llama stack build --distro starter
 ...
 You can now edit ~/.llama/distributions/llamastack-starter/starter-run.yaml and run `llama stack run ~/.llama/distributions/llamastack-starter/starter-run.yaml`
 ```
@@ -184,10 +184,10 @@ You can now edit ~/.llama/distributions/llamastack-my-local-stack/my-local-stack
 :::{tab-item} Building from a pre-existing build config file
 - In addition to templates, you may customize the build to your liking through editing config files and build from config files with the following command.
 
-- The config file will be of contents like the ones in `llama_stack/templates/*build.yaml`.
+- The config file will be of contents like the ones in `llama_stack/distributions/*build.yaml`.
 
 ```
-llama stack build --config llama_stack/templates/starter/build.yaml
+llama stack build --config llama_stack/distributions/starter/build.yaml
 ```
 :::
 
@@ -253,11 +253,11 @@ Podman is supported as an alternative to Docker. Set `CONTAINER_BINARY` to `podm
 To build a container image, you may start off from a template and use the `--image-type container` flag to specify `container` as the build image type.
 
 ```
-llama stack build --template starter --image-type container
+llama stack build --distro starter --image-type container
 ```
 
 ```
-$ llama stack build --template starter --image-type container
+$ llama stack build --distro starter --image-type container
 ...
 Containerfile created successfully in /tmp/tmp.viA3a3Rdsg/ContainerfileFROM python:3.10-slim
 ...

docs/source/distributions/importing_as_library.md

@@ -6,7 +6,7 @@ This avoids the overhead of setting up a server.
 ```bash
 # setup
 uv pip install llama-stack
-llama stack build --template starter --image-type venv
+llama stack build --distro starter --image-type venv
 ```
 
 ```python

docs/source/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
 python -m venv stack-fireworks
 source stack-fireworks/bin/activate  # On Windows: stack-fireworks\Scripts\activate
 pip install --no-cache llama-stack==0.2.2
-llama stack build --template fireworks --image-type venv
+llama stack build --distro fireworks --image-type venv
 export FIREWORKS_API_KEY=<SOME_KEY>
 llama stack run fireworks --port 5050
 ```

docs/source/distributions/self_hosted_distro/dell.md

@@ -153,7 +153,7 @@ docker run \
   --pull always \
   -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
   -v $HOME/.llama:/root/.llama \
-  -v ./llama_stack/templates/tgi/run-with-safety.yaml:/root/my-run.yaml \
+  -v ./llama_stack/distributions/tgi/run-with-safety.yaml:/root/my-run.yaml \
   llamastack/distribution-dell \
   --config /root/my-run.yaml \
   --port $LLAMA_STACK_PORT \
@@ -169,7 +169,7 @@ docker run \
 Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
 
 ```bash
-llama stack build --template dell --image-type venv
+llama stack build --distro dell --image-type venv
 llama stack run dell
   --port $LLAMA_STACK_PORT \
   --env INFERENCE_MODEL=$INFERENCE_MODEL \

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

@@ -109,7 +109,7 @@ docker run \
 Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
 
 ```bash
-llama stack build --template meta-reference-gpu --image-type venv
+llama stack build --distro meta-reference-gpu --image-type venv
 llama stack run distributions/meta-reference-gpu/run.yaml \
   --port 8321 \
   --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct

docs/source/distributions/self_hosted_distro/nvidia.md

@@ -158,7 +158,7 @@ If you've set up your local development environment, you can also build the imag
 
 ```bash
 INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct
-llama stack build --template nvidia --image-type venv
+llama stack build --distro nvidia --image-type venv
 llama stack run ./run.yaml \
   --port 8321 \
   --env NVIDIA_API_KEY=$NVIDIA_API_KEY \

docs/source/distributions/self_hosted_distro/starter.md

@@ -169,7 +169,7 @@ docker run \
 Ensure you have configured the starter distribution using the environment variables explained above.
 
 ```bash
-uv run --with llama-stack llama stack build --template starter --image-type venv --run
+uv run --with llama-stack llama stack build --distro starter --image-type venv --run
 ```
 
 ## Example Usage

docs/source/getting_started/detailed_tutorial.md

@@ -59,7 +59,7 @@ Now let's build and run the Llama Stack config for Ollama.
 We use `starter` as template. By default all providers are disabled, this requires enable ollama by passing environment variables.
 
 ```bash
-llama stack build --template starter --image-type venv --run
+llama stack build --distro starter --image-type venv --run
 ```
 :::
 :::{tab-item} Using `venv`
@@ -70,7 +70,7 @@ which defines the providers and their settings.
 Now let's build and run the Llama Stack config for Ollama.
 
 ```bash
-llama stack build --template starter --image-type venv --run
+llama stack build --distro starter --image-type venv --run
 ```
 :::
 :::{tab-item} Using a Container

docs/source/getting_started/quickstart.md

@@ -22,7 +22,7 @@ ollama run llama3.2:3b --keepalive 60m
 We will use `uv` to run the Llama Stack server.
 ```bash
 OLLAMA_URL=http://localhost:11434 \
-  uv run --with llama-stack llama stack build --template starter --image-type venv --run
+  uv run --with llama-stack llama stack build --distro starter --image-type venv --run
 ```
 #### Step 3: Run the demo
 Now open up a new terminal and copy the following script into a file named `demo_script.py`.

docs/source/references/evals_reference/index.md

@@ -366,7 +366,7 @@ The purpose of scoring function is to calculate the score for each example based
 Firstly, you can see if the existing [llama stack scoring functions](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/inline/scoring) can fulfill your need. If not, you need to write a new scoring function based on what benchmark author / other open source repo describe.
 
 ### Add new benchmark into template
-Firstly, you need to add the evaluation dataset associated with your benchmark under `datasets` resource in the [open-benchmark](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/templates/open-benchmark/run.yaml)
+Firstly, you need to add the evaluation dataset associated with your benchmark under `datasets` resource in the [open-benchmark](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/distributions/open-benchmark/run.yaml)
 
 Secondly, you need to add the new benchmark you just created under the `benchmarks` resource in the same template. To add the new benchmark, you need to have
 - `benchmark_id`: identifier of the benchmark
@@ -378,7 +378,7 @@ Secondly, you need to add the new benchmark you just created under the `benchmar
 
 Spin up llama stack server with 'open-benchmark' templates
 ```
-llama stack run llama_stack/templates/open-benchmark/run.yaml
+llama stack run llama_stack/distributions/open-benchmark/run.yaml
 
 ```