More simplification of the "Starting a Llama Stack" doc

docs/source/distributions/importing_as_library.md

@@ -1,6 +1,6 @@
-# Importing Llama Stack as a Python Library
+# Using Llama Stack as a Library
 
-Llama Stack is typically utilized in a client-server configuration. To get started quickly, you can import Llama Stack as a library and call the APIs directly without needing to set up a server. For [example](https://github.com/meta-llama/llama-stack-client-python/blob/main/src/llama_stack_client/lib/direct/test.py):
+If you are planning to use an external service for Inference (even Ollama or TGI counts as external), it is often easier to use Llama Stack as a library. This avoids the overhead of setting up a server. For [example](https://github.com/meta-llama/llama-stack-client-python/blob/main/src/llama_stack_client/lib/direct/test.py):
 
 ```python
 from llama_stack_client.lib.direct.direct import LlamaStackDirectClient

docs/source/distributions/index.md

@@ -4,31 +4,33 @@
 :hidden:
 
 importing_as_library
-self_hosted_distro/index
-remote_hosted_distro/index
 building_distro
-ondevice_distro/index
 ```
 
-You can start a Llama Stack server using "distributions" (see [Concepts](../concepts/index)) in one of the following ways:
-- **Docker**: we provide a number of pre-built Docker containers allowing you to get started instantly. If you are focused on application development, we recommend this option. You can also build your own custom Docker container.
-- **Conda**: the `llama` CLI provides a simple set of commands to build, configure and run a Llama Stack server containing the exact combination of providers you wish. We have provided various templates to make getting started easier.
+<!-- self_hosted_distro/index -->
+<!-- remote_hosted_distro/index -->
+<!-- ondevice_distro/index -->
 
-Which distribution to choose depends on the hardware you have for running LLM inference.
+You can instantiate a Llama Stack in one of the following ways:
+- **As a Library**: this is the simplest, especially if you are using an external inference service. See [Using Llama Stack as a Library](importing_as_library)
+- **Docker**: we provide a number of pre-built Docker containers so you can start a Llama Stack server instantly. You can also build your own custom Docker container.
+- **Conda**: finally, you can build a custom Llama Stack server using `llama stack build` containing the exact combination of providers you wish. We have provided various templates to make getting started easier.
+
+Which templates / distributions to choose depends on the hardware you have for running LLM inference.
 
 - **Do you have access to a machine with powerful GPUs?**
 If so, we suggest:
-  - [distribution-remote-vllm](self_hosted_distro/remote-vllm)
-  - [distribution-meta-reference-gpu](self_hosted_distro/meta-reference-gpu)
-  - [distribution-tgi](self_hosted_distro/tgi)
+  - {dockerhub}`distribution-remote-vllm` ([Guide](self_hosted_distro/remote-vllm))
+  - {dockerhub}`distribution-meta-reference-gpu` ([Guide](self_hosted_distro/meta-reference-gpu))
+  - {dockerhub}`distribution-tgi` ([Guide](self_hosted_distro/tgi))
 
 - **Are you running on a "regular" desktop machine?**
 If so, we suggest:
-  - [distribution-ollama](self_hosted_distro/ollama)
+  - {dockerhub}`distribution-ollama` ([Guide](self_hosted_distro/ollama))
 
 - **Do you have an API key for a remote inference provider like Fireworks, Together, etc.?** If so, we suggest:
-  - [distribution-together](remote_hosted_distro/index)
-  - [distribution-fireworks](remote_hosted_distro/index)
+  - {dockerhub}`distribution-together` ([Guide](remote_hosted_distro/index))
+  - {dockerhub}`distribution-fireworks` ([Guide](remote_hosted_distro/index))
 
 - **Do you want to run Llama Stack inference on your iOS / Android device** If so, we suggest:
   - [iOS](ondevice_distro/ios_sdk)