Write some docs

docs/source/getting_started/distributions/self_hosted_distro/ollama.md

@@ -2,17 +2,21 @@
 
 The `llamastack/distribution-ollama` distribution consists of the following provider configurations.
 
-| **API**         	| **Inference**  	| **Agents**     	| **Memory**                       	| **Safety**     	| **Telemetry**  	|
+| **API**         	| **Inference**  	| **Agents**     	| **Memory**                       	  | **Safety**     	| **Telemetry**  	|
-|-----------------	|----------------	|----------------	|----------------------------------	|----------------	|----------------	|
+|-----------------	|----------------	|----------------	|------------------------------------	|----------------	|----------------	|
-| **Provider(s)** 	| remote::ollama 	| meta-reference 	| remote::pgvector, remote::chroma 	| remote::ollama 	| meta-reference 	|
+| **Provider(s)** 	| remote::ollama 	| meta-reference 	| remote::pgvector, remote::chromadb 	| meta-reference 	| meta-reference 	|
 
 
+## Using Docker Compose
+
+You can use `docker compose` to start a Ollama server and connect with Llama Stack server in a single command.
+
 ### Docker: Start the Distribution (Single Node regular Desktop machine)
 
 > [!NOTE]
 > This will start an ollama server with CPU only, please see [Ollama Documentations](https://github.com/ollama/ollama) for serving models on CPU only.
 
-```
+```bash
 $ cd distributions/ollama; docker compose up
 ```
 
@@ -21,12 +25,12 @@ $ cd distributions/ollama; docker compose up
 > [!NOTE]
 > This assumes you have access to GPU to start a Ollama server with access to your GPU.
 
-```
+```bash
 $ cd distributions/ollama-gpu; docker compose up
 ```
 
 You will see outputs similar to following ---
-```
+```bash
 [ollama]               | [GIN] 2024/10/18 - 21:19:41 | 200 |     226.841µs |             ::1 | GET      "/api/ps"
 [ollama]               | [GIN] 2024/10/18 - 21:19:42 | 200 |      60.908µs |             ::1 | GET      "/api/ps"
 INFO:     Started server process [1]
@@ -40,24 +44,24 @@ INFO:     Uvicorn running on http://[::]:5000 (Press CTRL+C to quit)
 ```
 
 To kill the server
-```
+```bash
 docker compose down
 ```
 
-### Conda: ollama run + llama stack run
+## Starting Ollama and Llama Stack separately
 
-If you wish to separately spin up a Ollama server, and connect with Llama Stack, you may use the following commands.
+If you wish to separately spin up a Ollama server, and connect with Llama Stack, you should use the following commands.
 
-#### Start Ollama server.
+#### Start Ollama server
-- Please check the [Ollama Documentations](https://github.com/ollama/ollama) for more details.
+- Please check the [Ollama Documentation](https://github.com/ollama/ollama) for more details.
 
 **Via Docker**
-```
+```bash
 docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
 ```
 
 **Via CLI**
-```
+```bash
 ollama run <model_id>
 ```
 
@@ -65,7 +69,7 @@ ollama run <model_id>
 
 **Via Conda**
 
-```
+```bash
 llama stack build --template ollama --image-type conda
 llama stack run ./gpu/run.yaml
 ```
@@ -76,7 +80,7 @@ docker run --network host -it -p 5000:5000 -v ~/.llama:/root/.llama -v ./gpu/run
 ```
 
 Make sure in your `run.yaml` file, your inference provider is pointing to the correct Ollama endpoint. E.g.
-```
+```yaml
 inference:
   - provider_id: ollama0
     provider_type: remote::ollama
@@ -90,7 +94,7 @@ inference:
 
 You can use ollama for managing model downloads.
 
-```
+```bash
 ollama pull llama3.1:8b-instruct-fp16
 ollama pull llama3.1:70b-instruct-fp16
 ```
@@ -100,7 +104,7 @@ ollama pull llama3.1:70b-instruct-fp16
 
 
 To serve a new model with `ollama`
-```
+```bash
 ollama run <model_name>
 ```
 
@@ -113,7 +117,7 @@ llama3.1:8b-instruct-fp16    4aacac419454    17 GB    100% GPU     4 minutes fro
 ```
 
 To verify that the model served by ollama is correctly connected to Llama Stack server
-```
+```bash
 $ llama-stack-client models list
 +----------------------+----------------------+---------------+-----------------------------------------------+
 | identifier           | llama_model          | provider_id   | metadata                                      |

docs/source/getting_started/distributions/self_hosted_distro/remote_vllm.md

@@ -0,0 +1,83 @@
+# Remote vLLM Distribution
+
+The `llamastack/distribution-remote-vllm` distribution consists of the following provider configurations.
+
+| **API**           | **Inference**   | **Agents**      | **Memory**                          | **Safety**     	| **Telemetry**  	|
+|-----------------  |---------------- |---------------- |------------------------------------	|----------------	|----------------	|
+| **Provider(s)**   | remote::vllm  	| meta-reference 	| remote::pgvector, remote::chromadb 	| meta-reference 	| meta-reference 	|
+
+You can use this distribution if you have GPUs and want to run an independent vLLM server container for running inference.
+
+## Using Docker Compose
+
+You can use `docker compose` to start a vLLM container and Llama Stack server container together.
+
+> [!NOTE]
+> This assumes you have access to GPU to start a vLLM server with access to your GPU.
+
+```bash
+$ cd distributions/remote-vllm; docker compose up
+```
+
+You will see outputs similar to following ---
+```
+<TO BE FILLED>
+```
+
+To kill the server
+```bash
+docker compose down
+```
+
+## Starting vLLM and Llama Stack separately
+
+You may want to start a vLLM server and connect with Llama Stack manually. There are two ways to start a vLLM server and connect with Llama Stack.
+
+
+#### Start vLLM server.
+
+```bash
+docker run --runtime nvidia --gpus all \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
+    -p 8000:8000 \
+    --ipc=host \
+    vllm/vllm-openai:latest \
+    --model meta-llama/Llama-3.1-8B-Instruct
+```
+
+Please check the [vLLM Documentation](https://docs.vllm.ai/en/v0.5.5/serving/deploying_with_docker.html) for more details.
+
+
+#### Start Llama Stack server pointing to your vLLM server
+
+
+We have provided a template `run.yaml` file in the `distributions/remote-vllm` directory. Please make sure to modify the `inference.provider_id` to point to your vLLM server endpoint. As an example, if your vLLM server is running on `http://127.0.0.1:8000`, your `run.yaml` file should look like the following:
+```yaml
+inference:
+  - provider_id: vllm0
+    provider_type: remote::vllm
+    config:
+      url: http://127.0.0.1:8000
+```
+
+**Via Conda**
+
+If you are using Conda, you can build and run the Llama Stack server with the following commands:
+```bash
+cd distributions/remote-vllm
+llama stack build --template remote_vllm --image-type conda
+llama stack run run.yaml
+```
+
+**Via Docker**
+
+You can use the Llama Stack Docker image to start the server with the following command:
+```bash
+docker run --network host -it -p 5000:5000 \
+  -v ~/.llama:/root/.llama \
+  -v ./gpu/run.yaml:/root/llamastack-run-remote-vllm.yaml \
+  --gpus=all \
+  llamastack/distribution-remote-vllm \
+  --yaml_config /root/llamastack-run-remote-vllm.yaml
+```

docs/source/getting_started/index.md

@@ -80,6 +80,11 @@ Llama3.1-8B-Instruct  Llama3.2-1B                   Llama3.2-3B-Instruct  Llama-
 
 :::
 
+:::{tab-item} vLLM
+##### System Requirements
+Access to Single-Node GPU to start a vLLM server.
+:::
+
 :::{tab-item} tgi
 ##### System Requirements
 Access to Single-Node GPU to start a TGI server.
@@ -119,6 +124,22 @@ docker run -it -p 5000:5000 -v ~/.llama:/root/.llama -v ./run.yaml:/root/my-run.
 ```
 :::
 
+:::{tab-item} vLLM
+```
+$ cd llama-stack/distributions/remote-vllm && docker compose up
+```
+
+The script will first start up vLLM server on port 8000, then start up Llama Stack distribution server hooking up to it for inference. You should see the following outputs --
+```
+<TO BE FILLED>
+```
+
+To kill the server
+```
+docker compose down
+```
+:::
+
 :::{tab-item} tgi
 ```
 $ cd llama-stack/distributions/tgi && docker compose up