Add ollama/pull-models.sh

distributions/ollama/compose.yaml

@@ -1,30 +1,71 @@
 services:
   ollama:
     image: ollama/ollama:latest
-    network_mode: "host"
+    network_mode: ${NETWORK_MODE:-bridge}
     volumes:
-      - ollama:/root/.ollama # this solution synchronizes with the docker volume and loads the model rocket fast
+      - ~/.ollama:/root/.ollama
     ports:
       - "11434:11434"
+    environment:
+      OLLAMA_DEBUG: 1
     command: []
+    deploy:
+      resources:
+        limits:
+          memory: 8G    # Set maximum memory
+        reservations:
+          memory: 8G    # Set minimum memory reservation
+    # healthcheck:
+    #   # ugh, no CURL in ollama image
+    #   test: ["CMD", "curl", "-f", "http://ollama:11434"]
+    #   interval: 10s
+    #   timeout: 5s
+    #   retries: 5
+
+  ollama-init:
+    image: ollama/ollama:latest
+    depends_on:
+      - ollama
+        # condition: service_healthy
+    network_mode: ${NETWORK_MODE:-bridge}
+    environment:
+      - OLLAMA_HOST=ollama
+      - INFERENCE_MODEL=${INFERENCE_MODEL}
+      - SAFETY_MODEL=${SAFETY_MODEL:-}
+    volumes:
+      - ~/.ollama:/root/.ollama
+      - ./pull-models.sh:/pull-models.sh
+    entrypoint: ["/pull-models.sh"]
+
   llamastack:
     depends_on:
-    - ollama
+      ollama:
-    image: llamastack/distribution-ollama
+        condition: service_started
-    network_mode: "host"
+      ollama-init:
+        condition: service_started
+    image: ${LLAMA_STACK_IMAGE:-llamastack/distribution-ollama}
+    network_mode: ${NETWORK_MODE:-bridge}
     volumes:
       - ~/.llama:/root/.llama
       # Link to ollama run.yaml file
-      - ./run.yaml:/root/my-run.yaml
+      - ~/local/llama-stack/:/app/llama-stack-source
+      - ./run${SAFETY_MODEL:+-with-safety}.yaml:/root/my-run.yaml
     ports:
-      - "5000:5000"
+      - "${LLAMA_STACK_PORT:-5001}:${LLAMA_STACK_PORT:-5001}"
-    # Hack: wait for ollama server to start before starting docker
+    environment:
-    entrypoint: bash -c "sleep 60; python -m llama_stack.distribution.server.server --yaml_config /root/my-run.yaml"
+      - INFERENCE_MODEL=${INFERENCE_MODEL}
+      - SAFETY_MODEL=${SAFETY_MODEL:-}
+      - OLLAMA_URL=http://ollama:11434
+    entrypoint: >
+        python -m llama_stack.distribution.server.server /root/my-run.yaml \
+        --port ${LLAMA_STACK_PORT:-5001}
     deploy:
       restart_policy:
         condition: on-failure
-        delay: 3s
+        delay: 10s
-        max_attempts: 5
+        max_attempts: 3
         window: 60s
 volumes:
   ollama:
+  ollama-init:
+  llamastack:

distributions/ollama/pull-models.sh

@@ -0,0 +1,18 @@
+#!/bin/sh
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+echo "Preloading (${INFERENCE_MODEL}, ${SAFETY_MODEL})..."
+for model in ${INFERENCE_MODEL} ${SAFETY_MODEL}; do
+  echo "Preloading $model..."
+  if ! ollama run "$model"; then
+    echo "Failed to pull and run $model"
+    exit 1
+  fi
+done
+
+echo "All models pulled successfully"

distributions/ollama/run-with-safety.yaml

@@ -1,13 +1,12 @@
 version: '2'
-built_at: 2024-11-17 19:33:00
 image_name: ollama
 docker_image: null
 conda_env: null
 apis:
-- memory
 - agents
-- safety
 - inference
+- memory
+- safety
 - telemetry
 providers:
   inference:
@@ -46,11 +45,11 @@ metadata_store:
 models:
 - metadata: {}
   model_id: ${env.INFERENCE_MODEL}
-  provider_id: ollama-inference
+  provider_id: ollama
   provider_model_id: null
 - metadata: {}
   model_id: ${env.SAFETY_MODEL}
-  provider_id: ollama-safety
+  provider_id: ollama
   provider_model_id: null
 shields:
 - params: null

distributions/ollama/run.yaml

@@ -3,10 +3,10 @@ image_name: ollama
 docker_image: null
 conda_env: null
 apis:
-- memory
 - agents
-- safety
 - inference
+- memory
+- safety
 - telemetry
 providers:
   inference:
@@ -45,7 +45,7 @@ metadata_store:
 models:
 - metadata: {}
   model_id: ${env.INFERENCE_MODEL}
-  provider_id: ollama-inference
+  provider_id: ollama
   provider_model_id: null
 shields: []
 memory_banks: []

distributions/remote-vllm/run-with-safety.yaml

@@ -1,13 +1,12 @@
 version: '2'
-built_at: 2024-11-17 19:33:00
 image_name: remote-vllm
 docker_image: llamastack/distribution-remote-vllm:test-0.0.52rc3
 conda_env: null
 apis:
-- memory
 - agents
-- safety
 - inference
+- memory
+- safety
 - telemetry
 providers:
   inference:

distributions/remote-vllm/run.yaml

@@ -3,10 +3,10 @@ image_name: remote-vllm
 docker_image: llamastack/distribution-remote-vllm:test-0.0.52rc3
 conda_env: null
 apis:
-- memory
 - agents
-- safety
 - inference
+- memory
+- safety
 - telemetry
 providers:
   inference:

distributions/tgi/run-with-safety.yaml

@@ -1,13 +1,12 @@
 version: '2'
-built_at: 2024-11-17 19:33:00
 image_name: tgi
 docker_image: llamastack/distribution-tgi:test-0.0.52rc3
 conda_env: null
 apis:
-- memory
 - agents
-- safety
 - inference
+- memory
+- safety
 - telemetry
 providers:
   inference:
@@ -18,7 +17,7 @@ providers:
   - provider_id: tgi-safety
     provider_type: remote::tgi
     config:
-      url: ${env.SAFETY_TGI_URL}
+      url: ${env.TGI_SAFETY_URL}
   memory:
   - provider_id: faiss
     provider_type: inline::faiss

distributions/tgi/run.yaml

@@ -3,10 +3,10 @@ image_name: tgi
 docker_image: llamastack/distribution-tgi:test-0.0.52rc3
 conda_env: null
 apis:
-- memory
 - agents
-- safety
 - inference
+- memory
+- safety
 - telemetry
 providers:
   inference:

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

@@ -2,27 +2,16 @@
 
 The `llamastack/distribution-ollama` distribution consists of the following provider configurations.
 
-                        Provider Configuration
+| API | Provider(s) |
-┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+|-----|-------------|
-┃ API       ┃ Provider(s)                                             ┃
+| agents | `inline::meta-reference` |
-┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+| inference | `remote::ollama` |
-│ agents    │ `inline::meta-reference`                                │
+| memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
-│ inference │ `remote::ollama`                                        │
+| safety | `inline::llama-guard` |
-│ memory    │ `inline::faiss`, `remote::chromadb`, `remote::pgvector` │
+| telemetry | `inline::meta-reference` |
-│ safety    │ `inline::llama-guard`                                   │
-│ telemetry │ `inline::meta-reference`                                │
-└───────────┴─────────────────────────────────────────────────────────┘
 
 
-You should use this distribution if you have a regular desktop machine without very powerful GPUs. Of course, if you have powerful GPUs, you can still continue using this distribution since Ollama supports GPU acceleration.### Environment Variables
+You should use this distribution if you have a regular desktop machine without very powerful GPUs. Of course, if you have powerful GPUs, you can still continue using this distribution since Ollama supports GPU acceleration.### Models
-
-The following environment variables can be configured:
-
-- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
-- `INFERENCE_MODEL`: Inference model loaded into the TGI server (default: `meta-llama/Llama-3.2-3B-Instruct`)
-- `OLLAMA_URL`: URL of the Ollama server (default: `http://host.docker.internal:11434`)
-- `SAFETY_MODEL`: Name of the safety (Llama-Guard) model to use (default: `meta-llama/Llama-Guard-3-1B`)
-### Models
 
 The following models are configured by default:
 - `${env.INFERENCE_MODEL}`

docs/source/getting_started/distributions/self_hosted_distro/remote-vllm.md

@@ -2,29 +2,16 @@
 
 The `llamastack/distribution-remote-vllm` distribution consists of the following provider configurations:
 
-                        Provider Configuration
+| API | Provider(s) |
-┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+|-----|-------------|
-┃ API       ┃ Provider(s)                                             ┃
+| agents | `inline::meta-reference` |
-┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+| inference | `remote::vllm` |
-│ agents    │ `inline::meta-reference`                                │
+| memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
-│ inference │ `remote::vllm`                                          │
+| safety | `inline::llama-guard` |
-│ memory    │ `inline::faiss`, `remote::chromadb`, `remote::pgvector` │
+| telemetry | `inline::meta-reference` |
-│ safety    │ `inline::llama-guard`                                   │
-│ telemetry │ `inline::meta-reference`                                │
-└───────────┴─────────────────────────────────────────────────────────┘
 
 
-You can use this distribution if you have GPUs and want to run an independent vLLM server container for running inference.### Environment Variables
+You can use this distribution if you have GPUs and want to run an independent vLLM server container for running inference.
-
-The following environment variables can be configured:
-
-- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
-- `INFERENCE_MODEL`: Inference model loaded into the vLLM server (default: `meta-llama/Llama-3.2-3B-Instruct`)
-- `VLLM_URL`: URL of the vLLM server with the main inference model (default: `http://host.docker.internal:5100}/v1`)
-- `MAX_TOKENS`: Maximum number of tokens for generation (default: `4096`)
-- `SAFETY_VLLM_URL`: URL of the vLLM server with the safety model (default: `http://host.docker.internal:5101/v1`)
-- `SAFETY_MODEL`: Name of the safety (Llama-Guard) model to use (default: `meta-llama/Llama-Guard-3-1B`)
-
 ### Models
 
 The following models are configured by default:

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

@@ -2,128 +2,127 @@
 
 The `llamastack/distribution-tgi` distribution consists of the following provider configurations.
 
-                        Provider Configuration
+| API | Provider(s) |
-┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+|-----|-------------|
-┃ API       ┃ Provider(s)                                             ┃
+| agents | `inline::meta-reference` |
-┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+| inference | `remote::tgi` |
-│ agents    │ `inline::meta-reference`                                │
+| memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
-│ inference │ `remote::tgi`                                           │
+| safety | `inline::llama-guard` |
-│ memory    │ `inline::faiss`, `remote::chromadb`, `remote::pgvector` │
+| telemetry | `inline::meta-reference` |
-│ safety    │ `inline::llama-guard`                                   │
-│ telemetry │ `inline::meta-reference`                                │
-└───────────┴─────────────────────────────────────────────────────────┘
 
 
-You can use this distribution if you have GPUs and want to run an independent TGI server container for running inference.### Environment Variables
+You can use this distribution if you have GPUs and want to run an independent TGI server container for running inference.
+
+### Environment Variables
 
 The following environment variables can be configured:
 
 - `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
 - `INFERENCE_MODEL`: Inference model loaded into the TGI server (default: `meta-llama/Llama-3.2-3B-Instruct`)
-- `TGI_URL`: URL of the TGI server with the main inference model (default: `http://host.docker.internal:8080}/v1`)
+- `TGI_URL`: URL of the TGI server with the main inference model (default: `http://127.0.0.1:8080}/v1`)
-- `SAFETY_TGI_URL`: URL of the TGI server with the safety model (default: `http://host.docker.internal:8081/v1`)
+- `TGI_SAFETY_URL`: URL of the TGI server with the safety model (default: `http://127.0.0.1:8081/v1`)
 - `SAFETY_MODEL`: Name of the safety (Llama-Guard) model to use (default: `meta-llama/Llama-Guard-3-1B`)
-### Models
-
-The following models are configured by default:
-- `${env.INFERENCE_MODEL}`
-- `${env.SAFETY_MODEL}`
 
 
-## Using Docker Compose
+## Setting up TGI server
 
-You can use `docker compose` to start a TGI container and Llama Stack server container together.
+Please check the [TGI Getting Started Guide](https://github.com/huggingface/text-generation-inference?tab=readme-ov-file#get-started) to get a TGI endpoint. Here is a sample script to start a TGI server locally via Docker:
 
 ```bash
-$ cd distributions/tgi; docker compose up
+export TGI_INFERENCE_PORT=8080
+export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
+export CUDA_VISIBLE_DEVICES=0
+
+docker run --rm -it \
+  -v $HOME/.cache/huggingface:/data \
+  -p $TGI_INFERENCE_PORT:$TGI_INFERENCE_PORT \
+  --gpus $CUDA_VISIBLE_DEVICES \
+  ghcr.io/huggingface/text-generation-inference:2.3.1 \
+  --dtype bfloat16 \
+  --usage-stats off \
+  --sharded false \
+  --cuda-memory-fraction 0.7 \
+  --model-id $INFERENCE_MODEL \
+  --port $TGI_INFERENCE_PORT
 ```
 
-The script will first start up TGI server, then start up Llama Stack distribution server hooking up to the remote TGI provider for inference. You should be able to see the following outputs --
+If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a TGI with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
-```bash
-[text-generation-inference] | 2024-10-15T18:56:33.810397Z  INFO text_generation_router::server: router/src/server.rs:1813: Using config Some(Llama)
-[text-generation-inference] | 2024-10-15T18:56:33.810448Z  WARN text_generation_router::server: router/src/server.rs:1960: Invalid hostname, defaulting to 0.0.0.0
-[text-generation-inference] | 2024-10-15T18:56:33.864143Z  INFO text_generation_router::server: router/src/server.rs:2353: Connected
-INFO:     Started server process [1]
-INFO:     Waiting for application startup.
-INFO:     Application startup complete.
-INFO:     Uvicorn running on http://[::]:5001 (Press CTRL+C to quit)
-```
-
-To kill the server
-```bash
-docker compose down
-```
-
-
-### Conda: TGI server + llama stack run
-
-If you wish to separately spin up a TGI server, and connect with Llama Stack, you may use the following commands.
-
-#### Start TGI server locally
-- Please check the [TGI Getting Started Guide](https://github.com/huggingface/text-generation-inference?tab=readme-ov-file#get-started) to get a TGI endpoint.
 
 ```bash
-docker run --rm -it -v $HOME/.cache/huggingface:/data \
+export TGI_SAFETY_PORT=8081
-  -p 5009:5009 --gpus all \
+export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
-  ghcr.io/huggingface/text-generation-inference:latest \
+export CUDA_VISIBLE_DEVICES=1
-  --dtype bfloat16 --usage-stats on --sharded false \
+
-  --model-id meta-llama/Llama-3.2-3B-Instruct --port 5009
+docker run --rm -it \
+  -v $HOME/.cache/huggingface:/data \
+  -p $TGI_SAFETY_PORT:$TGI_SAFETY_PORT \
+  --gpus $CUDA_VISIBLE_DEVICES \
+  ghcr.io/huggingface/text-generation-inference:2.3.1 \
+  --dtype bfloat16 \
+  --usage-stats off \
+  --sharded false \
+  --model-id $SAFETY_MODEL \
+  --port $TGI_SAFETY_PORT
 ```
 
-#### Start Llama Stack server pointing to TGI server
+## Running Llama Stack with TGI as the inference provider
 
-**Via Conda**
+Now you are ready to run Llama Stack with TGI as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.
+
+### Via Docker
+
+This method allows you to get started quickly without having to build the distribution code.
+
+```bash
+LLAMA_STACK_PORT=5001
+docker run \
+  --network host \
+  -it \
+  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
+  -v ./run.yaml:/root/my-run.yaml \
+  llamastack/distribution-tgi \
+  /root/my-run.yaml \
+  --port $LLAMA_STACK_PORT \
+  --env INFERENCE_MODEL=$INFERENCE_MODEL \
+  --env TGI_URL=http://host.docker.internal:$TGI_INFERENCE_PORT
+```
+
+If you are using Llama Stack Safety / Shield APIs, use:
+
+```bash
+docker run \
+  --network host \
+  -it \
+  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
+  -v ./run-with-safety.yaml:/root/my-run.yaml \
+  llamastack/distribution-tgi \
+  /root/my-run.yaml \
+  --port $LLAMA_STACK_PORT \
+  --env INFERENCE_MODEL=$INFERENCE_MODEL \
+  --env TGI_URL=http://host.docker.internal:$TGI_INFERENCE_PORT \
+  --env SAFETY_MODEL=$SAFETY_MODEL \
+  --env TGI_SAFETY_URL=http://host.docker.internal:$TGI_SAFETY_PORT
+```
+
+### Via Conda
+
+Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
 
 ```bash
 llama stack build --template tgi --image-type conda
-# -- start a TGI server endpoint
+llama stack run ./run.yaml
-llama stack run ./gpu/run.yaml
+  --port 5001
+  --env INFERENCE_MODEL=$INFERENCE_MODEL
+  --env TGI_URL=http://127.0.0.1:$TGI_INFERENCE_PORT
 ```
 
-**Via Docker**
+If you are using Llama Stack Safety / Shield APIs, use:
+
 ```bash
-docker run --network host -it -p 5001:5001 \
+llama stack run ./run-with-safety.yaml
-  -v ./run.yaml:/root/my-run.yaml --gpus=all \
+  --port 5001
-  llamastack/distribution-tgi \
+  --env INFERENCE_MODEL=$INFERENCE_MODEL
-  --yaml_config /root/my-run.yaml
+  --env TGI_URL=http://127.0.0.1:$TGI_INFERENCE_PORT
-```
+  --env SAFETY_MODEL=$SAFETY_MODEL
-
+  --env TGI_SAFETY_URL=http://127.0.0.1:$TGI_SAFETY_PORT
-We have provided a template `run.yaml` file in the `distributions/tgi` directory. Make sure in your `run.yaml` file, you inference provider is pointing to the correct TGI server endpoint. E.g.
-```yaml
-inference:
-  - provider_id: tgi0
-    provider_type: remote::tgi
-    config:
-      url: http://127.0.0.1:5009
-```
-
-
-### (Optional) Update Model Serving Configuration
-To serve a new model with `tgi`, change the docker command flag `--model-id <model-to-serve>`.
-
-This can be done by edit the `command` args in `compose.yaml`. E.g. Replace "Llama-3.2-1B-Instruct" with the model you want to serve.
-
-```yaml
-command: >
-  --dtype bfloat16 --usage-stats on --sharded false
-  --model-id meta-llama/Llama-3.2-1B-Instruct
-  --port 5009 --cuda-memory-fraction 0.7
-```
-
-or by changing the docker run command's `--model-id` flag
-```bash
-docker run --rm -it -v $HOME/.cache/huggingface:/data \
-  -p 5009:5009 --gpus all \
-  ghcr.io/huggingface/text-generation-inference:latest \
-  --dtype bfloat16 --usage-stats off --sharded false \
-  --model-id meta-llama/Llama-3.2-3B-Instruct --port 5009
-```
-
-In `run.yaml`, make sure you point the correct server endpoint to the TGI server endpoint serving your model.
-```yaml
-inference:
-  - provider_id: tgi0
-    provider_type: remote::tgi
-    config:
-      url: http://127.0.0.1:5009
 ```

llama_stack/scripts/distro_codegen.py

@@ -21,7 +21,9 @@ def find_template_dirs(templates_dir: Path) -> Iterator[Path]:
     if not templates_dir.exists():
         raise FileNotFoundError(f"Templates directory not found: {templates_dir}")
 
-    return (d for d in templates_dir.iterdir() if d.is_dir())
+    return (
+        d for d in templates_dir.iterdir() if d.is_dir() and d.name != "__pycache__"
+    )
 
 
 def process_template(template_dir: Path, progress) -> None:

llama_stack/templates/ollama/ollama.py

@@ -28,11 +28,11 @@ def get_distribution_template() -> DistributionTemplate:
 
     inference_model = ModelInput(
         model_id="${env.INFERENCE_MODEL}",
-        provider_id="ollama-inference",
+        provider_id="ollama",
     )
     safety_model = ModelInput(
         model_id="${env.SAFETY_MODEL}",
-        provider_id="ollama-safety",
+        provider_id="ollama",
     )
 
     return DistributionTemplate(

llama_stack/templates/template.py

@@ -4,18 +4,13 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from io import StringIO
-
 from pathlib import Path
-from typing import Dict, List, Literal, Optional, Set, Tuple
+from typing import Dict, List, Literal, Optional, Tuple
 
 import jinja2
 import yaml
 from pydantic import BaseModel, Field
 
-from rich.console import Console
-from rich.table import Table
-
 from llama_stack.distribution.datatypes import (
     Api,
     BuildConfig,
@@ -80,12 +75,12 @@ class RunConfigSettings(BaseModel):
             ]
 
         # Get unique set of APIs from providers
-        apis: Set[str] = set(providers.keys())
+        apis = list(sorted(providers.keys()))
 
         return StackRunConfig(
             image_name=name,
             docker_image=docker_image,
-            apis=list(apis),
+            apis=apis,
             providers=provider_configs,
             metadata_store=SqliteKVStoreConfig.sample_run_config(
                 __distro_dir__=f"distributions/{name}",
@@ -111,7 +106,7 @@ class DistributionTemplate(BaseModel):
     template_path: Path
 
     # Optional configuration
-    docker_compose_env_vars: Optional[Dict[str, Tuple[str, str]]] = None
+    run_config_env_vars: Optional[Dict[str, Tuple[str, str]]] = None
     docker_image: Optional[str] = None
 
     default_models: Optional[List[ModelInput]] = None
@@ -128,20 +123,12 @@ class DistributionTemplate(BaseModel):
         )
 
     def generate_markdown_docs(self) -> str:
-        """Generate markdown documentation using both Jinja2 templates and rich tables."""
+        providers_table = "| API | Provider(s) |\n"
-        # First generate the providers table using rich
+        providers_table += "|-----|-------------|\n"
-        output = StringIO()
-        console = Console(file=output, force_terminal=False)
-
-        table = Table(title="Provider Configuration", show_header=True)
-        table.add_column("API", style="bold")
-        table.add_column("Provider(s)")
 
         for api, providers in sorted(self.providers.items()):
-            table.add_row(api, ", ".join(f"`{p}`" for p in providers))
+            providers_str = ", ".join(f"`{p}`" for p in providers)
-
+            providers_table += f"| {api} | {providers_str} |\n"
-        console.print(table)
-        providers_table = output.getvalue()
 
         template = self.template_path.read_text()
         # Render template with rich-generated table
@@ -152,7 +139,7 @@ class DistributionTemplate(BaseModel):
             description=self.description,
             providers=self.providers,
             providers_table=providers_table,
-            docker_compose_env_vars=self.docker_compose_env_vars,
+            run_config_env_vars=self.run_config_env_vars,
             default_models=self.default_models,
         )
 

llama_stack/templates/tgi/doc_template.md

@@ -6,120 +6,116 @@ The `llamastack/distribution-{{ name }}` distribution consists of the following
 
 You can use this distribution if you have GPUs and want to run an independent TGI server container for running inference.
 
-{%- if docker_compose_env_vars %}
+{% if run_config_env_vars %}
 ### Environment Variables
 
 The following environment variables can be configured:
 
-{% for var, (default_value, description) in docker_compose_env_vars.items() %}
+{% for var, (default_value, description) in run_config_env_vars.items() %}
 - `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
 {% endfor %}
 {% endif %}
 
-{%- if default_models %}
-### Models
 
-The following models are configured by default:
+## Setting up TGI server
-{% for model in default_models %}
-- `{{ model.model_id }}`
-{% endfor %}
-{% endif %}
 
-
+Please check the [TGI Getting Started Guide](https://github.com/huggingface/text-generation-inference?tab=readme-ov-file#get-started) to get a TGI endpoint. Here is a sample script to start a TGI server locally via Docker:
-## Using Docker Compose
-
-You can use `docker compose` to start a TGI container and Llama Stack server container together.
 
 ```bash
-$ cd distributions/{{ name }}; docker compose up
+export TGI_INFERENCE_PORT=8080
+export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
+export CUDA_VISIBLE_DEVICES=0
+
+docker run --rm -it \
+  -v $HOME/.cache/huggingface:/data \
+  -p $TGI_INFERENCE_PORT:$TGI_INFERENCE_PORT \
+  --gpus $CUDA_VISIBLE_DEVICES \
+  ghcr.io/huggingface/text-generation-inference:2.3.1 \
+  --dtype bfloat16 \
+  --usage-stats off \
+  --sharded false \
+  --cuda-memory-fraction 0.7 \
+  --model-id $INFERENCE_MODEL \
+  --port $TGI_INFERENCE_PORT
 ```
 
-The script will first start up TGI server, then start up Llama Stack distribution server hooking up to the remote TGI provider for inference. You should be able to see the following outputs --
+If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a TGI with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:
-```bash
-[text-generation-inference] | 2024-10-15T18:56:33.810397Z  INFO text_generation_router::server: router/src/server.rs:1813: Using config Some(Llama)
-[text-generation-inference] | 2024-10-15T18:56:33.810448Z  WARN text_generation_router::server: router/src/server.rs:1960: Invalid hostname, defaulting to 0.0.0.0
-[text-generation-inference] | 2024-10-15T18:56:33.864143Z  INFO text_generation_router::server: router/src/server.rs:2353: Connected
-INFO:     Started server process [1]
-INFO:     Waiting for application startup.
-INFO:     Application startup complete.
-INFO:     Uvicorn running on http://[::]:5001 (Press CTRL+C to quit)
-```
-
-To kill the server
-```bash
-docker compose down
-```
-
-
-### Conda: TGI server + llama stack run
-
-If you wish to separately spin up a TGI server, and connect with Llama Stack, you may use the following commands.
-
-#### Start TGI server locally
-- Please check the [TGI Getting Started Guide](https://github.com/huggingface/text-generation-inference?tab=readme-ov-file#get-started) to get a TGI endpoint.
 
 ```bash
-docker run --rm -it -v $HOME/.cache/huggingface:/data \
+export TGI_SAFETY_PORT=8081
-  -p 5009:5009 --gpus all \
+export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
-  ghcr.io/huggingface/text-generation-inference:latest \
+export CUDA_VISIBLE_DEVICES=1
-  --dtype bfloat16 --usage-stats on --sharded false \
+
-  --model-id meta-llama/Llama-3.2-3B-Instruct --port 5009
+docker run --rm -it \
+  -v $HOME/.cache/huggingface:/data \
+  -p $TGI_SAFETY_PORT:$TGI_SAFETY_PORT \
+  --gpus $CUDA_VISIBLE_DEVICES \
+  ghcr.io/huggingface/text-generation-inference:2.3.1 \
+  --dtype bfloat16 \
+  --usage-stats off \
+  --sharded false \
+  --model-id $SAFETY_MODEL \
+  --port $TGI_SAFETY_PORT
 ```
 
-#### Start Llama Stack server pointing to TGI server
+## Running Llama Stack with TGI as the inference provider
 
-**Via Conda**
+Now you are ready to run Llama Stack with TGI as the inference provider. You can do this via Conda (build code) or Docker which has a pre-built image.
+
+### Via Docker
+
+This method allows you to get started quickly without having to build the distribution code.
+
+```bash
+LLAMA_STACK_PORT=5001
+docker run \
+  --network host \
+  -it \
+  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
+  -v ./run.yaml:/root/my-run.yaml \
+  llamastack/distribution-{{ name }} \
+  /root/my-run.yaml \
+  --port $LLAMA_STACK_PORT \
+  --env INFERENCE_MODEL=$INFERENCE_MODEL \
+  --env TGI_URL=http://host.docker.internal:$TGI_INFERENCE_PORT
+```
+
+If you are using Llama Stack Safety / Shield APIs, use:
+
+```bash
+docker run \
+  --network host \
+  -it \
+  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
+  -v ./run-with-safety.yaml:/root/my-run.yaml \
+  llamastack/distribution-{{ name }} \
+  /root/my-run.yaml \
+  --port $LLAMA_STACK_PORT \
+  --env INFERENCE_MODEL=$INFERENCE_MODEL \
+  --env TGI_URL=http://host.docker.internal:$TGI_INFERENCE_PORT \
+  --env SAFETY_MODEL=$SAFETY_MODEL \
+  --env TGI_SAFETY_URL=http://host.docker.internal:$TGI_SAFETY_PORT
+```
+
+### Via Conda
+
+Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
 
 ```bash
 llama stack build --template {{ name }} --image-type conda
-# -- start a TGI server endpoint
+llama stack run ./run.yaml
-llama stack run ./gpu/run.yaml
+  --port 5001
+  --env INFERENCE_MODEL=$INFERENCE_MODEL
+  --env TGI_URL=http://127.0.0.1:$TGI_INFERENCE_PORT
 ```
 
-**Via Docker**
+If you are using Llama Stack Safety / Shield APIs, use:
+
 ```bash
-docker run --network host -it -p 5001:5001 \
+llama stack run ./run-with-safety.yaml
-  -v ./run.yaml:/root/my-run.yaml --gpus=all \
+  --port 5001
-  llamastack/distribution-{{ name }} \
+  --env INFERENCE_MODEL=$INFERENCE_MODEL
-  --yaml_config /root/my-run.yaml
+  --env TGI_URL=http://127.0.0.1:$TGI_INFERENCE_PORT
-```
+  --env SAFETY_MODEL=$SAFETY_MODEL
-
+  --env TGI_SAFETY_URL=http://127.0.0.1:$TGI_SAFETY_PORT
-We have provided a template `run.yaml` file in the `distributions/{{ name }}` directory. Make sure in your `run.yaml` file, you inference provider is pointing to the correct TGI server endpoint. E.g.
-```yaml
-inference:
-  - provider_id: tgi0
-    provider_type: remote::tgi
-    config:
-      url: http://127.0.0.1:5009
-```
-
-
-### (Optional) Update Model Serving Configuration
-To serve a new model with `tgi`, change the docker command flag `--model-id <model-to-serve>`.
-
-This can be done by edit the `command` args in `compose.yaml`. E.g. Replace "Llama-3.2-1B-Instruct" with the model you want to serve.
-
-```yaml
-command: >
-  --dtype bfloat16 --usage-stats on --sharded false
-  --model-id meta-llama/Llama-3.2-1B-Instruct
-  --port 5009 --cuda-memory-fraction 0.7
-```
-
-or by changing the docker run command's `--model-id` flag
-```bash
-docker run --rm -it -v $HOME/.cache/huggingface:/data \
-  -p 5009:5009 --gpus all \
-  ghcr.io/huggingface/text-generation-inference:latest \
-  --dtype bfloat16 --usage-stats off --sharded false \
-  --model-id meta-llama/Llama-3.2-3B-Instruct --port 5009
-```
-
-In `run.yaml`, make sure you point the correct server endpoint to the TGI server endpoint serving your model.
-```yaml
-inference:
-  - provider_id: tgi0
-    provider_type: remote::tgi
-    config:
-      url: http://127.0.0.1:5009
 ```

llama_stack/templates/tgi/tgi.py

@@ -60,7 +60,7 @@ def get_distribution_template() -> DistributionTemplate:
                             provider_id="tgi-safety",
                             provider_type="remote::tgi",
                             config=TGIImplConfig.sample_run_config(
-                                url="${env.SAFETY_TGI_URL}",
+                                url="${env.TGI_SAFETY_URL}",
                             ),
                         ),
                     ],
@@ -72,7 +72,7 @@ def get_distribution_template() -> DistributionTemplate:
                 default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}")],
             ),
         },
-        docker_compose_env_vars={
+        run_config_env_vars={
             "LLAMASTACK_PORT": (
                 "5001",
                 "Port for the Llama Stack distribution server",
@@ -82,11 +82,11 @@ def get_distribution_template() -> DistributionTemplate:
                 "Inference model loaded into the TGI server",
             ),
             "TGI_URL": (
-                "http://host.docker.internal:8080}/v1",
+                "http://127.0.0.1:8080}/v1",
                 "URL of the TGI server with the main inference model",
             ),
-            "SAFETY_TGI_URL": (
+            "TGI_SAFETY_URL": (
-                "http://host.docker.internal:8081/v1",
+                "http://127.0.0.1:8081/v1",
                 "URL of the TGI server with the safety model",
             ),
             "SAFETY_MODEL": (