verify dockers

distributions/meta-reference-gpu/README.md

@@ -11,13 +11,8 @@ The `llamastack/distribution-meta-reference-gpu` distribution consists of the fo
 ### Start the Distribution (Single Node GPU)
 
 > [!NOTE]
-> This assumes you have access to GPU to start a TGI server with access to your GPU.
+> This assumes you have access to GPU to start a local server with access to your GPU.
 
-> [!NOTE]
-> For GPU inference, you need to set these environment variables for specifying local directory containing your model checkpoints, and enable GPU inference to start running docker container.
-```
-export LLAMA_CHECKPOINT_DIR=~/.llama
-```
 
 > [!NOTE]
 > `~/.llama` should be the path containing downloaded weights of Llama models.
@@ -26,8 +21,8 @@ export LLAMA_CHECKPOINT_DIR=~/.llama
 To download and start running a pre-built docker container, you may use the following commands:
 
 ```
-docker run -it -p 5000:5000 -v ~/.llama:/root/.llama --gpus=all llamastack/llamastack-local-gpu
+docker run -it -p 5000:5000 -v ~/.llama:/root/.llama -v ./run.yaml:/root/my-run.yaml --gpus=all distribution-meta-reference-gpu --yaml_config /root/my-run.yaml
 ```
 
 ### Alternative (Build and start distribution locally via conda)
-- You may checkout the [Getting Started](../../docs/getting_started.md) for more details on starting up a meta-reference distribution.
+- You may checkout the [Getting Started](../../docs/getting_started.md) for more details on building locally via conda and starting up a meta-reference distribution.

distributions/meta-reference-gpu/build.yaml

@@ -1,4 +1,4 @@
-name: distribution-meta-reference-gpu
+name: meta-reference-gpu
 distribution_spec:
   description: Use code from `llama_stack` itself to serve all llama stack APIs
   providers:

distributions/ollama/README.md

@@ -71,10 +71,10 @@ ollama run <model_id>
 
 **Via Docker**
 ```
-docker run --network host -it -p 5000:5000 -v ~/.llama:/root/.llama -v ./ollama-run.yaml:/root/llamastack-run-ollama.yaml --gpus=all llamastack-local-cpu --yaml_config /root/llamastack-run-ollama.yaml
+docker run --network host -it -p 5000:5000 -v ~/.llama:/root/.llama -v ./gpu/run.yaml:/root/llamastack-run-ollama.yaml --gpus=all distribution-ollama --yaml_config /root/llamastack-run-ollama.yaml
 ```
 
-Make sure in you `ollama-run.yaml` file, you inference provider is pointing to the correct Ollama endpoint. E.g.
+Make sure in you `run.yaml` file, you inference provider is pointing to the correct Ollama endpoint. E.g.
 ```
 inference:
   - provider_id: ollama0

distributions/ollama/build.yaml

@@ -1,4 +1,4 @@
-name: distribution-ollama
+name: ollama
 distribution_spec:
   description: Use ollama for running LLM inference
   providers:
@@ -10,4 +10,4 @@ distribution_spec:
     safety: meta-reference
     agents: meta-reference
     telemetry: meta-reference
-image_type: conda
+image_type: docker

distributions/tgi/build.yaml

@@ -1,4 +1,4 @@
-name: distribution-tgi
+name: tgi
 distribution_spec:
   description: Use TGI for running LLM inference
   providers:
@@ -10,4 +10,4 @@ distribution_spec:
     safety: meta-reference
     agents: meta-reference
     telemetry: meta-reference
-image_type: conda
+image_type: docker

distributions/tgi/cpu/compose.yaml

@@ -6,28 +6,7 @@ services:
       - $HOME/.cache/huggingface:/data
     ports:
       - "5009:5009"
-    devices:
-      - nvidia.com/gpu=all
-    environment:
-      - CUDA_VISIBLE_DEVICES=0
-      - HF_HOME=/data
-      - HF_DATASETS_CACHE=/data
-      - HF_MODULES_CACHE=/data
-      - HF_HUB_CACHE=/data
     command: ["--dtype", "bfloat16", "--usage-stats", "on", "--sharded", "false", "--model-id", "meta-llama/Llama-3.1-8B-Instruct", "--port", "5009", "--cuda-memory-fraction", "0.3"]
-    deploy:
-      resources:
-        reservations:
-          devices:
-          - driver: nvidia
-            # that's the closest analogue to --gpus; provide
-            # an integer amount of devices or 'all'
-            count: 1
-            # Devices are reserved using a list of capabilities, making
-            # capabilities the only required field. A device MUST
-            # satisfy all the requested capabilities for a successful
-            # reservation.
-            capabilities: [gpu]
     runtime: nvidia
     healthcheck:
       test: ["CMD", "curl", "-f", "http://text-generation-inference:5009/health"]

distributions/together/README.md

@@ -0,0 +1,104 @@
+# Together Distribution
+
+### Connect to a Llama Stack Together Endpoint
+- You may connect to a hosted endpoint `https://llama-stack.together.ai`, serving a Llama Stack distribution
+
+### Start a Together distribution
+```
+
+```
+
+# TGI Distribution
+
+The `llamastack/distribution-tgi` distribution consists of the following provider configurations.
+
+
+| **API**         	| **Inference** 	| **Agents**     	| **Memory**                                       	| **Safety**     	| **Telemetry**  	|
+|-----------------	|---------------	|----------------	|--------------------------------------------------	|----------------	|----------------	|
+| **Provider(s)** 	| remote::tgi   	| meta-reference 	| meta-reference, remote::pgvector, remote::chroma 	| meta-reference 	| meta-reference 	|
+
+
+### Start the Distribution (Single Node GPU)
+
+> [!NOTE]
+> This assumes you have access to GPU to start a TGI server with access to your GPU.
+
+
+```
+$ cd llama_stack/distribution/docker/tgi
+$ ls
+compose.yaml  tgi-run.yaml
+$ docker compose up
+```
+
+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 --
+```
+[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://[::]:5000 (Press CTRL+C to quit)
+```
+
+To kill the server
+```
+docker compose down
+```
+
+### Start the Distribution (Single Node CPU)
+
+> [!NOTE]
+> This assumes you have an hosted endpoint compatible with TGI server.
+
+```
+$ cd llama-stack/distribution/tgi/cpu
+$ ls
+compose.yaml  run.yaml
+$ docker compose up
+```
+
+Replace <ENTER_YOUR_TGI_HOSTED_ENDPOINT> in `run.yaml` file with your TGI endpoint.
+```
+inference:
+  - provider_id: tgi0
+    provider_type: remote::tgi
+    config:
+      url: <ENTER_YOUR_TGI_HOSTED_ENDPOINT>
+```
+
+### (Alternative) TGI server + llama stack run (Single Node GPU)
+
+If you wish to separately spin up a TGI server, and connect with Llama Stack, you may use the following commands.
+
+#### (optional) 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.
+
+```
+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 on --sharded false --model-id meta-llama/Llama-3.1-8B-Instruct --port 5009
+```
+
+
+#### Start Llama Stack server pointing to TGI server
+
+```
+docker run --network host -it -p 5000:5000 -v ./run.yaml:/root/my-run.yaml --gpus=all llamastack-local-cpu --yaml_config /root/my-run.yaml
+```
+
+Make sure in you `run.yaml` file, you inference provider is pointing to the correct Together URL server endpoint. E.g.
+```
+inference:
+  - provider_id: together
+    provider_type: remote::together
+    config:
+      url: https://api.together.xyz/v1
+```
+
+**Via Conda**
+
+```bash
+llama stack build --config ./build.yaml
+# -- modify run.yaml to a valid Together server endpoint
+llama stack run ./run.yaml
+```

llama_stack/distribution/build_container.sh

@@ -15,7 +15,7 @@ special_pip_deps="$6"
 set -euo pipefail
 
 build_name="$1"
-image_name="llamastack-$build_name"
+image_name="distribution-$build_name"
 docker_base=$2
 build_file_path=$3
 host_build_dir=$4

llama_stack/providers/registry/inference.py

@@ -55,7 +55,7 @@ def available_providers() -> List[ProviderSpec]:
             api=Api.inference,
             adapter=AdapterSpec(
                 adapter_type="ollama",
-                pip_packages=["ollama"],
+                pip_packages=["ollama", "aiohttp"],
                 config_class="llama_stack.providers.adapters.inference.ollama.OllamaImplConfig",
                 module="llama_stack.providers.adapters.inference.ollama",
             ),