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91
distributions/ollama/README.md Normal file
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# Ollama Distribution
The `llamastack/distribution-ollama` distribution consists of the following provider configurations.
| **API** | **Inference** | **Agents** | **Memory** | **Safety** | **Telemetry** |
|----------------- |---------------- |---------------- |---------------------------------- |---------------- |---------------- |
| **Provider(s)** | remote::ollama | meta-reference | remote::pgvector, remote::chroma | remote::ollama | meta-reference |
### Start a Distribution (Single Node GPU)
> [!NOTE]
> This assumes you have access to GPU to start a Ollama server with access to your GPU.
```
$ cd llama-stack/distribution/ollama/gpu
$ ls
compose.yaml run.yaml
$ docker compose up
```
You will see outputs similar to following ---
```
[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]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://[::]:5000 (Press CTRL+C to quit)
[llamastack] | Resolved 12 providers
[llamastack] | inner-inference => ollama0
[llamastack] | models => __routing_table__
[llamastack] | inference => __autorouted__
```
To kill the server
```
docker compose down
```
### Start the Distribution (Single Node CPU)
> [!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.
```
$ cd llama-stack/distribution/ollama/cpu
$ ls
compose.yaml run.yaml
$ docker compose up
```
### (Alternative) ollama run + llama stack Run
If you wish to separately spin up a Ollama server, and connect with Llama Stack, you may use the following commands.
#### Start Ollama server.
- Please check the [Ollama Documentations](https://github.com/ollama/ollama) for more details.
**Via Docker**
```
docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
```
**Via CLI**
```
ollama run <model_id>
```
#### Start Llama Stack server pointing to Ollama server
**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
```
Make sure in you `ollama-run.yaml` file, you inference provider is pointing to the correct Ollama endpoint. E.g.
```
inference:
- provider_id: ollama0
provider_type: remote::ollama
config:
url: http://127.0.0.1:14343
```
**Via Conda**
```
llama stack build --config ./build.yaml
llama stack run ./gpu/run.yaml
```

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distributions/ollama/build.yaml Normal file
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name: local-ollama
distribution_spec:
description: Like local, but use ollama for running LLM inference
providers:
inference: remote::ollama
memory:
- meta-reference
- remote::chromadb
- remote::pgvector
safety: meta-reference
agents: meta-reference
telemetry: meta-reference
image_type: conda

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distributions/ollama/cpu/compose.yaml Normal file
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services:
ollama:
image: ollama/ollama:latest
network_mode: "host"
volumes:
- ollama:/root/.ollama # this solution synchronizes with the docker volume and loads the model rocket fast
ports:
- "11434:11434"
command: []
llamastack:
depends_on:
- ollama
image: llamastack/llamastack-local-cpu
network_mode: "host"
volumes:
- ~/.llama:/root/.llama
# Link to ollama run.yaml file
- ./run.yaml:/root/my-run.yaml
ports:
- "5000:5000"
# Hack: wait for ollama server to start before starting docker
entrypoint: bash -c "sleep 60; python -m llama_stack.distribution.server.server --yaml_config /root/my-run.yaml"
deploy:
restart_policy:
condition: on-failure
delay: 3s
max_attempts: 5
window: 60s
volumes:
ollama:

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distributions/ollama/cpu/run.yaml Normal file
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version: '2'
built_at: '2024-10-08T17:40:45.325529'
image_name: local
docker_image: null
conda_env: local
apis:
- shields
- agents
- models
- memory
- memory_banks
- inference
- safety
providers:
inference:
- provider_id: ollama0
provider_type: remote::ollama
config:
url: http://127.0.0.1:14343
safety:
- provider_id: meta0
provider_type: meta-reference
config:
llama_guard_shield:
model: Llama-Guard-3-1B
excluded_categories: []
disable_input_check: false
disable_output_check: false
prompt_guard_shield:
model: Prompt-Guard-86M
memory:
- provider_id: meta0
provider_type: meta-reference
config: {}
agents:
- provider_id: meta0
provider_type: meta-reference
config:
persistence_store:
namespace: null
type: sqlite
db_path: ~/.llama/runtime/kvstore.db
telemetry:
- provider_id: meta0
provider_type: meta-reference
config: {}

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distributions/ollama/gpu/compose.yaml Normal file
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services:
ollama:
image: ollama/ollama:latest
network_mode: "host"
volumes:
- ollama:/root/.ollama # this solution synchronizes with the docker volume and loads the model rocket fast
ports:
- "11434:11434"
devices:
- nvidia.com/gpu=all
environment:
- CUDA_VISIBLE_DEVICES=0
command: []
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
llamastack-local-cpu:
depends_on:
- ollama
image: llamastack/llamastack-local-cpu
network_mode: "host"
volumes:
- ~/.llama:/root/.llama
# Link to ollama run.yaml file
- ./ollama-run.yaml:/root/llamastack-run-ollama.yaml
ports:
- "5000:5000"
# Hack: wait for ollama server to start before starting docker
entrypoint: bash -c "sleep 60; python -m llama_stack.distribution.server.server --yaml_config /root/llamastack-run-ollama.yaml"
deploy:
restart_policy:
condition: on-failure
delay: 3s
max_attempts: 5
window: 60s
volumes:
ollama:

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version: '2'
built_at: '2024-10-08T17:40:45.325529'
image_name: local
docker_image: null
conda_env: local
apis:
- shields
- agents
- models
- memory
- memory_banks
- inference
- safety
providers:
inference:
- provider_id: ollama0
provider_type: remote::ollama
config:
url: http://127.0.0.1:14343
safety:
- provider_id: meta0
provider_type: meta-reference
config:
llama_guard_shield:
model: Llama-Guard-3-1B
excluded_categories: []
disable_input_check: false
disable_output_check: false
prompt_guard_shield:
model: Prompt-Guard-86M
memory:
- provider_id: meta0
provider_type: meta-reference
config: {}
agents:
- provider_id: meta0
provider_type: meta-reference
config:
persistence_store:
namespace: null
type: sqlite
db_path: ~/.llama/runtime/kvstore.db
telemetry:
- provider_id: meta0
provider_type: meta-reference
config: {}

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# 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
```
$ cd llama-stack/distribution/tgi/cpu
$ ls
compose.yaml run.yaml
$ docker compose up
```
### (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 TGI server endpoint. E.g.
```
inference:
- provider_id: tgi0
provider_type: remote::tgi
config:
url: http://127.0.0.1:5009
```
**Via Conda**
```bash
llama stack build --config ./build.yaml
# -- start a TGI server endpoint
llama stack run ./gpu/run.yaml
```

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services:
text-generation-inference:
image: ghcr.io/huggingface/text-generation-inference:latest
network_mode: "host"
volumes:
- $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"]
interval: 5s
timeout: 5s
retries: 30
llamastack:
depends_on:
text-generation-inference:
condition: service_healthy
image: llamastack/llamastack-local-cpu
network_mode: "host"
volumes:
- ~/.llama:/root/.llama
# Link to run.yaml file
- ./run.yaml:/root/my-run.yaml
ports:
- "5000:5000"
entrypoint: bash -c "python -m llama_stack.distribution.server.server --yaml_config /root/my-run.yaml"
restart_policy:
condition: on-failure
delay: 3s
max_attempts: 5
window: 60s

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version: '2'
built_at: '2024-10-08T17:40:45.325529'
image_name: local
docker_image: null
conda_env: local
apis:
- shields
- agents
- models
- memory
- memory_banks
- inference
- safety
providers:
inference:
- provider_id: tgi0
provider_type: remote::tgi
config:
url: <ENTER_YOUR_TGI_HOSTED_ENDPOINT>
safety:
- provider_id: meta0
provider_type: meta-reference
config:
llama_guard_shield:
model: Llama-Guard-3-1B
excluded_categories: []
disable_input_check: false
disable_output_check: false
prompt_guard_shield:
model: Prompt-Guard-86M
memory:
- provider_id: meta0
provider_type: meta-reference
config: {}
agents:
- provider_id: meta0
provider_type: meta-reference
config:
persistence_store:
namespace: null
type: sqlite
db_path: ~/.llama/runtime/kvstore.db
telemetry:
- provider_id: meta0
provider_type: meta-reference
config: {}

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services:
text-generation-inference:
image: ghcr.io/huggingface/text-generation-inference:latest
network_mode: "host"
volumes:
- $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"]
interval: 5s
timeout: 5s
retries: 30
llamastack:
depends_on:
text-generation-inference:
condition: service_healthy
image: llamastack/llamastack-local-cpu
network_mode: "host"
volumes:
- ~/.llama:/root/.llama
# Link to TGI run.yaml file
- ./run.yaml:/root/my-run.yaml
ports:
- "5000:5000"
# Hack: wait for TGI server to start before starting docker
entrypoint: bash -c "sleep 60; python -m llama_stack.distribution.server.server --yaml_config /root/my-run.yaml"
restart_policy:
condition: on-failure
delay: 3s
max_attempts: 5
window: 60s

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version: '2'
built_at: '2024-10-08T17:40:45.325529'
image_name: local
docker_image: null
conda_env: local
apis:
- shields
- agents
- models
- memory
- memory_banks
- inference
- safety
providers:
inference:
- provider_id: tgi0
provider_type: remote::tgi
config:
url: http://127.0.0.1:5009
safety:
- provider_id: meta0
provider_type: meta-reference
config:
llama_guard_shield:
model: Llama-Guard-3-1B
excluded_categories: []
disable_input_check: false
disable_output_check: false
prompt_guard_shield:
model: Prompt-Guard-86M
memory:
- provider_id: meta0
provider_type: meta-reference
config: {}
agents:
- provider_id: meta0
provider_type: meta-reference
config:
persistence_store:
namespace: null
type: sqlite
db_path: ~/.llama/runtime/kvstore.db
telemetry:
- provider_id: meta0
provider_type: meta-reference
config: {}