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fix: Default to port 8321 everywhere (#1734)

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As titled, moved all instances of 5001 to 8321
This commit is contained in:
Hardik Shah 2025-03-20 15:50:41 -07:00 • committed by GitHub
parent 581e8ae562
commit 127bac6869
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GPG key ID: B5690EEEBB952194
56 changed files with 2352 additions and 2305 deletions
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4
distributions/ollama/compose.yaml
View file

@ -51,14 +51,14 @@ services:
- ~/local/llama-stack/:/app/llama-stack-source
- ./run${SAFETY_MODEL:+-with-safety}.yaml:/root/my-run.yaml
ports:
- "${LLAMA_STACK_PORT:-5001}:${LLAMA_STACK_PORT:-5001}"
- "${LLAMA_STACK_PORT:-8321}:${LLAMA_STACK_PORT:-8321}"
environment:
- INFERENCE_MODEL=${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}
--port ${LLAMA_STACK_PORT:-8321}
deploy:
restart_policy:
condition: on-failure

4
distributions/remote-vllm/compose.yaml
View file

@ -84,9 +84,9 @@ services:
- SQLITE_STORE_DIR=${SQLITE_STORE_DIR:-$HOME/.llama/distributions/remote-vllm}
- SAFETY_MODEL=${SAFETY_MODEL:-meta-llama/Llama-Guard-3-1B}
ports:
- "${LLAMA_STACK_PORT:-5001}:${LLAMA_STACK_PORT:-5001}"
- "${LLAMA_STACK_PORT:-8321}:${LLAMA_STACK_PORT:-8321}"
# Hack: wait for vLLM server to start before starting docker
entrypoint: bash -c "sleep 60; python -m llama_stack.distribution.server.server --yaml_config /root/llamastack-run-remote-vllm.yaml --port 5001"
entrypoint: bash -c "sleep 60; python -m llama_stack.distribution.server.server --yaml_config /root/llamastack-run-remote-vllm.yaml --port 8321"
deploy:
restart_policy:
condition: on-failure

2
distributions/tgi/compose.yaml
View file

@ -83,7 +83,7 @@ services:
- ~/.llama:/root/.llama
- ./run${TGI_SAFETY_MODEL:+-with-safety}.yaml:/root/my-run.yaml
ports:
- "${LLAMA_STACK_PORT:-5001}:${LLAMA_STACK_PORT:-5001}"
- "${LLAMA_STACK_PORT:-8321}:${LLAMA_STACK_PORT:-8321}"
# 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:

4
docs/source/distributions/remote_hosted_distro/nvidia.md
View file

@ -58,7 +58,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
@ -75,7 +75,7 @@ docker run \
```bash
llama stack build --template nvidia --image-type conda
llama stack run ./run.yaml \
--port 5001 \
--port 8321 \
--env NVIDIA_API_KEY=$NVIDIA_API_KEY
--env INFERENCE_MODEL=$INFERENCE_MODEL
```

4
docs/source/distributions/self_hosted_distro/bedrock.md
View file

@ -28,7 +28,7 @@ The `llamastack/distribution-bedrock` distribution consists of the following pro
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
### Models
@ -53,7 +53,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \

6
docs/source/distributions/self_hosted_distro/cerebras.md
View file

@ -20,7 +20,7 @@ The `llamastack/distribution-cerebras` distribution consists of the following pr
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `CEREBRAS_API_KEY`: Cerebras API Key (default: ``)
### Models
@ -45,7 +45,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
@ -62,6 +62,6 @@ docker run \
```bash
llama stack build --template cerebras --image-type conda
llama stack run ./run.yaml \
--port 5001 \
--port 8321 \
--env CEREBRAS_API_KEY=$CEREBRAS_API_KEY
```

4
docs/source/distributions/self_hosted_distro/fireworks.md
View file

@ -30,7 +30,7 @@ The `llamastack/distribution-fireworks` distribution consists of the following p
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `FIREWORKS_API_KEY`: Fireworks.AI API Key (default: ``)
### Models
@ -63,7 +63,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \

4
docs/source/distributions/self_hosted_distro/groq.md
View file

@ -30,7 +30,7 @@ The `llamastack/distribution-groq` distribution consists of the following provid
The following environment variables can be configured:
- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `GROQ_API_KEY`: Groq API Key (default: ``)
### Models
@ -58,7 +58,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \

8
docs/source/distributions/self_hosted_distro/meta-reference-gpu.md
View file

@ -32,7 +32,7 @@ Note that you need access to nvidia GPUs to run this distribution. This distribu
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `INFERENCE_MODEL`: Inference model loaded into the Meta Reference server (default: `meta-llama/Llama-3.2-3B-Instruct`)
- `INFERENCE_CHECKPOINT_DIR`: Directory containing the Meta Reference model checkpoint (default: `null`)
- `SAFETY_MODEL`: Name of the safety (Llama-Guard) model to use (default: `meta-llama/Llama-Guard-3-1B`)
@ -77,7 +77,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
@ -109,7 +109,7 @@ Make sure you have done `uv pip install llama-stack` and have the Llama Stack CL
```bash
llama stack build --template meta-reference-gpu --image-type conda
llama stack run distributions/meta-reference-gpu/run.yaml \
--port 5001 \
--port 8321 \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
```
@ -117,7 +117,7 @@ If you are using Llama Stack Safety / Shield APIs, use:
```bash
llama stack run distributions/meta-reference-gpu/run-with-safety.yaml \
--port 5001 \
--port 8321 \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
```

4
docs/source/distributions/self_hosted_distro/meta-reference-quantized-gpu.md
View file

@ -34,7 +34,7 @@ Note that you need access to nvidia GPUs to run this distribution. This distribu
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `INFERENCE_MODEL`: Inference model loaded into the Meta Reference server (default: `meta-llama/Llama-3.2-3B-Instruct`)
- `INFERENCE_CHECKPOINT_DIR`: Directory containing the Meta Reference model checkpoint (default: `null`)
@ -77,7 +77,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \

6
docs/source/distributions/self_hosted_distro/nvidia.md
View file

@ -15,7 +15,7 @@ The `llamastack/distribution-nvidia` distribution consists of the following prov
The following environment variables can be configured:
- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `NVIDIA_API_KEY`: NVIDIA API Key (default: ``)
### Models
@ -39,7 +39,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
@ -56,6 +56,6 @@ docker run \
```bash
llama stack build --template nvidia --image-type conda
llama stack run ./run.yaml \
--port 5001 \
--port 8321 \
--env NVIDIA_API_KEY=$NVIDIA_API_KEY
```

6
docs/source/distributions/self_hosted_distro/ollama.md
View file

@ -32,7 +32,7 @@ You should use this distribution if you have a regular desktop machine without v
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `OLLAMA_URL`: URL of the Ollama server (default: `http://127.0.0.1:11434`)
- `INFERENCE_MODEL`: Inference model loaded into the Ollama server (default: `meta-llama/Llama-3.2-3B-Instruct`)
- `SAFETY_MODEL`: Safety model loaded into the Ollama server (default: `meta-llama/Llama-Guard-3-1B`)
@ -71,7 +71,7 @@ Now you are ready to run Llama Stack with Ollama as the inference provider. You
This method allows you to get started quickly without having to build the distribution code.
```bash
export LLAMA_STACK_PORT=5001
export LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
@ -109,7 +109,7 @@ docker run \
Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
```bash
export LLAMA_STACK_PORT=5001
export LLAMA_STACK_PORT=8321
llama stack build --template ollama --image-type conda
llama stack run ./run.yaml \

2
docs/source/distributions/self_hosted_distro/passthrough.md
View file

@ -30,7 +30,7 @@ The `llamastack/distribution-passthrough` distribution consists of the following
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `PASSTHROUGH_API_KEY`: Passthrough API Key (default: ``)
- `PASSTHROUGH_URL`: Passthrough URL (default: ``)

6
docs/source/distributions/self_hosted_distro/remote-vllm.md
View file

@ -31,7 +31,7 @@ You can use this distribution if you have GPUs and want to run an independent vL
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `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`)
@ -96,7 +96,7 @@ This method allows you to get started quickly without having to build the distri
```bash
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export LLAMA_STACK_PORT=5001
export LLAMA_STACK_PORT=8321
docker run \
-it \
@ -143,7 +143,7 @@ Make sure you have done `uv pip install llama-stack` and have the Llama Stack CL
```bash
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export LLAMA_STACK_PORT=5001
export LLAMA_STACK_PORT=8321
cd distributions/remote-vllm
llama stack build --template remote-vllm --image-type conda

4
docs/source/distributions/self_hosted_distro/sambanova.md
View file

@ -27,7 +27,7 @@ The `llamastack/distribution-sambanova` distribution consists of the following p
The following environment variables can be configured:
- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `SAMBANOVA_API_KEY`: SambaNova.AI API Key (default: ``)
### Models
@ -59,7 +59,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \

4
docs/source/distributions/self_hosted_distro/tgi.md
View file

@ -33,7 +33,7 @@ You can use this distribution if you have GPUs and want to run an independent TG
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `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://127.0.0.1:8080/v1`)
- `TGI_SAFETY_URL`: URL of the TGI server with the safety model (default: `http://127.0.0.1:8081/v1`)
@ -92,7 +92,7 @@ Now you are ready to run Llama Stack with TGI as the inference provider. You can
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \

4
docs/source/distributions/self_hosted_distro/together.md
View file

@ -30,7 +30,7 @@ The `llamastack/distribution-together` distribution consists of the following pr
The following environment variables can be configured:
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
- `TOGETHER_API_KEY`: Together.AI API Key (default: ``)
### Models
@ -64,7 +64,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \

9
docs/zero_to_hero_guide/00_Inference101.ipynb
View file

@ -48,7 +48,7 @@
"outputs": [],
"source": [
"HOST = \"localhost\" # Replace with your host\n",
"PORT = 5001 # Replace with your port\n",
"PORT = 8321 # Replace with your port\n",
"MODEL_NAME='meta-llama/Llama-3.2-3B-Instruct'"
]
},
@ -369,6 +369,9 @@
}
],
"metadata": {
"fileHeader": "",
"fileUid": "7da25939-a2a3-463c-958e-9cdfd710d158",
"isAdHoc": false,
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
@ -386,7 +389,5 @@
"pygments_lexer": "ipython3",
"version": "3.10.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
}

9
docs/zero_to_hero_guide/01_Local_Cloud_Inference101.ipynb
View file

@ -43,7 +43,7 @@
"source": [
"#### 2. Set Up Local and Cloud Clients\n",
"\n",
"Initialize both clients, specifying the `base_url` for each instance. In this case, we have the local distribution running on `http://localhost:8321` and the cloud distribution running on `http://localhost:5001`.\n"
"Initialize both clients, specifying the `base_url` for each instance. In this case, we have the local distribution running on `http://localhost:8321` and the cloud distribution running on `http://localhost:8322`.\n"
]
},
{
@ -236,6 +236,9 @@
}
],
"metadata": {
"fileHeader": "",
"fileUid": "e11939ac-dfbc-4a1c-83be-e494c7f803b8",
"isAdHoc": false,
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
@ -253,7 +256,5 @@
"pygments_lexer": "ipython3",
"version": "3.10.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
}

9
docs/zero_to_hero_guide/02_Prompt_Engineering101.ipynb
View file

@ -47,7 +47,7 @@
"outputs": [],
"source": [
"HOST = \"localhost\" # Replace with your host\n",
"PORT = 5001 # Replace with your port\n",
"PORT = 8321 # Replace with your port\n",
"MODEL_NAME='meta-llama/Llama-3.2-3B-Instruct'"
]
},
@ -281,6 +281,9 @@
}
],
"metadata": {
"fileHeader": "",
"fileUid": "b1b93b6e-22a2-4c24-8cb0-161fdafff29a",
"isAdHoc": false,
"kernelspec": {
"display_name": "base",
"language": "python",
@ -298,7 +301,5 @@
"pygments_lexer": "ipython3",
"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
}

9
docs/zero_to_hero_guide/03_Image_Chat101.ipynb
View file

@ -45,7 +45,7 @@
"outputs": [],
"source": [
"HOST = \"localhost\" # Replace with your host\n",
"CLOUD_PORT = 5001 # Replace with your cloud distro port\n",
"CLOUD_PORT = 8321 # Replace with your cloud distro port\n",
"MODEL_NAME='Llama3.2-11B-Vision-Instruct'"
]
},
@ -180,6 +180,9 @@
}
],
"metadata": {
"fileHeader": "",
"fileUid": "37bbbfda-8e42-446c-89c7-59dd49e2d339",
"isAdHoc": false,
"kernelspec": {
"display_name": "base",
"language": "python",
@ -197,7 +200,5 @@
"pygments_lexer": "ipython3",
"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
}

9
docs/zero_to_hero_guide/04_Tool_Calling101.ipynb
View file

@ -46,7 +46,7 @@
"nest_asyncio.apply()\n",
"\n",
"HOST = \"localhost\"\n",
"PORT = 5001\n",
"PORT = 8321\n",
"MODEL_NAME = \"meta-llama/Llama-3.2-3B-Instruct\"\n"
]
},
@ -335,6 +335,9 @@
}
],
"metadata": {
"fileHeader": "",
"fileUid": "f0abbf6d-ed52-40ad-afb4-f5ec99130249",
"isAdHoc": false,
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
@ -352,7 +355,5 @@
"pygments_lexer": "ipython3",
"version": "3.10.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
}

9
docs/zero_to_hero_guide/05_Memory101.ipynb
View file

@ -45,7 +45,7 @@
"outputs": [],
"source": [
"HOST = \"localhost\" # Replace with your host\n",
"PORT = 5001 # Replace with your port\n",
"PORT = 8321 # Replace with your port\n",
"MODEL_NAME='meta-llama/Llama-3.2-3B-Instruct'\n",
"MEMORY_BANK_ID=\"tutorial_bank\""
]
@ -378,6 +378,9 @@
}
],
"metadata": {
"fileHeader": "",
"fileUid": "73bc3357-0e5e-42ff-95b1-40b916d24c4f",
"isAdHoc": false,
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
@ -395,7 +398,5 @@
"pygments_lexer": "ipython3",
"version": "3.10.15"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
}

9
docs/zero_to_hero_guide/06_Safety101.ipynb
View file

@ -49,7 +49,7 @@
"outputs": [],
"source": [
"HOST = \"localhost\" # Replace with your host\n",
"PORT = 5001 # Replace with your port\n",
"PORT = 8321 # Replace with your port\n",
"SHEILD_NAME=\"meta-llama/Llama-Guard-3-1B\""
]
},
@ -112,6 +112,9 @@
}
],
"metadata": {
"fileHeader": "",
"fileUid": "9afaddb7-c2fb-4309-8fa0-761697de53f0",
"isAdHoc": false,
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
@ -129,7 +132,5 @@
"pygments_lexer": "ipython3",
"version": "3.11.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
}

9
docs/zero_to_hero_guide/07_Agents101.ipynb
View file

@ -50,7 +50,7 @@
"outputs": [],
"source": [
"HOST = \"localhost\" # Replace with your host\n",
"PORT = 5001 # Replace with your port\n",
"PORT = 8321 # Replace with your port\n",
"MODEL_NAME = \"meta-llama/Llama-3.2-3B-Instruct\"\n"
]
},
@ -168,6 +168,9 @@
}
],
"metadata": {
"fileHeader": "",
"fileUid": "8de24775-c4a0-49c7-904e-608264f69292",
"isAdHoc": false,
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
@ -185,7 +188,5 @@
"pygments_lexer": "ipython3",
"version": "3.10.15"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
}

10
docs/zero_to_hero_guide/README.md
View file

@ -96,7 +96,7 @@ If you're looking for more specific topics, we have a [Zero to Hero Guide](#next
3. **Set the ENV variables by exporting them to the terminal**:
```bash
export OLLAMA_URL="http://localhost:11434"
export LLAMA_STACK_PORT=5001
export LLAMA_STACK_PORT=8321
export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct"
export SAFETY_MODEL="meta-llama/Llama-Guard-3-1B"
```
@ -112,7 +112,7 @@ If you're looking for more specific topics, we have a [Zero to Hero Guide](#next
```
Note: Every time you run a new model with `ollama run`, you will need to restart the llama stack. Otherwise it won't see the new model.
The server will start and listen on `http://localhost:5001`.
The server will start and listen on `http://localhost:8321`.
---
## Test with `llama-stack-client` CLI
@ -120,11 +120,11 @@ After setting up the server, open a new terminal window and configure the llama-
1. Configure the CLI to point to the llama-stack server.
```bash
llama-stack-client configure --endpoint http://localhost:5001
llama-stack-client configure --endpoint http://localhost:8321
```
**Expected Output:**
```bash
Done! You can now use the Llama Stack Client CLI with endpoint http://localhost:5001
Done! You can now use the Llama Stack Client CLI with endpoint http://localhost:8321
```
2. Test the CLI by running inference:
```bash
@ -218,7 +218,7 @@ if INFERENCE_MODEL is None:
raise ValueError("The environment variable 'INFERENCE_MODEL' is not set.")
# Initialize the clien
client = LlamaStackClient(base_url="http://localhost:5001")
client = LlamaStackClient(base_url="http://localhost:8321")
# Create a chat completion reques
response = client.inference.chat_completion(

8
llama_stack/templates/bedrock/bedrock.py
View file

@ -9,7 +9,11 @@ from pathlib import Path
from llama_stack.distribution.datatypes import Provider, ToolGroupInput
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.remote.inference.bedrock.models import MODEL_ENTRIES
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
get_model_registry,
)
def get_distribution_template() -> DistributionTemplate:
@ -76,7 +80,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
},

2
llama_stack/templates/bedrock/doc_template.md
View file

@ -47,7 +47,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \

8
llama_stack/templates/cerebras/cerebras.py
View file

@ -14,7 +14,11 @@ from llama_stack.providers.inline.inference.sentence_transformers import (
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.remote.inference.cerebras import CerebrasImplConfig
from llama_stack.providers.remote.inference.cerebras.models import MODEL_ENTRIES
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
get_model_registry,
)
def get_distribution_template() -> DistributionTemplate:
@ -100,7 +104,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"CEREBRAS_API_KEY": (

4
llama_stack/templates/cerebras/doc_template.md
View file

@ -39,7 +39,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
@ -56,6 +56,6 @@ docker run \
```bash
llama stack build --template cerebras --image-type conda
llama stack run ./run.yaml \
--port 5001 \
--port 8321 \
--env CEREBRAS_API_KEY=$CEREBRAS_API_KEY
```

12
llama_stack/templates/ci-tests/ci_tests.py
View file

@ -15,10 +15,16 @@ from llama_stack.distribution.datatypes import (
from llama_stack.providers.inline.inference.sentence_transformers import (
SentenceTransformersInferenceConfig,
)
from llama_stack.providers.inline.vector_io.sqlite_vec.config import SQLiteVectorIOConfig
from llama_stack.providers.inline.vector_io.sqlite_vec.config import (
SQLiteVectorIOConfig,
)
from llama_stack.providers.remote.inference.fireworks.config import FireworksImplConfig
from llama_stack.providers.remote.inference.fireworks.models import MODEL_ENTRIES
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
get_model_registry,
)
def get_distribution_template() -> DistributionTemplate:
@ -104,7 +110,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"FIREWORKS_API_KEY": (

36
llama_stack/templates/dev/dev.py
View file

@ -16,20 +16,38 @@ from llama_stack.distribution.datatypes import (
from llama_stack.providers.inline.inference.sentence_transformers import (
SentenceTransformersInferenceConfig,
)
from llama_stack.providers.inline.vector_io.sqlite_vec.config import SQLiteVectorIOConfig
from llama_stack.providers.inline.vector_io.sqlite_vec.config import (
SQLiteVectorIOConfig,
)
from llama_stack.providers.remote.inference.anthropic.config import AnthropicConfig
from llama_stack.providers.remote.inference.anthropic.models import MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES
from llama_stack.providers.remote.inference.anthropic.models import (
MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES,
)
from llama_stack.providers.remote.inference.fireworks.config import FireworksImplConfig
from llama_stack.providers.remote.inference.fireworks.models import MODEL_ENTRIES as FIREWORKS_MODEL_ENTRIES
from llama_stack.providers.remote.inference.fireworks.models import (
MODEL_ENTRIES as FIREWORKS_MODEL_ENTRIES,
)
from llama_stack.providers.remote.inference.gemini.config import GeminiConfig
from llama_stack.providers.remote.inference.gemini.models import MODEL_ENTRIES as GEMINI_MODEL_ENTRIES
from llama_stack.providers.remote.inference.gemini.models import (
MODEL_ENTRIES as GEMINI_MODEL_ENTRIES,
)
from llama_stack.providers.remote.inference.groq.config import GroqConfig
from llama_stack.providers.remote.inference.groq.models import MODEL_ENTRIES as GROQ_MODEL_ENTRIES
from llama_stack.providers.remote.inference.groq.models import (
MODEL_ENTRIES as GROQ_MODEL_ENTRIES,
)
from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
from llama_stack.providers.remote.inference.openai.models import MODEL_ENTRIES as OPENAI_MODEL_ENTRIES
from llama_stack.providers.remote.inference.openai.models import (
MODEL_ENTRIES as OPENAI_MODEL_ENTRIES,
)
from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry
from llama_stack.providers.remote.vector_io.pgvector.config import (
PGVectorVectorIOConfig,
)
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
get_model_registry,
)
def get_inference_providers() -> Tuple[List[Provider], List[ModelInput]]:
@ -168,7 +186,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"FIREWORKS_API_KEY": (

2
llama_stack/templates/fireworks/doc_template.md
View file

@ -49,7 +49,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \

8
llama_stack/templates/fireworks/fireworks.py
View file

@ -19,7 +19,11 @@ from llama_stack.providers.inline.inference.sentence_transformers import (
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.remote.inference.fireworks.config import FireworksImplConfig
from llama_stack.providers.remote.inference.fireworks.models import MODEL_ENTRIES
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
get_model_registry,
)
def get_distribution_template() -> DistributionTemplate:
@ -158,7 +162,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"FIREWORKS_API_KEY": (

2
llama_stack/templates/groq/doc_template.md
View file

@ -49,7 +49,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \

14
llama_stack/templates/groq/groq.py
View file

@ -7,17 +7,17 @@
from pathlib import Path
from llama_stack.apis.models.models import ModelType
from llama_stack.distribution.datatypes import (
ModelInput,
Provider,
ToolGroupInput,
)
from llama_stack.distribution.datatypes import ModelInput, Provider, ToolGroupInput
from llama_stack.providers.inline.inference.sentence_transformers import (
SentenceTransformersInferenceConfig,
)
from llama_stack.providers.remote.inference.groq import GroqConfig
from llama_stack.providers.remote.inference.groq.models import MODEL_ENTRIES
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
get_model_registry,
)
def get_distribution_template() -> DistributionTemplate:
@ -97,7 +97,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMASTACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"GROQ_API_KEY": (

2
llama_stack/templates/hf-endpoint/hf_endpoint.py
View file

@ -127,7 +127,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"HF_API_TOKEN": (

2
llama_stack/templates/hf-serverless/hf_serverless.py
View file

@ -128,7 +128,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"HF_API_TOKEN": (

6
llama_stack/templates/meta-reference-gpu/doc_template.md
View file

@ -65,7 +65,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
@ -97,7 +97,7 @@ Make sure you have done `uv pip install llama-stack` and have the Llama Stack CL
```bash
llama stack build --template {{ name }} --image-type conda
llama stack run distributions/{{ name }}/run.yaml \
--port 5001 \
--port 8321 \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
```
@ -105,7 +105,7 @@ If you are using Llama Stack Safety / Shield APIs, use:
```bash
llama stack run distributions/{{ name }}/run-with-safety.yaml \
--port 5001 \
--port 8321 \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
```

2
llama_stack/templates/meta-reference-gpu/meta_reference.py
View file

@ -134,7 +134,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"INFERENCE_MODEL": (

2
llama_stack/templates/meta-reference-quantized-gpu/doc_template.md
View file

@ -67,7 +67,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \

2
llama_stack/templates/meta-reference-quantized-gpu/meta_reference.py
View file

@ -100,7 +100,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"INFERENCE_MODEL": (

4
llama_stack/templates/nvidia/doc_template.md
View file

@ -39,7 +39,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
@ -56,7 +56,7 @@ docker run \
```bash
llama stack build --template nvidia --image-type conda
llama stack run ./run.yaml \
--port 5001 \
--port 8321 \
--env NVIDIA_API_KEY=$NVIDIA_API_KEY
--env INFERENCE_MODEL=$INFERENCE_MODEL
```

4
llama_stack/templates/ollama/doc_template.md
View file

@ -60,7 +60,7 @@ Now you are ready to run Llama Stack with Ollama as the inference provider. You
This method allows you to get started quickly without having to build the distribution code.
```bash
export LLAMA_STACK_PORT=5001
export LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
@ -98,7 +98,7 @@ docker run \
Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
```bash
export LLAMA_STACK_PORT=5001
export LLAMA_STACK_PORT=8321
llama stack build --template {{ name }} --image-type conda
llama stack run ./run.yaml \

2
llama_stack/templates/ollama/ollama.py
View file

@ -138,7 +138,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"OLLAMA_URL": (

2
llama_stack/templates/open-benchmark/open_benchmark.py
View file

@ -279,7 +279,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"TOGETHER_API_KEY": (

7
llama_stack/templates/passthrough/passthrough.py
View file

@ -21,10 +21,7 @@ from llama_stack.providers.remote.inference.passthrough.config import (
PassthroughImplConfig,
)
from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
)
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
@ -186,7 +183,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"PASSTHROUGH_API_KEY": (

4
llama_stack/templates/remote-vllm/doc_template.md
View file

@ -83,7 +83,7 @@ This method allows you to get started quickly without having to build the distri
```bash
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export LLAMA_STACK_PORT=5001
export LLAMA_STACK_PORT=8321
docker run \
-it \
@ -130,7 +130,7 @@ Make sure you have done `uv pip install llama-stack` and have the Llama Stack CL
```bash
export INFERENCE_PORT=8000
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export LLAMA_STACK_PORT=5001
export LLAMA_STACK_PORT=8321
cd distributions/remote-vllm
llama stack build --template remote-vllm --image-type conda

2
llama_stack/templates/remote-vllm/vllm.py
View file

@ -135,7 +135,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"INFERENCE_MODEL": (

2
llama_stack/templates/sambanova/doc_template.md
View file

@ -49,7 +49,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \

18
llama_stack/templates/sambanova/sambanova.py
View file

@ -6,17 +6,19 @@
from pathlib import Path
from llama_stack.distribution.datatypes import (
Provider,
ShieldInput,
ToolGroupInput,
)
from llama_stack.distribution.datatypes import Provider, ShieldInput, ToolGroupInput
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.remote.inference.sambanova import SambaNovaImplConfig
from llama_stack.providers.remote.inference.sambanova.models import MODEL_ENTRIES
from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry
from llama_stack.providers.remote.vector_io.pgvector.config import (
PGVectorVectorIOConfig,
)
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
get_model_registry,
)
def get_distribution_template() -> DistributionTemplate:
@ -105,7 +107,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMASTACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"SAMBANOVA_API_KEY": (

2
llama_stack/templates/tgi/doc_template.md
View file

@ -80,7 +80,7 @@ Now you are ready to run Llama Stack with TGI as the inference provider. You can
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \

2
llama_stack/templates/tgi/tgi.py
View file

@ -129,7 +129,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"INFERENCE_MODEL": (

2
llama_stack/templates/together/doc_template.md
View file

@ -49,7 +49,7 @@ You can do this via Conda (build code) or Docker which has a pre-built image.
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=5001
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \

8
llama_stack/templates/together/together.py
View file

@ -19,7 +19,11 @@ from llama_stack.providers.inline.inference.sentence_transformers import (
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.remote.inference.together import TogetherImplConfig
from llama_stack.providers.remote.inference.together.models import MODEL_ENTRIES
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
get_model_registry,
)
def get_distribution_template() -> DistributionTemplate:
@ -154,7 +158,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"TOGETHER_API_KEY": (

2
llama_stack/templates/vllm-gpu/vllm.py
View file

@ -100,7 +100,7 @@ def get_distribution_template() -> DistributionTemplate:
},
run_config_env_vars={
"LLAMA_STACK_PORT": (
"5001",
"8321",
"Port for the Llama Stack distribution server",
),
"INFERENCE_MODEL": (