Docs for meta-reference-gpu

This commit is contained in:
Ashwin Bharambe 2024-11-18 13:58:12 -08:00
parent 38563d7c00
commit dd732f037f
9 changed files with 374 additions and 101 deletions

View file

@ -0,0 +1,7 @@
# 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.
from .meta_reference import get_distribution_template # noqa: F401

View file

@ -1,13 +1,19 @@
version: '2'
name: meta-reference-gpu
distribution_spec:
docker_image: pytorch/pytorch:2.5.0-cuda12.4-cudnn9-runtime
description: Use code from `llama_stack` itself to serve all llama stack APIs
description: Use Meta Reference for running LLM inference
docker_image: null
providers:
inference: meta-reference
inference:
- inline::meta-reference
memory:
- inline::faiss
- remote::chromadb
- remote::pgvector
safety: inline::llama-guard
agents: inline::meta-reference
telemetry: inline::meta-reference
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
image_type: conda

View file

@ -0,0 +1,82 @@
# Meta Reference Distribution
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations:
{{ providers_table }}
Note that you need access to nvidia GPUs to run this distribution. This distribution is not compatible with CPU-only machines or machines with AMD GPUs.
{% if run_config_env_vars %}
### Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
{% endfor %}
{% endif %}
## Prerequisite: Downloading Models
Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
```
$ ls ~/.llama/checkpoints
Llama3.1-8B Llama3.2-11B-Vision-Instruct Llama3.2-1B-Instruct Llama3.2-90B-Vision-Instruct Llama-Guard-3-8B
Llama3.1-8B-Instruct Llama3.2-1B Llama3.2-3B-Instruct Llama-Guard-3-1B Prompt-Guard-86M
```
## Running the Distribution
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 \
-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=meta-llama/Llama-3.2-3B-Instruct
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
docker run \
-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=meta-llama/Llama-3.2-3B-Instruct \
--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
```
### Via Conda
Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
```bash
llama stack build --template meta-reference-gpu --image-type conda
llama stack run ./run.yaml \
--port 5001 \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
```
If you are using Llama Stack Safety / Shield APIs, use:
```bash
llama stack run ./run-with-safety.yaml \
--port 5001 \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
```

View file

@ -0,0 +1,100 @@
# 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.
from pathlib import Path
from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput
from llama_stack.providers.inline.inference.meta_reference import (
MetaReferenceInferenceConfig,
)
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["inline::meta-reference"],
"memory": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
}
inference_provider = Provider(
provider_id="meta-reference-inference",
provider_type="inline::meta-reference",
config=MetaReferenceInferenceConfig.sample_run_config(
model="${env.INFERENCE_MODEL}",
checkpoint_dir="${env.INFERENCE_CHECKPOINT_DIR:null}",
),
)
inference_model = ModelInput(
model_id="${env.INFERENCE_MODEL}",
provider_id="meta-reference-inference",
)
safety_model = ModelInput(
model_id="${env.SAFETY_MODEL}",
provider_id="meta-reference-safety",
)
return DistributionTemplate(
name="meta-reference-gpu",
distro_type="self_hosted",
description="Use Meta Reference for running LLM inference",
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
default_models=[inference_model, safety_model],
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider],
},
default_models=[inference_model],
),
"run-with-safety.yaml": RunConfigSettings(
provider_overrides={
"inference": [
inference_provider,
Provider(
provider_id="meta-reference-safety",
provider_type="inline::meta-reference",
config=MetaReferenceInferenceConfig.sample_run_config(
model="${env.SAFETY_MODEL}",
checkpoint_dir="${env.SAFETY_CHECKPOINT_DIR:null}",
),
),
],
},
default_models=[
inference_model,
safety_model,
],
default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}")],
),
},
docker_compose_env_vars={
"LLAMASTACK_PORT": (
"5001",
"Port for the Llama Stack distribution server",
),
"INFERENCE_MODEL": (
"meta-llama/Llama-3.2-3B-Instruct",
"Inference model loaded into the Meta Reference server",
),
"INFERENCE_CHECKPOINT_DIR": (
"null",
"Directory containing the Meta Reference model checkpoint",
),
"SAFETY_MODEL": (
"meta-llama/Llama-Guard-3-1B",
"Name of the safety (Llama-Guard) model to use",
),
"SAFETY_CHECKPOINT_DIR": (
"null",
"Directory containing the Llama-Guard model checkpoint",
),
},
)