llama-stack/llama_stack/templates/vllm-gpu/run.yaml
Ashwin Bharambe e951852848 Miscellaneous fixes around telemetry, library client and run yaml autogen
Also add a `venv` image-type for llama stack build
2024-12-08 20:40:22 -08:00

85 lines
2.2 KiB
YAML

version: '2'
image_name: vllm-gpu
docker_image: null
conda_env: vllm-gpu
apis:
- agents
- datasetio
- eval
- inference
- memory
- safety
- scoring
- telemetry
providers:
inference:
- provider_id: vllm
provider_type: inline::vllm
config:
model: ${env.INFERENCE_MODEL:Llama3.2-3B-Instruct}
tensor_parallel_size: ${env.TENSOR_PARALLEL_SIZE:1}
max_tokens: ${env.MAX_TOKENS:4096}
enforce_eager: ${env.ENFORCE_EAGER:False}
gpu_memory_utilization: ${env.GPU_MEMORY_UTILIZATION:0.7}
memory:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/vllm-gpu}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/vllm-gpu}/agents_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
sinks: ${env.TELEMETRY_SINKS:console,sqlite}
sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/vllm-gpu/trace_store.db}
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference
config: {}
datasetio:
- provider_id: huggingface
provider_type: remote::huggingface
config: {}
- provider_id: localfs
provider_type: inline::localfs
config: {}
scoring:
- provider_id: basic
provider_type: inline::basic
config: {}
- provider_id: llm-as-judge
provider_type: inline::llm-as-judge
config: {}
- provider_id: braintrust
provider_type: inline::braintrust
config: {}
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/vllm-gpu}/registry.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: vllm
provider_model_id: null
shields: []
memory_banks: []
datasets: []
scoring_fns: []
eval_tasks: []