Configuring LMCache

class lmcache.config.GlobalConfig

enable_debug: bool = True

classmethod is_debug() → bool

classmethod set_debug(enable: bool)

class lmcache.config.LMCacheEngineConfig(chunk_size: int, local_device: str | None, remote_url: str | None, remote_serde: str | None, pipelined_backend: bool, save_decode_cache: bool, enable_blending: bool)

chunk_size: int

enable_blending: bool

static from_defaults(chunk_size: int = 256, local_device: str = 'cuda', remote_url: str = 'redis://localhost:6379', remote_serde: str = 'torch', pipelined_backend: bool = False, save_decode_cache: bool = False, enable_blending: bool = False) → LMCacheEngineConfig

static from_file(file_path: str) → LMCacheEngineConfig

Load the config from a yaml file

static from_legacy(chunk_size: int = 256, backend: str = 'cuda', persist_path: str | None = None, remote_serde: str | None = 'torch', pipelined_backend: bool = False, save_decode_cache: bool = False) → LMCacheEngineConfig

local_device: str | None

pipelined_backend: bool

remote_serde: str | None

remote_url: str | None

save_decode_cache: bool

class lmcache.config.LMCacheEngineMetadata(model_name: str, world_size: int, worker_id: int, fmt: str, dtype: str)

name of the LLM model

dtype: str

fmt: str

the data type of kv tensors

model_name: str

world size when running under a distributed setting

worker_id: int

the format of kv tensors

world_size: int

worker id when running under a distributed setting