Tensor Memory API

persisting.open()

Open a tensor namespace for multi-dimensional access.

def open(
    namespace: str,
    dims: tuple[Dimension, ...],
    order_dim: Dimension | None = None,
    partition_dims: tuple[Dimension, ...] | None = None,
    *,
    backend: str = "local",
    shape: tuple[int, ...],
    dtype: np.dtype | type = np.float32,
    catalog: dict[Dimension, dict[Any, int]] | None = None,
    block_tokens: int = 64,
) -> TensorNamespace

Argument	Required	Description
namespace	✅	Namespace name (e.g. "kvcache/v1")
dims	✅	Tuple of Dimension in declaration order
order_dim	tiered	Dimension for range scans and block partitioning
partition_dims	❌	Dimensions for cross-node routing (planned)
backend	❌	"local" (flat array) or "tiered" (L1+L3 BlockStore)
shape	✅	Tuple of sizes, one per dimension
dtype	❌	numpy dtype (default float32)
catalog	❌	{dim: {coord → index}} for str/bytes dimensions
block_tokens	❌	Tokens per block on order_dim (default 64)

Returns a TensorNamespace.

---

TensorNamespace

class TensorNamespace:
    def __getitem__(self, key) -> Handler
    def prefetch(self, key) -> None
    def wait(self, key) -> None

__getitem__(key) → Handler

Create a slice handler. Returns a new Handler every call.

h = kv["s1", 0, 0:512]                       # positional
h = kv[{SESSION: "s1"}, :, :, slice(0, 512)]  # dict + slice

prefetch(key) → None

Asynchronously pull blocks covering this slice from L3 into L1 (tiered backend only).

kv.prefetch(("s1", 0, 0:512))

wait(key) → None

Block until prefetched blocks for this slice are in L1 (tiered backend only).

kv.wait(("s1", 0, 0:512))

---

Handler

class Handler:
    def tensor(self) -> np.ndarray
    def put(self, data: np.ndarray) -> None

tensor() → ndarray

Materialize data from storage. This is the only operation that copies data.

arr = kv["s1", 0, 0:512].tensor()

put(data) → None

Write data back to the slice's address.

kv["s1", 0, 0:512].put(some_ndarray)

---

Storage Backends

These are internal implementations available from persisting.store. Most users should use persisting.open().

LocalTensorStore

Single-node flat tensor store.

store = LocalTensorStore(dims, shape, dtype=np.float32, catalog=None, backing=None)
store.get(region)    → ndarray
store.put(region, data)

BlockStore

Two-tier block-granularity store.

store = BlockStore(
    dims, shape, dtype=np.float32,
    order_dim=TIME, block_tokens=64,
    l1_backing=None,    # default: NumpyBacking
    l3_backing=None,    # default: NumpyBacking
)
store.get(region)      → ndarray
store.put(region, data)
store.prefetch(region)
store.wait(region)

ActorStore

BlockStore exposed as a Pulsing Actor.

from persisting.store import get_block_store_actor_class, ActorStore

actor_cls = get_block_store_actor_class()
actor = await actor_cls.spawn(dims_ser, shape, order_dim_name, block_tokens, catalog_ser)
store = ActorStore(actor.proxy, dims)
data = await store.get_async(region)

---

Backing Types

Available from persisting.store:

Backing	Description
NumpyBacking(shape, dtype)	In-memory numpy array (default)
MmapBacking(shape, path, dtype)	Memory-mapped file
SafetensorsBacking(shape, path, tensor_name, dtype)	safetensors file
BlockMappedBacking(shape, dtype, block_table, use_mmap)	Block-granularity async, optional mmap
RemoteBacking(shape, dtype, get_block, put_block)	Remote via callback/RPC