Persistence (neural_nets_lib.Ocannl.Persistence)

Tensor checkpoint persistence: save, load, and restore.

Checkpoint files use an S-expression header for metadata followed by contiguous binary payloads in native precision format.

val save : 
  ctx:Context.t ->
  appending:bool ->
  Ocannl_tensor.Tensor.tn_set ->
  string ->
  unit

save ~ctx ~appending t_set path writes tensor data to a checkpoint file.

When ~appending:false, creates a fresh checkpoint (overwriting any existing file). When ~appending:true and the file exists, replaces tensors with matching IDs and keeps non-overlapping entries from the existing file.

Each tensor's data is retrieved on demand from its device buffer in ctx via Context.to_host (gh-ocannl-333). Raises if any tnode in t_set is not present in ctx.

val load : 
  ctx:Context.t ->
  ?prefix_namespace:string ->
  string ->
  Context.t * Ocannl_tensor.Tensor.tn_set

load ~ctx ?prefix_namespace path reads tensors from a checkpoint file, creates new tnodes, uploads their data into ctx via Context.from_host, and returns the updated context together with the loaded set (gh-ocannl-333).

Raises if any loaded tensor ID clashes with an existing tnode in the registry. After loading, bumps the session ID floor so that subsequently created tensors get IDs strictly above any loaded ID.

?prefix_namespace is reserved for future namespace support (#372). Currently, only None or Some "" are accepted; any non-empty prefix raises an error.

val restore : 
  ctx:Context.t ->
  Ocannl_tensor.Tensor.tn_set ->
  string ->
  Context.t

restore ~ctx t_set path updates existing tensor device buffers from a checkpoint file, returning the updated context.

For each tnode in t_set, finds its data in the file by ID, reads it into a temporary host buffer, and uploads it into the node's device buffer in ctx via Context.from_host (gh-ocannl-333).

Raises if:

A tensor in t_set is missing from the file
Precision or dimensions don't match between live tensor and file