# `caffe2-mem`

---

Crate in the process of being translated from C++
to Rust. Some function bodies may still be
undergoing translation.

---

`ComputeBlobRecyclingForDag`

`compute_blob_recycling_for_dag`

These functions compute the set of blobs in
a directed acyclic graph (DAG) that can be safely
recycled. Given a DAG, blobs can be recycled if
they are not inputs to any nodes or if all the
nodes they are inputs to have already been
executed. The set of blobs that can be recycled is
determined by iterating over all the nodes in the
DAG and updating a set of "consumed blobs" as
nodes are executed. The set of blobs that can be
recycled is the set of blobs in the DAG that are
not in the set of consumed blobs.

---

`apply_assignments`

`apply_asyncif_blob_assignments`

`apply_recurrent_blob_assignments`

These functions apply assignments of blobs to
a specific device or memory type. These
assignments are represented as maps from blob
names to devices or memory
types. `apply_assignments` applies a one-time
assignment of blobs to devices or memory types,
while `apply_asyncif_blob_assignments` applies
assignments asynchronously, in parallel with other
computations. `apply_recurrent_blob_assignments`
applies assignments recurrently, meaning that the
assignments are applied repeatedly as long as the
corresponding blobs are in use.

---

`can_use_blob`

`get_blob_or_mapped_blob`

`get_free_blob`

`has_key_in_map`

`has_key_in_set`

`infer_blob_size`

These functions are all used to manage the
allocation and use of blobs, which are
multi-dimensional arrays used to represent data in
Caffe2. `can_use_blob` checks if a blob can be
reused for a new
computation. `get_blob_or_mapped_blob` retrieves
a blob or a mapped blob, depending on whether the
blob is resident in memory or mapped from
disk. `get_free_blob` retrieves a free blob for
use in a computation. `has_key_in_map` and
`has_key_in_set` are simple utility functions to
check if a key is in a map or set,
respectively. `infer_blob_size` infers the size of
a blob based on the dimensions of other blobs in
a computation.

---

`optimize_inference_net`

`optimize_net`

These functions optimize a neural network for
inference or training,
respectively. `optimize_inference_net` optimizes
a network by fusing multiple operations into
a single operation, thereby reducing the number of
memory accesses required. `optimize_net` optimizes
a network for training by modifying the network
structure or weights to improve accuracy or reduce
memory consumption.

---

`process_op`

`run_schema_check`

These functions are used to execute individual
operations in a neural network. `process_op` takes
an operation and its inputs and produces its
outputs. `run_schema_check` verifies that the
inputs and outputs of an operation conform to the
expected schema, raising an error if they do
not. Both of these functions are critical for
ensuring that a neural network operates correctly
and produces accurate results.

---

In summary, `caffe2-mem` is a Rust crate that
provides functions for managing the allocation and
reuse of memory in a neural network. These
functions are critical for optimizing the
performance of a neural network and ensuring that
it produces accurate results. The crate is still
in the process of being translated from C++ to
Rust, but many of the core functions are already
available.