serialization.md

# Serialization #

Torch provides 4 high-level methods to serialize/deserialize arbitrary Lua/Torch objects. These functions are just abstractions over the File object, and were created for convenience (these are very common routines).

The first two functions are useful to serialize/deserialize data to/from files:

• torch.save(filename, object [, format])
• [object] torch.load(filename [, format])

The next two functions are useful to serialize/deserialize data to/from strings:

• [str] torch.serialize(object)
• [object] torch.deserialize(str)

Serializing to files is useful to save arbitrary data structures, or share them with other people. Serializing to strings is useful to store arbitrary data structures in databases, or 3rd party software.

### torch.save(filename, object [, format]) ###

Writes object into a file named filename. The format can be set to ascii or binary (default is binary). Binary format is platform dependent, but typically more compact and faster to read/write. The ASCII format is platform-independent, and should be used to share data structures across platforms.

-- arbitrary object:
obj = {
   mat = torch.randn(10,10),
   name = '10',
   test = {
      entry = 1
   }
}

-- save to disk:
torch.save('test.dat', obj)

### [object] torch.load(filename [, format]) ###

Reads object from a file named filename. The format can be set to ascii or binary (default is binary). Binary format is platform dependent, but typically more compact and faster to read/write. The ASCII format is platform-independent, and should be used to share data structures across platforms.

-- given serialized object from section above, reload:
obj = torch.load('test.dat')

print(obj)
-- will print:
-- {[mat]  = DoubleTensor - size: 10x10
--  [name] = string : "10"
--  [test] = table - size: 0}

### [str] torch.serialize(object [, format]) ###

Serializes object into a string. The format can be set to ascii or binary (default is binary). Binary format is platform dependent, but typically more compact and faster to read/write. The ASCII format is platform-independent, and should be used to share data structures across platforms.

-- arbitrary object:
obj = {
   mat = torch.randn(10,10),
   name = '10',
   test = {
      entry = 1
   }
}

-- serialize:
str = torch.serialize(obj)

### [object] torch.deserialize(str [, format]) ###

Deserializes object from a string. The format can be set to ascii or binary (default is binary). Binary format is platform dependent, but typically more compact and faster to read/write. The ASCII format is platform-independent, and should be used to share data structures across platforms.

-- given serialized object from section above, deserialize:
obj = torch.deserialize(str)

print(obj)
-- will print:
-- {[mat]  = DoubleTensor - size: 10x10
--  [name] = string : "10"
--  [test] = table - size: 0}