Serialization in the Object Store

This document describes what Python objects Ray can and cannot serialize into the object store. Once an object is placed in the object store, it is immutable.

There are a number of situations in which Ray will place objects in the object store.

  1. The return values of a remote function.
  2. The value x in a call to ray.put(x).
  3. Arguments to remote functions (except for simple arguments like ints or floats).

A Python object may have an arbitrary number of pointers with arbitrarily deep nesting. To place an object in the object store or send it between processes, it must first be converted to a contiguous string of bytes. This process is known as serialization. The process of converting the string of bytes back into a Python object is known as deserialization. Serialization and deserialization are often bottlenecks in distributed computing.

Pickle is one example of a library for serialization and deserialization in Python.

Pickle (and the variant we use, cloudpickle) is general-purpose. It can serialize a large variety of Python objects. However, for numerical workloads, pickling and unpickling can be inefficient. For example, if multiple processes want to access a Python list of numpy arrays, each process must unpickle the list and create its own new copies of the arrays. This can lead to high memory overheads, even when all processes are read-only and could easily share memory.

In Ray, we optimize for numpy arrays by using the Apache Arrow data format. When we deserialize a list of numpy arrays from the object store, we still create a Python list of numpy array objects. However, rather than copy each numpy array, each numpy array object holds a pointer to the relevant array held in shared memory. There are some advantages to this form of serialization.

  • Deserialization can be very fast.
  • Memory is shared between processes so worker processes can all read the same data without having to copy it.

What Objects Does Ray Handle

Ray does not currently support serialization of arbitrary Python objects. The set of Python objects that Ray can serialize using Arrow includes the following.

  1. Primitive types: ints, floats, longs, bools, strings, unicode, and numpy arrays.
  2. Any list, dictionary, or tuple whose elements can be serialized by Ray.

For a more general object, Ray will first attempt to serialize the object by unpacking the object as a dictionary of its fields. This behavior is not correct in all cases. If Ray cannot serialize the object as a dictionary of its fields, Ray will fall back to using pickle. However, using pickle will likely be inefficient.

Notes and limitations

  • We currently handle certain patterns incorrectly, according to Python semantics. For example, a list that contains two copies of the same list will be serialized as if the two lists were distinct.

    l1 = [0]
    l2 = [l1, l1]
    l3 = ray.get(ray.put(l2))
    
    l2[0] is l2[1]  # True.
    l3[0] is l3[1]  # False.
    
  • For reasons similar to the above example, we also do not currently handle objects that recursively contain themselves (this may be common in graph-like data structures).

    l = []
    l.append(l)
    
    # Try to put this list that recursively contains itself in the object store.
    ray.put(l)
    

    This will throw an exception with a message like the following.

    This object exceeds the maximum recursion depth. It may contain itself recursively.
    
  • Whenever possible, use numpy arrays for maximum performance.

Last Resort Workaround

If you find cases where Ray serialization doesn’t work or does something unexpected, please let us know so we can fix it. In the meantime, you may have to resort to writing custom serialization and deserialization code (e.g., calling pickle by hand).

import pickle

@ray.remote
def f(complicated_object):
    # Deserialize the object manually.
    obj = pickle.loads(complicated_object)
    return "Successfully passed {} into f.".format(obj)

# Define a complicated object.
l = []
l.append(l)

# Manually serialize the object and pass it in as a string.
ray.get(f.remote(pickle.dumps(l)))  # prints 'Successfully passed [[...]] into f.'

Note: If you have trouble with pickle, you may have better luck with cloudpickle.