title: Ray object store running out of memory using out of core. How can I configure an external object store like s3 bucket?
body: <pre><code>import ray
  import numpy as np
  
  
  
  ray.init()
  
  @ray.remote
  def f():
    return np.zeros(10000000)
  
  results = []
  for i in range(100):
    print(i)
    results += ray.get([f.remote() for _ in range(50)])
  </code></pre>
  <p>Normally, when the object store fills up, it begins evicting objects that are not in use (in a least-recently used fashion). However, because all of the objects are numpy arrays that are being held in the results list, they are all still in use, and the memory that those numpy arrays live in is actually in the object store, so they are taking up space in the object store. The object store can't evict them until those objects go out of scope.</p>
  <p>Question: How can I specify an external object store like redis without exceeding memory on single machine? I don't want to use /dev/shm or /tmp as object store as only limited memory is available and it quickly fills up</p>