title: Python ray code working slower as compared to python multi-processing
body: <p>I am wanting to issue http requests in parallel and here is how my code (skeleton) looks like when using ray:</p>
  
  <pre><code>@ray.remote
  def issue_request(user_id):
      r = requests.post(url , json, headers)
  
   ray.get([issue_request.remote(id_token[user_id]) for _ in range(500)])
  </code></pre>
  
  <p>This is running much slower as compared to the following:</p>
  
  <pre><code>def issue_request(user_id):
      r = requests.post(url , json, headers)
  
  jobs = []
  for i in range(500):
      process = multiprocessing.Process(target=issue_request,
                                    args=(admin_id))
  jobs.append(process)
  for j in jobs:
      j.start()
  
  # Ensure all of the processes have finished
  for j in jobs:
      j.join()
  </code></pre>
  
  <p>The machine has two cores and it seems that ray only starts two processes to handle the 500 requests. Can someone please tell me how to tell ray to start 1 worker/process per request?</p>