Inability to assign values into shared memory objects using ray, i.e. it results in ValueError: assignment destination is read-only

ray_example_numpy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import psutil\n",
    "import numpy as np\n",
    "import ray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-11-30 12:47:34,442\tWARNING worker.py:1268 -- WARNING: Not updating worker name since `setproctitle` is not installed. Install this with `pip install setproctitle` (or ray[debug]) to enable monitoring of worker processes.\n",
      "2019-11-30 12:47:34,445\tINFO resource_spec.py:205 -- Starting Ray with 13.82 GiB memory available for workers and up to 6.92 GiB for objects. You can adjust these settings with ray.init(memory=<bytes>, object_store_memory=<bytes>).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'node_ip_address': '192.168.0.111',\n",
       " 'redis_address': '192.168.0.111:48348',\n",
       " 'object_store_address': '/tmp/ray/session_2019-11-30_12-47-34_443599_4531/sockets/plasma_store',\n",
       " 'raylet_socket_name': '/tmp/ray/session_2019-11-30_12-47-34_443599_4531/sockets/raylet',\n",
       " 'webui_url': None,\n",
       " 'session_dir': '/tmp/ray/session_2019-11-30_12-47-34_443599_4531'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cpus = psutil.cpu_count(logical=False)\n",
    "ray.init(num_cpus=num_cpus, ignore_reinit_error=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_average(\n",
    "        in_ary: np.ndarray,\n",
    "        out_ary: np.ndarray,\n",
    "        lat_index: int,\n",
    "        lon_index: int,\n",
    "):\n",
    "    ary = in_ary[lat_index, lon_index]\n",
    "    out_ary[lat_index, lon_index] = ary + np.mean(ary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_with_loop(\n",
    "        input_array: np.ndarray,\n",
    ") -> np.ndarray:\n",
    "    output_array = np.full(shape=input_array.shape, fill_value=np.NaN)\n",
    "    for lat_index in range(input_array.shape[0]):\n",
    "        for lon_index in range(input_array.shape[0]):\n",
    "            add_average(input_array, output_array, lat_index, lon_index)\n",
    "    return output_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "@ray.remote\n",
    "def add_average_ray(\n",
    "        in_ary: np.ndarray,\n",
    "        out_ary: np.ndarray,\n",
    "        lat_index: int,\n",
    "        lon_index: int,\n",
    "):\n",
    "    ary = in_ary[lat_index, lon_index]\n",
    "    out_ary[lat_index, lon_index] = ary + np.mean(ary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_with_ray(\n",
    "        input_array: np.ndarray,\n",
    ") -> np.ndarray:\n",
    "    output_array = np.full(shape=input_array.shape, fill_value=np.NaN)\n",
    "    in_array_id = ray.put(input_array)\n",
    "    out_array_id = ray.put(output_array)\n",
    "    futures = []\n",
    "    for lat_index in range(input_array.shape[0]):\n",
    "        for lon_index in range(input_array.shape[1]):\n",
    "            futures.append(add_average_ray.remote(in_array_id, out_array_id, lat_index, lon_index))\n",
    "\n",
    "    ray.get(futures)\n",
    "\n",
    "    return output_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an array that can be used to represent a 2x2 cell lat/lon map with 3 times\n",
    "tst_ary_1 = np.array([[[1, 6, 5], [3, 2, 7]], [[8, 4., 6.], [9, 4, 2]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.14 ms, sys: 0 ns, total: 1.14 ms\n",
      "Wall time: 2.49 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[ 5., 10.,  9.],\n",
       "        [ 7.,  6., 11.]],\n",
       "\n",
       "       [[14., 10., 12.],\n",
       "        [14.,  9.,  7.]]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "average_added_loop = compute_with_loop(tst_ary_1)\n",
    "average_added_loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "RayTaskError(ValueError)",
     "evalue": "\u001b[36mray_worker\u001b[39m (pid=4569, host=skypilot)\n  File \"<ipython-input-5-0bf9c2bf3f2e>\", line 9, in add_average_ray\nValueError: assignment destination is read-only",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRayTaskError(ValueError)\u001b[0m                  Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-6-2fc392838a4e>\u001b[0m in \u001b[0;36mcompute_with_ray\u001b[0;34m(input_array)\u001b[0m\n\u001b[1;32m     10\u001b[0m             \u001b[0mfutures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madd_average_ray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremote\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0min_array_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_array_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlat_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlon_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfutures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0moutput_array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/spi_ray/lib/python3.7/site-packages/ray/worker.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(object_ids)\u001b[0m\n\u001b[1;32m   2119\u001b[0m                     \u001b[0mworker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump_object_store_memory_usage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2120\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRayTaskError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2121\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_instanceof_cause\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2122\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2123\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRayTaskError(ValueError)\u001b[0m: \u001b[36mray_worker\u001b[39m (pid=4569, host=skypilot)\n  File \"<ipython-input-5-0bf9c2bf3f2e>\", line 9, in add_average_ray\nValueError: assignment destination is read-only"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "average_added_ray = compute_with_ray(tst_ary_1)\n",
    "average_added_ray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'average_added_ray' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-ef1521416d6f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maverage_added_loop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage_added_ray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'average_added_ray' is not defined"
     ]
    }
   ],
   "source": [
    "np.allclose(average_added_loop, average_added_ray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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