Use dask instead of multiprocessing for lunar lander tutorial (#346)

HISTORY.md

@@ -1,5 +1,14 @@
 # History
 
+## (Upcoming)
+
+### Changelog
+
+#### Documentation
+
+- Use dask instead of multiprocessing for lunar lander tutorial (#346)
+- pip install swig before gymnasium[box2d] in lunar lander tutorial (#346)
+
 ## 0.5.2
 
 This release contains miscellaneous edits to our documentation from v0.5.1.

tutorials/lunar_lander.ipynb

@@ -151,7 +151,8 @@
    },
    "outputs": [],
    "source": [
-    "%pip install ribs[visualize] gymnasium[box2d]==0.27.0 \"moviepy>=1.0.0\"\n",
+    "%pip install swig  # Must be installed before box2d\n",
+    "%pip install ribs[visualize] gymnasium[box2d]==0.27.0 \"moviepy>=1.0.0\" dask distributed\n",
     "\n",
     "# An uninstalled version of decorator is occasionally loaded. This loads the\n",
     "# newly installed version of decorator so that moviepy works properly -- see\n",
@@ -178,10 +179,10 @@
    },
    "outputs": [],
    "source": [
-    "import multiprocessing\n",
     "import sys\n",
     "import time\n",
     "\n",
+    "from dask.distributed import Client\n",
     "import gymnasium as gym\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -460,7 +461,7 @@
     "\n",
     "With the pyribs components defined, we start searching with CMA-ME. Since we use 5 emitters each with a batch size of 30 and we run 300 iterations, we run 5 x 30 x 300 = 45,000 lunar lander simulations. We also keep track of some logging info via `archive.stats`, which is an [`ArchiveStats`](https://docs.pyribs.org/en/latest/api/ribs.archives.ArchiveStats.html) object.\n",
     "\n",
-    "Since it takes a relatively long time to evaluate a lunar lander solution, we parallelize the evaluation of multiple solutions with Python's [multiprocessing module](https://docs.python.org/3/library/multiprocessing.html), specifically the [`starmap`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool.starmap) method of [`multiprocessing.Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool). With one worker, the loop should take **2 hours** to run. With two workers, it should take **1 hour** to run. Feel free to increase the number of workers based on the number of CPUs your system has available to speed up the loop further."
+    "Since it takes a relatively long time to evaluate a lunar lander solution, we parallelize the evaluation of multiple solutions with [Dask](https://distributed.dask.org/en/stable/quickstart.html). With one worker (i.e., one CPU), the loop should take **2 hours** to run. With two workers, it should take **1 hour** to run. Feel free to increase the number of workers based on the number of CPUs your system has available to speed up the loop further."
    ]
   },
   {
@@ -559,13 +560,18 @@
     "total_itrs = 300\n",
     "workers = 2  # Adjust the number of workers based on your available CPUs.\n",
     "\n",
+    "client = Client(\n",
+    "    n_workers=workers,  # Create this many worker processes using Dask LocalCluster.\n",
+    "    threads_per_worker=1,  # Each worker process is single-threaded.\n",
+    ")\n",
+    "\n",
     "for itr in trange(1, total_itrs + 1, file=sys.stdout, desc='Iterations'):\n",
     "    # Request models from the scheduler.\n",
     "    sols = scheduler.ask()\n",
     "\n",
     "    # Evaluate the models and record the objectives and measuress.\n",
-    "    with multiprocessing.Pool(workers) as pool:\n",
-    "        results = pool.starmap(simulate, [(model, env_seed) for model in sols])\n",
+    "    futures = client.map(lambda model: simulate(model, env_seed), sols)\n",
+    "    results = client.gather(futures)\n",
     "\n",
     "    objs, meas = [], []\n",
     "    for obj, impact_x_pos, impact_y_vel in results:\n",