pymc_ipyparallel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Sampling models in background in a Jupyter environment"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "db25f827f8e4d616"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting 1 engines with <class 'ipyparallel.cluster.launcher.LocalEngineSetLauncher'>\n",
      "100%|██████████| 1/1 [00:05<00:00,  5.78s/engine]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<AsyncResult(use_cloudpickle): pending>"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "import ipyparallel as ipp\n",
    "\n",
    "rc = ipp.Cluster(n=1).start_and_connect_sync()\n",
    "dv = rc[:]\n",
    "dv.use_cloudpickle()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-13T12:41:26.791127Z",
     "start_time": "2024-03-13T12:41:19.684806Z"
    }
   },
   "id": "5323323bb42d880d",
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "import pymc as pm"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-13T12:41:29.202533Z",
     "start_time": "2024-03-13T12:41:26.792834Z"
    }
   },
   "id": "b0e3d3beb75f1273",
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "@dv.remote(block=False)\n",
    "def sample(model):\n",
    "    return pm.sample(model=model, draws=5_000, progressbar=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-13T12:41:29.206844Z",
     "start_time": "2024-03-13T12:41:29.203651Z"
    }
   },
   "id": "bf90b72c692eeecb",
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<AsyncResult(sample): pending>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with pm.Model() as m:\n",
    "    x = pm.Normal('x', mu=0, sigma=1)\n",
    "    y = pm.Normal('y', mu=x, sigma=1, observed=[-2, -1, 0, 1, 2])\n",
    "    \n",
    "idata_res = sample(m)\n",
    "idata_res"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-13T12:41:29.239845Z",
     "start_time": "2024-03-13T12:41:29.208612Z"
    }
   },
   "id": "4af7a7ed81347bdf",
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "[{'msg_id': None,\n  'submitted': datetime.datetime(2024, 3, 13, 12, 41, 29, 234459, tzinfo=datetime.timezone.utc),\n  'started': None,\n  'completed': None,\n  'received': None,\n  'engine_uuid': 'f8f055cb-560482594b93e99858d3e1b4',\n  'engine_id': 0,\n  'follow': None,\n  'after': None,\n  'status': None,\n  'execute_input': None,\n  'execute_result': None,\n  'error': None,\n  'stdout': ' |██████████----------------------| 32.08% [7700/24000 00:01<00:03 Sampling 4 chains, 0 divergences]\\r',\n  'stderr': 'Auto-assigning NUTS sampler...\\nInitializing NUTS using jitter+adapt_diag...\\nMultiprocess sampling (4 chains in 4 jobs)\\nNUTS: [x]\\n',\n  'outputs': [],\n  'data': {}}]"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time.sleep(5)\n",
    "idata_res.metadata"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-13T12:41:34.246870Z",
     "start_time": "2024-03-13T12:41:29.241601Z"
    }
   },
   "id": "12e2bb318bcadd6",
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing some computation in the meantime\n"
     ]
    },
    {
     "data": {
      "text/plain": "{'x': -0.92, 'y': -9.59}"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Doing some computation in the meantime\")\n",
    "time.sleep(2)\n",
    "m.point_logps()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-13T12:41:36.601897Z",
     "start_time": "2024-03-13T12:41:34.248705Z"
    }
   },
   "id": "fb6c4f5285dfb436",
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<AsyncResult(sample): pending>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata_res"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-13T12:41:36.613538Z",
     "start_time": "2024-03-13T12:41:36.603735Z"
    }
   },
   "id": "7f9faff17d3d7679",
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "[{'msg_id': None,\n  'submitted': datetime.datetime(2024, 3, 13, 12, 41, 29, 234459, tzinfo=datetime.timezone.utc),\n  'started': None,\n  'completed': None,\n  'received': None,\n  'engine_uuid': 'f8f055cb-560482594b93e99858d3e1b4',\n  'engine_id': 0,\n  'follow': None,\n  'after': None,\n  'status': None,\n  'execute_input': None,\n  'execute_result': None,\n  'error': None,\n  'stdout': ' |███████████████████████████----| 87.16% [20919/24000 00:03<00:00 Sampling 4 chains, 0 divergences]\\r',\n  'stderr': 'Auto-assigning NUTS sampler...\\nInitializing NUTS using jitter+adapt_diag...\\nMultiprocess sampling (4 chains in 4 jobs)\\nNUTS: [x]\\n',\n  'outputs': [],\n  'data': {}}]"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata_res.metadata"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-13T12:41:36.623724Z",
     "start_time": "2024-03-13T12:41:36.616070Z"
    }
   },
   "id": "ee20ec1caafed268",
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "[Inference data with groups:\n \t> posterior\n \t> sample_stats\n \t> observed_data]"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata_res.get()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-13T12:41:45.966791Z",
     "start_time": "2024-03-13T12:41:36.625464Z"
    }
   },
   "id": "5c60d2735dde4285",
   "execution_count": 9
  }
 ],
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