fixup

Author: TomAugspurger

06_distributed_advanced.ipynb

@@ -491,104 +491,6 @@
     "point"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dask.distributed import as_completed\n",
-    "from random import uniform\n",
-    "\n",
-    "scale = 5                  # Intial random perturbation scale\n",
-    "best_point = (0, 0)        # Initial guess\n",
-    "best_score = float('inf')  # Best score so far\n",
-    "startx = [uniform(-scale, scale) for _ in range(10)]\n",
-    "starty = [uniform(-scale, scale) for _ in range(10)]\n",
-    "\n",
-    "# set up plot\n",
-    "source = ColumnDataSource({'x': startx, 'y': starty, 'c': ['grey'] * 10})\n",
-    "p.circle(source=source, x='x', y='y', color='c')\n",
-    "t = show(p, notebook_handle=True)\n",
-    "\n",
-    "# initial 10 random points\n",
-    "futures = [c.submit(rosenbrock, (x, y)) for x, y in zip(startx, starty)]\n",
-    "iterator = as_completed(futures)\n",
-    "\n",
-    "for res in iterator:\n",
-    "    # take a completed point, is it an improvement?\n",
-    "    point, score = res.result()\n",
-    "    if score < best_score:\n",
-    "        best_score, best_point = score, point\n",
-    "        print(score, point)\n",
-    "\n",
-    "    x, y = best_point\n",
-    "    newx, newy = (x + uniform(-scale, scale), y + uniform(-scale, scale))\n",
-    "    \n",
-    "    # update plot\n",
-    "    source.stream({'x': [newx], 'y': [newy], 'c': ['grey']}, rollover=20)\n",
-    "    push_notebook(t)\n",
-    "    \n",
-    "    # add new point, dynamically, to work on the cluster\n",
-    "    new_point = c.submit(rosenbrock, (newx, newy))\n",
-    "    iterator.add(new_point)  # Start tracking new task as well\n",
-    "\n",
-    "    # Narrow search and consider stopping\n",
-    "    scale *= 0.99\n",
-    "    if scale < 0.001:\n",
-    "        break\n",
-    "point"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dask.distributed import as_completed\n",
-    "from random import uniform\n",
-    "\n",
-    "scale = 5                  # Intial random perturbation scale\n",
-    "best_point = (0, 0)        # Initial guess\n",
-    "best_score = float('inf')  # Best score so far\n",
-    "startx = [uniform(-scale, scale) for _ in range(10)]\n",
-    "starty = [uniform(-scale, scale) for _ in range(10)]\n",
-    "\n",
-    "# set up plot\n",
-    "source = ColumnDataSource({'x': startx, 'y': starty, 'c': ['grey'] * 10})\n",
-    "p.circle(source=source, x='x', y='y', color='c')\n",
-    "t = show(p, notebook_handle=True)\n",
-    "\n",
-    "# initial 10 random points\n",
-    "futures = [c.submit(rosenbrock, (x, y)) for x, y in zip(startx, starty)]\n",
-    "iterator = as_completed(futures)\n",
-    "\n",
-    "for res in iterator:\n",
-    "    # take a completed point, is it an improvement?\n",
-    "    point, score = res.result()\n",
-    "    if score < best_score:\n",
-    "        best_score, best_point = score, point\n",
-    "        print(score, point)\n",
-    "\n",
-    "    x, y = best_point\n",
-    "    newx, newy = (x + uniform(-scale, scale), y + uniform(-scale, scale))\n",
-    "    \n",
-    "    # update plot\n",
-    "    source.stream({'x': [newx], 'y': [newy], 'c': ['grey']}, rollover=20)\n",
-    "    push_notebook(t)\n",
-    "    \n",
-    "    # add new point, dynamically, to work on the cluster\n",
-    "    new_point = c.submit(rosenbrock, (newx, newy))\n",
-    "    iterator.add(new_point)  # Start tracking new task as well\n",
-    "\n",
-    "    # Narrow search and consider stopping\n",
-    "    scale *= 0.99\n",
-    "    if scale < 0.001:\n",
-    "        break\n",
-    "point"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},