updated numpy as numexpr versions

Linyx1994 · Jan 21, 2019 · 49af421 · 49af421
1 parent c6e8375
commit 49af421
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Showing 2 changed files with 112 additions and 8 deletions.
diff --git a/Benchmark_Matrix_Multiplication/Benchmark.ipynb b/Benchmark_Matrix_Multiplication/Benchmark.ipynb
@@ -525,9 +525,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6.66 µs ± 9.51 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
+      "461 µs ± 35.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
+      "5.36 µs ± 43.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
+      "1.82 ms ± 4.06 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
+      "84.5 µs ± 2.91 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
+      "620 µs ± 17.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
+      "529 µs ± 1.36 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
+      "6.22 ms ± 39.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "38.4 ms ± 1.4 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "43.2 ms ± 516 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "211 ms ± 5.35 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "318 ms ± 39.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "367 ms ± 5.14 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "409 ms ± 6.44 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "2.06 s ± 8.51 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "3.2 s ± 6.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "3.71 s ± 161 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "3.63 s ± 110 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "26.2 s ± 7.23 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "OOM for size (100000,100000)\n"
+     ]
+    }
+   ],
    "source": [
     "for s1, s2 in size_combinations:\n",
     "    a, b, c = factors_float3(s1, s2)\n",
@@ -545,11 +572,53 @@
     "    df.loc[len(df)] = row"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Multiple matrix multiplication (5 factors)"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.5 µs ± 36.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
+      "495 µs ± 22.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
+      "8.77 µs ± 58.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
+      "2.68 ms ± 216 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "158 µs ± 1.48 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
+      "668 µs ± 14 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
+      "732 µs ± 4.28 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
+      "10.3 ms ± 58.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "68.2 ms ± 2.72 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "65.9 ms ± 1.34 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "338 ms ± 2.79 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "486 ms ± 41 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "654 ms ± 29 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "581 ms ± 26.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "2.85 s ± 6.48 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "OOM for size (100000,10000)\n"
+     ]
+    },
+    {
+     "ename": "MemoryError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-44-aba38e6dd697>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms2\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msize_combinations\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfactors_float5\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms2\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      3\u001b[0m     \u001b[0moperation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"a * b * c * d * e\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mr1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-o multiply5(a,b,c,d,e)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mr2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-o ne_multiply5(a,b,c,d,e)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-39-cf23e28c4542>\u001b[0m in \u001b[0;36mfactors_float5\u001b[0;34m(s1, s2)\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0ms2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m     \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0ms2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\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     22\u001b[0m     \u001b[0me\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0ms2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mMemoryError\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
     "for s1, s2 in size_combinations:\n",
     "    a, b, c, d, e = factors_float5(s1, s2)\n",
@@ -576,9 +645,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "28 µs ± 316 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
+      "472 µs ± 8.91 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
+      "249 µs ± 3.26 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
+      "1.85 ms ± 16 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
+      "590 µs ± 58.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
+      "1.36 ms ± 9.41 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
+      "25 ms ± 391 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "7.12 ms ± 89.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "57.3 ms ± 665 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "100 ms ± 379 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "2.57 s ± 18.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "347 ms ± 39.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "556 ms ± 20 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "982 ms ± 14.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "25.7 s ± 217 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "3.38 s ± 24.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    },
+    {
+     "ename": "MemoryError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-45-810794d23589>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms2\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msize_combinations\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfactors_float3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms2\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      3\u001b[0m     \u001b[0moperation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"a * exp(b) * sin(c)\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mr1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-o exponential_sine(a,b,c)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mr2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-o ne_exponential_sine(a,b,c)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-39-cf23e28c4542>\u001b[0m in \u001b[0;36mfactors_float3\u001b[0;34m(s1, s2)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfactors_float3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\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---> 12\u001b[0;31m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\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[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0ms2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mMemoryError\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
     "for s1, s2 in size_combinations:\n",
     "    a, b, c = factors_float3(s1, s2)\n",

diff --git a/Benchmark_Matrix_Multiplication/conda.yaml b/Benchmark_Matrix_Multiplication/conda.yaml
@@ -12,12 +12,12 @@ channels:
 - defaults
 dependencies:
 - python==3.6.7
-- numpy>=1.13.3
+- numpy>=1.16.0
 - pandas>=0.23.4
 - cudatoolkit>=9.2
 - pip:
   - ipykernel>=5.1.0
-  - numexpr>=2.6.8
+  - numexpr>=2.6.9
   - numba>=0.38.0
   - bokeh>=0.12.16
   - psutil>=5.4.8