From 42e3752212fc8adc3dd880f3da13af75b71e2d52 Mon Sep 17 00:00:00 2001
From: Michiel Cottaar <MichielCottaar@gmail.com>
Date: Wed, 21 Feb 2018 15:31:32 +0000
Subject: [PATCH] Updates to speeding up python talk
---
talks/speed/speed.ipynb | 1602 +++------------------------------------
1 file changed, 93 insertions(+), 1509 deletions(-)
diff --git a/talks/speed/speed.ipynb b/talks/speed/speed.ipynb
index e6eed32..ee75a45 100644
--- a/talks/speed/speed.ipynb
+++ b/talks/speed/speed.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {
"collapsed": true
},
@@ -20,20 +20,9 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "((nan, nan), (-1.0, 4.0), (0.0, 0.0))"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"def root(a, b, c):\n",
" D = b ** 2 - 4 * a * c\n",
@@ -47,22 +36,9 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "ename": "ValueError",
- "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-19-5d1fed3ed2df>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\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[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\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[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mroot\u001b[0m\u001b[0;34m(\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m<ipython-input-18-54b500cd66b1>\u001b[0m in \u001b[0;36mroot\u001b[0;34m(a, b, c)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m(\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[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mD\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m4\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mD\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mx1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mb\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
- ],
- "output_type": "error"
- }
- ],
+ "outputs": [],
"source": [
"a = np.random.randn(int(1e6))\n",
"b = np.random.randn(int(1e6))\n",
@@ -72,34 +48,9 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 3.64 s, sys: 65.9 ms, total: 3.71 s\n",
- "Wall time: 3.71 s\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "array([[-0.55797188, 1.11121928],\n",
- " [ nan, nan],\n",
- " [ 0.76166857, -1.70637551],\n",
- " ...,\n",
- " [-0.56859783, 0.98659831],\n",
- " [ nan, nan],\n",
- " [-0.53531175, 0.18339357]])"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"%%time\n",
"np.array([root(av, bv, cv) for av, bv, cv in zip(a, b, c)])"
@@ -107,7 +58,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {
"collapsed": true
},
@@ -119,31 +70,9 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 1.85 s, sys: 71.9 ms, total: 1.92 s\n",
- "Wall time: 1.92 s\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "(array([-0.55797188, nan, 0.76166857, ..., -0.56859783,\n",
- " nan, -0.53531175]),\n",
- " array([ 1.11121928, nan, -1.70637551, ..., 0.98659831,\n",
- " nan, 0.18339357]))"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"%%time\n",
"root_vec(a, b, c)"
@@ -151,31 +80,9 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 1.66 s, sys: 62.7 ms, total: 1.73 s\n",
- "Wall time: 1.73 s\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "(array([-1.72246968, -2.63787563, nan, ..., -2.20722406,\n",
- " nan, -1.84415698]),\n",
- " array([2.27571708, 2.04353033, nan, ..., 2.62522454, nan,\n",
- " 1.4922388 ]))"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"%%time\n",
"root_vec(a, b, 7)"
@@ -190,7 +97,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": null,
"metadata": {
"collapsed": true
},
@@ -201,7 +108,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": null,
"metadata": {
"collapsed": true
},
@@ -212,59 +119,27 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "3.08 µs ± 192 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%timeit root(23, 78, 19.0)"
]
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The slowest run took 11.09 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
- "786 ns ± 1.08 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%timeit root_jit(23, 78, 19.0)"
]
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "ename": "SystemError",
- "evalue": "CPUDispatcher(<function root at 0x114035620>) returned a result with an error set",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
- "\nThe above exception was the direct cause of the following exception:\n",
- "\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
- "\u001b[0;31mSystemError\u001b[0m: CPUDispatcher(<function root at 0x114035620>) returned a result with an error set"
- ],
- "output_type": "error"
- }
- ],
+ "outputs": [],
"source": [
"%%time\n",
"root_jit(a, b, 7)"
@@ -272,31 +147,9 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 406 ms, sys: 57.2 ms, total: 464 ms\n",
- "Wall time: 464 ms\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "(array([-1.72246968, -2.63787563, nan, ..., -2.20722406,\n",
- " nan, -1.84415698]),\n",
- " array([2.27571708, 2.04353033, nan, ..., 2.62522454, nan,\n",
- " 1.4922388 ]))"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"root_jit_vec = np.vectorize(root_jit)\n",
"%time root_jit_vec(a, b, 7)"
@@ -304,31 +157,9 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 391 ms, sys: 58.2 ms, total: 449 ms\n",
- "Wall time: 448 ms\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "(array([-1.72246968, -2.63787563, nan, ..., -2.20722406,\n",
- " nan, -1.84415698]),\n",
- " array([2.27571708, 2.04353033, nan, ..., 2.62522454, nan,\n",
- " 1.4922388 ]))"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"@np.vectorize\n",
"@numba.jit\n",
@@ -344,25 +175,9 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "ename": "NotImplementedError",
- "evalue": "(float64 x 2) cannot be represented as a Numpy dtype",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
- "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/numba/npyufunc/dufunc.py\u001b[0m in \u001b[0;36m_compile_for_args\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0margty\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[0margtys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margty\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compile_for_argtys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margtys\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 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_compile_for_argtys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margtys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/numba/npyufunc/dufunc.py\u001b[0m in \u001b[0;36m_compile_for_argtys\u001b[0;34m(self, argtys, return_type)\u001b[0m\n\u001b[1;32m 188\u001b[0m cres, argtys, return_type)\n\u001b[1;32m 189\u001b[0m dtypenums, ptr, env = ufuncbuilder._build_element_wise_ufunc_wrapper(\n\u001b[0;32m--> 190\u001b[0;31m cres, actual_sig)\n\u001b[0m\u001b[1;32m 191\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlongint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtypenums\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_keepalive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mptr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlibrary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/numba/npyufunc/ufuncbuilder.py\u001b[0m in \u001b[0;36m_build_element_wise_ufunc_wrapper\u001b[0;34m(cres, signature)\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0;31m# Get dtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 164\u001b[0m \u001b[0mdtypenums\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m \u001b[0mdtypenums\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 166\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdtypenums\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mptr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/numba/numpy_support.py\u001b[0m in \u001b[0;36mas_dtype\u001b[0;34m(nbtype)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m raise NotImplementedError(\"%r cannot be represented as a Numpy dtype\"\n\u001b[0;32m--> 136\u001b[0;31m % (nbtype,))\n\u001b[0m\u001b[1;32m 137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mNotImplementedError\u001b[0m: (float64 x 2) cannot be represented as a Numpy dtype"
- ],
- "output_type": "error"
- }
- ],
+ "outputs": [],
"source": [
"@numba.vectorize\n",
"def fast_root(a, b, c):\n",
@@ -384,28 +199,9 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 1.56 s, sys: 3.5 ms, total: 1.56 s\n",
- "Wall time: 1.56 s\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "5702887"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"def fib(n):\n",
" if n <= 1:\n",
@@ -417,28 +213,9 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 63.4 ms, sys: 1.79 ms, total: 65.2 ms\n",
- "Wall time: 64.2 ms\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "5702887"
- ]
- },
- "execution_count": 35,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"@numba.jit\n",
"def fib_jit(n):\n",
@@ -451,80 +228,36 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 1.28 s, sys: 5.26 ms, total: 1.28 s\n",
- "Wall time: 1.28 s\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "267914296"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"%time fib_jit(41)"
]
},
{
"cell_type": "code",
- "execution_count": 103,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " "
- ]
- }
- ],
+ "outputs": [],
"source": [
- "%prun fib(33)"
+ "%prun fib_jit(41)"
]
},
{
"cell_type": "code",
- "execution_count": 104,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " "
- ]
- }
- ],
+ "outputs": [],
"source": [
- "%prun fib_jit(41)"
+ "%prun fib(33)"
]
},
{
"cell_type": "code",
- "execution_count": 112,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 20 µs, sys: 1 µs, total: 21 µs\n",
- "Wall time: 24.1 µs\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from functools import lru_cache\n",
"@lru_cache(None)\n",
@@ -539,38 +272,8 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "lru_cache?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 111,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 261 µs, sys: 1e+03 ns, total: 262 µs\n",
- "Wall time: 267 µs\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "225591516161936330872512695036072072046011324913758190588638866418474627738686883405015987052796968498626"
- ]
- },
- "execution_count": 111,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"%time fib_cache(500)"
]
@@ -591,17 +294,9 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "139 ms ± 1.16 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"a = np.random.randn(int(1e7))\n",
"b = np.random.randn(int(1e7))\n",
@@ -610,17 +305,9 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "11.4 ms ± 484 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import numexpr as ne\n",
"%timeit ne.evaluate('a * b + 4.1 * a < 3 * b')"
@@ -635,36 +322,21 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "141 ms ± 1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "%timeit a * np.sqrt(abs(b)) < 3 * b"
+ "c = np.random.randn(int(1e7))\n",
+ "%timeit (-b - np.sqrt(b ** 2 - 4 * a * c) ) / (2 * a)"
]
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "12.6 ms ± 986 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "%timeit ne.evaluate('a * sqrt(abs(b)) < 3 * b')"
+ "%timeit ne.evaluate('(-b - sqrt(b ** 2 - 4 * a * c) ) / (2 * a)')"
]
},
{
@@ -687,37 +359,18 @@
},
{
"cell_type": "code",
- "execution_count": 69,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The cython extension is already loaded. To reload it, use:\n",
- " %reload_ext cython\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%load_ext cython"
]
},
{
"cell_type": "code",
- "execution_count": 70,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\n",
- "CPU times: user 67.4 ms, sys: 1.22 ms, total: 68.6 ms\n",
- "Wall time: 68 ms\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"def primes(kmax):\n",
" p = {}\n",
@@ -735,528 +388,23 @@
" k = k + 1\n",
" result.append(n)\n",
" n = n + 1\n",
- " return result"
+ " return result\n",
+ "print(primes(10))\n",
+ "%time _ = primes(10000)"
]
},
{
"cell_type": "code",
- "execution_count": 91,
+ "execution_count": null,
"metadata": {},
- "outputs": [
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- "metadata": {},
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"source": [
"%%cython -a\n",
- "def cprimes(int kmax):\n",
- " cdef int[1000] p\n",
+ "def cprimes(kmax):\n",
+ " p = {}\n",
" result = []\n",
" if kmax > 1000:\n",
" kmax = 1000\n",
- " cdef int k, n, i\n",
" k = 0\n",
" n = 2\n",
" while k < kmax:\n",
@@ -1273,19 +421,9 @@
},
{
"cell_type": "code",
- "execution_count": 92,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\n",
- "CPU times: user 2.2 ms, sys: 10 µs, total: 2.21 ms\n",
- "Wall time: 2.22 ms\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"print(cprimes(10))\n",
"%time _ = cprimes(10000)"
@@ -1300,535 +438,41 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "#%%cython -a\n",
- "def fib(n):\n",
+ "%%cython -a\n",
+ "def cfib(n):\n",
" if n <= 1:\n",
" return 1\n",
" else:\n",
- " return fib(n-2) + fib(n-1)"
+ " return cfib(n-2) + cfib(n-1)"
]
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 1.48 s, sys: 1.79 ms, total: 1.48 s\n",
- "Wall time: 1.48 s\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "5702887"
- ]
- },
- "execution_count": 48,
- "metadata": {},
- "output_type": "execute_result"
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+ "outputs": [],
"source": [
- "%time fib(33)"
+ "%time cfib(33)"
]
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 27.5 ms, sys: 73 µs, total: 27.5 ms\n",
- "Wall time: 27.5 ms\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "5702887"
- ]
- },
- "execution_count": 49,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"%time fib_jit(33)"
]
},
{
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+ "execution_count": null,
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+ "outputs": [],
"source": [
"%%cython -a\n",
"cdef int cfib(int n) nogil:\n",
@@ -1843,28 +487,9 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 10.2 ms, sys: 138 µs, total: 10.4 ms\n",
- "Wall time: 10.4 ms\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "5702887"
- ]
- },
- "execution_count": 57,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"%time fib(33)"
]
@@ -1879,78 +504,36 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"def root(a, b, c):\n",
" D = b ** 2 - 4 * a * c\n",
" if D < 0:\n",
- " return NAN, NAN\n",
- " x1 = (-b + sqrt(D)) / (2 * a)\n",
- " x2 = (-b - sqrt(D)) / (2 * a)\n",
- " return x1, x2"
+ " return np.nan, np.nan\n",
+ " x1 = (-b + np.sqrt(D)) / (2 * a)\n",
+ " x2 = (-b - np.sqrt(D)) / (2 * a)\n",
+ " return x1, x2\n",
+ "root(1, 3, 4), root(-1, 3, 4), root(1, 0, 0)"
]
},
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 236 ms, sys: 53.7 ms, total: 290 ms\n",
- "Wall time: 288 ms\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "(array([-1.72246969, -2.6378758 , nan, ..., -2.20722389,\n",
- " nan, -1.84415698]),\n",
- " array([2.27571702, 2.04353046, nan, ..., 2.62522459, nan,\n",
- " 1.49223876]))"
- ]
- },
- "execution_count": 68,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
+ "a = np.random.randn(int(1e6))\n",
+ "b = np.random.randn(int(1e6))\n",
+ "c = np.random.randn(int(1e6))\n",
"%time np.vectorize(root)(a, b, 7)"
]
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 353 ms, sys: 57.8 ms, total: 411 ms\n",
- "Wall time: 410 ms\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "(array([-1.72246968, -2.63787563, nan, ..., -2.20722406,\n",
- " nan, -1.84415698]),\n",
- " array([2.27571708, 2.04353033, nan, ..., 2.62522454, nan,\n",
- " 1.4922388 ]))"
- ]
- },
- "execution_count": 66,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"%time root_jit_vec(a, b, 7)"
]
@@ -1981,7 +564,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Cython in production code"
+ "## More cython"
]
},
{
@@ -1997,6 +580,7 @@
"source": [
"# Steps to optimizing code:\n",
"- Profile where the bottleneck is (%prun)\n",
+ " - You can install a [line profiler](https://github.com/rkern/line_profiler) (available as %lprun after installation)\n",
"- Is there a faster algorithm for the bottleneck?\n",
"- If the bottleneck is vectorized: can we optimize with numexpr?\n",
"- If the internal part of the loop can not be vectorized:\n",
--
GitLab