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mrocklin/svd-low-memory.ipynb

Created July 5, 2019 10:41
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svd-low-memory.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Low Memory Approximate SVD\n",
"\n",
"This notebook shows doing an approximate SVD computation on a large array in small memory.\n",
"\n",
"It was used in the development of [dask/dask #5041](https://github.com/dask/dask/pull/5041)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ['NUMPY_EXPERIMENTAL_ARRAY_FUNCTION'] = '1'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dask.array.utils import IS_NEP18_ACTIVE\n",
"assert IS_NEP18_ACTIVE"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<tr>\n",
"<td>\n",
"<table> <thead> <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 800.00 GB </td> <td> 80.00 MB </td></tr>\n",
" <tr><th> Shape </th><td> (1000000, 100000) </td> <td> (2000, 5000) </td></tr>\n",
" <tr><th> Count </th><td> 10000 Tasks </td><td> 10000 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float64 </td><td> cupy.ndarray </td></tr>\n",
" </tbody></table>\n",
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"</table>"
],
"text/plain": [
"dask.array<random_sample, shape=(1000000, 100000), dtype=float64, chunksize=(2000, 5000)>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import dask.array as da\n",
"import cupy\n",
"import numpy\n",
"\n",
"rs = da.random.RandomState(RandomState=cupy.random.RandomState)\n",
"x = rs.random(size=(1_000_000, 100_000), chunks=(2000, 5000))\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<dask.config.set at 0x7f7cfc3f1550>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import dask\n",
"dask.config.set(fuse_ave_width=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set up Local Cuda Cluster"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "14637e2fee8541dc83b80cd0c9e8c10a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(HTML(value='<h2>LocalCUDACluster</h2>'), HBox(children=(HTML(value='\\n<div>\\n <style scoped>\\n…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from dask_cuda import LocalCUDACluster\n",
"cluster = LocalCUDACluster()\n",
"cluster"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table style=\"border: 2px solid white;\">\n",
"<tr>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Client</h3>\n",
"<ul>\n",
" <li><b>Scheduler: </b>tcp://127.0.0.1:42863\n",
" <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
"</ul>\n",
"</td>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Cluster</h3>\n",
"<ul>\n",
" <li><b>Workers: </b>4</li>\n",
" <li><b>Cores: </b>4</li>\n",
" <li><b>Memory: </b>257.78 GB</li>\n",
"</ul>\n",
"</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<Client: scheduler='tcp://127.0.0.1:42863' processes=4 cores=4>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dask.distributed import Client\n",
"client = Client(cluster)\n",
"client"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"u, s, v = da.linalg.svd_compressed(x, k=10, compute=True, seed=rs)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.0031599 , 0.00315846, 0.00316353, ..., 0.0031613 ,\n",
" 0.00316399, 0.00315826],\n",
" [ 0.00227812, -0.00375382, 0.00146421, ..., 0.00144928,\n",
" -0.00045726, 0.003376 ],\n",
" [ 0.00247836, 0.00060906, -0.00046026, ..., -0.00258691,\n",
" 0.00253948, 0.00147831],\n",
" ...,\n",
" [ 0.00500711, 0.00218733, 0.00068634, ..., -0.00252443,\n",
" -0.00735898, -0.00273903],\n",
" [ 0.00083847, -0.00186231, 0.00026126, ..., -0.006059 ,\n",
" 0.00232307, -0.00084597],\n",
" [ 0.00440147, 0.0015874 , -0.00347741, ..., -0.00066526,\n",
" 0.00883817, 0.0084228 ]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v.compute()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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