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May 5, 2020 14:52
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Configure Client for NVLink " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import os" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"os.environ['UCX_TLS']='tcp,sockcm,cuda_copy,cuda_ipc'\n", | |
"os.environ['UCX_SOCKADDR_TLS_PRIORITY']='sockcm'\n", | |
"os.environ[\"UCX_RNDV_SCHEME\"] = \"get_zcopy\"" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Create DASK Cluster on a DGX2" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"from dask.distributed import Client\n", | |
"from dask_cuda import LocalCUDACluster\n", | |
"from dask_cuda.initialize import initialize\n", | |
"from dask.utils import parse_bytes\n", | |
"from dask.distributed import performance_report\n", | |
"from dask.distributed import wait\n", | |
"from dask.distributed import get_task_stream\n", | |
"from collections import defaultdict\n", | |
"\n", | |
"\n", | |
"import cupy\n", | |
"import rmm\n", | |
"import cudf\n", | |
"\n", | |
"# Configurations\n", | |
"protocol = \"ucx\"\n", | |
"interface = \"enp134s0f1\" # DGX-2\n", | |
"enable_tcp_over_ucx = True\n", | |
"enable_nvlink = True\n", | |
"enable_infiniband = False\n", | |
"\n", | |
"initialize(\n", | |
" create_cuda_context=True,\n", | |
" enable_tcp_over_ucx=enable_tcp_over_ucx,\n", | |
" enable_infiniband=enable_infiniband,\n", | |
" enable_nvlink=enable_nvlink,\n", | |
")\n", | |
"\n", | |
"cluster = LocalCUDACluster(local_directory=\"/tmp/bzaitlen\", \n", | |
" protocol=protocol,\n", | |
" interface=interface,\n", | |
" enable_tcp_over_ucx=enable_tcp_over_ucx,\n", | |
" enable_infiniband=enable_infiniband,\n", | |
" enable_nvlink=enable_nvlink,\n", | |
" )" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"client = Client(cluster)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Configure GPU Memory Pool" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"def setup_rmm_pool(client):\n", | |
" client.run(\n", | |
" cudf.set_allocator,\n", | |
" pool=True,\n", | |
" initial_pool_size= parse_bytes(\"26GB\"),\n", | |
" allocator=\"default\"\n", | |
" )\n", | |
" client.run(\n", | |
" cupy.cuda.set_allocator,\n", | |
" rmm.rmm_cupy_allocator\n", | |
" )" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"setup_rmm_pool(client)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Create Random Data on GPUs " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import cupy\n", | |
"import dask.array as da\n", | |
"\n", | |
"# generate chunked dask arrays of many cupy random arrays\n", | |
"rs = da.random.RandomState(RandomState=cupy.random.RandomState) " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
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"<table>\n", | |
"<tr>\n", | |
"<td>\n", | |
"<table>\n", | |
" <thead>\n", | |
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr><th> Bytes </th><td> 200.00 GB </td> <td> 100.00 MB </td></tr>\n", | |
" <tr><th> Shape </th><td> (10000000, 20000) </td> <td> (20000, 5000) </td></tr>\n", | |
" <tr><th> Count </th><td> 2000 Tasks </td><td> 2000 Chunks </td></tr>\n", | |
" <tr><th> Type </th><td> uint8 </td><td> cupy.ndarray </td></tr>\n", | |
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" <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
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"\n", | |
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"\n", | |
" <!-- Text -->\n", | |
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" <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">10000000</text>\n", | |
"</svg>\n", | |
"</td>\n", | |
"</tr>\n", | |
"</table>" | |
], | |
"text/plain": [ | |
"dask.array<randint, shape=(10000000, 20000), dtype=uint8, chunksize=(20000, 5000), chunktype=cupy.ndarray>" | |
] | |
}, | |
"execution_count": 8, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"x = rs.randint(0, 3, size=(10_000_000, 20_000), \n", | |
" chunks=(20_000, 5_000), dtype=\"uint8\")\n", | |
"x" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"CPU times: user 5.47 s, sys: 810 ms, total: 6.28 s\n", | |
"Wall time: 31 s\n" | |
] | |
} | |
], | |
"source": [ | |
"%%time\n", | |
"x = x.persist()\n", | |
"_ = wait(x);" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Calculate SVD" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"CPU times: user 6.82 s, sys: 655 ms, total: 7.47 s\n", | |
"Wall time: 13.5 s\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"array([[ 0.00708035, 0.0070814 , 0.00706951, ..., 0.00705772,\n", | |
" 0.00706385, 0.00706919],\n", | |
" [-0.00828586, 0.00272798, -0.00012425, ..., -0.0008114 ,\n", | |
" 0.00578989, 0.00011516],\n", | |
" [-0.00212376, 0.0038137 , -0.00507415, ..., -0.00657831,\n", | |
" 0.00651364, 0.00707903],\n", | |
" ...,\n", | |
" [ 0.00490506, 0.0027471 , 0.01441292, ..., -0.00598521,\n", | |
" -0.0035699 , -0.00508925],\n", | |
" [-0.00202074, 0.00021128, 0.00283297, ..., -0.00494039,\n", | |
" 0.00225633, 0.00758967],\n", | |
" [ 0.00480698, 0.00153497, -0.01142091, ..., -0.00982944,\n", | |
" 0.01104207, -0.0008731 ]])" | |
] | |
}, | |
"execution_count": 10, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"%%time\n", | |
"u, s, v = da.linalg.svd_compressed(x, k=10, seed=rs)\n", | |
"v.compute()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"with performance_report(filename=\"dask-large-svd-report.html\"):\n", | |
" u, s, v = da.linalg.svd_compressed(x, k=10, seed=rs)\n", | |
" v.compute()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"%%time\n", | |
"with get_task_stream() as data:\n", | |
" u, s, v = da.linalg.svd_compressed(x, k=10, seed=rs)\n", | |
" v.compute();" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"timespent = defaultdict(int)\n", | |
"\n", | |
"for d in data.data:\n", | |
" for q in d['startstops']:\n", | |
" start = q['start']\n", | |
" stop = q['stop']\n", | |
" compute = q['action']\n", | |
" timespent[compute] += stop-start\n", | |
"timespent" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python [conda env:20200501]", | |
"language": "python", | |
"name": "conda-env-20200501-py" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.7.6" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 4 | |
} |
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