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Created July 2, 2024 01:04
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dask-basics.ipynb
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"source": [
"import dask.array"
]
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"source": [
"# Create a dask array - just a whole bunch of ones here. Dask arrays are broken up\n",
"# into chunks, so that each chunk can be computed one at a time\n",
"\n",
"a = dask.array.ones((1000,1000),chunks=(20,20))\n",
"a"
]
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{
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"execution_count": 3,
"id": "f2d1d746-5821-477d-a4cc-6aae4ad2bb6c",
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"source": [
"# Normally you'd get a dask array from opening a file with xarray with chunking\n",
"\n",
"# xarray.open_dataset(path, chunks={'lat': 100, 'lon': 100})"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "446db633-23e7-4411-b306-c3e6a9c036d2",
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],
"source": [
"# Dask arrays work just like numpy arrays, but rather than computing the output \n",
"# they save the formula to compute the output\n",
"\n",
"a + 10\n",
"\n",
"# Note how now the graph says '2 graph layers' - this is dask storing the addition"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c3d34cdf-78fc-412c-a227-07cd9419a633",
"metadata": {},
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{
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"<div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-f41b334d-380e-11ef-87f5-000007a9fe80</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
"\n",
" <tr>\n",
" \n",
" <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
" <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
" \n",
" </tr>\n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
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"\n",
" \n",
"\n",
" \n",
" <details>\n",
" <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
" <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
" </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">dcb1f411</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Workers:</strong> 2\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads:</strong> 2\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total memory:</strong> 9.00 GiB\n",
" </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
" <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
"</tr>\n",
"\n",
" \n",
" </table>\n",
"\n",
" <details>\n",
" <summary style=\"margin-bottom: 20px;\">\n",
" <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
" </summary>\n",
"\n",
" <div style=\"\">\n",
" <div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-da5fd438-b948-4dd4-841c-07050c09ec62</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm:</strong> tcp://127.0.0.1:35891\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Workers:</strong> 2\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads:</strong> 2\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Started:</strong> Just now\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total memory:</strong> 9.00 GiB\n",
" </td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"\n",
" <details style=\"margin-left: 48px;\">\n",
" <summary style=\"margin-bottom: 20px;\">\n",
" <h3 style=\"display: inline;\">Workers</h3>\n",
" </summary>\n",
"\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:45793\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 1\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"/proxy/40279/status\" target=\"_blank\">/proxy/40279/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 4.50 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:35313\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> /jobfs/119519481.gadi-pbs/dask-scratch-space/worker-tr3kw6o6\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
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" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:35223\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 1\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"/proxy/34381/status\" target=\"_blank\">/proxy/34381/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 4.50 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:34787\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> /jobfs/119519481.gadi-pbs/dask-scratch-space/worker-08o1ukty\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
"\n",
" </details>\n",
"</div>\n",
"\n",
" </details>\n",
" </div>\n",
"</div>\n",
" </details>\n",
" \n",
"\n",
" </div>\n",
"</div>"
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"text/plain": [
"<Client: 'tcp://127.0.0.1:35891' processes=2 threads=2, memory=9.00 GiB>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Dask will run the actual calculation when either we run `dask.compute()` or when we \n",
"# save the data to a file\n",
"\n",
"# We can get Dask to run the calculation in parallel by starting up a \n",
"# dask.distributed.Client - by default this will use your PBS session's resources\n",
"\n",
"import dask.distributed\n",
"dask.distributed.Client()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "6c5fd780-0b82-4703-b7ad-67a61c46c588",
"metadata": {},
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" [2., 2., 2., ..., 2., 2., 2.],\n",
" [2., 2., 2., ..., 2., 2., 2.],\n",
" [2., 2., 2., ..., 2., 2., 2.]]),)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now we can compute the output in parallel\n",
"\n",
"dask.compute(a + 1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "71f9c881-c313-4667-aa60-7de858370971",
"metadata": {},
"outputs": [],
"source": [
"# For working with real data, use numpy whole-array operations rather than loops\n",
"# More efficient, plus it can easily be converted with dask\n",
"\n",
"# E.g. a test lat and lon 2d array\n",
"import numpy\n",
"lats = numpy.linspace(-90,90,100)\n",
"lons = numpy.linspace(0, 360,100)\n",
"LL, LN = numpy.meshgrid(lats, lons)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "738e6fb5-5626-437d-8327-eb47c3c33553",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x1541786d6490>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# LL and LN are now the lat and lon of every grid point\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.pcolormesh(LN)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "fbdd1480-0769-4ac7-b8e0-8d58e45fd25f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x154178750810>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# And we can use whole-array operations on them\n",
"\n",
"plt.pcolormesh(numpy.cos(LN))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "801dc453-6ec8-4006-970c-aa643638b6d3",
"metadata": {},
"outputs": [],
"source": [
"# To convert to dask, use dask.array functions instead of numpy to create the array\n",
"# Once the array is created you can use numpy\n",
"\n",
"lons = dask.array.linspace(0, 360, 100000, chunks=100)\n",
"lats = dask.array.linspace(-90, 90, 100000, chunks=100)\n",
"LL, LN = numpy.meshgrid(lats, lons)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "73c0dc99-42e9-4d1b-b485-cb9beab89bb1",
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
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" <th> Chunk </th>\n",
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" </thead>\n",
" <tbody>\n",
" \n",
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" <th> Bytes </th>\n",
" <td> 74.51 GiB </td>\n",
" <td> 78.12 kiB </td>\n",
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" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (100000, 100000) </td>\n",
" <td> (100, 100) </td>\n",
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" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 1000000 chunks in 3 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
"\n",
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" <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >100000</text>\n",
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"</svg>\n",
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" </tr>\n",
"</table>"
],
"text/plain": [
"dask.array<broadcast_to, shape=(100000, 100000), dtype=float64, chunksize=(100, 100), chunktype=numpy.ndarray>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# LL is now a chunked dask array\n",
"\n",
"LL"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "e75bc222-5d44-4573-8cd4-e6d70c0c9e3c",
"metadata": {},
"outputs": [
{
"data": {
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" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 74.51 GiB </td>\n",
" <td> 1.91 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (100000, 100000) </td>\n",
" <td> (500, 500) </td>\n",
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" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 40000 chunks in 4 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
" </tr>\n",
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"text/plain": [
"dask.array<rechunk-merge, shape=(100000, 100000), dtype=float64, chunksize=(500, 500), chunktype=numpy.ndarray>"
]
},
"execution_count": 12,
"metadata": {},
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}
],
"source": [
"# You can rechunk a dask array - but dask will need to load all of the data from the\n",
"# original chunking to load one of the new chunks - be careful going from lat-lon chunked\n",
"# to time chunked\n",
"\n",
"LL.rechunk((500,500))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "dcdef544-1268-4c02-9876-8be80819082d",
"metadata": {},
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" <td style=\"text-align: left;\">1000000</td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th style=\"text-align: left; width: 150px;\">shape</th>\n",
" <td style=\"text-align: left;\">(100000, 100000)</td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th style=\"text-align: left; width: 150px;\">dtype</th>\n",
" <td style=\"text-align: left;\">float64</td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th style=\"text-align: left; width: 150px;\">chunksize</th>\n",
" <td style=\"text-align: left;\">(100, 100)</td>\n",
" </tr>\n",
" \n",
" <tr>\n",
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" <td style=\"text-align: left;\">dask.array.core.Array</td>\n",
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" <th style=\"text-align: left; width: 150px;\">chunk_type</th>\n",
" <td style=\"text-align: left;\">numpy.ndarray</td>\n",
" </tr>\n",
" \n",
" \n",
" \n",
" <tr>\n",
" <th style=\"text-align: left; width: 150px;\"> depends on </th>\n",
" <td style=\"text-align: left;\">getitem-b7365dad23f76be99767791b4204c193</td>\n",
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" \n",
" \n",
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" \n",
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" </div>\n",
"</div>"
],
"text/plain": [
"HighLevelGraph with 3 layers.\n",
"<dask.highlevelgraph.HighLevelGraph object at 0x1541442c6cd0>\n",
" 0. linspace-91e3237b3f6176a7349f63d0722747aa\n",
" 1. getitem-b7365dad23f76be99767791b4204c193\n",
" 2. broadcast_to-625a86c729522fa9c78be490c9b5a02e"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# You can see the saved computations by looking at the array's .dask parameter\n",
"\n",
"LL.dask"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4bdd227b-20cd-4b19-8943-2ef3dd7a04c8",
"metadata": {},
"outputs": [],
"source": []
}
],
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"display_name": "Python 3 (ipykernel)",
"language": "python",
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