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dmcg/cross-datacentre-dask.ipynb

Last active June 15, 2022 15:58
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cross-datacentre-dask.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "2623edd2-9e27-4422-8516-c356b7342055",
"metadata": {},
"source": [
"# Distributing a Dask Cluster Between Data Centres\n",
"\n",
"## Abstract\n",
"\n",
"We have devised a technique for creating a Dask cluster where worker nodes are hosted in different data centres, connected by a mesh VPN that allows the scheduler and workers to communicate and exchange results. A novel Dask hack means that we can run data processing tasks on the workers in the cluster closest to the source data, so that communication between data centres is minimised. When combined with zarr to give access to huge hyper-cube datasets in object storage, we believe that the technique has the potential to allow data-proximate distributed computing in the Cloud.\n",
"\n",
"## Introduction\n",
"\n",
"This notebook shows our approach to distributing Dask across data centres, with networking and scheduling and workers as a service.\n",
"\n",
"It runs a computation in 3 locations.\n",
"\n",
"1. This computer, where the client and scheduler are running.\n",
"2. The ECMWF data centre. This has compute resources, and hosts data containing *predictions*.\n",
"3. The EUMETSAT data centre, with compute resources and data on *observations*."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a41856ef-99cf-44f8-aa74-e56a8849ff27",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(filename=\"images/datacentres.png\") # this because GitHub doesn't render markup images"
]
},
{
"cell_type": "markdown",
"id": "20f225ad-7e9c-4bfe-9d86-2f0308dbb80b",
"metadata": {},
"source": [
"The idea is that tasks accessing data available in a location should be run there. Meanwhile the computation can be defined, run, and results rendered, elsewhere. All this with minimal hinting to the computation as to how this should be done."
]
},
{
"cell_type": "markdown",
"id": "9c65c2dc-3e42-499a-b193-dec0ccbd9675",
"metadata": {
"tags": []
},
"source": [
"## Setup \n",
"\n",
"First some imports and conveniences"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c0c63610-3372-432f-ab65-c740fd8c7724",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from time import sleep\n",
"import dask\n",
"from dask.distributed import Client\n",
"from dask.distributed import performance_report, get_task_stream\n",
"from dask_worker_pools import pool, propagate_pools\n",
"import pytest\n",
"import ipytest\n",
"import xarray\n",
"import matplotlib.pyplot as plt\n",
"from orgs import my_org\n",
"from tree import tree\n",
"\n",
"ipytest.autoconfig()"
]
},
{
"cell_type": "markdown",
"id": "43db728b-2bce-4aa4-97ad-3ca49f36212a",
"metadata": {
"tags": []
},
"source": [
"In this case we are using a control plane IPv4 WireGuard network on 10.8.0.0/24 to set up the cluster. WireGuard is running on ECMWF and EUMETSAT, but we have to start it here"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "080df188-21ce-4381-b917-4ebf4006a7ab",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4: mo-aws-ec2: <POINTOPOINT,NOARP,UP,LOWER_UP> mtu 8921 qdisc noqueue state UNKNOWN group default qlen 1000\n",
" link/none \n",
" inet 10.8.0.3/24 scope global mo-aws-ec2\n",
" valid_lft forever preferred_lft forever\n"
]
}
],
"source": [
"!./start-wg.sh"
]
},
{
"cell_type": "markdown",
"id": "44a7488f-81c5-41fd-84ae-7df30ede6fad",
"metadata": {},
"source": [
"We have worker machines configured in both ECMWF and EUMETSAT, one in each. They are accessible on the control plane network as"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "85153c5c-1bef-47e4-98d2-c0fe4f5d725b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"env: ECMWF_HOST=10.8.0.4\n",
"env: EUMETSAT_HOST=10.8.0.2\n"
]
}
],
"source": [
"ecmwf_host='10.8.0.4'\n",
"%env ECMWF_HOST=$ecmwf_host\n",
"eumetsat_host='10.8.0.2'\n",
"%env EUMETSAT_HOST=$eumetsat_host"
]
},
{
"cell_type": "markdown",
"id": "4f45031d-4a4f-48e8-add0-a3cf13a0c2fb",
"metadata": {
"tags": []
},
"source": [
"## Mount the Data\n",
"\n",
"This machine needs access to the data files over the network in order to read NetCDF metadata. The workers are sharing their data with NFS, so we mount them here (over the control plane network, but see Future Work below)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "03d3d6f4-e529-4f28-88ab-1bac076d7ba8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%%bash\n",
"sudo ./data-reset.sh\n",
"\n",
"mkdir -p /data/ecmwf\n",
"mkdir -p /data/eumetsat\n",
"sudo mount $ECMWF_HOST:/data/ecmwf /data/ecmwf\n",
"sudo mount $EUMETSAT_HOST:/eumetsatdata/ /data/eumetsat"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3908f8ae-5088-4167-a662-0f6741f19acb",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"images/datacentres-data.png\")"
]
},
{
"cell_type": "markdown",
"id": "019688d4-fb39-4ef9-8d4a-42bd0d17f2b0",
"metadata": {
"tags": []
},
"source": [
"## Access to Data"
]
},
{
"cell_type": "markdown",
"id": "71934bb9-b53e-4e41-93d4-8c3099ffbb9d",
"metadata": {},
"source": [
"For this demonstration, we have two large data files that we want to process. On ECMWF we have predictions in `/data/ecmwf/000490262cdd067721a34112963bcaa2b44860ab.nc`. Workers running in ECMWF can see the file"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9bc5f590-3364-4d1e-9ddd-efe3c17c6da2",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"['/data',\n",
" '/data/ecmwf',\n",
" '/data/ecmwf/000490262cdd067721a34112963bcaa2b44860ab.nc',\n",
" '/data/eumetsat',\n",
" '/data/eumetsat/nwcsaf',\n",
" '/data/eumetsat/ascat',\n",
" '/data/eumetsat/geo-hsr-test-data',\n",
" '/data/eumetsat/mviri',\n",
" '/data/eumetsat/nwcsaf-cf',\n",
" '/data/eumetsat/psdc',\n",
" '/data/eumetsat/seviri',\n",
" '/data/eumetsat/iasi',\n",
" '/data/eumetsat/cloud',\n",
" '/data/eumetsat/ad-hoc',\n",
" '/data/eumetsat/ad-hoc/observations.nc']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tree('/data').compute(workers='ecmwf-1-0')"
]
},
{
"cell_type": "markdown",
"id": "98629012-8edd-4e13-bdce-5133846ce0eb",
"metadata": {},
"source": [
"and because that directory is mounted here over NFS, so can this computer"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "8d1061d8-a630-4ad5-a182-1fdd067a3d4b",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/data/ecmwf\n",
"└── 000490262cdd067721a34112963bcaa2b44860ab.nc\n",
"\n",
"0 directories, 1 file\n"
]
}
],
"source": [
"!tree /data/ecmwf"
]
},
{
"cell_type": "markdown",
"id": "604f9e6e-2508-46f5-9aab-34c19d80ad78",
"metadata": {},
"source": [
"On EUMETSAT we have `observations.nc`"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ce170c8d-9f81-4ed3-a98b-d8ff73378cec",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"['/data/eumetsat/ad-hoc', '/data/eumetsat/ad-hoc/observations.nc']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tree('/data/eumetsat/ad-hoc').compute(workers='eumetsat-2-0')"
]
},
{
"cell_type": "markdown",
"id": "6bb22358-d35c-41b7-a915-67b8a5de9581",
"metadata": {},
"source": [
"similarly visible on this computer"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e994a546-4f5f-479d-9555-9159337812e4",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/data/eumetsat/ad-hoc\n",
"└── observations.nc\n",
"\n",
"0 directories, 1 file\n"
]
}
],
"source": [
"!tree /data/eumetsat/ad-hoc"
]
},
{
"cell_type": "markdown",
"id": "c99a540f-5bfa-4c64-87d5-fed9724bef95",
"metadata": {
"tags": []
},
"source": [
"## Our Calculation\n",
"\n",
"We want to compare the predictions against the observations."
]
},
{
"cell_type": "markdown",
"id": "503ba3d5-98e6-40f8-a4b7-5a68b7a42b48",
"metadata": {},
"source": [
"We can open the predictions file with xarray"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d4b8f647-b3e9-4073-8bde-3b352e8bee7f",
"metadata": {},
"outputs": [
{
"data": {
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" --xr-disabled-color: #515151;\n",
" --xr-background-color: #111111;\n",
" --xr-background-color-row-even: #111111;\n",
" --xr-background-color-row-odd: #313131;\n",
"}\n",
"\n",
".xr-wrap {\n",
" display: block !important;\n",
" min-width: 300px;\n",
" max-width: 700px;\n",
"}\n",
"\n",
".xr-text-repr-fallback {\n",
" /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
" display: none;\n",
"}\n",
"\n",
".xr-header {\n",
" padding-top: 6px;\n",
" padding-bottom: 6px;\n",
" margin-bottom: 4px;\n",
" border-bottom: solid 1px var(--xr-border-color);\n",
"}\n",
"\n",
".xr-header > div,\n",
".xr-header > ul {\n",
" display: inline;\n",
" margin-top: 0;\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-obj-type,\n",
".xr-array-name {\n",
" margin-left: 2px;\n",
" margin-right: 10px;\n",
"}\n",
"\n",
".xr-obj-type {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-section-item input {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label {\n",
" cursor: pointer;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2 {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (realization: 18, height: 33, latitude: 960,\n",
" longitude: 1280, bnds: 2)\n",
"Coordinates:\n",
" * realization (realization) int32 0 18 19 20 21 ... 31 32 33 34\n",
" * height (height) float32 5.0 10.0 20.0 ... 5.5e+03 6e+03\n",
" * latitude (latitude) float32 -89.91 -89.72 ... 89.72 89.91\n",
" * longitude (longitude) float32 -179.9 -179.6 ... 179.6 179.9\n",
" forecast_period timedelta64[ns] 1 days 18:00:00\n",
" forecast_reference_time datetime64[ns] 2021-11-07T06:00:00\n",
" time datetime64[ns] 2021-11-09\n",
"Dimensions without coordinates: bnds\n",
"Data variables:\n",
" air_pressure (realization, height, latitude, longitude) float32 dask.array&lt;chunksize=(18, 33, 192, 160), meta=np.ndarray&gt;\n",
" latitude_longitude int32 -2147483647\n",
" latitude_bnds (latitude, bnds) float32 dask.array&lt;chunksize=(960, 2), meta=np.ndarray&gt;\n",
" longitude_bnds (longitude, bnds) float32 dask.array&lt;chunksize=(1280, 2), meta=np.ndarray&gt;\n",
"Attributes:\n",
" history: 2021-11-07T10:27:38Z: StaGE Decoupler\n",
" institution: Met Office\n",
" least_significant_digit: 1\n",
" mosg__forecast_run_duration: PT198H\n",
" mosg__grid_domain: global\n",
" mosg__grid_type: standard\n",
" mosg__grid_version: 1.6.0\n",
" mosg__model_configuration: gl_ens\n",
" source: Met Office Unified Model\n",
" title: MOGREPS-G Model Forecast on Global 20 km St...\n",
" um_version: 11.5\n",
" Conventions: CF-1.7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-31c05081-957d-4041-9c37-a94153d29095' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-31c05081-957d-4041-9c37-a94153d29095' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>realization</span>: 18</li><li><span class='xr-has-index'>height</span>: 33</li><li><span class='xr-has-index'>latitude</span>: 960</li><li><span class='xr-has-index'>longitude</span>: 1280</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-94871a7e-117f-4a3a-bd02-427e972664e7' class='xr-section-summary-in' type='checkbox' checked><label for='section-94871a7e-117f-4a3a-bd02-427e972664e7' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>realization</span></div><div class='xr-var-dims'>(realization)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 18 19 20 21 22 ... 30 31 32 33 34</div><input id='attrs-c544e1db-173d-4604-a871-55d8c68f1426' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c544e1db-173d-4604-a871-55d8c68f1426' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74cb640c-f8d2-46a4-a7f8-107b2ce33910' class='xr-var-data-in' type='checkbox'><label for='data-74cb640c-f8d2-46a4-a7f8-107b2ce33910' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>realization</dd></dl></div><div class='xr-var-data'><pre>array([ 0, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34],\n",
" dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 10.0 20.0 ... 5.5e+03 6e+03</div><input id='attrs-b4393a6c-96d6-4106-9c23-e2e94d738d23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4393a6c-96d6-4106-9c23-e2e94d738d23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbe432ef-2e83-4fef-9653-864dfed19d97' class='xr-var-data-in' type='checkbox'><label for='data-fbe432ef-2e83-4fef-9653-864dfed19d97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array([5.00e+00, 1.00e+01, 2.00e+01, 3.00e+01, 5.00e+01, 7.50e+01, 1.00e+02,\n",
" 1.50e+02, 2.00e+02, 2.50e+02, 3.00e+02, 4.00e+02, 5.00e+02, 6.00e+02,\n",
" 7.00e+02, 8.00e+02, 1.00e+03, 1.25e+03, 1.50e+03, 1.75e+03, 2.00e+03,\n",
" 2.25e+03, 2.50e+03, 2.75e+03, 3.00e+03, 3.25e+03, 3.50e+03, 3.75e+03,\n",
" 4.00e+03, 4.50e+03, 5.00e+03, 5.50e+03, 6.00e+03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.91 -89.72 ... 89.72 89.91</div><input id='attrs-5b7afec3-ec24-4944-8504-c10695df8822' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b7afec3-ec24-4944-8504-c10695df8822' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48a6c247-10b4-4b7c-89e9-080385dcbde4' class='xr-var-data-in' type='checkbox'><label for='data-48a6c247-10b4-4b7c-89e9-080385dcbde4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>latitude_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.90625, -89.71875, -89.53125, ..., 89.53125, 89.71875, 89.90625],\n",
" dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-78907302-474d-46ce-97cd-58905aa5334c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78907302-474d-46ce-97cd-58905aa5334c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-986d9646-b2cd-4a55-9eaf-2782c740bc56' class='xr-var-data-in' type='checkbox'><label for='data-986d9646-b2cd-4a55-9eaf-2782c740bc56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>longitude_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-179.85938, -179.57812, -179.29688, ..., 179.29688, 179.57812,\n",
" 179.85938], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_period</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4c1ce6ff-f1d1-4eac-88e9-f467201fd9c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c1ce6ff-f1d1-4eac-88e9-f467201fd9c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81c78030-4ce4-466d-905b-e08862b36537' class='xr-var-data-in' type='checkbox'><label for='data-81c78030-4ce4-466d-905b-e08862b36537' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_period</dd></dl></div><div class='xr-var-data'><pre>array(151200000000000, dtype=&#x27;timedelta64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>forecast_reference_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fdfbfcaf-385c-400d-82f8-5d4ceeddad09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdfbfcaf-385c-400d-82f8-5d4ceeddad09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-608de08a-5fc3-4ac1-a755-888e7eb8d8c1' class='xr-var-data-in' type='checkbox'><label for='data-608de08a-5fc3-4ac1-a755-888e7eb8d8c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2021-11-07T06:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9bbc0d03-1127-423d-9175-690a0e548d13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bbc0d03-1127-423d-9175-690a0e548d13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24f982d1-dbac-405e-af15-fd1dc9cb59d3' class='xr-var-data-in' type='checkbox'><label for='data-24f982d1-dbac-405e-af15-fd1dc9cb59d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2021-11-09T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2c9789d6-f37d-4350-ad10-48906f48987c' class='xr-section-summary-in' type='checkbox' checked><label for='section-2c9789d6-f37d-4350-ad10-48906f48987c' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air_pressure</span></div><div class='xr-var-dims'>(realization, height, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(18, 33, 192, 160), meta=np.ndarray&gt;</div><input id='attrs-311b43c7-4be8-4f41-babb-8445ff0bae57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-311b43c7-4be8-4f41-babb-8445ff0bae57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72244e6e-a5b2-4993-a7f0-71f8588c6705' class='xr-var-data-in' type='checkbox'><label for='data-72244e6e-a5b2-4993-a7f0-71f8588c6705' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>grid_mapping :</span></dt><dd>latitude_longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_longitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-160a1cce-af8a-4bf3-a0a8-046fe5f072aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-160a1cce-af8a-4bf3-a0a8-046fe5f072aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c098e3cc-69a3-45be-9a18-28b041d19df5' class='xr-var-data-in' type='checkbox'><label for='data-c098e3cc-69a3-45be-9a18-28b041d19df5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>earth_radius :</span></dt><dd>6371229.0</dd></dl></div><div class='xr-var-data'><pre>array(-2147483647, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_bnds</span></div><div class='xr-var-dims'>(latitude, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(960, 2), meta=np.ndarray&gt;</div><input id='attrs-9a062293-d9a7-4d15-a1fa-3e2034401104' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a062293-d9a7-4d15-a1fa-3e2034401104' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2e07527-d739-486f-8438-48dec9c502bf' class='xr-var-data-in' type='checkbox'><label for='data-b2e07527-d739-486f-8438-48dec9c502bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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" <td> 7.50 kiB </td>\n",
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" <td> 2 Tasks </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_bnds</span></div><div class='xr-var-dims'>(longitude, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1280, 2), meta=np.ndarray&gt;</div><input id='attrs-db92cbf0-c75e-43a0-8bb1-dc301fe4c0bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db92cbf0-c75e-43a0-8bb1-dc301fe4c0bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-781a0d26-ecaf-4d58-85bb-67f787692481' class='xr-var-data-in' type='checkbox'><label for='data-781a0d26-ecaf-4d58-85bb-67f787692481' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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"\n",
" <!-- Text -->\n",
" <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
" <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">1280</text>\n",
"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-bc066f9e-a8b8-4993-b073-10d975716e3c' class='xr-section-summary-in' type='checkbox' ><label for='section-bc066f9e-a8b8-4993-b073-10d975716e3c' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>history :</span></dt><dd>2021-11-07T10:27:38Z: StaGE Decoupler</dd><dt><span>institution :</span></dt><dd>Met Office</dd><dt><span>least_significant_digit :</span></dt><dd>1</dd><dt><span>mosg__forecast_run_duration :</span></dt><dd>PT198H</dd><dt><span>mosg__grid_domain :</span></dt><dd>global</dd><dt><span>mosg__grid_type :</span></dt><dd>standard</dd><dt><span>mosg__grid_version :</span></dt><dd>1.6.0</dd><dt><span>mosg__model_configuration :</span></dt><dd>gl_ens</dd><dt><span>source :</span></dt><dd>Met Office Unified Model</dd><dt><span>title :</span></dt><dd>MOGREPS-G Model Forecast on Global 20 km Standard Grid</dd><dt><span>um_version :</span></dt><dd>11.5</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (realization: 18, height: 33, latitude: 960,\n",
" longitude: 1280, bnds: 2)\n",
"Coordinates:\n",
" * realization (realization) int32 0 18 19 20 21 ... 31 32 33 34\n",
" * height (height) float32 5.0 10.0 20.0 ... 5.5e+03 6e+03\n",
" * latitude (latitude) float32 -89.91 -89.72 ... 89.72 89.91\n",
" * longitude (longitude) float32 -179.9 -179.6 ... 179.6 179.9\n",
" forecast_period timedelta64[ns] ...\n",
" forecast_reference_time datetime64[ns] ...\n",
" time datetime64[ns] ...\n",
"Dimensions without coordinates: bnds\n",
"Data variables:\n",
" air_pressure (realization, height, latitude, longitude) float32 dask.array<chunksize=(18, 33, 192, 160), meta=np.ndarray>\n",
" latitude_longitude int32 ...\n",
" latitude_bnds (latitude, bnds) float32 dask.array<chunksize=(960, 2), meta=np.ndarray>\n",
" longitude_bnds (longitude, bnds) float32 dask.array<chunksize=(1280, 2), meta=np.ndarray>\n",
"Attributes:\n",
" history: 2021-11-07T10:27:38Z: StaGE Decoupler\n",
" institution: Met Office\n",
" least_significant_digit: 1\n",
" mosg__forecast_run_duration: PT198H\n",
" mosg__grid_domain: global\n",
" mosg__grid_type: standard\n",
" mosg__grid_version: 1.6.0\n",
" mosg__model_configuration: gl_ens\n",
" source: Met Office Unified Model\n",
" title: MOGREPS-G Model Forecast on Global 20 km St...\n",
" um_version: 11.5\n",
" Conventions: CF-1.7"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictions = xarray.open_dataset('/data/ecmwf/000490262cdd067721a34112963bcaa2b44860ab.nc').chunk('auto')\n",
"predictions"
]
},
{
"cell_type": "markdown",
"id": "86c31690-9531-4132-afcd-63b553d9d91a",
"metadata": {},
"source": [
"Dask code running on this machine has read the metadata for the file via NFS. \n",
"\n",
"Likewise we can see the observations, so we can perform a calculation locally. Here we average the predictions over the realisations and then compare them with the observations at a particular height. (This is a deliberately inefficient calculation, as we could average at only the required height, but this is pedagogical.)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "80854a5c-4611-4c61-b225-0ee9dce4a732",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 10 µs, sys: 1e+03 ns, total: 11 µs\n",
"Wall time: 14.5 µs\n"
]
}
],
"source": [
"%%time\n",
"def thing():\n",
" client = Client()\n",
" predictions = xarray.open_dataset('/data/ecmwf/000490262cdd067721a34112963bcaa2b44860ab.nc').chunk('auto')\n",
" observations = xarray.open_dataset('/data/eumetsat/ad-hoc/observations.nc').chunk('auto') \n",
"\n",
" averages = predictions.mean('realization')\n",
" diff = averages.isel(height=10) - observations\n",
" diff.compute()\n",
" \n",
"#thing() "
]
},
{
"cell_type": "markdown",
"id": "166135da-29b5-4501-9237-cd355d8789ad",
"metadata": {},
"source": [
"If you actually run this, it takes some 18 minutes to complete! Accessing the data over NFS between data centres (this notebook is running in AWS) is just too slow.\n",
"\n",
"Instead we should obviously run the Dask tasks where the data is. We can do that on a Dask cluster."
]
},
{
"cell_type": "markdown",
"id": "00e450bc-876a-4460-97be-d076727ee9d9",
"metadata": {
"tags": []
},
"source": [
"## Run Up a Cluster"
]
},
{
"cell_type": "markdown",
"id": "a928222c-77a8-4b8e-9dd7-135d10500c72",
"metadata": {},
"source": [
"The cluster is run up with a single command. It takes a while though"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "a9524c9b-44ed-4891-8d78-f0953e31dd6d",
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[#] ip link add dasklocal type wireguard\n",
"[#] wg setconf dasklocal /dev/fd/63\n",
"[#] ip -6 address add fda5:c0ff:eeee:0::1/64 dev dasklocal\n",
"[#] ip link set mtu 1420 up dev dasklocal\n",
"[#] ip -6 route add fda5:c0ff:eeee:2::/64 dev dasklocal\n",
"[#] ip -6 route add fda5:c0ff:eeee:1::/64 dev dasklocal\n",
"2022-06-15 13:30:02,946 - distributed.scheduler - INFO - -----------------------------------------------\n",
"2022-06-15 13:30:03,526 - distributed.http.proxy - INFO - To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy\n",
"2022-06-15 13:30:03,566 - distributed.scheduler - INFO - -----------------------------------------------\n",
"2022-06-15 13:30:03,568 - distributed.scheduler - INFO - Clear task state\n",
"2022-06-15 13:30:03,570 - distributed.scheduler - INFO - Scheduler at: tcp://172.17.0.2:8786\n",
"2022-06-15 13:30:03,570 - distributed.scheduler - INFO - dashboard at: :8787\n",
"2022-06-15 13:30:39,906 - distributed.comm.tcp - INFO - Connection from tcp://[fda5:c0ff:eeee:1::11]:45214 closed before handshake completed\n",
"2022-06-15 13:30:39,906 - distributed.comm.tcp - INFO - Connection from tcp://[fda5:c0ff:eeee:1::11]:45210 closed before handshake completed\n",
"2022-06-15 13:30:39,906 - distributed.comm.tcp - INFO - Connection from tcp://[fda5:c0ff:eeee:1::11]:45212 closed before handshake completed\n",
"2022-06-15 13:30:39,906 - distributed.comm.tcp - INFO - Connection from tcp://[fda5:c0ff:eeee:1::11]:45216 closed before handshake completed\n",
"2022-06-15 13:30:44,598 - distributed.comm.tcp - INFO - Connection from tcp://[fda5:c0ff:eeee:2::11]:59964 closed before handshake completed\n",
"2022-06-15 13:30:44,599 - distributed.comm.tcp - INFO - Connection from tcp://[fda5:c0ff:eeee:2::11]:59960 closed before handshake completed\n",
"2022-06-15 13:30:44,599 - distributed.comm.tcp - INFO - Connection from tcp://[fda5:c0ff:eeee:2::11]:59958 closed before handshake completed\n",
"2022-06-15 13:30:44,599 - distributed.comm.tcp - INFO - Connection from tcp://[fda5:c0ff:eeee:2::11]:59962 closed before handshake completed\n",
"2022-06-15 13:30:49,001 - distributed.scheduler - INFO - Register worker <WorkerState 'tcp://[fda5:c0ff:eeee:1::11]:41237', name: ecmwf-1-3, status: undefined, memory: 0, processing: 0>\n",
"2022-06-15 13:30:49,009 - distributed.scheduler - INFO - Starting worker compute stream, tcp://[fda5:c0ff:eeee:1::11]:41237\n",
"2022-06-15 13:30:49,009 - distributed.core - INFO - Starting established connection\n",
"2022-06-15 13:30:49,011 - distributed.scheduler - INFO - Register worker <WorkerState 'tcp://[fda5:c0ff:eeee:1::11]:39735', name: ecmwf-1-2, status: undefined, memory: 0, processing: 0>\n",
"2022-06-15 13:30:49,012 - distributed.scheduler - INFO - Starting worker compute stream, tcp://[fda5:c0ff:eeee:1::11]:39735\n",
"2022-06-15 13:30:49,012 - distributed.core - INFO - Starting established connection\n",
"2022-06-15 13:30:49,012 - distributed.scheduler - INFO - Register worker <WorkerState 'tcp://[fda5:c0ff:eeee:1::11]:45051', name: ecmwf-1-1, status: undefined, memory: 0, processing: 0>\n",
"2022-06-15 13:30:49,013 - distributed.scheduler - INFO - Starting worker compute stream, tcp://[fda5:c0ff:eeee:1::11]:45051\n",
"2022-06-15 13:30:49,013 - distributed.core - INFO - Starting established connection\n",
"2022-06-15 13:30:49,013 - distributed.scheduler - INFO - Register worker <WorkerState 'tcp://[fda5:c0ff:eeee:1::11]:37545', name: ecmwf-1-0, status: undefined, memory: 0, processing: 0>\n",
"2022-06-15 13:30:49,014 - distributed.scheduler - INFO - Starting worker compute stream, tcp://[fda5:c0ff:eeee:1::11]:37545\n",
"2022-06-15 13:30:49,014 - distributed.core - INFO - Starting established connection\n",
"2022-06-15 13:30:52,423 - distributed.scheduler - INFO - Register worker <WorkerState 'tcp://[fda5:c0ff:eeee:2::11]:45025', name: eumetsat-2-1, status: undefined, memory: 0, processing: 0>\n",
"2022-06-15 13:30:52,424 - distributed.scheduler - INFO - Starting worker compute stream, tcp://[fda5:c0ff:eeee:2::11]:45025\n",
"2022-06-15 13:30:52,424 - distributed.core - INFO - Starting established connection\n",
"2022-06-15 13:30:52,426 - distributed.scheduler - INFO - Register worker <WorkerState 'tcp://[fda5:c0ff:eeee:2::11]:36061', name: eumetsat-2-2, status: undefined, memory: 0, processing: 0>\n",
"2022-06-15 13:30:52,427 - distributed.scheduler - INFO - Starting worker compute stream, tcp://[fda5:c0ff:eeee:2::11]:36061\n",
"2022-06-15 13:30:52,427 - distributed.core - INFO - Starting established connection\n",
"2022-06-15 13:30:52,444 - distributed.scheduler - INFO - Register worker <WorkerState 'tcp://[fda5:c0ff:eeee:2::11]:38705', name: eumetsat-2-0, status: undefined, memory: 0, processing: 0>\n",
"2022-06-15 13:30:52,445 - distributed.scheduler - INFO - Starting worker compute stream, tcp://[fda5:c0ff:eeee:2::11]:38705\n",
"2022-06-15 13:30:52,445 - distributed.core - INFO - Starting established connection\n",
"2022-06-15 13:30:52,449 - distributed.scheduler - INFO - Register worker <WorkerState 'tcp://[fda5:c0ff:eeee:2::11]:39377', name: eumetsat-2-3, status: undefined, memory: 0, processing: 0>\n",
"2022-06-15 13:30:52,450 - distributed.scheduler - INFO - Starting worker compute stream, tcp://[fda5:c0ff:eeee:2::11]:39377\n",
"2022-06-15 13:30:52,450 - distributed.core - INFO - Starting established connection\n"
]
}
],
"source": [
"import subprocess\n",
"\n",
"scheduler_process = subprocess.Popen([\n",
" '../dask_multicloud/start-scheduler.sh', \n",
" f\"rcar@{ecmwf_host}\",\n",
" f\"rcar@{eumetsat_host}\"\n",
" ])"
]
},
{
"cell_type": "markdown",
"id": "b61001c9-0564-4a47-81db-4ecbed24d031",
"metadata": {
"tags": []
},
"source": [
"We need to wait for 8 `distributed.core - INFO - Starting established connection` lines - one from each of 4 worker processes on each of 2 worker machines.\n",
"\n",
"What has happened here is:\n",
"\n",
"1. `start-scheduler.sh` runs up a Docker container on this computer.\n",
"2. The container creates a WireGuard IPv6 data plane VPN. This involves generating shared keys for all the nodes and a network interface inside itself. This data plane VPN is transient and unique to this cluster.\n",
"3. The container runs a Dask scheduler, hosted on the data plane network.\n",
"4. It then asks each data centre to provision workers and routing.\n",
"\n",
"Each data centre hosts a control process, accessible over the control plane network. On invocation:\n",
"\n",
"1. The control process creates a WireGuard network interface on the data plane network. This acts as a router between the data centres and the scheduler.\n",
"2. It starts Docker containers on compute instances. These containers have their own WireGuard network interface on the data plane network.\n",
"3. The Docker containers spawn (4) Dask worker processes, each of which connects via the data plane network back to the scheduler created at the beginning.\n",
"\n",
"The result is one container on this computer running the scheduler, talking to a container on each worker machine, over a throw-away data plane WireGuard IPv6 network which allows each of the (in this case 8) Dask worker processes to communicate with each other and the scheduler, even though they are partitioned over 3 data centres.\n",
"\n",
"Something like this"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "fc3aa9c2-aec5-425d-b7af-ac7825c92ae6",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"images/datacentres-dask.png\")"
]
},
{
"cell_type": "markdown",
"id": "93567f6c-1b87-436f-832e-c62a9d71a63f",
"metadata": {},
"source": [
"Key\n",
"\n",
"* <span style='color: blue'>Data plane network</span>\n",
"* <span style='color: green'>Dask</span>\n",
"* <span style='color: red'>NetCDF data</span>"
]
},
{
"cell_type": "markdown",
"id": "deded5d9-3f4d-43f7-a7d5-9939f1319eab",
"metadata": {},
"source": [
"The scheduler for the cluster is running in a Docker container on this machine and is exposed on `localhost`, so we can create a client talking to it"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ec654ac8-2d48-437a-8bec-8bb4864b7d76",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"client = Client(\"localhost:8786\")"
]
},
{
"cell_type": "markdown",
"id": "ba936432-a1a6-4174-ab75-8f3b71036c36",
"metadata": {
"tags": []
},
"source": [
"If you click through the client you should see the workers under the `Scheduler Info` node"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "d507445b-f9c4-424e-b71f-addb214d0f06",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# client"
]
},
{
"cell_type": "markdown",
"id": "36cfc5c0-9d84-42e2-8e65-329c97525a27",
"metadata": {},
"source": [
"You can click through to the Dashboard on http://localhost:8787/status. There we can show the workers on the task stream"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "3ad44555-b657-46c0-801c-9c1a46e174bd",
"metadata": {},
"outputs": [],
"source": [
"def show_all_workers():\n",
" my_org().compute(workers='ecmwf-1-0')\n",
" my_org().compute(workers='ecmwf-1-1')\n",
" my_org().compute(workers='ecmwf-1-2')\n",
" my_org().compute(workers='ecmwf-1-3')\n",
" my_org().compute(workers='eumetsat-2-0')\n",
" my_org().compute(workers='eumetsat-2-1')\n",
" my_org().compute(workers='eumetsat-2-2')\n",
" my_org().compute(workers='eumetsat-2-3')\n",
" sleep(0.5)\n",
"\n",
"show_all_workers()"
]
},
{
"cell_type": "markdown",
"id": "2b84266d-7dba-42a0-be15-458e8b5a09c8",
"metadata": {},
"source": [
"## Running on the Cluster"
]
},
{
"cell_type": "markdown",
"id": "1940772b-54b5-4905-a72e-f35265935fec",
"metadata": {},
"source": [
"Now that there is a Dask client in scope, calculations will be run on the cluster. We can define the tasks to be run"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "1d157864-aef6-4dc8-8f65-ed933b1aa6e8",
"metadata": {},
"outputs": [],
"source": [
"predictions = xarray.open_dataset('/data/ecmwf/000490262cdd067721a34112963bcaa2b44860ab.nc').chunk('auto')\n",
"observations = xarray.open_dataset('/data/eumetsat/ad-hoc/observations.nc').chunk('auto') \n",
" \n",
"averages = predictions.mean('realization')\n",
"diff = averages.isel(height=10) - observations"
]
},
{
"cell_type": "markdown",
"id": "0b832edd-d387-437e-9e06-40b79c4d0edd",
"metadata": {},
"source": [
"But when we try to perform the calculation it fails"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "1014ee5a-8311-4875-aa66-c6fc198e9322",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"[Errno 2] No such file or directory: b'/data/ecmwf/000490262cdd067721a34112963bcaa2b44860ab.nc'\""
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with pytest.raises(FileNotFoundError) as excinfo:\n",
" show_all_workers()\n",
" diff.compute() \n",
"\n",
"str(excinfo.value)"
]
},
{
"cell_type": "markdown",
"id": "e39ec4db-f9dd-4fd9-b913-d15e8222150d",
"metadata": {},
"source": [
"It fails because some of the tasks to read the data have been run on the workers running in EUMETSAT. They cannot see the data in ECMWF, and nor do we want them too, because reading all that data between data centres would be too slow."
]
},
{
"cell_type": "markdown",
"id": "afba74a7-19bb-47ac-8702-df92b5cd62b4",
"metadata": {},
"source": [
"# Data-Proximate Computation\n",
"\n",
"Dask has the concept of resources - tasks can be scheduled to run only where a resource is available. Having assigned each data centre a resource pool, we can run the calculation only where it can access the data. A [special Python context manager](https://github.com/gjoseph92/dask-worker-pools) makes all the arrangements. So we can run a calculation only where the data is available"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "ec541046-dd1f-486a-b763-7a642e4f781e",
"metadata": {},
"outputs": [],
"source": [
"with pool('ecmwf'): \n",
" predictions.isel(height=10).compute()"
]
},
{
"cell_type": "markdown",
"id": "80abff95-7ee6-4069-88a1-4baf5cc43c97",
"metadata": {},
"source": [
"Better still, we can load the data inside the context manager block and it will carry the information with it"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "6e177233-a3a7-4c78-8e89-f4e74a7c952f",
"metadata": {},
"outputs": [],
"source": [
"with pool('ecmwf'):\n",
" predictions = xarray.open_dataset('/data/ecmwf/000490262cdd067721a34112963bcaa2b44860ab.nc').chunk('auto')"
]
},
{
"cell_type": "markdown",
"id": "1f78c332-2342-46fd-8a38-f0ba08dcf3f1",
"metadata": {},
"source": [
"Another context manager, `propagate_pools` ensures that this resource pinning is respected"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "d1d965bb-a005-4573-bb6b-69beaa118872",
"metadata": {},
"outputs": [],
"source": [
"with propagate_pools():\n",
" predictions.isel(height=10).compute()"
]
},
{
"cell_type": "markdown",
"id": "4662b226-b57f-407d-ac4e-1e885d26c41d",
"metadata": {},
"source": [
"This allows us to mark data with its pool"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "0e0dded3-7ba1-416d-b220-9531a3d22d57",
"metadata": {},
"outputs": [],
"source": [
"with pool('ecmwf'): \n",
" predictions = xarray.open_dataset('/data/ecmwf/000490262cdd067721a34112963bcaa2b44860ab.nc').chunk('auto')\n",
"\n",
"with pool('eumetsat'):\n",
" observations = xarray.open_dataset('/data/eumetsat/ad-hoc/observations.nc').chunk('auto')"
]
},
{
"cell_type": "markdown",
"id": "d0d796ce-4989-42f9-b649-7b8a3deaccc7",
"metadata": {},
"source": [
"define some deferred calculations oblivious to its provenance"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "9883a8f8-e34f-4c20-bc16-547fb7785d00",
"metadata": {},
"outputs": [],
"source": [
"averaged_predictions = predictions.mean('realization')\n",
"diff = averaged_predictions.isel(height=10) - observations"
]
},
{
"cell_type": "markdown",
"id": "e41c95f0-5a10-4321-a259-7f0f5a1e630c",
"metadata": {},
"source": [
"and then perform the final calculation"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "d29994de-ad36-4b17-923a-0bc2a0437925",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 139 ms, sys: 20.2 ms, total: 160 ms\n",
"Wall time: 28.2 s\n"
]
}
],
"source": [
"%%time \n",
"show_all_workers()\n",
"with propagate_pools():\n",
" diff.compute()"
]
},
{
"cell_type": "markdown",
"id": "75c4d775-59a9-4a0c-8ad7-0f4d80b01d69",
"metadata": {},
"source": [
"Remember, our aim was to prevent workers reading bulk data from foreign data centres.\n",
"\n",
"Here, we know that data is only being read by workers in the appropriate location, because neither data centre can read the other's data. Once data is in memory, Dask prefers to schedule tasks on the workers that have it, so that the local workers will tend to perform follow-on calcuations.\n",
"\n",
"Ordinarily though, Dask would use idle workers to perform calculations even if they don't have the data. If allowed, this work stealing would result in unreduced data being moved between data centres, a potentially expensive operation, so the `propagate_pools` context manager also prevents work-stealing between workers in different pools.\n",
"\n",
"Once data loaded in one pool needs to be combined with data from another (the substraction in `averaged_predictions.isel(height=10) - observations` above) this is no longer classified as work stealing, and Dask will move data between data centres as required."
]
},
{
"cell_type": "markdown",
"id": "450a87cb-ce38-4bc9-b118-482cae094af6",
"metadata": {},
"source": [
"That calculation in one go looks like this"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "c8ea9254-ccd3-4a08-914b-21d17848e5e7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 132 ms, sys: 24.8 ms, total: 157 ms\n",
"Wall time: 22.3 s\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%%time\n",
"with pool('ecmwf'): \n",
" predictions = xarray.open_dataset('/data/ecmwf/000490262cdd067721a34112963bcaa2b44860ab.nc').chunk('auto')\n",
"\n",
"with pool('eumetsat'):\n",
" observations = xarray.open_dataset('/data/eumetsat/ad-hoc/observations.nc').chunk('auto') \n",
"\n",
"averages = predictions.mean('realization')\n",
"diff = averages.isel(height=10) - observations\n",
" \n",
"with propagate_pools():\n",
" plt.figure(figsize=(6, 6))\n",
" plt.imshow(diff.to_array()[0,...,0], origin='lower')"
]
},
{
"cell_type": "markdown",
"id": "b844e16a-3f87-4040-b94b-a738541cedfc",
"metadata": {},
"source": [
"In terms of code, compared with the local version above, this has only added the use of `with` blocks to control execution, and executes some 40 times faster."
]
},
{
"cell_type": "markdown",
"id": "22adf3b4-cdb3-4d0d-92c4-a6167c2bc9b3",
"metadata": {},
"source": [
"## Catalogs\n",
"\n",
"Because the data-loading tasks are labelled with their resource pool, this can be opaque to the scientist. So we can write"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "c9e7c55c-6626-4e88-9ce2-210887dde1af",
"metadata": {},
"outputs": [],
"source": [
"def load_from_catalog(path):\n",
" with pool(path.split('/')[2]):\n",
" return xarray.open_dataset(path).chunk('auto')"
]
},
{
"cell_type": "markdown",
"id": "f4dd30b5-5df7-471b-9146-e2e687011e0f",
"metadata": {},
"source": [
"allowing us to ignore where the data came from"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "ff3b293f-7e37-4195-b050-17601fd59499",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 195 ms, sys: 64.5 ms, total: 259 ms\n",
"Wall time: 35.1 s\n"
]
}
],
"source": [
"%%time\n",
"predictions = load_from_catalog('/data/ecmwf/000490262cdd067721a34112963bcaa2b44860ab.nc')\n",
"observations = load_from_catalog('/data/eumetsat/ad-hoc/observations.nc') \n",
"\n",
"averages = predictions.mean('realization')\n",
"diff = averages.isel(height=10) - observations\n",
"\n",
"show_all_workers()\n",
"with propagate_pools():\n",
" predictions.isel(height=10).compute()"
]
},
{
"cell_type": "markdown",
"id": "641ca383-9132-480d-ade5-f34186200c37",
"metadata": {},
"source": [
"Of course the cluster would have to be provisioned with compute resources in the appropriate data centres, although with some work this could be made dynamic as part of the catalog code.\n"
]
},
{
"cell_type": "markdown",
"id": "7bc779e7-98ee-4b21-bfcd-103a40829c20",
"metadata": {},
"source": [
"## Next Steps\n",
"\n",
"For details of the prototype implementation, and ideas for enhancements, see [dask-multi-cloud-details.ipynb](./dask-multi-cloud-details.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9ca64d14-afda-4bf6-9c31-94189816eb5b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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