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high_mem.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "5d3e13c5-f9dd-495f-83f7-b6bd740f7cdd",
"metadata": {},
"source": [
"# Dask experiments with mean() and groupby() over time dimension"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1f432595-053e-45b5-a750-a884d3bded4c",
"metadata": {},
"outputs": [
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],
"source": [
"from dask.distributed import Client\n",
"\n",
"# Setup a local cluster.\n",
"# By default this sets up 1 worker per core\n",
"client = Client(memory_limit=\"2 GiB\")\n",
"client.cluster"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8f8a687c-5700-4268-bcef-c246fe74582c",
"metadata": {},
"outputs": [],
"source": [
"import time"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "0cd0527f-48c7-4b05-b063-0ed672a8285e",
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"import numpy as np\n",
"from dask.diagnostics import ProgressBar"
]
},
{
"cell_type": "markdown",
"id": "1f1e39c2-8c0e-496e-90ce-7acac919bec5",
"metadata": {},
"source": [
"## The dataset"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b3d52252-a2ac-4f32-b33e-ec3665d13977",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tp&#x27; (time: 8760, latitude: 721, longitude: 1440)&gt;\n",
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" <text x=\"35.294118\" y=\"92.785877\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,92.785877)\">8760</text>\n",
"</svg>\n",
" </td>\n",
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" 3.5975e+02], 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'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-2bb50db2-7565-4391-8674-98bdc7823fd8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bb50db2-7565-4391-8674-98bdc7823fd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66996adf-2239-456c-9efa-4aca532aaac7' class='xr-var-data-in' type='checkbox'><label for='data-66996adf-2239-456c-9efa-4aca532aaac7' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 90. , 89.75, 89.5 , ..., -89.5 , -89.75, -90. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-01-01 ... 2021-12-31T23:00:00</div><input id='attrs-db4521c4-7aca-443b-9c06-cce68c449a62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db4521c4-7aca-443b-9c06-cce68c449a62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f18c9622-c604-43e6-8aff-2d2d8ebdbb90' class='xr-var-data-in' type='checkbox'><label for='data-f18c9622-c604-43e6-8aff-2d2d8ebdbb90' 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>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T01:00:00.000000000&#x27;,\n",
" &#x27;2021-01-01T02:00:00.000000000&#x27;, ..., &#x27;2021-12-31T21:00:00.000000000&#x27;,\n",
" &#x27;2021-12-31T22:00:00.000000000&#x27;, &#x27;2021-12-31T23:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0a654543-521a-4515-bd88-2923a8aaf518' class='xr-section-summary-in' type='checkbox' checked><label for='section-0a654543-521a-4515-bd88-2923a8aaf518' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'tp' (time: 8760, latitude: 721, longitude: 1440)>\n",
"dask.array<concatenate, shape=(8760, 721, 1440), dtype=float32, chunksize=(744, 721, 1440), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
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"Attributes:\n",
" units: m\n",
" long_name: Total precipitation"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds = xr.open_mfdataset('hourly_tp/ERA5_HiRes_Hourly_tp_2021_*.nc')\n",
"ds.tp"
]
},
{
"cell_type": "markdown",
"id": "6e41490a-5af6-4914-8653-6374751bf1e9",
"metadata": {},
"source": [
"## Exp 1: Averages over time"
]
},
{
"cell_type": "markdown",
"id": "7c223012-696a-456a-ab28-4df26579abf3",
"metadata": {},
"source": [
"### dask + `open_mfdataset`"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "229af93b-d424-4da5-90c4-aa42db12f9ae",
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tp&#x27; (time: 8760, latitude: 721, longitude: 1440)&gt;\n",
"dask.array&lt;concatenate, shape=(8760, 721, 1440), dtype=float32, chunksize=(744, 50, 1440), chunktype=numpy.ndarray&gt;\n",
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"Attributes:\n",
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" long_name: Total precipitation</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 8760</li><li><span class='xr-has-index'>latitude</span>: 721</li><li><span class='xr-has-index'>longitude</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7d29a1ad-e7f7-48b7-a2f0-b178d9905f53' class='xr-array-in' type='checkbox' checked><label for='section-7d29a1ad-e7f7-48b7-a2f0-b178d9905f53' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(744, 50, 1440), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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" 3.5975e+02], 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'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-2275a5ca-2a76-4933-81cf-fff6b5f8bbd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2275a5ca-2a76-4933-81cf-fff6b5f8bbd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a600247-0eaf-477e-978b-62b87c047dfe' class='xr-var-data-in' type='checkbox'><label for='data-5a600247-0eaf-477e-978b-62b87c047dfe' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 90. , 89.75, 89.5 , ..., -89.5 , -89.75, -90. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-01-01 ... 2021-12-31T23:00:00</div><input id='attrs-365fba73-d14c-4a90-aa51-0197103e80e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-365fba73-d14c-4a90-aa51-0197103e80e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98037545-cc06-4198-aa0b-932f2a08fc08' class='xr-var-data-in' type='checkbox'><label for='data-98037545-cc06-4198-aa0b-932f2a08fc08' 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>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T01:00:00.000000000&#x27;,\n",
" &#x27;2021-01-01T02:00:00.000000000&#x27;, ..., &#x27;2021-12-31T21:00:00.000000000&#x27;,\n",
" &#x27;2021-12-31T22:00:00.000000000&#x27;, &#x27;2021-12-31T23:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3f129ce3-8717-4523-a268-c5a2b1ac4607' class='xr-section-summary-in' type='checkbox' checked><label for='section-3f129ce3-8717-4523-a268-c5a2b1ac4607' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'tp' (time: 8760, latitude: 721, longitude: 1440)>\n",
"dask.array<concatenate, shape=(8760, 721, 1440), dtype=float32, chunksize=(744, 50, 1440), chunktype=numpy.ndarray>\n",
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]
},
"execution_count": 9,
"metadata": {},
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}
],
"source": [
"ds = xr.open_mfdataset('hourly_tp/ERA5_HiRes_Hourly_tp_2021_*.nc', chunks={'latitude':50})\n",
"ds.tp"
]
},
{
"cell_type": "code",
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"id": "d8343dd1-ad45-411d-8c50-03b28bc41e94",
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".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.DataArray &#x27;tp&#x27; (latitude: 721, longitude: 1440)&gt;\n",
"dask.array&lt;mean_agg-aggregate, shape=(721, 1440), dtype=float32, chunksize=(50, 1440), chunktype=numpy.ndarray&gt;\n",
"Coordinates:\n",
" * longitude (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
" * latitude (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 721</li><li><span class='xr-has-index'>longitude</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c333b17b-837c-4d8a-8ce5-db9ba0c97c70' class='xr-array-in' type='checkbox' checked><label for='section-c333b17b-837c-4d8a-8ce5-db9ba0c97c70' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(50, 1440), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 3.96 MiB </td>\n",
" <td> 281.25 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (721, 1440) </td>\n",
" <td> (50, 1440) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 612 Tasks </td>\n",
" <td> 15 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float32 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"170\" height=\"110\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"4\" x2=\"120\" y2=\"4\" />\n",
" <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n",
" <line x1=\"0\" y1=\"12\" x2=\"120\" y2=\"12\" />\n",
" <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n",
" <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" />\n",
" <line x1=\"0\" y1=\"29\" x2=\"120\" y2=\"29\" />\n",
" <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" />\n",
" <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
" <line x1=\"0\" y1=\"41\" x2=\"120\" y2=\"41\" />\n",
" <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
" <line x1=\"0\" y1=\"50\" x2=\"120\" y2=\"50\" />\n",
" <line x1=\"0\" y1=\"54\" x2=\"120\" y2=\"54\" />\n",
" <line x1=\"0\" y1=\"58\" x2=\"120\" y2=\"58\" />\n",
" <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
" <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></div></li><li class='xr-section-item'><input id='section-f7ce3bb7-009f-4f6e-a96b-3d2383d98e1c' class='xr-section-summary-in' type='checkbox' checked><label for='section-f7ce3bb7-009f-4f6e-a96b-3d2383d98e1c' class='xr-section-summary' >Coordinates: <span>(2)</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'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-b508ea31-2853-4449-9316-e00422279f50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b508ea31-2853-4449-9316-e00422279f50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98507bd6-c905-4e41-aeb3-345dc8c5b25b' class='xr-var-data-in' type='checkbox'><label for='data-98507bd6-c905-4e41-aeb3-345dc8c5b25b' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
" 3.5975e+02], 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'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-1f81a2e6-08f8-49cd-9a9b-5c660f3403eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f81a2e6-08f8-49cd-9a9b-5c660f3403eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97c077c3-6724-4261-b027-65381265209e' class='xr-var-data-in' type='checkbox'><label for='data-97c077c3-6724-4261-b027-65381265209e' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 90. , 89.75, 89.5 , ..., -89.5 , -89.75, -90. ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-41829fc3-ef94-442e-ae38-424c68d86f92' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-41829fc3-ef94-442e-ae38-424c68d86f92' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'tp' (latitude: 721, longitude: 1440)>\n",
"dask.array<mean_agg-aggregate, shape=(721, 1440), dtype=float32, chunksize=(50, 1440), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" * longitude (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
" * latitude (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tpm = ds.tp.mean(dim='time')\n",
"tpm"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "6e80dcb6-eccd-4cbf-bc44-65bf466dfec4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Yep: 52.2s\n"
]
}
],
"source": [
"t0 = time.time()\n",
"tpm = tpm.load()\n",
"print(f'Yep: {time.time() - t0:.1f}s')"
]
},
{
"cell_type": "markdown",
"id": "b859d98a-8269-40ea-9d09-eb56644f114a",
"metadata": {},
"source": [
"Process manager:\n",
"\n",
"![]( data:image/png;base64,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)"
]
},
{
"cell_type": "markdown",
"id": "afba48ef-3251-4aa6-824a-327b7553e593",
"metadata": {},
"source": [
"**Summary: it works fine, BUT some swap memory pollution -> why?**"
]
},
{
"cell_type": "markdown",
"id": "fda18196-4fd1-4f1f-a945-88aae167f98e",
"metadata": {},
"source": [
"### Dask + `open_dataset` + for loop "
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "97edd093-3203-45a2-b9ee-ad855f28c6c1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n",
"10\n",
"11\n",
"12\n",
"Yep: 53.1s\n"
]
}
],
"source": [
"t0 = time.time()\n",
"out_ds = []\n",
"nn = []\n",
"for i in range(1, 13):\n",
" print(i)\n",
" with xr.open_dataset(f'/home/mowglie/tmp/xarray_adv/hourly_tp/ERA5_HiRes_Hourly_tp_2021_{i:02d}.nc',\n",
" chunks={'latitude':50}) as ds:\n",
" tph = ds.tp.mean(dim='time').load()\n",
" out_ds.append(tph)\n",
" nn.append(len(ds.time))\n",
"out = 0\n",
"for ds, n in zip(out_ds, nn):\n",
" out += ds * n\n",
"out /= np.sum(nn)\n",
"print(f'Yep: {time.time() - t0:.1f}s')"
]
},
{
"cell_type": "markdown",
"id": "7c1be79c-8041-44e6-b9fc-96589f99e91a",
"metadata": {},
"source": [
"Process manager:\n",
"\n",
"![]( data:image/png;base64,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)"
]
},
{
"cell_type": "markdown",
"id": "55f3b859-d4b8-4bd1-84ca-7e80c6652a98",
"metadata": {},
"source": [
"**Summary: similar performance, negligible swap memory pollution.**"
]
},
{
"cell_type": "markdown",
"id": "bafdad6e-37f2-4a16-a32a-39b26c160481",
"metadata": {},
"source": [
"### No Dask + for loop "
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "643a72b6-8121-4b1d-8c0c-6a14c26b51b9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n",
"10\n",
"11\n",
"12\n",
"Yep: 126.4s\n"
]
}
],
"source": [
"t0 = time.time()\n",
"out_ds = []\n",
"nn = []\n",
"for i in range(1, 13):\n",
" print(i)\n",
" with xr.open_dataset(f'/home/mowglie/tmp/xarray_adv/hourly_tp/ERA5_HiRes_Hourly_tp_2021_{i:02d}.nc') as ds:\n",
" tph = ds.tp.mean(dim='time').load()\n",
" out_ds.append(tph)\n",
" nn.append(len(ds.time))\n",
"out = 0\n",
"for ds, n in zip(out_ds, nn):\n",
" out += ds * n\n",
"out /= np.sum(nn)\n",
"print(f'Yep: {time.time() - t0:.1f}s')"
]
},
{
"cell_type": "markdown",
"id": "854221cc-5c02-4978-83b8-c9ce00039b11",
"metadata": {},
"source": [
"**Summary: all good: worse performance, bad memory performance.**"
]
},
{
"cell_type": "markdown",
"id": "42bc606d-d705-4bb6-a2b6-5221b33110df",
"metadata": {},
"source": [
"## Exp 2: groupby + averages over time"
]
},
{
"cell_type": "markdown",
"id": "b6d5b248-b865-4822-8411-3f0ecf6e2fd4",
"metadata": {},
"source": [
"### dask + `open_mfdataset`"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "81a52c78-08a5-4346-b222-59dfcecccc19",
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tp&#x27; (time: 8760, latitude: 721, longitude: 1440)&gt;\n",
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"</table></div></div></li><li class='xr-section-item'><input id='section-7740edda-3e69-459c-8754-2feab9a0d900' class='xr-section-summary-in' type='checkbox' checked><label for='section-7740edda-3e69-459c-8754-2feab9a0d900' class='xr-section-summary' >Coordinates: <span>(3)</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'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-f7826ed1-d317-46b2-b090-f1512ebe2d78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7826ed1-d317-46b2-b090-f1512ebe2d78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa1e8484-3aea-47c5-b651-ff8cb891b436' class='xr-var-data-in' type='checkbox'><label for='data-aa1e8484-3aea-47c5-b651-ff8cb891b436' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
" 3.5975e+02], 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'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-2c71fb23-e983-4eb8-ac45-dfb9bdc0a3fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c71fb23-e983-4eb8-ac45-dfb9bdc0a3fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8c3fbe4-fe2f-46e5-a53e-bf9a332c3e6d' class='xr-var-data-in' type='checkbox'><label for='data-a8c3fbe4-fe2f-46e5-a53e-bf9a332c3e6d' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 90. , 89.75, 89.5 , ..., -89.5 , -89.75, -90. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-01-01 ... 2021-12-31T23:00:00</div><input id='attrs-ab17e4ae-2b07-49a2-9d80-f3a2fff6ad38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab17e4ae-2b07-49a2-9d80-f3a2fff6ad38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ec64b82-b663-4481-b858-aa748dc21d0b' class='xr-var-data-in' type='checkbox'><label for='data-5ec64b82-b663-4481-b858-aa748dc21d0b' 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>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T01:00:00.000000000&#x27;,\n",
" &#x27;2021-01-01T02:00:00.000000000&#x27;, ..., &#x27;2021-12-31T21:00:00.000000000&#x27;,\n",
" &#x27;2021-12-31T22:00:00.000000000&#x27;, &#x27;2021-12-31T23:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e8ae387d-36d4-423c-afa0-7d070f8f362c' class='xr-section-summary-in' type='checkbox' checked><label for='section-e8ae387d-36d4-423c-afa0-7d070f8f362c' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'tp' (time: 8760, latitude: 721, longitude: 1440)>\n",
"dask.array<concatenate, shape=(8760, 721, 1440), dtype=float32, chunksize=(744, 50, 1440), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
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"Attributes:\n",
" units: m\n",
" long_name: Total precipitation"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds = xr.open_mfdataset('hourly_tp/ERA5_HiRes_Hourly_tp_2021_*.nc', chunks={'latitude':50})\n",
"ds.tp"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "c5e382de-2b7b-4882-bb7f-75bae1d268c7",
"metadata": {},
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tp&#x27; (hour: 24, latitude: 721, longitude: 1440)&gt;\n",
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" * hour (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 15 16 17 18 19 20 21 22 23</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>hour</span>: 24</li><li><span class='xr-has-index'>latitude</span>: 721</li><li><span class='xr-has-index'>longitude</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8e1e463e-3be5-420f-82c4-bbfd0ea9529e' class='xr-array-in' type='checkbox' checked><label for='section-8e1e463e-3be5-420f-82c4-bbfd0ea9529e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 50, 1440), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 95.05 MiB </td>\n",
" <td> 281.25 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (24, 721, 1440) </td>\n",
" <td> (1, 50, 1440) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 10812 Tasks </td>\n",
" <td> 360 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float32 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
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"</table></div></div></li><li class='xr-section-item'><input id='section-010b282c-7bbe-4a45-9f79-e53dc51a08a1' class='xr-section-summary-in' type='checkbox' checked><label for='section-010b282c-7bbe-4a45-9f79-e53dc51a08a1' class='xr-section-summary' >Coordinates: <span>(3)</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'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-5b31b086-8a40-464a-ae08-a22555c4fa19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b31b086-8a40-464a-ae08-a22555c4fa19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00d39ca7-d5ad-4bf8-8f5e-3f90ec3e83ae' class='xr-var-data-in' type='checkbox'><label for='data-00d39ca7-d5ad-4bf8-8f5e-3f90ec3e83ae' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
" 3.5975e+02], 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'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-0d0d8604-5203-4082-bd8f-e8e26e835b06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d0d8604-5203-4082-bd8f-e8e26e835b06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b21958ad-542a-419d-8302-36b0cd987fb7' class='xr-var-data-in' type='checkbox'><label for='data-b21958ad-542a-419d-8302-36b0cd987fb7' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([ 90. , 89.75, 89.5 , ..., -89.5 , -89.75, -90. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>hour</span></div><div class='xr-var-dims'>(hour)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 18 19 20 21 22 23</div><input id='attrs-475d6a89-a660-409e-ace7-83fde7331a95' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-475d6a89-a660-409e-ace7-83fde7331a95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f882634-34bc-48d0-9249-c6c834873200' class='xr-var-data-in' type='checkbox'><label for='data-0f882634-34bc-48d0-9249-c6c834873200' 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'><pre>array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
" 18, 19, 20, 21, 22, 23])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-36a9b49b-846a-4350-a5c9-45de60714940' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-36a9b49b-846a-4350-a5c9-45de60714940' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'tp' (hour: 24, latitude: 721, longitude: 1440)>\n",
"dask.array<stack, shape=(24, 721, 1440), dtype=float32, chunksize=(1, 50, 1440), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" * longitude (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
" * latitude (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
" * hour (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 15 16 17 18 19 20 21 22 23"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tpm = ds.tp.groupby('time.hour').mean(dim='time')\n",
"tpm"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "7069dc59-dbde-43b7-8903-21f9dd500427",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-05-08 16:07:36,732 - distributed.worker_memory - WARNING - Worker is at 86% memory usage. Pausing worker. Process memory: 1.74 GiB -- Worker memory limit: 2.00 GiB\n",
"2022-05-08 16:07:36,957 - distributed.worker_memory - WARNING - Worker exceeded 95% memory budget. Restarting\n",
"2022-05-08 16:07:37,309 - distributed.nanny - WARNING - Restarting worker\n",
"2022-05-08 16:07:37,979 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:42667 -> tcp://127.0.0.1:44165\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/tornado/iostream.py\", line 971, in _handle_write\n",
" num_bytes = self.write_to_fd(self._write_buffer.peek(size))\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/tornado/iostream.py\", line 1148, in write_to_fd\n",
" return self.socket.send(data) # type: ignore\n",
"BrokenPipeError: [Errno 32] Broken pipe\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 1728, in get_data\n",
" response = await comm.read(deserializers=serializers)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 242, in read\n",
" convert_stream_closed_error(self, e)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 148, in convert_stream_closed_error\n",
" raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n",
"distributed.comm.core.CommClosedError: in <TCP (closed) local=tcp://127.0.0.1:42667 remote=tcp://127.0.0.1:44488>: BrokenPipeError: [Errno 32] Broken pipe\n",
"2022-05-08 16:07:39,032 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:44165\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 236, in read\n",
" n = await stream.read_into(chunk)\n",
"tornado.iostream.StreamClosedError: Stream is closed\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 3162, in gather_dep\n",
" response = await get_data_from_worker(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4488, in get_data_from_worker\n",
" return await retry_operation(_get_data, operation=\"get_data_from_worker\")\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 381, in retry_operation\n",
" return await retry(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 366, in retry\n",
" return await coro()\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4468, in _get_data\n",
" response = await send_recv(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/core.py\", line 708, in send_recv\n",
" response = await comm.read(deserializers=deserializers)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 242, in read\n",
" convert_stream_closed_error(self, e)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 150, in convert_stream_closed_error\n",
" raise CommClosedError(f\"in {obj}: {exc}\") from exc\n",
"distributed.comm.core.CommClosedError: in <TCP (closed) Ephemeral Worker->Worker for gather local=tcp://127.0.0.1:56808 remote=tcp://127.0.0.1:44165>: Stream is closed\n",
"2022-05-08 16:07:39,863 - distributed.worker_memory - WARNING - Worker exceeded 95% memory budget. Restarting\n",
"2022-05-08 16:07:40,268 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:42667\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/tornado/iostream.py\", line 867, in _read_to_buffer\n",
" bytes_read = self.read_from_fd(buf)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/tornado/iostream.py\", line 1140, in read_from_fd\n",
" return self.socket.recv_into(buf, len(buf))\n",
"ConnectionResetError: [Errno 104] Connection reset by peer\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/core.py\", line 326, in connect\n",
" handshake = await asyncio.wait_for(comm.read(), time_left())\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/asyncio/tasks.py\", line 481, in wait_for\n",
" return fut.result()\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 242, in read\n",
" convert_stream_closed_error(self, e)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 148, in convert_stream_closed_error\n",
" raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n",
"distributed.comm.core.CommClosedError: in <TCP (closed) local=tcp://127.0.0.1:44494 remote=tcp://127.0.0.1:42667>: ConnectionResetError: [Errno 104] Connection reset by peer\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 3162, in gather_dep\n",
" response = await get_data_from_worker(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4488, in get_data_from_worker\n",
" return await retry_operation(_get_data, operation=\"get_data_from_worker\")\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 381, in retry_operation\n",
" return await retry(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 366, in retry\n",
" return await coro()\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4465, in _get_data\n",
" comm = await rpc.connect(worker)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/core.py\", line 1162, in connect\n",
" return await connect_attempt\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/core.py\", line 1098, in _connect\n",
" comm = await connect(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/core.py\", line 331, in connect\n",
" raise OSError(\n",
"OSError: Timed out during handshake while connecting to tcp://127.0.0.1:42667 after 30 s\n",
"2022-05-08 16:07:40,601 - distributed.nanny - WARNING - Restarting worker\n",
"2022-05-08 16:07:40,658 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:42667\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/tornado/iostream.py\", line 867, in _read_to_buffer\n",
" bytes_read = self.read_from_fd(buf)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/tornado/iostream.py\", line 1140, in read_from_fd\n",
" return self.socket.recv_into(buf, len(buf))\n",
"ConnectionResetError: [Errno 104] Connection reset by peer\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 3162, in gather_dep\n",
" response = await get_data_from_worker(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4488, in get_data_from_worker\n",
" return await retry_operation(_get_data, operation=\"get_data_from_worker\")\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 381, in retry_operation\n",
" return await retry(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 366, in retry\n",
" return await coro()\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4468, in _get_data\n",
" response = await send_recv(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/core.py\", line 708, in send_recv\n",
" response = await comm.read(deserializers=deserializers)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 242, in read\n",
" convert_stream_closed_error(self, e)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 148, in convert_stream_closed_error\n",
" raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n",
"distributed.comm.core.CommClosedError: in <TCP (closed) Ephemeral Worker->Worker for gather local=tcp://127.0.0.1:44484 remote=tcp://127.0.0.1:42667>: ConnectionResetError: [Errno 104] Connection reset by peer\n",
"2022-05-08 16:07:42,675 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:44723 -> tcp://127.0.0.1:42667\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 226, in read\n",
" frames_nbytes = await stream.read_bytes(fmt_size)\n",
"tornado.iostream.StreamClosedError: Stream is closed\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 1728, in get_data\n",
" response = await comm.read(deserializers=serializers)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 242, in read\n",
" convert_stream_closed_error(self, e)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 150, in convert_stream_closed_error\n",
" raise CommClosedError(f\"in {obj}: {exc}\") from exc\n",
"distributed.comm.core.CommClosedError: in <TCP (closed) local=tcp://127.0.0.1:44723 remote=tcp://127.0.0.1:43488>: Stream is closed\n",
"2022-05-08 16:08:00,141 - distributed.worker_memory - WARNING - Worker is at 84% memory usage. Pausing worker. Process memory: 1.68 GiB -- Worker memory limit: 2.00 GiB\n",
"2022-05-08 16:08:01,102 - distributed.worker_memory - WARNING - Worker is at 71% memory usage. Resuming worker. Process memory: 1.43 GiB -- Worker memory limit: 2.00 GiB\n",
"2022-05-08 16:08:13,453 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:42667\n",
"ConnectionRefusedError: [Errno 111] Connection refused\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/core.py\", line 289, in connect\n",
" comm = await asyncio.wait_for(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/asyncio/tasks.py\", line 481, in wait_for\n",
" return fut.result()\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 451, in connect\n",
" convert_stream_closed_error(self, e)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 148, in convert_stream_closed_error\n",
" raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n",
"distributed.comm.core.CommClosedError: in <distributed.comm.tcp.TCPConnector object at 0x7f71f139d820>: ConnectionRefusedError: [Errno 111] Connection refused\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 3162, in gather_dep\n",
" response = await get_data_from_worker(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4488, in get_data_from_worker\n",
" return await retry_operation(_get_data, operation=\"get_data_from_worker\")\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 381, in retry_operation\n",
" return await retry(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 366, in retry\n",
" return await coro()\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4465, in _get_data\n",
" comm = await rpc.connect(worker)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/core.py\", line 1162, in connect\n",
" return await connect_attempt\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/core.py\", line 1098, in _connect\n",
" comm = await connect(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/core.py\", line 315, in connect\n",
" raise OSError(\n",
"OSError: Timed out trying to connect to tcp://127.0.0.1:42667 after 30 s\n",
"2022-05-08 16:08:28,757 - distributed.worker_memory - WARNING - Worker exceeded 95% memory budget. Restarting\n",
"2022-05-08 16:08:28,807 - distributed.worker_memory - WARNING - Worker exceeded 95% memory budget. Restarting\n",
"2022-05-08 16:08:28,847 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:44723\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 226, in read\n",
" frames_nbytes = await stream.read_bytes(fmt_size)\n",
"tornado.iostream.StreamClosedError: Stream is closed\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 3162, in gather_dep\n",
" response = await get_data_from_worker(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4488, in get_data_from_worker\n",
" return await retry_operation(_get_data, operation=\"get_data_from_worker\")\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 381, in retry_operation\n",
" return await retry(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 366, in retry\n",
" return await coro()\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4468, in _get_data\n",
" response = await send_recv(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/core.py\", line 708, in send_recv\n",
" response = await comm.read(deserializers=deserializers)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 242, in read\n",
" convert_stream_closed_error(self, e)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 150, in convert_stream_closed_error\n",
" raise CommClosedError(f\"in {obj}: {exc}\") from exc\n",
"distributed.comm.core.CommClosedError: in <TCP (closed) Ephemeral Worker->Worker for gather local=tcp://127.0.0.1:43498 remote=tcp://127.0.0.1:44723>: Stream is closed\n",
"2022-05-08 16:08:28,852 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:44723\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/tornado/iostream.py\", line 867, in _read_to_buffer\n",
" bytes_read = self.read_from_fd(buf)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/tornado/iostream.py\", line 1140, in read_from_fd\n",
" return self.socket.recv_into(buf, len(buf))\n",
"ConnectionResetError: [Errno 104] Connection reset by peer\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 3162, in gather_dep\n",
" response = await get_data_from_worker(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4488, in get_data_from_worker\n",
" return await retry_operation(_get_data, operation=\"get_data_from_worker\")\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 381, in retry_operation\n",
" return await retry(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 366, in retry\n",
" return await coro()\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4468, in _get_data\n",
" response = await send_recv(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/core.py\", line 708, in send_recv\n",
" response = await comm.read(deserializers=deserializers)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 242, in read\n",
" convert_stream_closed_error(self, e)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 148, in convert_stream_closed_error\n",
" raise CommClosedError(f\"in {obj}: {exc.__class__.__name__}: {exc}\") from exc\n",
"distributed.comm.core.CommClosedError: in <TCP (closed) Ephemeral Worker->Worker for gather local=tcp://127.0.0.1:43496 remote=tcp://127.0.0.1:44723>: ConnectionResetError: [Errno 104] Connection reset by peer\n",
"2022-05-08 16:08:28,903 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:36537\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 226, in read\n",
" frames_nbytes = await stream.read_bytes(fmt_size)\n",
"tornado.iostream.StreamClosedError: Stream is closed\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 3162, in gather_dep\n",
" response = await get_data_from_worker(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4488, in get_data_from_worker\n",
" return await retry_operation(_get_data, operation=\"get_data_from_worker\")\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 381, in retry_operation\n",
" return await retry(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 366, in retry\n",
" return await coro()\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4468, in _get_data\n",
" response = await send_recv(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/core.py\", line 708, in send_recv\n",
" response = await comm.read(deserializers=deserializers)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 242, in read\n",
" convert_stream_closed_error(self, e)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 150, in convert_stream_closed_error\n",
" raise CommClosedError(f\"in {obj}: {exc}\") from exc\n",
"distributed.comm.core.CommClosedError: in <TCP (closed) Ephemeral Worker->Worker for gather local=tcp://127.0.0.1:36416 remote=tcp://127.0.0.1:36537>: Stream is closed\n",
"2022-05-08 16:08:28,904 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:36537\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 236, in read\n",
" n = await stream.read_into(chunk)\n",
"tornado.iostream.StreamClosedError: Stream is closed\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 3162, in gather_dep\n",
" response = await get_data_from_worker(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4488, in get_data_from_worker\n",
" return await retry_operation(_get_data, operation=\"get_data_from_worker\")\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 381, in retry_operation\n",
" return await retry(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 366, in retry\n",
" return await coro()\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4468, in _get_data\n",
" response = await send_recv(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/core.py\", line 708, in send_recv\n",
" response = await comm.read(deserializers=deserializers)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 242, in read\n",
" convert_stream_closed_error(self, e)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 150, in convert_stream_closed_error\n",
" raise CommClosedError(f\"in {obj}: {exc}\") from exc\n",
"distributed.comm.core.CommClosedError: in <TCP (closed) Ephemeral Worker->Worker for gather local=tcp://127.0.0.1:36414 remote=tcp://127.0.0.1:36537>: Stream is closed\n",
"2022-05-08 16:08:29,232 - distributed.nanny - WARNING - Restarting worker\n",
"2022-05-08 16:08:29,238 - distributed.nanny - WARNING - Restarting worker\n",
"2022-05-08 16:08:32,963 - distributed.worker_memory - WARNING - Worker exceeded 95% memory budget. Restarting\n",
"2022-05-08 16:08:33,032 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:33975\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 226, in read\n",
" frames_nbytes = await stream.read_bytes(fmt_size)\n",
"tornado.iostream.StreamClosedError: Stream is closed\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 3162, in gather_dep\n",
" response = await get_data_from_worker(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4488, in get_data_from_worker\n",
" return await retry_operation(_get_data, operation=\"get_data_from_worker\")\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 381, in retry_operation\n",
" return await retry(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 366, in retry\n",
" return await coro()\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4468, in _get_data\n",
" response = await send_recv(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/core.py\", line 708, in send_recv\n",
" response = await comm.read(deserializers=deserializers)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 242, in read\n",
" convert_stream_closed_error(self, e)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 150, in convert_stream_closed_error\n",
" raise CommClosedError(f\"in {obj}: {exc}\") from exc\n",
"distributed.comm.core.CommClosedError: in <TCP (closed) Ephemeral Worker->Worker for gather local=tcp://127.0.0.1:33040 remote=tcp://127.0.0.1:33975>: Stream is closed\n",
"2022-05-08 16:08:33,032 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:33975\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 226, in read\n",
" frames_nbytes = await stream.read_bytes(fmt_size)\n",
"tornado.iostream.StreamClosedError: Stream is closed\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 3162, in gather_dep\n",
" response = await get_data_from_worker(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4488, in get_data_from_worker\n",
" return await retry_operation(_get_data, operation=\"get_data_from_worker\")\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 381, in retry_operation\n",
" return await retry(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 366, in retry\n",
" return await coro()\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4468, in _get_data\n",
" response = await send_recv(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/core.py\", line 708, in send_recv\n",
" response = await comm.read(deserializers=deserializers)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 242, in read\n",
" convert_stream_closed_error(self, e)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 150, in convert_stream_closed_error\n",
" raise CommClosedError(f\"in {obj}: {exc}\") from exc\n",
"distributed.comm.core.CommClosedError: in <TCP (closed) Ephemeral Worker->Worker for gather local=tcp://127.0.0.1:33042 remote=tcp://127.0.0.1:33975>: Stream is closed\n",
"2022-05-08 16:08:33,173 - distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:44595 -> tcp://127.0.0.1:33975\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 226, in read\n",
" frames_nbytes = await stream.read_bytes(fmt_size)\n",
"tornado.iostream.StreamClosedError: Stream is closed\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 1728, in get_data\n",
" response = await comm.read(deserializers=serializers)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 242, in read\n",
" convert_stream_closed_error(self, e)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 150, in convert_stream_closed_error\n",
" raise CommClosedError(f\"in {obj}: {exc}\") from exc\n",
"distributed.comm.core.CommClosedError: in <TCP (closed) local=tcp://127.0.0.1:44595 remote=tcp://127.0.0.1:58932>: Stream is closed\n",
"2022-05-08 16:08:33,347 - distributed.nanny - WARNING - Restarting worker\n"
]
},
{
"ename": "KilledWorker",
"evalue": "(\"('getitem-mean_chunk-36794663cd3dbdf4b2b7cd7445c39a39', 5, 14, 0)\", <WorkerState 'tcp://127.0.0.1:33975', name: 0, status: closed, memory: 0, processing: 87>)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKilledWorker\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_155890/4239941597.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mt0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtpm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\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 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Yep: {time.time() - t0:.1f}s'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.miniconda3/envs/climate/lib/python3.9/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 919\u001b[0m \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 920\u001b[0m \"\"\"\n\u001b[0;32m--> 921\u001b[0;31m \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_temp_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 922\u001b[0m \u001b[0mnew\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_temp_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 923\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.miniconda3/envs/climate/lib/python3.9/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 859\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 860\u001b[0m \u001b[0;31m# evaluate all the dask arrays simultaneously\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 861\u001b[0;31m \u001b[0mevaluated_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlazy_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 862\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 863\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlazy_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluated_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.miniconda3/envs/climate/lib/python3.9/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[0mpostcomputes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_postcompute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 574\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 575\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 576\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mrepack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpostcomputes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 577\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/client.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, dsk, keys, workers, allow_other_workers, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs)\u001b[0m\n\u001b[1;32m 3002\u001b[0m \u001b[0mshould_rejoin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3003\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3004\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpacked\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masynchronous\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0masynchronous\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdirect\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 3005\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3006\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfutures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/client.py\u001b[0m in \u001b[0;36mgather\u001b[0;34m(self, futures, errors, direct, asynchronous)\u001b[0m\n\u001b[1;32m 2176\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2177\u001b[0m \u001b[0mlocal_worker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2178\u001b[0;31m return self.sync(\n\u001b[0m\u001b[1;32m 2179\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gather\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2180\u001b[0m \u001b[0mfutures\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(self, func, asynchronous, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 318\u001b[0;31m return sync(\n\u001b[0m\u001b[1;32m 319\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback_timeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback_timeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 320\u001b[0m )\n",
"\u001b[0;32m~/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(loop, func, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m 383\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 384\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 385\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\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 386\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils.py\u001b[0m in \u001b[0;36mf\u001b[0;34m()\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0mfuture\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masyncio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait_for\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfuture\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 357\u001b[0m \u001b[0mfuture\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masyncio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_future\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfuture\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 358\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 359\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[0merror\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.miniconda3/envs/climate/lib/python3.9/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 761\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 762\u001b[0;31m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\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 763\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 764\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/client.py\u001b[0m in \u001b[0;36m_gather\u001b[0;34m(self, futures, errors, direct, local_worker)\u001b[0m\n\u001b[1;32m 2039\u001b[0m \u001b[0mexc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2040\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2041\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraceback\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 2042\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2043\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"skip\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKilledWorker\u001b[0m: (\"('getitem-mean_chunk-36794663cd3dbdf4b2b7cd7445c39a39', 5, 14, 0)\", <WorkerState 'tcp://127.0.0.1:33975', name: 0, status: closed, memory: 0, processing: 87>)"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-05-08 16:08:34,682 - distributed.worker_memory - WARNING - Worker is at 83% memory usage. Pausing worker. Process memory: 1.67 GiB -- Worker memory limit: 2.00 GiB\n",
"2022-05-08 16:08:34,757 - distributed.worker_memory - WARNING - Worker exceeded 95% memory budget. Restarting\n",
"2022-05-08 16:08:34,835 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:44595\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 226, in read\n",
" frames_nbytes = await stream.read_bytes(fmt_size)\n",
"tornado.iostream.StreamClosedError: Stream is closed\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 3162, in gather_dep\n",
" response = await get_data_from_worker(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4488, in get_data_from_worker\n",
" return await retry_operation(_get_data, operation=\"get_data_from_worker\")\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 381, in retry_operation\n",
" return await retry(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 366, in retry\n",
" return await coro()\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4468, in _get_data\n",
" response = await send_recv(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/core.py\", line 708, in send_recv\n",
" response = await comm.read(deserializers=deserializers)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 242, in read\n",
" convert_stream_closed_error(self, e)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 150, in convert_stream_closed_error\n",
" raise CommClosedError(f\"in {obj}: {exc}\") from exc\n",
"distributed.comm.core.CommClosedError: in <TCP (closed) Ephemeral Worker->Worker for gather local=tcp://127.0.0.1:58938 remote=tcp://127.0.0.1:44595>: Stream is closed\n",
"2022-05-08 16:08:34,835 - distributed.worker - ERROR - Worker stream died during communication: tcp://127.0.0.1:44595\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 226, in read\n",
" frames_nbytes = await stream.read_bytes(fmt_size)\n",
"tornado.iostream.StreamClosedError: Stream is closed\n",
"\n",
"The above exception was the direct cause of the following exception:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 3162, in gather_dep\n",
" response = await get_data_from_worker(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4488, in get_data_from_worker\n",
" return await retry_operation(_get_data, operation=\"get_data_from_worker\")\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 381, in retry_operation\n",
" return await retry(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/utils_comm.py\", line 366, in retry\n",
" return await coro()\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/worker.py\", line 4468, in _get_data\n",
" response = await send_recv(\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/core.py\", line 708, in send_recv\n",
" response = await comm.read(deserializers=deserializers)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 242, in read\n",
" convert_stream_closed_error(self, e)\n",
" File \"/home/mowglie/.miniconda3/envs/climate/lib/python3.9/site-packages/distributed/comm/tcp.py\", line 150, in convert_stream_closed_error\n",
" raise CommClosedError(f\"in {obj}: {exc}\") from exc\n",
"distributed.comm.core.CommClosedError: in <TCP (closed) Ephemeral Worker->Worker for gather local=tcp://127.0.0.1:58940 remote=tcp://127.0.0.1:44595>: Stream is closed\n",
"2022-05-08 16:08:34,878 - distributed.nanny - WARNING - Restarting worker\n"
]
}
],
"source": [
"t0 = time.time()\n",
"tpm = tpm.load()\n",
"print(f'Yep: {time.time() - t0:.1f}s')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0a44c789-596d-42a4-ada6-c92ce29bdd33",
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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