Commit 62db7642 authored by Chirag Chandrahas Shetty's avatar Chirag Chandrahas Shetty
Browse files

Getting slurm data using slurm2sql

parent f60adca2
astroid==2.3.3
astropy==4.0
attr==0.3.1
attrs==19.3.0
backcall==0.1.0
bleach==3.1.3
certifi==2019.11.28
chardet==3.0.4
confuse==1.0.0
cycler==0.10.0
decorator==4.4.2
defusedxml==0.6.0
entrypoints==0.3
htmlmin==0.1.12
idna==2.8
ipykernel==5.1.4
ipython==7.13.0
ipython-genutils==0.2.0
ipywidgets==7.5.1
isort==4.3.21
jedi==0.16.0
Jinja2==2.11.1
joblib==0.14.1
jsonschema==3.2.0
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jupyter-core==4.6.1
kaggle==1.5.6
kiwisolver==1.1.0
lazy-object-proxy==1.4.3
llvmlite==0.31.0
MarkupSafe==1.1.1
matplotlib==3.2.0
mccabe==0.6.1
missingno==0.4.2
mistune==0.8.4
more-itertools==8.2.0
nbconvert==5.6.1
nbformat==5.0.4
networkx==2.4
notebook==6.0.3
numba==0.48.0
numpy==1.18.2
packaging==20.3
pandas==0.25.3
pandas-profiling==2.5.0
pandocfilters==1.4.2
parso==0.6.2
pexpect==4.8.0
phik==0.9.9
pickleshare==0.7.5
pluggy==0.13.1
prometheus-client==0.7.1
prompt-toolkit==3.0.3
ptyprocess==0.6.0
py==1.8.1
Pygments==2.6.1
pylint==2.4.4
pyparsing==2.4.6
pyrsistent==0.15.7
pytest==5.4.1
pytest-pylint==0.15.1
python-dateutil==2.8.1
python-slugify==4.0.0
pytz==2019.3
PyYAML==5.3
pyzmq==18.1.1
requests==2.22.0
scipy==1.4.1
seaborn==0.10.0
Send2Trash==1.5.0
six==1.14.0
slurm2sql==0.9.0
tangled-up-in-unicode==0.0.3
terminado==0.8.3
testpath==0.4.4
text-unidecode==1.3
tornado==6.0.4
tqdm==4.42.0
traitlets==4.3.3
urllib3==1.25.8
visions==0.2.2
wcwidth==0.1.8
webencodings==0.5.1
widgetsnbextension==3.5.1
wrapt==1.11.2
{
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import sqlite3\n",
"import slurm2sql\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db = sqlite3.connect('test.db')\n",
"slurm2sql.slurm2sql(db, ['-S', '2020-03-18', '-a'])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# For example, you can then convert to a dataframe:\n",
"df1 = pd.read_sql('SELECT * FROM slurm', db)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
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" <th>4</th>\n",
" <td>3927199</td>\n",
" <td>3887451</td>\n",
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" <td>None</td>\n",
" <td>3887451_31</td>\n",
" <td>100kCrC20MPa</td>\n",
" <td>user</td>\n",
" <td>user</td>\n",
" <td>user</td>\n",
" <td>COMPLETED</td>\n",
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" <td></td>\n",
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"<p>5 rows × 63 columns</p>\n",
"</div>"
],
"text/plain": [
" JobID ArrayJobID ArrayTaskID JobStep JobIDSlurm \\\n",
"0 3319116 3319116 NaN None 3319116_[43-45,47%5] \n",
"1 3927198 3887451 30.0 None 3887451_30 \n",
"2 3927198 3887451 30.0 batch 3887451_30.batch \n",
"3 3927198 3887451 30.0 extern 3887451_30.extern \n",
"4 3927199 3887451 31.0 None 3887451_31 \n",
"\n",
" JobName User Group Account State ... \\\n",
"0 1mUD1MPa user user user PENDING ... \n",
"1 100kCrC20MPa user user user COMPLETED ... \n",
"2 batch user COMPLETED ... \n",
"3 extern user COMPLETED ... \n",
"4 100kCrC20MPa user user user COMPLETED ... \n",
"\n",
" MaxDiskReadNode MaxDiskReadTask MaxDiskWrite MaxDiskWriteNode \\\n",
"0 NaN \n",
"1 NaN \n",
"2 c0088 0 1.222336e+10 c0088 \n",
"3 c0088 0 0.000000e+00 c0088 \n",
"4 NaN \n",
"\n",
" MaxDiskWriteTask ReqGPUS Comment GPUMem GPUEff NGPU \n",
"0 NaN None None None None \n",
"1 NaN None None None None \n",
"2 0 NaN None None None None \n",
"3 0 NaN None None None None \n",
"4 NaN None None None None \n",
"\n",
"[5 rows x 63 columns]"
]
},
"execution_count": 11,
"metadata": {},
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"source": [
"df1.head(5)"
]
},
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"source": []
}
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