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John-Paul Robinson
createAndParseSACCT
Commits
c1f05b91
Commit
c1f05b91
authored
Mar 19, 2020
by
Saisri Vegesna
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parent
c1be2976
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c1f05b91
{
"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": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>JobID</th>\n",
" <th>ArrayJobID</th>\n",
" <th>ArrayTaskID</th>\n",
" <th>JobStep</th>\n",
" <th>JobIDSlurm</th>\n",
" <th>JobName</th>\n",
" <th>User</th>\n",
" <th>Group</th>\n",
" <th>Account</th>\n",
" <th>State</th>\n",
" <th>...</th>\n",
" <th>MaxDiskReadNode</th>\n",
" <th>MaxDiskReadTask</th>\n",
" <th>MaxDiskWrite</th>\n",
" <th>MaxDiskWriteNode</th>\n",
" <th>MaxDiskWriteTask</th>\n",
" <th>ReqGPUS</th>\n",
" <th>Comment</th>\n",
" <th>GPUMem</th>\n",
" <th>GPUEff</th>\n",
" <th>NGPU</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3319116</td>\n",
" <td>3319116</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>3319116_[43-45,47%5]</td>\n",
" <td>1mUD1MPa</td>\n",
" <td>user</td>\n",
" <td>user</td>\n",
" <td>user</td>\n",
" <td>PENDING</td>\n",
" <td>...</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3927198</td>\n",
" <td>3887451</td>\n",
" <td>30.0</td>\n",
" <td>None</td>\n",
" <td>3887451_30</td>\n",
" <td>100kCrC20MPa</td>\n",
" <td>user</td>\n",
" <td>user</td>\n",
" <td>user</td>\n",
" <td>COMPLETED</td>\n",
" <td>...</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3927198</td>\n",
" <td>3887451</td>\n",
" <td>30.0</td>\n",
" <td>batch</td>\n",
" <td>3887451_30.batch</td>\n",
" <td>batch</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>user</td>\n",
" <td>COMPLETED</td>\n",
" <td>...</td>\n",
" <td>c0088</td>\n",
" <td>0</td>\n",
" <td>1.222336e+10</td>\n",
" <td>c0088</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3927198</td>\n",
" <td>3887451</td>\n",
" <td>30.0</td>\n",
" <td>extern</td>\n",
" <td>3887451_30.extern</td>\n",
" <td>extern</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>user</td>\n",
" <td>COMPLETED</td>\n",
" <td>...</td>\n",
" <td>c0088</td>\n",
" <td>0</td>\n",
" <td>0.000000e+00</td>\n",
" <td>c0088</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3927199</td>\n",
" <td>3887451</td>\n",
" <td>31.0</td>\n",
" <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",
" <td>...</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<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": {},
"output_type": "execute_result"
}
],
"source": [
"df1.head(5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:.conda-slurm-ds]",
"language": "python",
"name": "conda-env-.conda-slurm-ds-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
%% Cell type:code id: tags:
```
python
import
sqlite3
import
slurm2sql
import
pandas
as
pd
```
%% Cell type:code id: tags:
```
python
db
=
sqlite3
.
connect
(
'test.db'
)
slurm2sql
.
slurm2sql
(
db
,
[
'-S'
,
'2020-03-18'
,
'-a'
])
```
%% Output
0
%% Cell type:code id: tags:
```
python
# For example, you can then convert to a dataframe:
df1
=
pd
.
read_sql
(
'SELECT * FROM slurm'
,
db
)
```
%% Cell type:code id: tags:
```
python
df1
.
head
(
5
)
```
%% Output
JobID ArrayJobID ArrayTaskID JobStep JobIDSlurm \
0 3319116 3319116 NaN None 3319116_[43-45,47%5]
1 3927198 3887451 30.0 None 3887451_30
2 3927198 3887451 30.0 batch 3887451_30.batch
3 3927198 3887451 30.0 extern 3887451_30.extern
4 3927199 3887451 31.0 None 3887451_31
JobName User Group Account State ... \
0 1mUD1MPa user user user PENDING ...
1 100kCrC20MPa user user user COMPLETED ...
2 batch user COMPLETED ...
3 extern user COMPLETED ...
4 100kCrC20MPa user user user COMPLETED ...
MaxDiskReadNode MaxDiskReadTask MaxDiskWrite MaxDiskWriteNode \
0 NaN
1 NaN
2 c0088 0 1.222336e+10 c0088
3 c0088 0 0.000000e+00 c0088
4 NaN
MaxDiskWriteTask ReqGPUS Comment GPUMem GPUEff NGPU
0 NaN None None None None
1 NaN None None None None
2 0 NaN None None None None
3 0 NaN None None None None
4 NaN None None None None
[5 rows x 63 columns]
%% Cell type:code id: tags:
```
python
```
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