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Commit 6538fdb6 authored by Ryan Randles Jones's avatar Ryan Randles Jones
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beautification of docstrings

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%% Cell type:code id: tags:
```
# must run
import sqlite3
import slurm2sql
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import plotly.express as px
```
%% Cell type:code id: tags:
```
# must run
# creates database of info from March 2020 using sqlite 3
db = sqlite3.connect('/data/rc/rc-team/slurm-since-March.sqlite3')
```
%% Cell type:code id: tags:
```
# creates database of allocation info from March 2020 using sqlite 3
# not using this right now, but is here as an option
#db_allocation = sqlite3.connect('/data/rc/rc-team/slurm-since-March-allocation.sqlite3')
```
%% Cell type:code id: tags:
```
# must run
# variable for max gigs of RAM requested - Charts range from 0 to upperRAMlimit gigs
upperRAMlimit = 50e+10 # 5 gigs
```
%% Cell type:code id: tags:
```
# must run
# df_1 is starting database
df_1 = pd.read_sql('SELECT * FROM slurm', db)
```
%% Cell type:code id: tags:
```
# voluntary
# for displaying all available column options
pd.set_option('display.max_columns', None)
df_1.head(5)
```
%% Cell type:code id: tags:
```
# must run
# df_2 is database with only ReqMemCpu and ReqMemNode, and ArrayTaskID
df_2 = df_1.loc[:,['JobName','ReqMemCPU', 'ReqMemNode', 'ArrayJobID','ArrayTaskID']]
#df_2.head(5)
```
%% Cell type:code id: tags:
```
# must run
# df_batch is df_2 with only batch jobs
df_batch = df_1.JobName.str.contains('batch')
#df_2[df_batch]
```
%% Cell type:code id: tags:
```
# must run
# creates database from df_batch of ReqMemCPU batch jobs that are < or = a given point
CPU_cutoff = df_2[df_batch][(df_2[df_batch].ReqMemCPU <= upperRAMlimit)]
#CPU_cutoff
Node_cutoff = df_2[df_batch][(df_2[df_batch].ReqMemNode <= upperRAMlimit)]
```
%% Cell type:code id: tags:
```
# voluntary
# gives mean, min, max, std, and 3 percentiles for cutoff data
# can change what to include or exclude
CPU_cutoff.describe(include=None, exclude=None)
```
%% Cell type:code id: tags:
```
# voluntary
# gives mean, min, max, std, and 3 percentiles for cutoff data
# can change what to include or exclude
Node_cutoff.describe(include=None, exclude=None)
```
%% Cell type:code id: tags:
```
# msut run
# creates databases of Requested Ram per CPU and per Node that have an array task id using the upper RAM limit cutoff
CPU_arraytask = CPU_cutoff.dropna(subset=['ArrayTaskID'])
Node_arraytask = Node_cutoff.dropna(subset=['ArrayTaskID'])
```
%% Cell type:code id: tags:
```
# must run
# creates databases of Requested Ram per CPU and per Node that do not have an array task id using the upper RAM limit cutoff
CPU_nonarraytask = CPU_cutoff[CPU_cutoff['ArrayTaskID'].isnull()]
Node_nonarraytask = Node_cutoff[Node_cutoff['ArrayTaskID'].isnull()]
#CPU_nonarraytask.head(5)
```
%% Cell type:markdown id: tags:
Graphs: User Requested RAM per CPU for all Jobs
Graphs: <br>
User Requested RAM per CPU for all Jobs
<br>
User Requested RAM per Node for all Jobs
<br>
User Requested RAM per CPU and per Node together for all Jobs
<br>
User Requested RAM per CPU for Array Jobs vs Not Array Jobs
<br>
User Requested RAM per Node for Array Jobs vs Not Array Jobs
<br>
These graphs create histograms using the data for the month of March 2020.
The x axis measures the amount of requested RAM in gigs per CPU/Node, from 0 to the max declared in the upperRAMlimit variable above - 5 gigs.
The y axis measures how many users requested that amount RAM per CPU or Node.
%% Cell type:code id: tags:
```
# shows all user requested cpu memory for array and non array jobs
CPU_fig = sns.distplot(CPU_cutoff['ReqMemCPU'], kde=False, label='All CPU', color = "green")
CPU_fig.set_yscale('log')
plt.legend(prop={'size': 12})
plt.title('User Requested RAM per CPU for all Jobs')
plt.xlabel('Requested Gigs of RAM')
plt.ylabel('Number of Users Requesting')
```
%% Cell type:code id: tags:
```
# shows all user requested node memory for array and non array jobs
Node_fig = sns.distplot(Node_cutoff['ReqMemNode'], kde=False, label='All Node')
Node_fig.set_yscale('log')
plt.legend(prop={'size': 12})
plt.title('User Requested RAM per Node for all Jobs')
plt.xlabel('Requested Gigs of RAM')
plt.ylabel('Number of Users Requesting')
```
%% Cell type:code id: tags:
```
#shows requested cpu and node for all job types (array and non array jobs) side by side for easy comparison.
CPU_fig = sns.distplot(CPU_cutoff['ReqMemCPU'], kde=False, label='All CPU', color = "green")
CPU_fig.set_yscale('log')
Node_fig = sns.distplot(Node_cutoff['ReqMemNode'], kde=False, label='All Node') #color = 'darkblue')
Node_fig.set_yscale('log')
plt.legend(prop={'size': 12})
plt.title('User Requested RAM per CPU and per Node together for all Jobs')
plt.xlabel('Requested Gigs of RAM')
plt.ylabel('Number of Users Requesting')
```
%% Cell type:code id: tags:
```
#shows requested cpu memory for array jobs alongside requested cpu memory for non array jobs for easy comparison.
CPU_arraytask_fig = sns.distplot(CPU_arraytask['ReqMemCPU'], kde=False, label='CPU Array Task', color = "green")
CPU_arraytask_fig.set_yscale('log')
CPU_nonarraytask_fig = sns.distplot(CPU_nonarraytask['ReqMemCPU'], kde=False, label='CPU Non Array Task')
CPU_nonarraytask_fig.set_yscale('log')
plt.legend(prop={'size': 12})
plt.title('User Requested RAM per CPU for Array Jobs vs Not Array Jobs')
plt.xlabel('Requested Gigs of RAM')
plt.ylabel('Number of Users Requesting')
```
%% Cell type:code id: tags:
```
#shows requested node memory for array jobs alongside requested node memory for non array jobs for easy comparison.
Node_arraytask_fig = sns.distplot(Node_arraytask['ReqMemCPU'], kde=False, label='Node Array Task', color = "green")
Node_arraytask_fig.set_yscale('log')
Node_nonarraytask_fig = sns.distplot(Node_nonarraytask['ReqMemNode'], kde=False, label='Node Non Array Task')
Node_nonarraytask_fig.set_yscale('log')
plt.legend(prop={'size': 12})
plt.title('User Requested RAM per Node for Array Jobs vs Not Array Jobs')
plt.xlabel('Requested Gigs of RAM')
plt.ylabel('Number of Users Requesting')
```
%% Cell type:markdown id: tags:
# These are Plotly Express Graphs of the some of the above Seaborn graphs. Run them only if you need more details about the data in the graph. They will make your notebook run slower.
%% Cell type:markdown id: tags:
Graphs: User Requested RAM per CPU for all Jobs
Graphs: <br>
User Requested RAM per CPU for all Jobs
<br>
User Requested RAM per CPU for Non Array Jobs
<br>
User Requested RAM per CPU for Array Jobs
<br>
User Requested RAM per Node for all Jobs
<br>
User Requested RAM per Node for Non Array Jobs
<br>
User Requested RAM per Node for Array Jobs
<br>
These graphs create histograms using the data for the month of March 2020.
The x axis measures the amount of requested RAM in gigs per CPU/Node, from 0 to the max declared in the upperRAMlimit variable above - 5 gigs.
The y axis measures how many users requested that amount RAM per CPU or Node.
Can also show box or violin graph above to show where min, max, median, and 3rd quartile is.
%% Cell type:code id: tags:
```
CPU_fig = px.histogram(CPU_cutoff, x="ReqMemCPU",
title='User Requested RAM per CPU for all Jobs',
labels={'ReqMemCPU':'ReqMemCPU'}, # can specify one label per df column
opacity=0.8,
log_y=True, # represent bars with log scale
marginal="box", # can be `box`, `violin`
hover_data=CPU_cutoff.columns,
nbins=30,
color_discrete_sequence=['goldenrod'] # color of histogram bars
)
CPU_fig.show()
```
%% Cell type:code id: tags:
```
CPU_nonarraytask_fig = px.histogram(CPU_nonarraytask, x="ReqMemCPU",
title='User Requested RAM per CPU for Non Array Jobs',
labels={'ReqMemCPU':'ReqMemCPU'}, # can specify one label per df column
opacity=0.8,
log_y=True, # represent bars with log scale
marginal="box", # can be `box`, `violin`
hover_data=CPU_nonarraytask.columns,
nbins=30,
color_discrete_sequence=['goldenrod'] # color of histogram bars
)
CPU_nonarraytask_fig.show()
```
%% Cell type:code id: tags:
```
CPU_arraytask_fig = px.histogram(CPU_arraytask, x="ReqMemCPU",
title='User Requested RAM per CPU for Array Jobs',
labels={'ReqMemCPU':'ReqMemCPU'}, # can specify one label per df column
opacity=0.8,
log_y=True, # represent bars with log scale
marginal="box", # can be `box`, `violin`
hover_data=CPU_arraytask.columns,
nbins=30,
color_discrete_sequence=['goldenrod'] # color of histogram bars
)
CPU_arraytask_fig.show()
```
%% Cell type:code id: tags:
```
Node_fig = px.histogram(Node_cutoff, x="ReqMemNode",
title='User Requested RAM per Node for all Jobs',
labels={'ReqMemNode':'ReqMemNode'}, # can specify one label per df column
opacity=0.8,
log_y=True, # represent bars with log scale
marginal="box", # can be `box`, `violin`
hover_data=Node_cutoff.columns,
nbins=30,
color_discrete_sequence=['darkblue'] # color of histogram bars
)
Node_fig.show()
```
%% Cell type:code id: tags:
```
Node_nonarraytask_fig = px.histogram(Node_nonarraytask, x="ReqMemNode",
title='User Requested RAM per Node for Non Array Jobs',
labels={'ReqMemNode':'ReqMemNode'}, # can specify one label per df column
opacity=0.8,
log_y=True, # represent bars with log scale
marginal="box", # can be `box`, `violin`
hover_data=Node_nonarraytask.columns,
nbins=30,
color_discrete_sequence=['darkblue'] # color of histogram bars
)
Node_nonarraytask_fig.show()
```
%% Cell type:code id: tags:
```
Node_arraytask_fig = px.histogram(Node_arraytask, x="ReqMemNode",
title='User Requested RAM per Node for Array Jobs',
labels={'ReqMemNode':'ReqMemNode'}, # can specify one label per df column
opacity=0.8,
log_y=True, # represent bars with log scale
marginal="box", # can be `box`, `violin`
hover_data=Node_arraytask.columns,
nbins=30,
color_discrete_sequence=['darkblue'] # color of histogram bars
)
Node_arraytask_fig.show()
```
......
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