2021-04-17 10:50:40 +00:00
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "black-funeral",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import sys\n",
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"import glob\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import matplotlib as mpl\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "68c2f54a-f838-4d41-af1e-d63f13f3d894",
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"metadata": {},
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"outputs": [],
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"source": [
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"DIR = \"/home/claudio/hdd/git/bachelorThesis/figure_8/\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "87bfcdfc-6fee-4cb5-8054-d278700bbbee",
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"metadata": {},
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"outputs": [],
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"source": [
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"def read_json(cluster, filetype, read_ab, read_cd, read_ef):\n",
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" jsondata = None\n",
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" with open(DIR + cluster + \"_figure8\" + filetype + \".json\", \"r\") as f:\n",
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" jsondata = json.loads(f.read())\n",
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" \n",
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" terms = [\"-1\",\"4\",\"5\",\"6\",\"7\",\"8\"]\n",
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" intervals = range(0, 41)\n",
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" \n",
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" cd_measures = [0, 0.2, 0.4, 0.6, 0.8, 1]\n",
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" measures = set()\n",
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" if read_ab:\n",
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" measures.add('rcpu')\n",
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" measures.add('rram')\n",
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" if read_ef:\n",
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" measures.add('ucpu')\n",
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" measures.add('uram')\n",
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" \n",
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" dfd = {'term': [], 'measure': [], 'interval': [], 'count': []}\n",
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" \n",
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" for t in terms:\n",
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" for m in measures:\n",
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" if jsondata[t][m] is None:\n",
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" continue\n",
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" for i in intervals:\n",
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" dfd[\"term\"].append(int(t))\n",
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" dfd[\"measure\"].append(m)\n",
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" dfd[\"interval\"].append(i / 40)\n",
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" dfd[\"count\"].append(jsondata[t][m][i])\n",
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" if read_cd:\n",
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" for m in [\"rscpu\", \"rsram\"]:\n",
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" if jsondata[m][t] is None:\n",
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" continue\n",
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" for i, mi in enumerate(cd_measures):\n",
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" dfd[\"term\"].append(int(t))\n",
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" dfd[\"measure\"].append(m)\n",
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" dfd[\"interval\"].append(mi)\n",
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" if len(jsondata[m][t]) == 41 and i != 0:\n",
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" dfd[\"count\"].append([sum(jsondata[m][t][k]) for k in range(i, i+8)])\n",
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" else:\n",
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" dfd[\"count\"].append(jsondata[m][t][i])\n",
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"\n",
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" return pd.DataFrame(dfd, columns=dfd.keys())\n",
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"\n",
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"dfs = {}\n",
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"\n",
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"dfs[\"a\"] = pd.concat([read_json(\"a\", \"abef\", True, False, True), read_json(\"a\", \"\", False, True, False)])\n",
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"for c in \"bcd\":\n",
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" dfs[c] = read_json(c, \"abef\", True, False, True)\n",
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"for c in \"efgh\":\n",
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" dfs[c] = read_json(c, \"ab\", True, False, False) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"id": "fe6f1eb5-bf36-49b7-854f-6b9dfa51f766",
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"metadata": {},
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"outputs": [],
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"source": [
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"def categorical_plot(df, key, title, sorted_xs = None):\n",
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" if sorted_xs == None:\n",
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" xs = set()\n",
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" lines = []\n",
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" for t in [4,5,6,7,8]:\n",
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" xs = xs.union(np.array(df[df.term == t][key]).reshape(-1))\n",
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" xs = [str(x) for x in sorted(xs)]\n",
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" else:\n",
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" xs = sorted_xs\n",
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" \n",
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" plt.figure(figsize=(25,8))\n",
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" ax = plt.gca()\n",
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" lines = []\n",
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" for t in [4,5,6,7,8]:\n",
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" ys = []\n",
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" for x in xs:\n",
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" partsum = df[(df.term != -1) & (df[key] == float(x))][\"count\"].sum().astype(int)\n",
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" y = df[(df.term == t) & (df[key] == float(x))][\"count\"]\n",
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" ys.append((y.values.astype(int)[0] / partsum if partsum > 0 else y.values.astype(int)[0]) if y.any() else 0)\n",
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" lines.append(ax.plot(xs,ys, marker='.')[0])\n",
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"\n",
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" plt.xticks(xs)\n",
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" plt.legend(lines, [\"EVICT\", \"FAIL\", \"FINISH\", \"KILL\", \"LOST\"],\n",
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" bbox_to_anchor=(1,1))\n",
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" plt.title(title)\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"id": "2eb1f8de-81ef-4301-abd3-262fdfd29eda",
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"metadata": {},
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"outputs": [],
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"source": [
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"def prepare_plot(cluster, measure):\n",
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" titles = {\n",
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" \"rscpu\": \"CPU reservation w.r.t. event rate\",\n",
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" \"rsram\": \"Memory reservation w.r.t. event rate\",\n",
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" \"rcpu\": \"CPU request w.r.t. event rate\",\n",
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" \"rram\": \"Memory request w.r.t. event rate\",\n",
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" \"ucpu\": \"CPU utilization w.r.t. event rate\",\n",
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" \"uram\": \"Memory utilization w.r.t. event rate\",\n",
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" }\n",
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" \n",
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" df = dfs[cluster]\n",
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" df = df[df.measure == measure]\n",
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" del df[\"measure\"]\n",
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" df = df.sort_values([\"term\", \"interval\"])\n",
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" \n",
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" \n",
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" categorical_plot(df, \"interval\", titles[measure] + \" (cluster \" + cluster + \")\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"id": "a9a09b9c-4281-41f4-a069-eb954e0a018f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1800x576 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1800x576 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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|
|
"for cluster in \"abcdefgh\":\n",
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" prepare_plot(cluster, \"rcpu\")\n",
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" prepare_plot(cluster, \"rram\")\n",
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"prepare_plot(\"a\", \"rscpu\")\n",
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"prepare_plot(\"a\", \"rsram\")\n",
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"for cluster in \"abcd\":\n",
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" prepare_plot(cluster, \"ucpu\")\n",
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" prepare_plot(cluster, \"uram\")"
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]
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},
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{
|
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"cell_type": "code",
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"execution_count": null,
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"id": "f388dc45-2649-479d-a3dc-4ee2ccbd0bbd",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
|
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"metadata": {
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"kernelspec": {
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"display_name": "venv",
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"language": "python",
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"name": "venv"
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},
|
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"language_info": {
|
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|
|
"codemirror_mode": {
|
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"name": "ipython",
|
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"version": 3
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},
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"file_extension": ".py",
|
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"mimetype": "text/x-python",
|
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|
|
"name": "python",
|
|
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|
|
"nbconvert_exporter": "python",
|
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|
|
"pygments_lexer": "ipython3",
|
2021-04-27 14:37:09 +00:00
|
|
|
"version": "3.9.4"
|
2021-04-17 10:50:40 +00:00
|
|
|
}
|
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},
|
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"nbformat": 4,
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"nbformat_minor": 5
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}
|
