examples/plots_and_comparisons.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# hposuite Plotting and Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and printing available Benchmarks and Optimizers in hposuite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['yahpo-lcbench-3945', 'yahpo-lcbench-7593', 'yahpo-lcbench-34539', 'yahpo-lcbench-126025', 'yahpo-lcbench-126026', 'yahpo-lcbench-126029', 'yahpo-lcbench-146212', 'yahpo-lcbench-167104', 'yahpo-lcbench-167149', 'yahpo-lcbench-167152', 'yahpo-lcbench-167161', 'yahpo-lcbench-167168', 'yahpo-lcbench-167181', 'yahpo-lcbench-167184', 'yahpo-lcbench-167185', 'yahpo-lcbench-167190', 'yahpo-lcbench-167200', 'yahpo-lcbench-167201', 'yahpo-lcbench-168329', 'yahpo-lcbench-168330', 'yahpo-lcbench-168331', 'yahpo-lcbench-168335', 'yahpo-lcbench-168868', 'yahpo-lcbench-168908', 'yahpo-lcbench-168910', 'yahpo-lcbench-189354', 'yahpo-lcbench-189862', 'yahpo-lcbench-189865', 'yahpo-lcbench-189866', 'yahpo-lcbench-189873', 'yahpo-lcbench-189905', 'yahpo-lcbench-189906', 'yahpo-lcbench-189908', 'yahpo-lcbench-189909', 'mfh3_bad', 'mfh6_bad', 'mfh3_good', 'mfh6_good', 'mfh3_moderate', 'mfh6_moderate', 'mfh3_terrible', 'mfh6_terrible', 'pd1-cifar100-wide_resnet-2048', 'pd1-imagenet-resnet-512', 'pd1-lm1b-transformer-2048', 'pd1-translate_wmt-xformer_translate-64', 'pymoo-ackley', 'pymoo-griewank', 'pymoo-himmelblau', 'pymoo-rastrigin', 'pymoo-rosenbrock', 'pymoo-schwefel', 'pymoo-sphere', 'pymoo-zakharov', 'pymoo-kursawe', 'pymoo-zdt1', 'pymoo-zdt2', 'pymoo-zdt3', 'pymoo-zdt4', 'pymoo-zdt5', 'pymoo-zdt6', 'pymoo-omnitest', 'pymoo-sympart', 'pymoo-sympart_rotated', 'pymoo-dtlz1', 'pymoo-dtlz2', 'pymoo-dtlz3', 'pymoo-dtlz4', 'pymoo-dtlz5', 'pymoo-dtlz6', 'pymoo-dtlz7', 'ackley', 'branin'])\n",
      "dict_keys(['RandomSearch', 'RandomSearchWithPriors', 'DEHB', 'Nevergrad', 'SMAC_BO', 'SMAC_Hyperband', 'SyneTune_BO', 'SyneTune_BOHB', 'Scikit_Optimize', 'Optuna'])\n"
     ]
    }
   ],
   "source": [
    "from hposuite.benchmarks import BENCHMARKS\n",
    "from hposuite.optimizers import OPTIMIZERS\n",
    "\n",
    "print(BENCHMARKS.keys())\n",
    "print(OPTIMIZERS.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing RandomSearch and RandomSearchWithPriors on Ackley Functional Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:hposuite.study:Generated 10 runs\n",
      "INFO:hposuite.study:Created study at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp\n",
      "INFO:hposuite.study:Dumped experiments to /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/dump_all.txt\n",
      "INFO:hposuite.study:Overwrite flag is set to True. Existing results will be overwritten!\n",
      "INFO:hposuite.study:Running 10 experiments sequentially\n",
      "INFO:hposuite.study:Running experiment 1/10\n",
      "INFO:hposuite.run:Overwriting optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=383329928 in `state=<State.PENDING: 'PENDING'>` at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=383329928.\n",
      "WARNING:hposuite.run:Continuations are not supported for the optimizer: <class 'hposuite.optimizers.random_search.RandomSearch'>. Ignoring continuations=True.\n",
      "INFO:hpoglue._run:COMPLETED running optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=383329928\n",
      "INFO:hposuite.run:Results dumped at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=383329928/optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=383329928.parquet\n",
      "INFO:hposuite.study:Running experiment 2/10\n",
      "INFO:hposuite.run:Overwriting optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=3324115917 in `state=<State.PENDING: 'PENDING'>` at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=3324115917.\n",
      "WARNING:hposuite.run:Continuations are not supported for the optimizer: <class 'hposuite.optimizers.random_search.RandomSearch'>. Ignoring continuations=True.\n",
      "INFO:hpoglue._run:COMPLETED running optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=3324115917\n",
      "INFO:hposuite.run:Results dumped at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=3324115917/optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=3324115917.parquet\n",
      "INFO:hposuite.study:Running experiment 3/10\n",
      "INFO:hposuite.run:Overwriting optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=2811363265 in `state=<State.PENDING: 'PENDING'>` at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=2811363265.\n",
      "WARNING:hposuite.run:Continuations are not supported for the optimizer: <class 'hposuite.optimizers.random_search.RandomSearch'>. Ignoring continuations=True.\n",
      "INFO:hpoglue._run:COMPLETED running optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=2811363265\n",
      "INFO:hposuite.run:Results dumped at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=2811363265/optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=2811363265.parquet\n",
      "INFO:hposuite.study:Running experiment 4/10\n",
      "INFO:hposuite.run:Overwriting optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1884968545 in `state=<State.PENDING: 'PENDING'>` at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1884968545.\n",
      "WARNING:hposuite.run:Continuations are not supported for the optimizer: <class 'hposuite.optimizers.random_search.RandomSearch'>. Ignoring continuations=True.\n",
      "INFO:hpoglue._run:COMPLETED running optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1884968545\n",
      "INFO:hposuite.run:Results dumped at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1884968545/optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1884968545.parquet\n",
      "INFO:hposuite.study:Running experiment 5/10\n",
      "INFO:hposuite.run:Overwriting optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1859786276 in `state=<State.PENDING: 'PENDING'>` at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1859786276.\n",
      "WARNING:hposuite.run:Continuations are not supported for the optimizer: <class 'hposuite.optimizers.random_search.RandomSearch'>. Ignoring continuations=True.\n",
      "INFO:hpoglue._run:COMPLETED running optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1859786276\n",
      "INFO:hposuite.run:Results dumped at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1859786276/optimizer=RandomSearch.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1859786276.parquet\n",
      "INFO:hposuite.study:Running experiment 6/10\n",
      "INFO:hposuite.run:Overwriting optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=383329928 in `state=<State.PENDING: 'PENDING'>` at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=383329928.\n",
      "WARNING:hposuite.run:Continuations are not supported for the optimizer: <class 'hposuite.optimizers.random_search.RandomSearchWithPriors'>. Ignoring continuations=True.\n",
      "INFO:hpoglue._run:COMPLETED running optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=383329928\n",
      "INFO:hposuite.run:Results dumped at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=383329928/optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=383329928.parquet\n",
      "INFO:hposuite.study:Running experiment 7/10\n",
      "INFO:hposuite.run:Overwriting optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=3324115917 in `state=<State.PENDING: 'PENDING'>` at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=3324115917.\n",
      "WARNING:hposuite.run:Continuations are not supported for the optimizer: <class 'hposuite.optimizers.random_search.RandomSearchWithPriors'>. Ignoring continuations=True.\n",
      "INFO:hpoglue._run:COMPLETED running optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=3324115917\n",
      "INFO:hposuite.run:Results dumped at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=3324115917/optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=3324115917.parquet\n",
      "INFO:hposuite.study:Running experiment 8/10\n",
      "INFO:hposuite.run:Overwriting optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=2811363265 in `state=<State.PENDING: 'PENDING'>` at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=2811363265.\n",
      "WARNING:hposuite.run:Continuations are not supported for the optimizer: <class 'hposuite.optimizers.random_search.RandomSearchWithPriors'>. Ignoring continuations=True.\n",
      "INFO:hpoglue._run:COMPLETED running optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=2811363265\n",
      "INFO:hposuite.run:Results dumped at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=2811363265/optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=2811363265.parquet\n",
      "INFO:hposuite.study:Running experiment 9/10\n",
      "INFO:hposuite.run:Overwriting optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1884968545 in `state=<State.PENDING: 'PENDING'>` at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1884968545.\n",
      "WARNING:hposuite.run:Continuations are not supported for the optimizer: <class 'hposuite.optimizers.random_search.RandomSearchWithPriors'>. Ignoring continuations=True.\n",
      "INFO:hpoglue._run:COMPLETED running optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1884968545\n",
      "INFO:hposuite.run:Results dumped at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1884968545/optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1884968545.parquet\n",
      "INFO:hposuite.study:Running experiment 10/10\n",
      "INFO:hposuite.run:Overwriting optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1859786276 in `state=<State.PENDING: 'PENDING'>` at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1859786276.\n",
      "WARNING:hposuite.run:Continuations are not supported for the optimizer: <class 'hposuite.optimizers.random_search.RandomSearchWithPriors'>. Ignoring continuations=True.\n",
      "INFO:hpoglue._run:COMPLETED running optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1859786276\n",
      "INFO:hposuite.run:Results dumped at /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1859786276/optimizer=RandomSearchWithPriors.benchmark=ackley.objectives=y.TrialBudget=1000.seed=1859786276.parquet\n",
      "INFO:hposuite.study:Completed study with 10 runs\n"
     ]
    }
   ],
   "source": [
    "from hposuite import create_study\n",
    "\n",
    "ackley = BENCHMARKS[\"ackley\"]\n",
    "\n",
    "study = create_study(\n",
    "    name=\"ex-rs-comp\",\n",
    "    output_dir=\"example-outputs\",\n",
    "    optimizers=[\n",
    "        \"RandomSearch\",\n",
    "        \"RandomSearchWithPriors\",\n",
    "    ],\n",
    "    benchmarks=(\n",
    "        \"ackley\",\n",
    "        {\n",
    "            \"priors\": {\n",
    "                \"y\": ackley.desc.predefined_points[\"min\"]\n",
    "            }\n",
    "        }\n",
    "    ),\n",
    "    num_seeds=5,\n",
    "    budget=1000,\n",
    ")\n",
    "\n",
    "study.optimize(overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG:matplotlib:matplotlib data path: /home/soham/repos/envs/hposuite_env/lib/python3.10/site-packages/matplotlib/mpl-data\n",
      "DEBUG:matplotlib:CONFIGDIR=/home/soham/.config/matplotlib\n",
      "DEBUG:matplotlib:interactive is False\n",
      "DEBUG:matplotlib:platform is linux\n",
      "DEBUG:matplotlib:CACHEDIR=/home/soham/.cache/matplotlib\n",
      "DEBUG:matplotlib.font_manager:Using fontManager instance from /home/soham/.cache/matplotlib/fontlist-v390.json\n",
      "INFO:__main__:Found benchmarks: ['ackley']\n",
      "INFO:__main__:Plotting RandomSearch_default\n",
      "INFO:__main__:Plotting RandomSearchWithPriors_default\n",
      "INFO:__main__:Saved plot to /home/soham/repos/hposuite/examples/example-outputs/ex-rs-comp/plots/ackley_performance.png\n"
     ]
    }
   ],
   "source": [
    "!python -m hposuite.plotting.utils --study_dir ex-rs-comp --output_dir example-outputs --figsize 10 5 --logscale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Displaying the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "Image(filename=\"example-outputs/ex-rs-comp/plots/ackley_performance.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the effect of Continuations using a Multi-fidelity Optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example we use the DEHB on the PD1 Cifar100 Wide Resnet 2048 Benchmark \\\n",
    "In hposuite, continuations is set to True by default for each run, unless the optimizer doesn't support it. In that case, it automatically gets set to False."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating and Running the Study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:hposuite.study:Generated 5 runs\n",
      "INFO:hposuite.study:Created study at /home/soham/repos/hposuite/examples/example-outputs/continuations-comp\n",
      "INFO:hposuite.study:Dumped experiments to /home/soham/repos/hposuite/examples/example-outputs/continuations-comp/dump_all.txt\n",
      "INFO:hposuite.study:Overwrite flag is set to True. Existing results will be overwritten!\n",
      "INFO:hposuite.study:Running 5 experiments sequentially\n",
      "INFO:hposuite.study:Running experiment 1/5\n",
      "INFO:hposuite.run:Overwriting optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=383329928 in `state=<State.PENDING: 'PENDING'>` at /home/soham/repos/hposuite/examples/example-outputs/continuations-comp/optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=383329928.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-02-11 12:53:11.676\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdehb.optimizers.dehb\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m264\u001b[0m - \u001b[33m\u001b[1mA checkpoint already exists, results could potentially be overwritten.\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:hpoglue._run:COMPLETED running optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=383329928\n",
      "INFO:hposuite.run:Results dumped at /home/soham/repos/hposuite/examples/example-outputs/continuations-comp/optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=383329928/optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=383329928.parquet\n",
      "INFO:hposuite.study:Running experiment 2/5\n",
      "INFO:hposuite.run:Overwriting optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=3324115917 in `state=<State.PENDING: 'PENDING'>` at /home/soham/repos/hposuite/examples/example-outputs/continuations-comp/optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=3324115917.\n",
      "INFO:hpoglue._run:COMPLETED running optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=3324115917\n",
      "INFO:hposuite.run:Results dumped at /home/soham/repos/hposuite/examples/example-outputs/continuations-comp/optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=3324115917/optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=3324115917.parquet\n",
      "INFO:hposuite.study:Running experiment 3/5\n",
      "INFO:hposuite.run:Overwriting optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=2811363265 in `state=<State.PENDING: 'PENDING'>` at /home/soham/repos/hposuite/examples/example-outputs/continuations-comp/optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=2811363265.\n",
      "INFO:hpoglue._run:COMPLETED running optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=2811363265\n",
      "INFO:hposuite.run:Results dumped at /home/soham/repos/hposuite/examples/example-outputs/continuations-comp/optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=2811363265/optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=2811363265.parquet\n",
      "INFO:hposuite.study:Running experiment 4/5\n",
      "INFO:hposuite.run:Overwriting optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=1884968545 in `state=<State.PENDING: 'PENDING'>` at /home/soham/repos/hposuite/examples/example-outputs/continuations-comp/optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=1884968545.\n",
      "INFO:hpoglue._run:COMPLETED running optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=1884968545\n",
      "INFO:hposuite.run:Results dumped at /home/soham/repos/hposuite/examples/example-outputs/continuations-comp/optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=1884968545/optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=1884968545.parquet\n",
      "INFO:hposuite.study:Running experiment 5/5\n",
      "INFO:hposuite.run:Overwriting optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=1859786276 in `state=<State.PENDING: 'PENDING'>` at /home/soham/repos/hposuite/examples/example-outputs/continuations-comp/optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=1859786276.\n",
      "INFO:hpoglue._run:COMPLETED running optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=1859786276\n",
      "INFO:hposuite.run:Results dumped at /home/soham/repos/hposuite/examples/example-outputs/continuations-comp/optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=1859786276/optimizer=DEHB.benchmark=pd1-cifar100-wide_resnet-2048.objectives=valid_error_rate.TrialBudget=100.seed=1859786276.parquet\n",
      "INFO:hposuite.study:Completed study with 5 runs\n"
     ]
    }
   ],
   "source": [
    "from hposuite import create_study\n",
    "\n",
    "study = create_study(\n",
    "    name=\"continuations-comp\",\n",
    "    output_dir=\"example-outputs\",\n",
    "    optimizers=\"DEHB\",\n",
    "    benchmarks=(\n",
    "        \"pd1-cifar100-wide_resnet-2048\",\n",
    "        {\n",
    "            \"objectives\": \"valid_error_rate\"\n",
    "        }\n",
    "    ),\n",
    "    num_seeds=5,\n",
    "    budget=100,\n",
    ")\n",
    "\n",
    "study.optimize(overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting the Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEBUG:matplotlib:matplotlib data path: /home/soham/repos/envs/hposuite_env/lib/python3.10/site-packages/matplotlib/mpl-data\n",
      "DEBUG:matplotlib:CONFIGDIR=/home/soham/.config/matplotlib\n",
      "DEBUG:matplotlib:interactive is False\n",
      "DEBUG:matplotlib:platform is linux\n",
      "DEBUG:matplotlib:CACHEDIR=/home/soham/.cache/matplotlib\n",
      "DEBUG:matplotlib.font_manager:Using fontManager instance from /home/soham/.cache/matplotlib/fontlist-v390.json\n",
      "INFO:__main__:Found benchmarks: ['pd1-cifar100-wide_resnet-2048']\n",
      "INFO:__main__:Plotting DEHB_default\n",
      "INFO:__main__:Saved plot to /home/soham/repos/hposuite/examples/example-outputs/continuations-comp/plots/pd1-cifar100-wide_resnet-2048_performance.png\n"
     ]
    }
   ],
   "source": [
    "!python -m hposuite.plotting.utils --study_dir continuations-comp --output_dir example-outputs --figsize 12 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Displaying the Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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zJ6VTCZK9aMK5U2ezdPx0uuyslw8AxZJhsSorIFx2n5JFfmwPq0XhJXwV7F+Mfk4DAOBGBFi47pUKryBJsgeG6ExoPyWG11Ltcs30gS1Lp08mKDMtScHlHlX8PxUvecH2HOFVyfIuUxFzax/s8YVsdun7kEfUu1mM7qtXViV8vZRyNlPzt/6jT9fGad/RNGXYpNFrz2jcbVZVK+1x2edvKqcSpNMnpMzTRXK4pKQzOpOepYxMRrMBQHFk9/RTZlqGTgf7Fvmx/bwdP5sJsAAAKBwEWLjuBYeEKjgk9FxD7UaSpH2//3quzeJx5eFVmxFS/W7SlObnFomXpMAIqc9yxxUO/10k/kWvL/R8w6rybNzK2S3I10vdG1fQQw1v0KA52/Td9nhl2KTJW85qfNuAS6rNtOw2R3h1+rhkvfofT5azZ2Q/k6n0szZZmVANAMWKxbApw6uU0u0BSvHOKtJj2+yO0dVBvsZFegIAgIIiwAKugsDDa3OOvKrfXQoMlR78RPrsXsmwSxar435gqCPc2vSpcySW54rRUolwqdqdLvv2kfRe+xv0zz+HdOD4acUfk3b8fUblg3KOwvJKTVWmX4CyrHaln8m84vM6dTZTAT5FMNrL6imVrnTVD5NppOmMZ4bOeNtUtmTR/3UeAHD1eCfHye7toyx/P5UuXbR/6Ik7llakxwMA4HpAgAVcBallG+tYjW4qvfO/ssjuCKWmNHeEVTG3Sq2GSitel1oNk2JaSHGrpLl9XEdmSdKC/rnu31vSN5KUnblszLuWFMNfwzN76Vt780I4M8eUiAGtK6tGZFCh7A8AAAAAgIth0gxwNVg8dLLawzrc7DVl+ZRytKUmSJ91lH56S2r+nNT6Fan5QGnlm44RWReGV4UkyHJao71myEOFs87TmQybJq34S1k2Vj4HAAAAABQNRmABF2HYspT8967L2jZZ3kqu9Jhq7Bwvq2FzTBtc8boki9TyBemnt6WVYwu34FwEWU4rWGk6ocIZNZWWYdNvW35VSd/CzcAt9kx5nDkta8YZZRpXf/pFlp21SQAAAADgWkCABeTB6uEhT7/AK9pHwMldqvDXJ47wSnKsedVqmGPk1U9vO77KkFbGOsKta0jq8b/l4V34+7Xazspi2HTWM6Pwd56LjCwbQ1EBAAAAwOQIsIA8lAqvIEmyB4Zc+saGTSV3/k+ldsx2rIElOa422GmaFN3cMY1wxeuO9pZDpPJNpLmPn5tGaLFKzZ6VGj0lWXNfNP3LjQf1xuLdkqTW5T3VrbaP8zHPjBTVWeO6ftZnLU7K5nV5V2FKyrSq5+qSLm2Zp1OUmVn4oZth8VCGdyllpRfOlMeLsUoK9uMS5wAA/H979x0fVZX4//89M8mkF0hCmgRCL9KRGBAsZAVlFVcXkUUpK3Z3dVEW0bV/PoLY8Lci7tcSXXWtH8u6CqtLkSKCZEGlRcVQhAQIkErKZOb8/hgyYUhCEkiZhNfz8cgjmXPP3HtnztzJ3Pecew4AAL6MAAuoRUT7GEW0jzm1O2/9WNr696rbkUnSjGXu2QazVrp7XEnuyweTznUP5H7zGumli9wDvhuXtPoZKWGQ1Gd8tdWXVTj1wobNnksCf9U7RKHHzUIYvqv6qO7BUWcp6NBWFSWOaPDDKS91SSryKguP76aIRr6EEAAAoNU4nCWV5kmBkVL75GbddP5Rh/YXlsrJcAhoJjarRbFhgYoI5otftBwCLKAp9BkvjX5QWvqw+3bebmnj69Kga92zDVZeLmhc7ts3r3Yvz9vtWUXFhQ/Ir5bwauY73+rng+4xovpG29S1XVV41e6H9xS99bVq94vc8bHa//i+coun6kiPCQ16ODvzq/eGevHbUl3ZI8Br2wAAAG1OyRGpMEdynfB5yOVw/y7Na7JN1xZUVTiNjjoqVFLePD3WgSC7+zM/ARZaEgEW0FRGznT/rgyxlj4sZaRXXSZo9ZNcFe7ZCSt7Xh3zuOMa/XvDEE2379T4QYkKD/RXQalDH2/cq/SvdnrCqwCbdMugQM/9aguvJKndj/8nSZ7l9QmxMg859fzGUm3Nrf7h6POsCn2eVaG+0TbdMihQPaMIsgAAQBtUmCMdPSw5jlZf5h8s2YObbNP7C0t15Gh5jUFVqcOpknKXbFZLk20fkOQJUMMD6fGHlkWABTSlE0OsypAqOMo9mPuyR6TSfK/w6knnNVrkvFw6WKz7P96i+z/eIrufVeUV3uNNBdik+0cEeYKjk4VXkmRR1T+c+oRY6/Y59MiaEp34eSnAz6qy4/ZlS65TM5cV64ERQUpJ4BsZAADQxric7vDq6CH3F5Ce8gopWE0aYDldRiXlTh0pdlQLqqwWKS48UKGBnNKhaWXlNv3s4EB98G4HNLUTQyyLVRr9kBQeL51zo7T6qapLCofdoF/FT9DXKwu0IbtqFr4Tw6sTez1VC68ik7xCsZrKTxZiZR5yeoVXXWJCNH1Esq4YmKCwyt5gm/YpfU2Wfj5YrHKn9MiaEj19kZWeWAAAoG2y+klRXatuH9pxrLzpv8CzWS1Kjg5p8u0AgC9jBOYzwMKFC9W5c2cFBgYqJSVF69evr7XuBx98oKFDhyoyMlIhISEaOHCgXn/99Wbc2zZq5Ez3mFiSu+dV4mD33x16SUOmuf8edoM0cLIGxNr1/oRofTYpRtf1C1bfaJuSwi3qFmnRZd389cKYEC1IC6k9vBr9oDRjqRQa670PoXHugeQr90PuEKvdD+9V293nN5Z6wqtx/eO15I5Ruu7cTgoLdH9ACw/013XndtLiO0ZqXL94SVK5U1q0sfT0nicAAAAAAGpAD6w27p133tHMmTP1wgsvKCUlRQsWLNCYMWOUmZmpDh06VKvfvn173XffferVq5fsdrv+9a9/afr06erQoYPGjBnTAo+gDRk50/2tXa/LpANbq8oHTpYiO0ldzveq3ifGX49eGKms3GIdKi5XcZlTCZGBXnVC966p3vNq0HXu2Q6vekn6+3h37y6L1X07NMY9kHxGuldPLGdApIrjhkmSsvKcysk9qvaSOkUF65lxZ8ledlgqq/6QAiQ98+uztG/fHu06dFTZudLWX0qUFH7qvbAq/EIla/P14gqzWxg7AgAAAAB8nMUYw0hsbVhKSorOOeccPffcc5Ikl8uljh076g9/+IPuueeeeq1j8ODBGjdunB599NF61S8oKFBERITy8/MVHh5+yvvepuVslgqz3eNfHd8VvQYnC7BknGr3w3uK2vYPWXTsMsPQOHdYlTxS+nK+tPx/pQv/Ip0/S8pa6Z71sHIgeR9UYIL1gGOaPnKd1yzbC/GXbh8SqLTO9mbZHgCgedjzs+Syh6siKEqyNO8YjXvzShQSYFNkkF02G1+StHYWl0O2kkOylhfIEZHsKfevfI2FxKksqneTbLvCaXSouEwFJRVcQogWk5VbrPAgP8WGB6p3vO+e33Ee2vbRA6sNKy8vV0ZGhubMmeMps1qtSktL09q1a+u8vzFGy5YtU2Zmph5//PGm3FWcKotNR3peo9L2fRS34Qn5lR1xz2r498ulC+ZI5/1JkkU6705pxePSl/OqxtvyUeGWo3rE/1V9UpYqp5q+J1axQ3ouo1QXJvnTEwsA2hKLVRZnqfxKDjX7pgNLyySHdLSE0TraCquzVBbjVKlf1RilQSUOVVSUq9yUqdi/6YZRKHU4xUcUACDAatNyc3PldDoVG+s9FlJsbKy2b99e6/3y8/OVmJiosrIy2Ww2Pf/88/rVr35Va/2ysjKVlVVdX1ZQUHD6O48GKYnpr73DH1XSijtlMRXukGr5/0qyuHteffmEtOKxlt7Negu3HFWEinVYzfPNSbFDKiw3igzk0yEAtBUVgVHyKz0ki7P5P5e0t7pUWOqQo9m3jKZiLDaV29upouy46ZnLnXK4XCqzOlVQUtFk27ZapKiQgCZbPwC0FgRYqCYsLEybNm1SUVGRli5dqpkzZ6pLly664IILaqw/d+5cPfzww827k/ASdPA7xW14wh1eSe4xry64193z6ssn3L9lpBVzfb4HFgAAjcFlD1W5PbRFtm2RmukrGLQkuyVALnuAKoKDFBXF5X0A0NQIsNqw6Oho2Ww27d/vPd7R/v37FRcXV+v9rFarunXrJkkaOHCgtm3bprlz59YaYM2ZM0czZ8703C4oKFDHjh1P/wGgbsap9pnvqv32t7zHwPrty1Ln86rGwJKk8/8sJaVK/3e9ZwwsI4uOdL9KeV3Hu0MvSS9uKtG/s9xB2D2X9tLVQ+puy3c37Na8xZmSpAuT/HTt2Q37ltCvvED9Vt/mVfbypSGqaKITj/wyoxmLi5tk3QAAAACAxkeA1YbZ7XYNGTJES5cu1RVXXCHJPYj70qVLdfvtt9d7PS6Xy+sSwRMFBAQoIIBuzS0hdN/Xitr+ZlVBZJI0Y5l7tsGsle4eV5L78sGkc90Du9+8RnrpIilvtywyav/j+yqL7K6ixBGSpFE9QvVWljvceeGbfF0xfIDsfrWP4VFW4dQLGzZ7Lvf7Ve8QhTZwFkJbWfX1RwRY5QxoqrFD6IUGAAAAAK0JI0u2cTNnztSLL76o1157Tdu2bdMtt9yi4uJiTZ8+XZI0ZcoUr0He586dqy+++EI///yztm3bpqeeekqvv/66rr322pZ6CDiJosQRyu0ztaogb7e08XWp6IB7tsHKywWN69jsgwfdy/N2e+6S22eqJ7ySpG7tbOoT7Q6gfj5YrD+9u0nlFTUHPmUVTs1851v9fNAdePWNtqlru6YfeB0AAAAAcGahB1YbN3HiRB08eFAPPPCAcnJyNHDgQC1ZssQzsPvu3btltVblmMXFxbr11lv1yy+/KCgoSL169dIbb7yhiRMnttRDaNtcFdKhHSet4p9foqASh1ylTuUXVV+eH9xPpYmX6qy9n7kLlj4sZaR7LhN0WWyyGqd7dsJjPa8q/Zw8WXs6XCblec+cM6G7RY8dlhwu6dPvsrUtu0DTh3fW+EGJCg/0V0GpQx9v3Kv0r3Z6wqsAm3TLoMBTehpCctZXLzQuhe5d4xWuAQAAAADOTBZjjGnpnUDbUlBQoIiICOXn5ys8nCFMa3Rgm3T0sOQ4WmfVPXklKiqrULnDedJ6HXZ/pvisD7zKHPYIZfW9VcmbF8rf4T0L0y/dr1VOl6tqXV9GjlNPfVOu8hM6X9n9rNV6ZAXYpPtHBCklwb/Ox3Oidj+8p+itr1UrP9z9t2r/4/vK7TNVR3pMaPB6T2bTfodmLS/xKrs42U9X9gigBxkAAKgXe36WXPZwVQTHqTSqd0vvDtBksnKLFR7kp9jwQPWO993zO85D2z56YAEtIezYIPquiDqrRvpXyHm0TAF1DNtUEnOzDoVEKmrzK5IkI6tyU+5RYGii8jRN0Ruf8wz0frjvVDl6Xq2ok6zv4q5SUqS/Fv63VJsPVoVnJ4ZXfaNtumVQoHpGNTz4qS28kqR2P/6fJHmWN0aIlXnIqec3lmprbvUw8POsCn2eVXFajwcAAAAA0DQIsICWENTO/VMPYcd+6uXsFCm2o7T0YVkuvFfxPUdJhdlS8ADJNk3a8Io07Aa1HzhZ7euxuuRo6ZKeEdp60KG3Nhfrv9llKix3yW6Vekf7aVxX+yn3WDpZeCVJFlV1Dm2MEGvdPoceWVOi8hOyqwA/q8qOC+W25Do1c1mxHjjFHmUAAAAAgMZHgAW0NSNnSlFdpV6XSQe2VpUPnCxFdpK6nN/gVfaJ8dejF0YqK7dYh4rLVVzmVELkqY13JdUQXkUmeY3NVVP56YRYmYecXuFVl5gQTR+RrCsGJiisckyvTfuUviZLPx8sVrlTemRNiZ6+yEpPLAAAAADwAQRYQFvUZ7z3bVeFdGSnFHFWnYPGn0xdA8rXR1z2MkVXDjgvSaMflAZdK71wnmfgeUlSaJw0Y5l71sSlD0tyh1gFJRXa0+k3Ddrmgm8cnvBqXP94PXP1QNn9qiYvCA/013XndtLVQ8/SzHe+1affZ6vcKS3aWKoFaSGn9kABAAAAAI2GAAtoy6w2yT9YCm6c1ZlAu6yWCgX4n3xA+dpEHMyomi1RcvewGnSdFBojXfWS9PfxknFJFqv7dmiMO9zKSPf0xOqS9aYswZHK73BOvba5q8Clw3nlai+pU1Swnhl3luxlh6Wy6nUDJD3z67O0b98e7Tp0VNm50tZfSpQUTi8sAGgtIg58o/yYIZLFqgq/UPf/QuNUu/3rdCRueLPtR5jdIpvV0mzbAwCgrSPAAtqyBgwWXx/1HVC+Nq7oRB22HFW7La+7B5TP2+3ueXXVS1LyKOmCOdLy/5UuuFdKHillrZT+b4Z3zyxJyVsWSlvqt82BksZXXu1YLOmZk9e3S/pQkirvs6GeDw4A4HMKTLAecExTR8sB3eX/vh53XKNFzsubZdsWSfGhFg2JO70xIwEAgBsBFtCWNWCw+Ppo0IDytek3XBo6Qfq/693BVFGO9PfL3eHVeX+SZJHOu1Na8bj05Tx3jywAAE5BuOWo/tf/ZQWqXJI02/9tSWqWEMtI2ldktO8nhz75ycEstwAAnCZr3VUAoJElj5Su+1CyHpvlz7jcPa9WL5DOn+X+veIxwisAwGkLsZTJZqma2Xa2/9u6xfbPJt9ugJ/3x+zKWW7X7XM0+bbROEL3rqn+WcQ43eUAgGZHDywAza/y0kDXsQ/xFqv7ssHz7pS+fML9W0ZaMZcQCwDQMPWY2bapemIxy23bUTljcm7va3Wk5zWe8vaZ7ypq+5vKLZqi4rjjxuN0ORW+698qSL60UbaflVukxZtzlJVbrJJyp4LsNiVHh+iSs+OUHB3aKNsAgNbGYowxdVcD6q+goEARERHKz89XeHh4S+8OfInLKa16yjuYCo2Tfvuy1Pk86cv57p5YF/7F3RMra1XVpYaSO+jqf4109lXuQXnr4bHV+Xp/W4kk6Z5Le+nqIR3rvM+7G3Zr3uJMSdKFSX669uyAhj9WAECTi935iRJ+freq4GQz29682mtmW0na02OKcrr89pS3/5eVR5V52P3/rKZZbiuVVTg9s9xKUt9oG7Pc+rDK8EqSjKzaO+J/VBLTX0EHv1Pimvtkkfv06UiXy5TX/beqCI5T+O7PFZvxlHLOma2DA2475W3/sL9QL636WdtyCmutExnsr4ggf8mIYAtNqjJI3ZZdqAqnS2FBfhrSqZ1+N6yT+iT43nke56FtHwEWGh1vHKjV1o+ld6dU3Y5MkmYsc882mLXSexbCKf90X2pYdFB66SLvb9PTHpa6nF+vTW456NC4tw5Kcn8zvuSOUTWeXFQqq3DqkmdX6eeDxZKkF8aEMPAuAPig0L1rFP/N3KqCev1POSC9NNrrf8r+QXeoOG5Yg7eflefUrOVHJblnuX3nxtST/n8pr3Bp4v9bq12H3Pd58LxAZrn1QdVCUUkOe4QyhzysXuvnyM9Z4rVsX/IEFcQMUc9v7pXl2JdzOwfO0i99b27wtjfuPqL/b9mPcji9T88C/Kwqq6i7R3rv+HDNOC9ZPWJPe8RSnOHqE6QO7dRO9/+6jwZ0jGy+HasD56FtHwEWGh1vHDipVU97ffvd0G/LNewGaeDkBm3yqvcOKiPbfbki35ADQBthnGr3w3uK2vYP98y2kvt/x1UvucOqar16a57ZFqjVcZedGiNZLDVXKzKBCrWUepWd7oyXx1+O+s3Ow7rljf9WC7FqCrbsNqvuuaSXzunc/pS3jTPbNzsPa97i7Sp31v16C/CzatG1g3VRr9jm3MVacR7a9hFgodHxxoE6nRhiHT9eidVPclVUL5dOKbySpG/3l+vq93NV5nTf7hITounDO2v8oESFV45RsnGv0r/a6el5FWCTnroohDFKAMDHBR38TnEbnpBf2RF3gcVaNbPt6gXucRVXPc3MtmiY2r5gk+o1zpp06iHW8V+2fbsnT1f/ba0nOKhrnDXJHWLNvbIfPbHQYD/sL9ScD773hFf1eb0F+Fn17k2pPtETi/PQto8AC42ONw7Uy4khliQFR7kHc1/2iFSa773sFMOrSsuySnXLZ4c9IVYlu59V5Sd+m2ST7h8RpJQE/1PeHgCg+djzdyppxZ2ymIqqwsqeV18+IS3/n5bbObQ+x1+O+s8/SP/9e9WyBvYcb2iIdeJwB1ct+koZu9zhbEN6kfeOD9f8q/o35FED+vP733ouG2zI621op3Z6/5bhzbqvNeE8tO1jFkIALWPkTPfvyg95Fqs0+iEpPF4650Zp9VNV35SfZnglSRclB+rd30br0ZUF2pBd7ik/MbzqG23TLYMC6XkFAK1EZQ8sT3jFzLY4XXm73QHV0One4VVkkjToOnewddVL3uOsXfWSu3zQtVJGuteMl1kmTktc9RtnbfqIZE9gsGVfvie86hITUmuYIEkBfjY9M3GgtuUU6OeDxdqWXaDvfslTpyiGQkD97DpU7AmvGvp627DriLbuK/DJgd3RthBgAWg5x4dYF9wrJQ6WCrOlDr2kIdOkDa80SnhVaUCsXe9PiNbWgw69tblY/80uU2G5S3ar1DvaT+O62hmwHQBaC+NU+8x31X77W95jYJ04s60knf9nKSnVa2ZbI4uOdL9KeV3HuwOIBnpxU4n+neUOzZjltm0IPbJNnbcslH95vlSUI62YJyWfL+1c5Q6qKoOtq16Skke5L1Vd/r/uzzDJI6uNs2YsVu3qf6eu7XuLrj3Ja+zhT7boxwNFkqTxAxM85W+tr7oc8fhgqzZ2P6umD++s+z/eIkm676PNp/xc4Mx2Kq+3t9bv1qNXnN0cu4czGAEWgJY1cqYU1VXqdZl0YGtV+cDJUmSnes822BB9Yvz16IWRysot1qHichWXOZUQGdjo2wEANJ3QfV8ravubVQUnzkK44tgMhSsek5LOdQcMN6/xzGxrkVH7H99XWWR3FSWOaPD2R/UI1VtZx2as/SZfVwwfUOcsty9s2KzDcvdQ+FXvEIUyC6FvCR+ufZEJx12OaqSsL6WB10o/feEOpopypL9fXjXOmizuXn4rHvcaZ80RFKM9Fz6n4oRURdSx2cre4AF+VoUHVg1fsHVfgefv44Otkxk/KNETKACn6lReb1uzC+qoDZy+hn/dBACNrc94yXrC25HV1iThFQCgbShKHKHcPlOrCvJ2u8cfKjrg7gVTebmgcR3rFXPQvfy4QbZz+0w9pfBKkrq1s6lPtDuA+vlgsf707qZql6VXqhwvpnLQ477RNnr8+qCgg98p8av7vS9HvfAv0mULpN7jJeuxcMm43D2vVi9wj7O2eoE7KD32mnNZ/ZV1yZsqTkit33bt7tdCWYVLBaUOT3nxsYE7Twy2TiY80L/OnjPAyZzq6624rKKO2sDp490NAAAArdKRHhO8Q6ylD0svjT7uEq5jFxsU5bh7Xh03uHZun6k60mPCaW3/1kGBOpY96NPvsjX22ZV6fe1OTwhRUOrQ62t36pJnV3kGOw6wSbcMotevTzFOtd/+lhLX/KVqNsvQOGnqJ+6A6pM7pG/+n+Q6Fi5VBlvHj7N24X2eS1GtLoeSF09WyL619dp8cnTVOFUfb9rn+TskoOZg62QKSh21BqlAfZzq6y0kgIu70PR4lQEAAKDVqgyhore+5i441sOqIrC99qY+osSv7neHEif0vDrd8EqSekbZ9MCIID2ypkTlTndPrPs/3qL7P95y0llumSjEt9R6OWpwe+mjW6VNxy2r5zhr/iUHlbx4kvYPnqmDA25z9yyvxdi+cVq8OUeSlL4mSxOHdpTdz6o+CeH67+48Se5g67pzO9X5WD7euNfzd1rvDpo2PLn+TwTOaOlrsrR0+wFJp/Z66xPPAO5oegRYAAAAaNVODLGMrMoZcrfKIzorZ+gsJa75i2eg98YKryqlJPjr6YusWrSxVFtynZ5yZrltPYoSRyi3eKp3CLrxdSkk2ju8aug4a8aluIwnVRbZTQXJl9a6/S4xoeodF6ZtOYWey1GfuXqgJg1L0htfu4PX44Ot2pRVOJX+1U7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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "Image(filename=\"example-outputs/continuations-comp/plots/pd1-cifar100-wide_resnet-2048_performance.png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hposuite_env",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}