{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# parPE example: steadystate model - minibatch optimization\n", "\n", "This example demonstrates some basic use of the mini-batch optimizer in parPE.\n", "\n", "## Prerequisites\n", "\n", "The prerequisites mention in `parpeExampleSteadystateBasic.ipynb` also apply to this notebook." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import amici\n", "import os\n", "import sys\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import h5py\n", "from importlib import reload\n", "\n", "# set paths\n", "parpe_source_root = os.path.abspath('../../../')\n", "parpe_build_root = os.path.join(parpe_source_root, 'build')\n", "\n", "model_source_dir = f'{parpe_build_root}/examples/parpeamici/steadystate/steadystate_scaled-prefix/src/steadystate_scaled/model_steadystate_scaled'\n", "example_binary_dir = f'{parpe_build_root}/examples/parpeamici/steadystate/'\n", "example_data_dir = f'{parpe_build_root}/examples/parpeamici/steadystate/steadystate_scaled-prefix/src/steadystate_scaled'\n", "optimization_options_py = f'{parpe_source_root}/misc/optimizationOptions.py'\n", "\n", "# MPI launcher and options\n", "mpiexec = \"mpiexec -n 4 --allow-run-as-root --oversubscribe\"\n", "\n", "# load parpe module from source tree\n", "sys.path.insert(0, os.path.join(parpe_source_root, 'python'))\n", "import parpe" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-- Found Git: /usr/bin/git (found version \"2.25.1\") \r\n", "-- Building version parPE-v0.4.3-43-gdc50b-dirty\r\n", "[ 0%] Built target get_version\r\n", "[ 10%] Built target parpecommon\r\n", "\u001B[35m\u001B[1mScanning dependencies of target parpeoptimization\u001B[0m\r\n", "[ 11%] \u001B[32mBuilding CXX object src/parpeoptimization/CMakeFiles/parpeoptimization.dir/optimizationResultWriter.cpp.o\u001B[0m\r\n", "[ 12%] \u001B[32m\u001B[1mLinking CXX static library libparpeoptimization-dbg.a\u001B[0m\r\n", "[ 21%] Built target parpeoptimization\r\n", "[ 25%] Built target parpeloadbalancer\r\n", "\u001B[35m\u001B[1mScanning dependencies of target parpeamici\u001B[0m\r\n", "[ 26%] \u001B[32mBuilding CXX object src/parpeamici/CMakeFiles/parpeamici.dir/optimizationApplication.cpp.o\u001B[0m\r\n", "[ 27%] \u001B[32m\u001B[1mLinking CXX static library libparpeamici-dbg.a\u001B[0m\r\n", "[ 37%] Built target parpeamici\r\n", "[ 39%] Built target parpe\r\n", "[ 45%] Built target unittests_common\r\n", "[ 50%] Built target unittests_loadbalancer\r\n", "[ 51%] \u001B[32m\u001B[1mLinking CXX executable unittests_optimization\u001B[0m\r\n", "[ 58%] Built target unittests_optimization\r\n", "\u001B[34m\u001B[1mSetting up virtual environment...\u001B[0m\r\n", "[ 58%] Built target setup_venv\r\n", "[ 59%] \u001B[34m\u001B[1mCreating test data using hierarchicalOptimizationTest.py\u001B[0m\r\n", "...\r\n", "----------------------------------------------------------------------\r\n", "Ran 3 tests in 0.001s\r\n", "\r\n", "OK\r\n", "[ 59%] Built target prepare_test_hierarchical_optimization\r\n", "[ 60%] \u001B[32m\u001B[1mLinking CXX executable unittests_amici\u001B[0m\r\n", "[ 67%] Built target unittests_amici\r\n", "[ 69%] Built target example_loadbalancer\r\n", "[ 70%] \u001B[34m\u001B[1mPerforming build step for 'steadystate_scaled'\u001B[0m\r\n", "[ 90%] Built target model_steadystate_scaled\r\n", "[ 94%] Built target simulate_model_steadystate_scaled\r\n", "[ 96%] Built target model_steadystate_scaled_swig_compilation\r\n", "[100%] Built target _model_steadystate_scaled\r\n", "[ 72%] \u001B[34m\u001B[1mNo install step for 'steadystate_scaled'\u001B[0m\r\n", "[ 73%] \u001B[34m\u001B[1mNo test step for 'steadystate_scaled'\u001B[0m\r\n", "[ 74%] \u001B[34m\u001B[1mCompleted 'steadystate_scaled'\u001B[0m\r\n", "[ 81%] Built target steadystate_scaled\r\n", "[ 82%] \u001B[32m\u001B[1mLinking CXX executable example_steadystate\u001B[0m\r\n", "[ 84%] Built target example_steadystate\r\n", "[ 86%] \u001B[32m\u001B[1mLinking CXX executable example_steadystate_multi\u001B[0m\r\n", "[ 88%] Built target example_steadystate_multi\r\n", "[ 89%] \u001B[32m\u001B[1mLinking CXX executable example_steadystate_multi_simulator\u001B[0m\r\n", "[ 92%] Built target example_steadystate_multi_simulator\r\n", "[ 93%] \u001B[32m\u001B[1mLinking CXX executable test_steadystate\u001B[0m\r\n", "[100%] Built target test_steadystate\r\n" ] } ], "source": [ "# rebuild example\n", "!cd {parpe_build_root} && make" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# run make test to generated all output files required below\n", "# !cd {parpe_build_root} && make test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mini-batch optimization" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " hierarchicalOptimization 0\r\n", " numStarts 1\r\n", " optimizer 10\r\n", " retryOptimization 0\r\n", " ceres/max_num_iterations 100\r\n", " fmincon/GradObj b'on'\r\n", " fmincon/MaxFunEvals 10000000.0\r\n", " fmincon/MaxIter 100\r\n", " fmincon/TolFun 0\r\n", " fmincon/TolX 1e-08\r\n", " fmincon/algorithm b'interior-point'\r\n", " fmincon/display b'iter'\r\n", " ipopt/acceptable_iter 1\r\n", " ipopt/acceptable_obj_change_tol 1e-05\r\n", " ipopt/acceptable_tol 1e-05\r\n", " ipopt/hessian_approximation b'limited-memory'\r\n", " ipopt/limited_memory_update_type b'bfgs'\r\n", " ipopt/max_iter 20\r\n", " ipopt/tol 1e-09\r\n", " ipopt/watchdog_shortened_iter_trigger 0\r\n", " minibatch/batchSize 2\r\n", " minibatch/endLearningRate 1e-05\r\n", " minibatch/maxEpochs 40\r\n", " minibatch/parameterUpdater b'Vanilla'\r\n", " minibatch/startLearningRate 1e-05\r\n", " toms611/mxfcal 100000000.0\r\n" ] } ], "source": [ "hdf5_file_minibatch = f'{example_data_dir}/example_data_minibatch.h5'\n", "!cp {example_data_dir}/example_data.h5 {hdf5FileMinibatch}\n", "\n", "# Generic options:\n", "# One optimizer run\n", "!{optimization_options_py} {hdf5_file_minibatch} -s numStarts 1\n", "# Hierarchical optimization not yet supported with minibatch (#118)\n", "!{optimization_options_py} {hdf5_file_minibatch} -s hierarchicalOptimization 0\n", "# Do not repeat on failure\n", "!{optimization_options_py} {hdf5_file_minibatch} -s retryOptimization 0\n", "\n", "# Mini-batch options:\n", "# Select mini-batch optimizer\n", "!{optimization_options_py} {hdf5_file_minibatch} -s optimizer 10\n", "# Set number of epochs\n", "!{optimization_options_py} {hdf5_file_minibatch} -s minibatch/maxEpochs 40\n", "# Set batch-size\n", "!{optimization_options_py} {hdf5_file_minibatch} -s minibatch/batchSize 2\n", "# Set parameter updating scheme\n", "!{optimization_options_py} {hdf5_file_minibatch} -s minibatch/parameterUpdater Vanilla\n", "# Set learning rate\n", "!{optimization_options_py} {hdf5_file_minibatch} -s minibatch/startLearningRate 1e-5\n", "!{optimization_options_py} {hdf5_file_minibatch} -s minibatch/endLearningRate 1e-5\n", "\n", "# Print settings\n", "!{optimization_options_py} {hdf5_file_minibatch}" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating starting point backup in /optimizationOptions/randomStartsBackup\r\n", "Selecting starting points [9]\r\n" ] } ], "source": [ "# Optional\n", "!{parpe_source_root}/misc/selectStartingPoints.py {hdf5_file_minibatch} 9 # select starting point" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m[2020-06-24 18:03:20] [INF] [0:140270737766336/dweindl-ThinkPad-L480] Running with 4 MPI processes.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:20] [INF] [0:140270737766336/dweindl-ThinkPad-L480] Reading random initial theta 0 from /optimizationOptions/randomStarts\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:20] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e0b1] iter: 0 cost: 3554.69 time_iter: wall: 0.0476933s cpu: 0.0854863s time_optim: wall: 0.0476936s cpu: 0.0854863s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:20] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e1b1] iter: 1 cost: 2584.42 time_iter: wall: 0.0437777s cpu: 0.0775371s time_optim: wall: 0.0914716s cpu: 0.163023s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:20] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e2b1] iter: 2 cost: 1628.43 time_iter: wall: 0.0415349s cpu: 0.0745043s time_optim: wall: 0.133007s cpu: 0.237528s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:20] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e3b1] iter: 3 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[INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e8b1] iter: 8 cost: 2597.73 time_iter: wall: 0.0499246s cpu: 0.0864627s time_optim: wall: 0.419814s cpu: 0.73874s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:20] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e9b1] iter: 9 cost: 2575.88 time_iter: wall: 0.0454407s cpu: 0.0816869s time_optim: wall: 0.465255s cpu: 0.820427s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:20] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e10b1] iter: 10 cost: 2583.17 time_iter: wall: 0.0545201s cpu: 0.0869733s time_optim: wall: 0.519775s cpu: 0.907401s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:20] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e11b1] iter: 11 cost: 2575.6 time_iter: wall: 0.0554565s cpu: 0.0902942s time_optim: wall: 0.575232s cpu: 0.997695s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:20] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e12b1] iter: 12 cost: 1627.08 time_iter: wall: 0.0570406s cpu: 0.090515s time_optim: wall: 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[o0e31b1] iter: 31 cost: 2594.61 time_iter: wall: 0.0437171s cpu: 0.0783228s time_optim: wall: 1.61248s cpu: 2.7172s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:22] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e32b1] iter: 32 cost: 1624.37 time_iter: wall: 0.0439255s cpu: 0.0783519s time_optim: wall: 1.65641s cpu: 2.79555s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:22] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e33b1] iter: 33 cost: 2572.55 time_iter: wall: 0.0425016s cpu: 0.0756871s time_optim: wall: 1.69891s cpu: 2.87124s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:22] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e34b1] iter: 34 cost: 2594.2 time_iter: wall: 0.0439749s cpu: 0.0782568s time_optim: wall: 1.74288s cpu: 2.94949s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:22] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e35b1] iter: 35 cost: 1623.97 time_iter: wall: 0.0482329s cpu: 0.08319s time_optim: wall: 1.79112s cpu: 3.03268s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:22] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e36b1] iter: 36 cost: 2601.35 time_iter: wall: 0.0563434s cpu: 0.0899884s time_optim: wall: 1.84746s cpu: 3.12267s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:22] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e37b1] iter: 37 cost: 2593.79 time_iter: wall: 0.0568368s cpu: 0.0906086s time_optim: wall: 1.9043s cpu: 3.21328s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:22] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e38b1] iter: 38 cost: 2593.66 time_iter: wall: 0.0575143s cpu: 0.0902695s time_optim: wall: 1.96181s cpu: 3.30355s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:22] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0e39b1] iter: 39 cost: 2579.14 time_iter: wall: 0.0555223s cpu: 0.0883681s time_optim: wall: 2.01733s cpu: 3.39192s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:22] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0] Number of epochs exceeded.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:22] [INF] [0:140270251255552/dweindl-ThinkPad-L480] [o0] Optimizer status 1, final llh: 2.586261e+03, time: wall: 2.059439 cpu: 3.479924.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:24] [INF] [0:140270737766336/dweindl-ThinkPad-L480] Walltime on master: 4.300712s, CPU time of all processes: 17.190522s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:03:24] [INF] [0:140270737766336/dweindl-ThinkPad-L480] Sent termination signal to workers.\u001B[0m\r\n" ] } ], "source": [ "!PARPE_MAX_SIMULATIONS_PER_PACKAGE=1 PARPE_NO_DEBUG=1 \\\n", " {mpiexec} {example_binary_dir}/example_steadystate_multi \\\n", " --mpi -o deleteme-minibatch/ {hdf5_file_minibatch}" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": "
", "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "filename = 'deleteme-minibatch/_rank00000.h5'\n", "with h5py.File(filename, 'r') as f:\n", " trajectory = f['/multistarts/0/iterCostFunCost'][:]\n", "for start in range(trajectory.shape[0]):\n", " plt.plot(trajectory[start])\n", " plt.gca().set_xlabel(\"Function evaluation\")\n", " plt.gca().set_ylabel(\"Cost\")\n", "#trajectory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compare learning rates" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128563488704/dweindl-ThinkPad-L480] Running with 4 MPI processes.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128563488704/dweindl-ThinkPad-L480] Reading random initial theta 0 from /optimizationOptions/randomStarts\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e0b1] iter: 0 cost: 1628.7 time_iter: wall: 0.0651095s cpu: 0.101819s time_optim: wall: 0.0651098s cpu: 0.101819s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e1b1] iter: 1 cost: 1628.57 time_iter: wall: 0.0477085s cpu: 0.0842107s time_optim: wall: 0.112819s cpu: 0.18603s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e2b1] iter: 2 cost: 2598.55 time_iter: wall: 0.0467025s cpu: 0.081893s time_optim: wall: 0.159521s cpu: 0.267923s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e3b1] iter: 3 cost: 2584.15 time_iter: wall: 0.0488849s cpu: 0.0847768s time_optim: wall: 0.208406s cpu: 0.3527s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e4b1] iter: 4 cost: 2598.28 time_iter: wall: 0.0470017s cpu: 0.0824214s time_optim: wall: 0.255408s cpu: 0.435121s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e5b1] iter: 5 cost: 3553.99 time_iter: wall: 0.0471031s cpu: 0.082031s time_optim: wall: 0.302512s cpu: 0.517152s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e6b1] iter: 6 cost: 2598.01 time_iter: wall: 0.0443932s cpu: 0.0794086s time_optim: wall: 0.346905s cpu: 0.596561s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e7b1] iter: 7 cost: 2597.87 time_iter: wall: 0.0585004s cpu: 0.0935554s time_optim: wall: 0.405406s cpu: 0.690116s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e8b1] iter: 8 cost: 3553.57 time_iter: wall: 0.0474411s cpu: 0.082477s time_optim: wall: 0.452847s cpu: 0.772593s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e9b1] iter: 9 cost: 2583.31 time_iter: wall: 0.0491929s cpu: 0.0852675s time_optim: wall: 0.50204s cpu: 0.857861s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e10b1] iter: 10 cost: 3553.3 time_iter: wall: 0.0506385s cpu: 0.0866106s time_optim: wall: 0.552679s cpu: 0.944471s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e11b1] iter: 11 cost: 2583.04 time_iter: wall: 0.0505453s cpu: 0.0842736s time_optim: wall: 0.603224s cpu: 1.02874s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e12b1] iter: 12 cost: 2597.19 time_iter: wall: 0.0515001s cpu: 0.0877196s time_optim: wall: 0.654725s cpu: 1.11646s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:11] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e13b1] iter: 13 cost: 1626.95 time_iter: wall: 0.0516208s cpu: 0.088255s time_optim: wall: 0.706346s cpu: 1.20472s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e14b1] iter: 14 cost: 2575.19 time_iter: wall: 0.0522611s cpu: 0.0872468s time_optim: wall: 0.758607s cpu: 1.29197s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e15b1] iter: 15 cost: 2604.21 time_iter: wall: 0.0539821s cpu: 0.0892329s time_optim: wall: 0.812589s cpu: 1.3812s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e16b1] iter: 16 cost: 2596.65 time_iter: wall: 0.0486306s cpu: 0.0838493s time_optim: wall: 0.86122s cpu: 1.46505s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e17b1] iter: 17 cost: 3552.32 time_iter: wall: 0.0468001s cpu: 0.0833688s time_optim: wall: 0.90802s cpu: 1.54842s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e18b1] iter: 18 cost: 3552.18 time_iter: wall: 0.0461051s cpu: 0.0819807s time_optim: wall: 0.954126s cpu: 1.6304s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e19b1] iter: 19 cost: 3552.04 time_iter: wall: 0.0565551s cpu: 0.0897935s time_optim: wall: 1.01068s cpu: 1.72019s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e20b1] iter: 20 cost: 2596.11 time_iter: wall: 0.0578384s cpu: 0.0915457s time_optim: wall: 1.06852s cpu: 1.81174s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e21b1] iter: 21 cost: 2603.4 time_iter: wall: 0.0575343s cpu: 0.0936316s time_optim: wall: 1.12605s cpu: 1.90537s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e22b1] iter: 22 cost: 1625.73 time_iter: wall: 0.0576452s cpu: 0.0938341s time_optim: wall: 1.1837s cpu: 1.9992s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e23b1] iter: 23 cost: 2595.7 time_iter: wall: 0.0573879s cpu: 0.0921114s time_optim: wall: 1.24109s cpu: 2.09131s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e24b1] iter: 24 cost: 1625.46 time_iter: wall: 0.0561392s cpu: 0.0893061s time_optim: wall: 1.29723s cpu: 2.18062s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e25b1] iter: 25 cost: 2595.43 time_iter: wall: 0.0546376s cpu: 0.0916923s time_optim: wall: 1.35187s cpu: 2.27231s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e26b1] iter: 26 cost: 2602.72 time_iter: wall: 0.0581859s cpu: 0.0912279s time_optim: wall: 1.41005s cpu: 2.36354s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e27b1] iter: 27 cost: 2602.58 time_iter: wall: 0.0470694s cpu: 0.0836855s time_optim: wall: 1.45712s cpu: 2.44723s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e28b1] iter: 28 cost: 2573.25 time_iter: wall: 0.0480904s cpu: 0.0840095s time_optim: wall: 1.50521s cpu: 2.53124s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e29b1] iter: 29 cost: 2602.31 time_iter: wall: 0.0588577s cpu: 0.103163s time_optim: wall: 1.56407s cpu: 2.6344s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e30b1] iter: 30 cost: 2594.75 time_iter: wall: 0.058872s cpu: 0.0957537s time_optim: wall: 1.62294s cpu: 2.73015s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:12] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e31b1] iter: 31 cost: 2594.61 time_iter: wall: 0.057997s cpu: 0.0920542s time_optim: wall: 1.68094s cpu: 2.82221s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:13] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e32b1] iter: 32 cost: 2572.7 time_iter: wall: 0.0587806s cpu: 0.0933812s time_optim: wall: 1.73972s cpu: 2.91559s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:13] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e33b1] iter: 33 cost: 1624.24 time_iter: wall: 0.0493689s cpu: 0.0846965s time_optim: wall: 1.78909s cpu: 3.00028s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:13] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e34b1] iter: 34 cost: 1624.11 time_iter: wall: 0.0479775s cpu: 0.0844509s time_optim: wall: 1.83707s cpu: 3.08474s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:13] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e35b1] iter: 35 cost: 2594.07 time_iter: wall: 0.0559033s cpu: 0.0929584s time_optim: wall: 1.89297s cpu: 3.17769s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:13] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e36b1] iter: 36 cost: 2579.57 time_iter: wall: 0.0611445s cpu: 0.108288s time_optim: wall: 1.95411s cpu: 3.28598s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:13] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e37b1] iter: 37 cost: 2593.8 time_iter: wall: 0.0492099s cpu: 0.0865385s time_optim: wall: 2.00332s cpu: 3.37252s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:13] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e38b1] iter: 38 cost: 1623.57 time_iter: wall: 0.0489464s cpu: 0.0848573s time_optim: wall: 2.05227s cpu: 3.45738s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:13] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0e39b1] iter: 39 cost: 2600.95 time_iter: wall: 0.0451686s cpu: 0.079828s time_optim: wall: 2.09744s cpu: 3.53721s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:13] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0] Number of epochs exceeded.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:13] [INF] [0:140128139867904/dweindl-ThinkPad-L480] [o0] Optimizer status 1, final llh: 2.586273e+03, time: wall: 2.148572 cpu: 3.630505.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:15] [INF] [0:140128563488704/dweindl-ThinkPad-L480] Walltime on master: 4.318119s, CPU time of all processes: 16.971442s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:15] [INF] [0:140128563488704/dweindl-ThinkPad-L480] Sent termination signal to workers.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:18] [INF] [0:140040514709440/dweindl-ThinkPad-L480] Running with 4 MPI processes.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:18] [INF] [0:140040514709440/dweindl-ThinkPad-L480] Reading random initial theta 0 from /optimizationOptions/randomStarts\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:18] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e0b1] iter: 0 cost: 1628.7 time_iter: wall: 0.0620374s cpu: 0.0965123s time_optim: wall: 0.0620377s cpu: 0.0965123s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:18] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e1b1] iter: 1 cost: 2606.12 time_iter: wall: 0.0619933s cpu: 0.0968211s time_optim: wall: 0.124031s cpu: 0.193333s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:18] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e2b1] iter: 2 cost: 1628.43 time_iter: wall: 0.062497s cpu: 0.0970936s time_optim: wall: 0.186528s cpu: 0.290427s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:18] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e3b1] iter: 3 cost: 2584.15 time_iter: wall: 0.0628173s cpu: 0.0975342s time_optim: wall: 0.249346s cpu: 0.387961s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:18] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e4b1] iter: 4 cost: 2576.57 time_iter: wall: 0.0630493s cpu: 0.102265s time_optim: wall: 0.312428s cpu: 0.490226s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:18] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e5b1] iter: 5 cost: 2598.14 time_iter: wall: 0.0627134s cpu: 0.10023s time_optim: wall: 0.375142s cpu: 0.590456s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:18] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e6b1] iter: 6 cost: 2605.44 time_iter: wall: 0.0462647s cpu: 0.0822424s time_optim: wall: 0.421407s cpu: 0.672698s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:18] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e7b1] iter: 7 cost: 2576.15 time_iter: wall: 0.0446779s cpu: 0.0798674s time_optim: wall: 0.466085s cpu: 0.752566s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:18] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e8b1] iter: 8 cost: 2605.16 time_iter: wall: 0.0459914s cpu: 0.0820788s time_optim: wall: 0.512076s cpu: 0.834645s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:18] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e9b1] iter: 9 cost: 2605.03 time_iter: wall: 0.0460034s cpu: 0.0822501s time_optim: wall: 0.55808s cpu: 0.916895s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:18] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e10b1] iter: 10 cost: 2583.17 time_iter: wall: 0.0485059s cpu: 0.086197s time_optim: wall: 0.606586s cpu: 1.00309s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:18] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e11b1] iter: 11 cost: 2583.03 time_iter: wall: 0.0455353s cpu: 0.0812845s time_optim: wall: 0.652122s cpu: 1.08438s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e12b1] iter: 12 cost: 2575.46 time_iter: wall: 0.0487622s cpu: 0.0848217s time_optim: wall: 0.700884s cpu: 1.1692s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e13b1] iter: 13 cost: 2597.05 time_iter: wall: 0.0459065s cpu: 0.0815944s time_optim: wall: 0.746791s cpu: 1.25079s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e14b1] iter: 14 cost: 2575.18 time_iter: wall: 0.0538032s cpu: 0.0896016s time_optim: wall: 0.800594s cpu: 1.34039s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e15b1] iter: 15 cost: 3552.59 time_iter: wall: 0.0570818s cpu: 0.090847s time_optim: wall: 0.857676s cpu: 1.43124s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e16b1] iter: 16 cost: 2604.07 time_iter: wall: 0.0582828s cpu: 0.0929545s time_optim: wall: 0.915959s cpu: 1.5242s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e17b1] iter: 17 cost: 2603.93 time_iter: wall: 0.0572582s cpu: 0.0936604s time_optim: wall: 0.973218s cpu: 1.61786s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e18b1] iter: 18 cost: 3552.17 time_iter: wall: 0.0604522s cpu: 0.0956316s time_optim: wall: 1.03367s cpu: 1.71349s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e19b1] iter: 19 cost: 2574.49 time_iter: wall: 0.0533441s cpu: 0.0905578s time_optim: wall: 1.08701s cpu: 1.80405s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e20b1] iter: 20 cost: 1625.99 time_iter: wall: 0.0601746s cpu: 0.0969461s time_optim: wall: 1.14719s cpu: 1.90099s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e21b1] iter: 21 cost: 2574.21 time_iter: wall: 0.0592998s cpu: 0.105119s time_optim: wall: 1.20649s cpu: 2.00611s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e22b1] iter: 22 cost: 1625.72 time_iter: wall: 0.0596036s cpu: 0.0961369s time_optim: wall: 1.26609s cpu: 2.10225s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e23b1] iter: 23 cost: 2573.94 time_iter: wall: 0.0591221s cpu: 0.0945617s time_optim: wall: 1.32522s cpu: 2.19681s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e24b1] iter: 24 cost: 2595.56 time_iter: wall: 0.0595344s cpu: 0.0985397s time_optim: wall: 1.38475s cpu: 2.29535s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e25b1] iter: 25 cost: 2581.08 time_iter: wall: 0.0579072s cpu: 0.0970798s time_optim: wall: 1.44266s cpu: 2.39243s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e26b1] iter: 26 cost: 2602.71 time_iter: wall: 0.0517033s cpu: 0.0917851s time_optim: wall: 1.49436s cpu: 2.48421s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e27b1] iter: 27 cost: 2573.38 time_iter: wall: 0.0583215s cpu: 0.102639s time_optim: wall: 1.55268s cpu: 2.58685s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:19] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e28b1] iter: 28 cost: 3550.77 time_iter: wall: 0.0573798s cpu: 0.0999289s time_optim: wall: 1.61006s cpu: 2.68678s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:20] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e29b1] iter: 29 cost: 1624.78 time_iter: wall: 0.055416s cpu: 0.0946081s time_optim: wall: 1.66548s cpu: 2.78139s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:20] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e30b1] iter: 30 cost: 2594.74 time_iter: wall: 0.0597373s cpu: 0.0974391s time_optim: wall: 1.72522s cpu: 2.87883s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:20] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e31b1] iter: 31 cost: 2602.03 time_iter: wall: 0.059422s cpu: 0.0976637s time_optim: wall: 1.78464s cpu: 2.97649s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:20] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e32b1] iter: 32 cost: 2594.47 time_iter: wall: 0.0603956s cpu: 0.096248s time_optim: wall: 1.84504s cpu: 3.07274s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:20] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e33b1] iter: 33 cost: 2579.97 time_iter: wall: 0.059686s cpu: 0.0955575s time_optim: wall: 1.90472s cpu: 3.1683s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:20] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e34b1] iter: 34 cost: 3549.94 time_iter: wall: 0.059981s cpu: 0.0954454s time_optim: wall: 1.9647s cpu: 3.26374s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:20] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e35b1] iter: 35 cost: 1623.97 time_iter: wall: 0.0601407s cpu: 0.0941339s time_optim: wall: 2.02484s cpu: 3.35788s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:20] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e36b1] iter: 36 cost: 2572.14 time_iter: wall: 0.0590763s cpu: 0.0937153s time_optim: wall: 2.08392s cpu: 3.45159s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:20] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e37b1] iter: 37 cost: 3549.52 time_iter: wall: 0.0544017s cpu: 0.0886217s time_optim: wall: 2.13832s cpu: 3.54021s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:20] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e38b1] iter: 38 cost: 2571.86 time_iter: wall: 0.0442645s cpu: 0.079001s time_optim: wall: 2.18259s cpu: 3.61922s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:20] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0e39b1] iter: 39 cost: 2593.52 time_iter: wall: 0.0511828s cpu: 0.0884544s time_optim: wall: 2.23377s cpu: 3.70767s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:20] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0] Number of epochs exceeded.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:20] [INF] [0:140040025929472/dweindl-ThinkPad-L480] [o0] Optimizer status 1, final llh: 2.586263e+03, time: wall: 2.275734 cpu: 3.796501.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:22] [INF] [0:140040514709440/dweindl-ThinkPad-L480] Walltime on master: 4.291548s, CPU time of all processes: 17.236513s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:22] [INF] [0:140040514709440/dweindl-ThinkPad-L480] Sent termination signal to workers.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:25] [INF] [0:139861087696832/dweindl-ThinkPad-L480] Running with 4 MPI processes.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:25] [INF] [0:139861087696832/dweindl-ThinkPad-L480] Reading random initial theta 0 from /optimizationOptions/randomStarts\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:25] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e0b1] iter: 0 cost: 2598.82 time_iter: wall: 0.0622845s cpu: 0.0999832s time_optim: wall: 0.0622848s cpu: 0.0999832s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:25] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e1b1] iter: 1 cost: 2606.12 time_iter: wall: 0.0630269s cpu: 0.102431s time_optim: wall: 0.125312s cpu: 0.202415s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:25] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e2b1] iter: 2 cost: 2598.55 time_iter: wall: 0.0628939s cpu: 0.099197s time_optim: wall: 0.188206s cpu: 0.301612s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:25] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e3b1] iter: 3 cost: 2598.41 time_iter: wall: 0.0632113s cpu: 0.0997596s time_optim: wall: 0.251418s cpu: 0.401371s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:25] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e4b1] iter: 4 cost: 2598.27 time_iter: wall: 0.0630509s cpu: 0.100739s time_optim: wall: 0.314469s cpu: 0.50211s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:25] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e5b1] iter: 5 cost: 2583.86 time_iter: wall: 0.0630902s cpu: 0.0993292s time_optim: wall: 0.37756s cpu: 0.601439s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:25] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e6b1] iter: 6 cost: 2583.72 time_iter: wall: 0.0641154s cpu: 0.106232s time_optim: wall: 0.441675s cpu: 0.707671s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:25] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e7b1] iter: 7 cost: 2597.86 time_iter: wall: 0.063322s cpu: 0.104737s time_optim: wall: 0.504998s cpu: 0.812409s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:25] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e8b1] iter: 8 cost: 1627.61 time_iter: wall: 0.0602295s cpu: 0.10042s time_optim: wall: 0.565227s cpu: 0.912828s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:26] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e9b1] iter: 9 cost: 1627.48 time_iter: wall: 0.0614533s cpu: 0.100033s time_optim: wall: 0.626681s cpu: 1.01286s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:26] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e10b1] iter: 10 cost: 1627.35 time_iter: wall: 0.0613093s cpu: 0.0965668s time_optim: wall: 0.68799s cpu: 1.10943s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:26] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e11b1] iter: 11 cost: 2597.32 time_iter: wall: 0.0610382s cpu: 0.100296s time_optim: wall: 0.749029s cpu: 1.20972s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:26] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e12b1] iter: 12 cost: 3553.01 time_iter: wall: 0.0620061s cpu: 0.0983909s time_optim: wall: 0.811035s cpu: 1.30811s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:26] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e13b1] iter: 13 cost: 2597.05 time_iter: wall: 0.0660115s cpu: 0.118999s time_optim: wall: 0.877047s cpu: 1.42711s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:26] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e14b1] iter: 14 cost: 2582.61 time_iter: wall: 0.0636964s cpu: 0.111761s time_optim: wall: 0.940744s cpu: 1.53888s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:26] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e15b1] iter: 15 cost: 3552.59 time_iter: wall: 0.0719714s cpu: 0.111735s time_optim: wall: 1.01272s cpu: 1.65061s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:26] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e16b1] iter: 16 cost: 2582.34 time_iter: wall: 0.0763708s cpu: 0.11809s time_optim: wall: 1.08909s cpu: 1.7687s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:26] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e17b1] iter: 17 cost: 2574.77 time_iter: wall: 0.0676391s cpu: 0.114936s time_optim: wall: 1.15673s cpu: 1.88364s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:26] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e18b1] iter: 18 cost: 1626.27 time_iter: wall: 0.0677754s cpu: 0.120742s time_optim: wall: 1.2245s cpu: 2.00438s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:26] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e19b1] iter: 19 cost: 2596.24 time_iter: wall: 0.0628398s cpu: 0.101457s time_optim: wall: 1.28734s cpu: 2.10583s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:26] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e20b1] iter: 20 cost: 3551.9 time_iter: wall: 0.0641185s cpu: 0.111732s time_optim: wall: 1.35146s cpu: 2.21757s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:26] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e21b1] iter: 21 cost: 2574.22 time_iter: wall: 0.0664898s cpu: 0.12155s time_optim: wall: 1.41795s cpu: 2.33912s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:26] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e22b1] iter: 22 cost: 3551.62 time_iter: wall: 0.0676231s cpu: 0.121595s time_optim: wall: 1.48557s cpu: 2.46071s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:26] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e23b1] iter: 23 cost: 2573.94 time_iter: wall: 0.0675655s cpu: 0.121896s time_optim: wall: 1.55314s cpu: 2.58261s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:27] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e24b1] iter: 24 cost: 2595.56 time_iter: wall: 0.0685067s cpu: 0.122986s time_optim: wall: 1.62165s cpu: 2.70559s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:27] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e25b1] iter: 25 cost: 1625.32 time_iter: wall: 0.0680839s cpu: 0.123413s time_optim: wall: 1.68973s cpu: 2.82901s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:27] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e26b1] iter: 26 cost: 3551.06 time_iter: wall: 0.0672257s cpu: 0.122315s time_optim: wall: 1.75696s cpu: 2.95132s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:27] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e27b1] iter: 27 cost: 3550.93 time_iter: wall: 0.066949s cpu: 0.121663s time_optim: wall: 1.82391s cpu: 3.07299s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:27] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e28b1] iter: 28 cost: 2580.68 time_iter: wall: 0.0670295s cpu: 0.122009s time_optim: wall: 1.89094s cpu: 3.195s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:27] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e29b1] iter: 29 cost: 2602.31 time_iter: wall: 0.0672325s cpu: 0.122463s time_optim: wall: 1.95817s cpu: 3.31746s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:27] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e30b1] iter: 30 cost: 2594.75 time_iter: wall: 0.0673297s cpu: 0.122729s time_optim: wall: 2.0255s cpu: 3.44019s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:27] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e31b1] iter: 31 cost: 3550.37 time_iter: wall: 0.0686383s cpu: 0.125382s time_optim: wall: 2.09414s cpu: 3.56557s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:27] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e32b1] iter: 32 cost: 2601.9 time_iter: wall: 0.0725371s cpu: 0.127239s time_optim: wall: 2.16667s cpu: 3.69281s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:27] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e33b1] iter: 33 cost: 2594.34 time_iter: wall: 0.0698901s cpu: 0.127167s time_optim: wall: 2.23656s cpu: 3.81997s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:27] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e34b1] iter: 34 cost: 2579.84 time_iter: wall: 0.0696602s cpu: 0.127001s time_optim: wall: 2.30623s cpu: 3.94698s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:27] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e35b1] iter: 35 cost: 3549.81 time_iter: wall: 0.0690396s cpu: 0.125777s time_optim: wall: 2.37527s cpu: 4.07275s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:27] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e36b1] iter: 36 cost: 2572.15 time_iter: wall: 0.0686984s cpu: 0.125473s time_optim: wall: 2.44396s cpu: 4.19823s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:27] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e37b1] iter: 37 cost: 2572.01 time_iter: wall: 0.0689723s cpu: 0.126265s time_optim: wall: 2.51294s cpu: 4.32449s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:27] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e38b1] iter: 38 cost: 3549.39 time_iter: wall: 0.0690215s cpu: 0.126387s time_optim: wall: 2.58196s cpu: 4.45088s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:28] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0e39b1] iter: 39 cost: 2593.53 time_iter: wall: 0.0673973s cpu: 0.122811s time_optim: wall: 2.64936s cpu: 4.57369s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:28] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0] Number of epochs exceeded.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:28] [INF] [0:139860668487424/dweindl-ThinkPad-L480] [o0] Optimizer status 1, final llh: 2.586273e+03, time: wall: 2.705125 cpu: 4.687131.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:29] [INF] [0:139861087696832/dweindl-ThinkPad-L480] Walltime on master: 4.296504s, CPU time of all processes: 17.140153s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:29] [INF] [0:139861087696832/dweindl-ThinkPad-L480] Sent termination signal to workers.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:32] [INF] [0:139846431197120/dweindl-ThinkPad-L480] Running with 4 MPI processes.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:32] [INF] [0:139846431197120/dweindl-ThinkPad-L480] Reading random initial theta 0 from /optimizationOptions/randomStarts\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:32] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e0b1] iter: 0 cost: 2606.26 time_iter: wall: 0.0724632s cpu: 0.128046s time_optim: wall: 0.0724636s cpu: 0.128046s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:32] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e1b1] iter: 1 cost: 2576.99 time_iter: wall: 0.0686715s cpu: 0.119099s time_optim: wall: 0.141135s cpu: 0.247144s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:32] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e2b1] iter: 2 cost: 2584.28 time_iter: wall: 0.0659187s cpu: 0.107962s time_optim: wall: 0.207054s cpu: 0.355107s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:32] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e3b1] iter: 3 cost: 2605.84 time_iter: wall: 0.0734749s cpu: 0.122744s time_optim: wall: 0.280529s cpu: 0.477851s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:32] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e4b1] iter: 4 cost: 2584 time_iter: wall: 0.0733659s cpu: 0.129842s time_optim: wall: 0.353896s cpu: 0.607693s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:32] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e5b1] iter: 5 cost: 2576.43 time_iter: wall: 0.0822124s cpu: 0.138493s time_optim: wall: 0.436108s cpu: 0.746185s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:32] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e6b1] iter: 6 cost: 1627.88 time_iter: wall: 0.0684215s cpu: 0.117872s time_optim: wall: 0.50453s cpu: 0.864057s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:32] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e7b1] iter: 7 cost: 3553.71 time_iter: wall: 0.0669413s cpu: 0.113626s time_optim: wall: 0.571471s cpu: 0.977683s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:33] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e8b1] iter: 8 cost: 2576.02 time_iter: wall: 0.0643325s cpu: 0.112588s time_optim: wall: 0.635804s cpu: 1.09027s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:33] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e9b1] iter: 9 cost: 1627.48 time_iter: wall: 0.0634549s cpu: 0.10862s time_optim: wall: 0.699259s cpu: 1.19889s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:33] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e10b1] iter: 10 cost: 2604.89 time_iter: wall: 0.0794393s cpu: 0.122471s time_optim: wall: 0.778699s cpu: 1.32136s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:33] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e11b1] iter: 11 cost: 2604.75 time_iter: wall: 0.078345s cpu: 0.131707s time_optim: wall: 0.857044s cpu: 1.45307s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:33] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e12b1] iter: 12 cost: 3553.01 time_iter: wall: 0.0711897s cpu: 0.118781s time_optim: wall: 0.928234s cpu: 1.57185s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:33] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e13b1] iter: 13 cost: 2582.75 time_iter: wall: 0.0768808s cpu: 0.125589s time_optim: wall: 1.00512s cpu: 1.69744s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:33] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e14b1] iter: 14 cost: 2604.35 time_iter: wall: 0.0849613s cpu: 0.127864s time_optim: wall: 1.09008s cpu: 1.8253s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:33] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e15b1] iter: 15 cost: 2582.47 time_iter: wall: 0.0600042s cpu: 0.106648s time_optim: wall: 1.15008s cpu: 1.93195s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:33] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e16b1] iter: 16 cost: 2604.07 time_iter: wall: 0.0570566s cpu: 0.101367s time_optim: wall: 1.20714s cpu: 2.03332s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:33] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e17b1] iter: 17 cost: 2582.19 time_iter: wall: 0.0716678s cpu: 0.116911s time_optim: wall: 1.27881s cpu: 2.15023s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:33] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e18b1] iter: 18 cost: 2582.05 time_iter: wall: 0.0645508s cpu: 0.113642s time_optim: wall: 1.34336s cpu: 2.26387s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:33] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e19b1] iter: 19 cost: 1626.13 time_iter: wall: 0.0627529s cpu: 0.108511s time_optim: wall: 1.40611s cpu: 2.37238s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:33] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e20b1] iter: 20 cost: 2581.78 time_iter: wall: 0.064229s cpu: 0.105655s time_optim: wall: 1.47034s cpu: 2.47804s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:33] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e21b1] iter: 21 cost: 2574.21 time_iter: wall: 0.0626647s cpu: 0.104949s time_optim: wall: 1.533s cpu: 2.58298s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e22b1] iter: 22 cost: 2581.5 time_iter: wall: 0.0644101s cpu: 0.115989s time_optim: wall: 1.59742s cpu: 2.69897s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e23b1] iter: 23 cost: 2595.69 time_iter: wall: 0.0647155s cpu: 0.118407s time_optim: wall: 1.66213s cpu: 2.81738s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e24b1] iter: 24 cost: 2595.55 time_iter: wall: 0.0649485s cpu: 0.119045s time_optim: wall: 1.72708s cpu: 2.93643s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e25b1] iter: 25 cost: 2573.65 time_iter: wall: 0.066019s cpu: 0.120768s time_optim: wall: 1.7931s cpu: 3.05719s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e26b1] iter: 26 cost: 2580.94 time_iter: wall: 0.0654555s cpu: 0.114922s time_optim: wall: 1.85855s cpu: 3.17212s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e27b1] iter: 27 cost: 3550.91 time_iter: wall: 0.0661213s cpu: 0.108651s time_optim: wall: 1.92468s cpu: 3.28077s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e28b1] iter: 28 cost: 2595.01 time_iter: wall: 0.0608098s cpu: 0.101498s time_optim: wall: 1.98549s cpu: 3.38226s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e29b1] iter: 29 cost: 2580.52 time_iter: wall: 0.0558579s cpu: 0.100219s time_optim: wall: 2.04134s cpu: 3.48248s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e30b1] iter: 30 cost: 2572.96 time_iter: wall: 0.0576749s cpu: 0.0964017s time_optim: wall: 2.09902s cpu: 3.57888s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e31b1] iter: 31 cost: 2602.02 time_iter: wall: 0.0658728s cpu: 0.116765s time_optim: wall: 2.16489s cpu: 3.69565s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e32b1] iter: 32 cost: 3550.21 time_iter: wall: 0.0657637s cpu: 0.110906s time_optim: wall: 2.23066s cpu: 3.80656s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e33b1] iter: 33 cost: 1624.23 time_iter: wall: 0.0642731s cpu: 0.102772s time_optim: wall: 2.29493s cpu: 3.90933s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e34b1] iter: 34 cost: 3549.94 time_iter: wall: 0.064435s cpu: 0.105091s time_optim: wall: 2.35937s cpu: 4.01442s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e35b1] iter: 35 cost: 2594.06 time_iter: wall: 0.0647286s cpu: 0.110586s time_optim: wall: 2.42409s cpu: 4.125s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e36b1] iter: 36 cost: 3549.66 time_iter: wall: 0.0652132s cpu: 0.113212s time_optim: wall: 2.48931s cpu: 4.23822s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:34] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e37b1] iter: 37 cost: 2579.42 time_iter: wall: 0.0674677s cpu: 0.114828s time_optim: wall: 2.55678s cpu: 4.35304s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:35] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e38b1] iter: 38 cost: 1623.56 time_iter: wall: 0.0658553s cpu: 0.114328s time_optim: wall: 2.62263s cpu: 4.46737s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:35] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0e39b1] iter: 39 cost: 1623.43 time_iter: wall: 0.0654116s cpu: 0.112918s time_optim: wall: 2.68804s cpu: 4.58029s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:35] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0] Number of epochs exceeded.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:35] [INF] [0:139846005810944/dweindl-ThinkPad-L480] [o0] Optimizer status 1, final llh: 2.586267e+03, time: wall: 2.752903 cpu: 4.703666.\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:36] [INF] [0:139846431197120/dweindl-ThinkPad-L480] Walltime on master: 4.303210s, CPU time of all processes: 16.887346s\u001B[0m\r\n", "\u001B[32m[2020-06-24 18:04:36] [INF] [0:139846431197120/dweindl-ThinkPad-L480] Sent termination signal to workers.\u001B[0m\r\n" ] } ], "source": [ "learningRates = np.logspace(-2, -5, 4)\n", "for i, learningRate in enumerate(learningRates):\n", " curInputFile = \"example-data-minibatch-rate-%d.h5\" % i\n", " outprefix = \"deleteme-minibatch-rate-%d/\" % i\n", " !cp {hdf5FileMinibatch} {curInputFile}\n", " !{optimizationOptionsPy} {curInputFile} -s minibatch/startLearningRate learningRate\n", " !{optimizationOptionsPy} {curInputFile} -s minibatch/endLearningRate 1e-5\n", " !PARPE_MAX_SIMULATIONS_PER_PACKAGE=1 PARPE_NO_DEBUG=1 \\\n", " {mpiexec} {example_binary_dir}/example_steadystate_multi \\\n", " --mpi -o {outprefix} {hdf5FileMinibatch}" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": "
", "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(nrows=len(learningRates) + 1, figsize=(10, 20))\n", "for i, learningRate in enumerate(learningRates):\n", " filename = \"deleteme-minibatch-rate-%d/_rank00000.h5\" % i\n", " with h5py.File(filename, 'r') as f:\n", " trajectory = f['/multistarts/0/iterCostFunCost'][:]\n", " for start in range(trajectory.shape[0]):\n", " # Individual plot + overlay\n", " for ax in (axs[i], axs[-1]):\n", " ax.plot(trajectory[start], label=\"r%f-s%d\"%(learningRate, start), color=\"C%d\"%i)\n", " ax.set_xlabel(\"Function evaluation\")\n", " ax.set_ylabel(\"Cost\")\n", " ax.set_title(\"Comparison of learning rates\")\n", " ax.legend()\n", "plt.subplots_adjust(hspace=0.5)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }