From 31e0e31b0e1b2ec801ede6642f209b4403f6609f Mon Sep 17 00:00:00 2001 From: Craig Michoski Date: Wed, 21 Mar 2018 11:32:11 -0500 Subject: [PATCH 1/3] Made some changes for running on Maverick -- cem --- data/signals.py | 7 +- examples/conf.yaml | 34 +- examples/conf.yaml_old | 133 ++++++++ examples/maverick_script | 29 ++ examples/mpi_learn.py | 3 + examples/notebooks/FRNN_scaling.ipynb | 450 ++++++++++++++------------ examples/performance_analysis.py | 5 +- examples/slurm.cmd | 20 +- examples/tune_hyperparams.py | 24 +- plasma/conf_parser.py | 39 ++- plasma/models/builder.py | 2 +- plasma/models/loader.py | 20 +- plasma/models/mpi_runner.py | 25 +- plasma/models/targets.py | 18 +- plasma/preprocessor/normalize.py | 7 +- plasma/preprocessor/preprocess.py | 13 +- plasma/primitives/data.py | 3 +- plasma/utils/batch_jobs.py | 2 +- plasma/utils/performance.py | 153 ++++----- 19 files changed, 633 insertions(+), 354 deletions(-) create mode 100644 examples/conf.yaml_old create mode 100644 examples/maverick_script diff --git a/data/signals.py b/data/signals.py index 68fde894..e6083dc9 100644 --- a/data/signals.py +++ b/data/signals.py @@ -240,10 +240,15 @@ def fetch_nstx_data(signal_path,shot_num,c): fully_defined_signals = {sig_name: sig for (sig_name, sig) in all_signals_restricted.items() if sig.is_defined_on_machines(all_machines)} fully_defined_signals_0D = {sig_name: sig for (sig_name, sig) in all_signals_restricted.items() if ( sig.is_defined_on_machines(all_machines) and sig.num_channels == 1) } +fully_defined_signals_1D = {sig_name: sig for (sig_name, sig) in all_signals_restricted.items() if ( sig.is_defined_on_machines(all_machines) and sig.num_channels > 1) } + d3d_signals = {sig_name: sig for (sig_name, sig) in all_signals_restricted.items() if sig.is_defined_on_machine(d3d)} +d3d_signals_0D = {sig_name: sig for (sig_name, sig) in all_signals_restricted.items() if (sig.is_defined_on_machine(d3d) and sig.num_channels == 1)} +d3d_signals_1D = {sig_name: sig for (sig_name, sig) in all_signals_restricted.items() if (sig.is_defined_on_machine(d3d) and sig.num_channels > 1)} + jet_signals = {sig_name: sig for (sig_name, sig) in all_signals_restricted.items() if sig.is_defined_on_machine(jet)} jet_signals_0D = {sig_name: sig for (sig_name, sig) in all_signals_restricted.items() if (sig.is_defined_on_machine(jet) and sig.num_channels == 1)} - +jet_signals_1D = {sig_name: sig for (sig_name, sig) in all_signals_restricted.items() if (sig.is_defined_on_machine(jet) and sig.num_channels > 1)} #['pcechpwrf'] #Total ECH Power Not always on! ### 0D EFIT signals ### diff --git a/examples/conf.yaml b/examples/conf.yaml index e93f0b6a..1f68139f 100644 --- a/examples/conf.yaml +++ b/examples/conf.yaml @@ -2,23 +2,24 @@ #will do stuff in fs_path / [username] / signal_data | shot_lists | processed shots, etc. -fs_path: '/tigress' -target: 'maxhinge' #'maxhinge' #'maxhinge' #'binary' #'hinge' -num_gpus: 4 +fs_path: '/work/00004/michoski/maverick' +target: 'hinge' #'maxhinge' #'maxhinge' #'binary' #'hinge' +num_gpus: 1 #40 paths: - signal_prepath: '/signal_data/' #/signal_data/jet/ - shot_list_dir: '/shot_lists/' + signal_prepath: '/../../../05447/merlo/maverick/plasma_python/signal_data_new/' #/signal_data/jet/ + shot_list_dir: '/shot_lists/d3d/' tensorboard_save_path: '/Graph/' - data: jet_data #'d3d_to_jet_data' #'d3d_to_jet_data' # 'jet_to_d3d_data' #jet_data - specific_signals: [] #['q95','li','ip','betan','energy','lm','pradcore','pradedge','pradtot','pin','torquein','tmamp1','tmamp2','tmfreq1','tmfreq2','pechin','energydt','ipdirect','etemp_profile','edens_profile'] #if left empty will use all valid signals defined on a machine. Only use if need a custom set + data: 'd3d_data_all' #'d3d_to_jet_data' #'d3d_to_jet_data' # 'jet_to_d3d_data' #jet_data + specific_signals: ['q95'] #['q95','li','ip','betan','energy','lm','pradcore','pradedge','pradtot','pin','torquein','tmamp1','tmamp2','tmfreq1','tmfreq2','pechin','energydt','ipdirect','etemp_profile','edens_profile'] #if left empty will use all valid signals defined on a machine. Only use if need a custom set executable: "mpi_learn.py" shallow_executable: "learn.py" data: bleed_in: 0 #how many shots from the test sit to use in training? + bleed_in_repeat_fac: 1 #how many times to repeat shots in training and validation? bleed_in_remove_from_test: True - bleed_in_equalize_sets: True + bleed_in_equalize_sets: False signal_to_augment: None #'plasma current' #or None augmentation_mode: 'none' augment_during_training: False @@ -31,7 +32,7 @@ data: plotting: False #train/validate split #how many shots to use - use_shots: 200000 #1000 #200000 + use_shots: 20 #1000 #200000 #1000 #200000 positive_example_penalty: 1.0 #by what factor to upweight positive examples? #normalization timescale dt: 0.001 @@ -53,12 +54,13 @@ data: floatx: 'float32' model: + loss_scale_factor: 1.0 use_bidirectional: false use_batch_norm: false shallow: False shallow_model: num_samples: 1000000 #1000000 #the number of samples to use for training - type: "mlp" #"xgboost" #"xgboost" #"random_forest" "xgboost" + type: "xgboost" #"xgboost" #"xgboost" #"random_forest" "xgboost" n_estimators: 100 #for random forest max_depth: 3 #for random forest and xgboost (def = 3) C: 1.0 #for svm @@ -92,8 +94,8 @@ model: #have not found a difference yet optimizer: 'adam' clipnorm: 10.0 - regularization: 0.0 - dense_regularization: 0.01 + regularization: 0.001 + dense_regularization: 0.001 #1e-4 is too high, 5e-7 is too low. 5e-5 seems best at 256 batch size, full dataset and ~10 epochs, and lr decay of 0.90. 1e-4 also works well if we decay a lot (i.e ~0.7 or more) lr: 0.00002 #0.00001 #0.0005 #for adam plots 0.0000001 #0.00005 #0.00005 #0.00005 lr_decay: 0.97 #0.98 #0.9 @@ -109,17 +111,17 @@ training: shuffle_training: True train_frac: 0.75 validation_frac: 0.33 - batch_size: 128 #256 + batch_size: 16 #128 #256 #THIS WAS THE CULPRIT FOR NO TRAINING! Lower than 1000 performs very poorly max_patch_length: 100000 #How many shots are we loading at once? - num_shots_at_once: 200 - num_epochs: 1000 + num_shots_at_once: 100 + num_epochs: 10 use_mock_data: False data_parallel: False hyperparam_tuning: False batch_generator_warmup_steps: 0 - num_batches_minimum: 200 #minimum number of batches per epoch + num_batches_minimum: 20 #minimum number of batches per epoch ranking_difficulty_fac: 1.0 #how much to upweight incorrectly classified shots during training callbacks: list: ['earlystop'] diff --git a/examples/conf.yaml_old b/examples/conf.yaml_old new file mode 100644 index 00000000..e93f0b6a --- /dev/null +++ b/examples/conf.yaml_old @@ -0,0 +1,133 @@ +#conf.py will parse the yaml and extract parameters based on what is specified + +#will do stuff in fs_path / [username] / signal_data | shot_lists | processed shots, etc. + +fs_path: '/tigress' +target: 'maxhinge' #'maxhinge' #'maxhinge' #'binary' #'hinge' +num_gpus: 4 + +paths: + signal_prepath: '/signal_data/' #/signal_data/jet/ + shot_list_dir: '/shot_lists/' + tensorboard_save_path: '/Graph/' + data: jet_data #'d3d_to_jet_data' #'d3d_to_jet_data' # 'jet_to_d3d_data' #jet_data + specific_signals: [] #['q95','li','ip','betan','energy','lm','pradcore','pradedge','pradtot','pin','torquein','tmamp1','tmamp2','tmfreq1','tmfreq2','pechin','energydt','ipdirect','etemp_profile','edens_profile'] #if left empty will use all valid signals defined on a machine. Only use if need a custom set + executable: "mpi_learn.py" + shallow_executable: "learn.py" + +data: + bleed_in: 0 #how many shots from the test sit to use in training? + bleed_in_remove_from_test: True + bleed_in_equalize_sets: True + signal_to_augment: None #'plasma current' #or None + augmentation_mode: 'none' + augment_during_training: False + cut_shot_ends: True + T_min_warn: 30 + recompute: False + recompute_normalization: False + #specifies which of the signals in the signals_dirs order contains the plasma current info + current_index: 0 + plotting: False + #train/validate split + #how many shots to use + use_shots: 200000 #1000 #200000 + positive_example_penalty: 1.0 #by what factor to upweight positive examples? + #normalization timescale + dt: 0.001 + #maximum TTD considered + T_max: 1000.0 + #The shortest works best so far: less overfitting. log TTd prediction also works well. 0.5 better than 0.2 + T_warning: 1.024 #1.024 #1.024 #0.512 #0.25 #1.0 #1.0 #warning time in seconds + current_thresh: 750000 + current_end_thresh: 10000 + #the characteristic decay length of the decaying moving average window + window_decay: 2 + #the width of the actual window + window_size: 10 + #TODO optimize + normalizer: 'var' + norm_stat_range: 100.0 + equalize_classes: False + # shallow_sample_prob: 0.01 #the fraction of samples with which to train the shallow model + floatx: 'float32' + +model: + use_bidirectional: false + use_batch_norm: false + shallow: False + shallow_model: + num_samples: 1000000 #1000000 #the number of samples to use for training + type: "mlp" #"xgboost" #"xgboost" #"random_forest" "xgboost" + n_estimators: 100 #for random forest + max_depth: 3 #for random forest and xgboost (def = 3) + C: 1.0 #for svm + kernel: "rbf" #rbf, sigmoid, linear, poly, for svm + learning_rate: 0.1 #xgboost + scale_pos_weight: 10.0 #xgboost + final_hidden_layer_size: 10 #final layers has this many neurons, every layer before twice as many + num_hidden_layers: 3 + learning_rate_mlp: 0.0001 + mlp_regularization: 0.0001 + skip_train: False #should a finished model be loaded if available + #length of LSTM memory + pred_length: 200 + pred_batch_size: 128 + #TODO optimize + length: 128 + skip: 1 + #hidden layer size + #TODO optimize + rnn_size: 200 + #size 100 slight overfitting, size 20 no overfitting. 200 is not better than 100. Prediction much better with size 100, size 20 cannot capture the data. + rnn_type: 'LSTM' + #TODO optimize + rnn_layers: 2 + num_conv_filters: 128 + size_conv_filters: 3 + num_conv_layers: 3 + pool_size: 2 + dense_size: 128 + extra_dense_input: False + #have not found a difference yet + optimizer: 'adam' + clipnorm: 10.0 + regularization: 0.0 + dense_regularization: 0.01 + #1e-4 is too high, 5e-7 is too low. 5e-5 seems best at 256 batch size, full dataset and ~10 epochs, and lr decay of 0.90. 1e-4 also works well if we decay a lot (i.e ~0.7 or more) + lr: 0.00002 #0.00001 #0.0005 #for adam plots 0.0000001 #0.00005 #0.00005 #0.00005 + lr_decay: 0.97 #0.98 #0.9 + stateful: True + return_sequences: True + dropout_prob: 0.1 + #only relevant if we want to do mpi training. The number of steps with a single replica + warmup_steps: 0 + ignore_timesteps: 100 #how many initial timesteps to ignore during evaluation (to let the internal state settle) + backend: 'tensorflow' +training: + as_array_of_shots: True + shuffle_training: True + train_frac: 0.75 + validation_frac: 0.33 + batch_size: 128 #256 + #THIS WAS THE CULPRIT FOR NO TRAINING! Lower than 1000 performs very poorly + max_patch_length: 100000 + #How many shots are we loading at once? + num_shots_at_once: 200 + num_epochs: 1000 + use_mock_data: False + data_parallel: False + hyperparam_tuning: False + batch_generator_warmup_steps: 0 + num_batches_minimum: 200 #minimum number of batches per epoch + ranking_difficulty_fac: 1.0 #how much to upweight incorrectly classified shots during training +callbacks: + list: ['earlystop'] + metrics: ['val_loss','val_roc','train_loss'] + mode: 'max' + monitor: 'val_roc' + patience: 5 + write_grads: False +env: + name: 'frnn' + type: 'anaconda' diff --git a/examples/maverick_script b/examples/maverick_script new file mode 100644 index 00000000..69282c8e --- /dev/null +++ b/examples/maverick_script @@ -0,0 +1,29 @@ +#!/bin/bash +#SBATCH -J plasma-python # Job name +#SBATCH -o plasma.o%j # Name of stdout output file +#SBATCH -e plasma.e%j # Name of stderr error file +#SBATCH -p gpu # Queue (partition) name +#SBATCH -t 05:00:00 +#SBATCH -N 1 +#SBATCH -n 20 # Total # of mpi tasks (should be 1 for serial) +#SBATCH --mail-user=michoski@gmail.com +#SBATCH --mail-type=all # Send email at begin and end of job +#SBATCH -A Magnetic-Confinement # Allocation name (req'd if you have more than 1) + +module load gcc/4.9.3 +module load python3/3.5.2 +module load cuda/8.0 +module load cudnn/5.1 +module load tensorflow-gpu/1.0.0 +module load mvapich2 +module load git + +#remove checkpoints for a benchmark run +#rm /scratch/gpfs/$USER/model_checkpoints/* +#rm /scratch/gpfs/$USER/results/* +#rm /scratch/gpfs/$USER/csv_logs/* +#rm /scratch/gpfs/$USER/Graph/* +#rm /scratch/gpfs/$USER/normalization/* + +#export OMPI_MCA_btl="tcp,self,sm" +ibrun python3 mpi_learn.py \ No newline at end of file diff --git a/examples/mpi_learn.py b/examples/mpi_learn.py index a965a86b..7bc4ce8b 100644 --- a/examples/mpi_learn.py +++ b/examples/mpi_learn.py @@ -86,6 +86,9 @@ loader = Loader(conf,normalizer) print("...done") +#ensure training has a separate random seed for every worker +np.random.seed(task_index) +random.seed(task_index) if not only_predict: mpi_train(conf,shot_list_train,shot_list_validate,loader) diff --git a/examples/notebooks/FRNN_scaling.ipynb b/examples/notebooks/FRNN_scaling.ipynb index 52c09f0e..40109fd0 100644 --- a/examples/notebooks/FRNN_scaling.ipynb +++ b/examples/notebooks/FRNN_scaling.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 29, "metadata": { "collapsed": false }, @@ -19,21 +19,33 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 13, + "execution_count": 212, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ + "import re\n", + "from os import listdir\n", + "from os.path import isfile, join\n", + "def plot_loss_vs_step(raw_path,ending='.out',save_path=False):\n", + " files = [join(raw_path,f) for f in listdir(raw_path) if (isfile(join(raw_path, f)) and ending in f)]\n", + " loss_arrays = [get_losses(open(f).read()) for f in files]\n", + " node_counts = np.array([get_num_gpus(open(f).read()) for f in files])\n", + " for (i,loss_arr) in enumerate(loss_arrays):\n", + " plt.semilogy(np.array(range(len(loss_arr)))*node_counts[i],loss_arr,label=r\"$N_{{GPU}} = {{{}}}$\".format(node_counts[i]))\n", + " plt.legend(loc=(1,0))\n", + " if save_path:\n", + " plt.savefig(save_path)\n", + "\n", + "def get_from_csv(f):\n", + " dat = np.genfromtxt(f,delimiter=',',names=True)\n", + " epochs = dat['epoch']\n", + " train_loss = dat['train_loss']\n", + " val_loss = dat['val_loss']\n", + " val_roc = dat['val_roc']\n", + " return epochs,train_loss,val_loss,val_roc\n", + "\n", "#regexes for parsing the log files\n", "def get_num_gpus(text):\n", " p = re.compile('\\[batch = \\d+ = \\d+\\*\\d+\\]')\n", @@ -54,6 +66,30 @@ " nums = [float(re.findall(r'\\d+.\\d+',match)[-1]) for match in matches]\n", " return nums[-1]\n", "\n", + "def get_effective_epoch(text):\n", + " p = re.compile(' \\[\\d+.\\d+\\/\\d+\\]')\n", + " matches = p.findall(text)\n", + " nums = [float(re.findall(r'\\d+.\\d+',match)[0]) for match in matches]\n", + " return nums\n", + "\n", + "def get_effective_epoch_and_loss(text):\n", + " p = re.compile(' \\[\\d+.\\d+\\/\\d+\\], ' + 'loss: \\d+\\.\\d+ \\[\\d+\\.\\d+\\]')\n", + " matches = p.findall(text)\n", + " e_eff = [float(re.findall(r'\\d+.\\d+',match)[0]) for match in matches]\n", + " e_eff = np.linspace(0,e_eff[-1],len(e_eff))\n", + " loss = np.array([float(re.findall(r'\\d+.\\d+',match)[-1]) for match in matches])\n", + " return e_eff,loss\n", + "\n", + "\n", + " \n", + "def get_epoch_size(text):\n", + " p = re.compile(' \\[\\d+.\\d+\\/\\d+\\]')\n", + " matches = p.findall(text)\n", + " nums = [float(re.findall(r'\\d+.\\d+',match)[0]) for match in matches]\n", + " epoch_size = float(re.findall(r'\\d+.\\d+',matches[0])[1])\n", + " return epoch_size\n", + " \n", + "\n", "def get_losses(text):\n", " p = re.compile('loss: \\d+\\.\\d+ \\[\\d+\\.\\d+\\]')\n", " #'loss: \\d+\\.\\d+ \\[\\d+\\.\\d+\\]'\n", @@ -67,7 +103,7 @@ "def get_execution_time(text):\n", " p = re.compile('Epoch \\d+.\\d+ finished \\(\\d+.\\d+ epochs passed\\) in \\d+.\\d+ seconds')\n", " #p = re.compile('Epoch 2 finished in \\d+.\\d+ seconds')\n", - " match = p.findall(text)[0]\n", + " match = p.findall(text)[1]\n", " execution_time = float(re.findall(r'\\d+\\.\\d',match)[-1])\n", " effective_epochs_passed = float(re.findall(r'\\d+\\.\\d',match)[-2])\n", " return execution_time/effective_epochs_passed\n", @@ -77,16 +113,161 @@ " return arr/arr[0]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot from CSV log files" + ] + }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 249, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "raw_path = './newtest/'\n", - "data_size = 'full' #'Titan' #full, large, medium\n", + "#only works if in the same order as the GPU counts!\n", + "csvfiles = [join(raw_path,f) for f in listdir(raw_path) if (isfile(join(raw_path, f)) and 'callback' in f)]\n", + "csvfiles = list(zip(*sorted(zip(node_counts,csvfiles)))[1])\n", + "f = csvfiles[0]\n", + "\n", + "\n", + "by_epoch_stats = [get_from_csv(f) for f in csvfiles]" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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6gfWeQpyVYlJQcriGDcHb24hbMaOzYrUji1bSYyUtIHrMjMtaGjQwnImSpEED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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors = ['b','g','r']\n", + "\n", + "for (i,loss_arr) in enumerate(loss_arrays):\n", + " by_epoch = by_epoch_stats[i]\n", + " epoch,t_loss,val_loss,val_roc = by_epoch\n", + " alpha = 1.0\n", + " if node_counts[i] == 4:\n", + " alpha = 0.4\n", + "# plt.semilogy(effective_epochs[i],np.array(range(len(loss_arr)))*node_counts[i],loss_arr,alpha=alpha,label=r\"$N_{{GPU}} = {{{}}}$\".format(node_counts[i]))\n", + " plt.plot(epoch,t_loss,color=colors[i],label=r\"$N_{{GPU}} = {{{}}}$\".format(node_counts[i]))\n", + " plt.plot(epoch,val_loss,'--',color=colors[i])\n", + "\n", + " \n", + "plt.plot([],[],'--k',label=\"Validation\")\n", + "plt.plot([],[],'-k',label=\"Train\")\n", + "plt.xlim([0,9])\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Loss\")\n", + "plt.legend(loc=\"best\")\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot from CSV log files" + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a = effective_epochs_and_loss[0][1]\n", + "a\n", + "import pandas as pd\n", + "def get_rolling_mean(arr,N):\n", + " ret = np.zeros_like(arr)\n", + " cutoff=N-1\n", + " for i in range(cutoff):\n", + " ret[i] = np.mean(arr[:i])\n", + "# ret[:cutoff] = arr[:cutoff]\n", + " ret[cutoff:] = pd.rolling_mean(arr,N)[cutoff:]\n", + " return ret\n" + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/julian/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:10: FutureWarning: pd.rolling_mean is deprecated for ndarrays and will be removed in a future version\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 250, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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eSHePEKIF6Nq1K1u3bpVF5UTI0tPT6dq1qynXipskBZgH5ABBr1ucYLFRLt09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\nXt44+RIuvhqMUABuvpn0io1Yn/40Jz35Vc85MxmIGftZmZlLoeg01t3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YUWuJlEDU9u0O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\n8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HvLt5myH6r9zcx87sdMeeYnTH/hZ6iZph6/GVq64KvHmtf4ZedF/OHJVjZvZsQwRlJG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\nZlZITlLMzMyskJykmJmZWSH9PxgCqSrdq9WpAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for (i,loss_arr) in enumerate(loss_arrays):\n", + " alpha = 1.0\n", + " to_plot = effective_epochs_and_loss[i][1]\n", + " if node_counts[i] == 4:\n", + " to_plot = get_rolling_mean(to_plot,15) #very noisy\n", + " alpha = 0.4\n", + "# plt.semilogy(effective_epochs[i],np.array(range(len(loss_arr)))*node_counts[i],loss_arr,alpha=alpha,label=r\"$N_{{GPU}} = {{{}}}$\".format(node_counts[i]))\n", + " plt.semilogy(effective_epochs_and_loss[i][0]/(epoch_lengths[i]),to_plot,alpha=alpha,label=r\"$N_{{GPU}} = {{{}}}$\".format(node_counts[i]))\n", + "plt.legend(loc=\"best\")#(1,0))\n", + "plt.xlim([1e-2,10])\n", + "plt.ylim([1e-3,1])\n", + "plt.grid()\n", + "plt.xlabel(\"Epoch\",size=12)\n", + "plt.ylabel(\"Training Loss\",size=12)" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "raw_path = '../../plots/'#'./newtest/'\n", + "data_size = 'out' #full' #'Titan' #full, large, medium\n", "from os import listdir\n", "from os.path import isfile, join\n", "files = [join(raw_path,f) for f in listdir(raw_path) if (isfile(join(raw_path, f)) and data_size in f)]" @@ -94,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 213, "metadata": { "collapsed": false }, @@ -103,7 +284,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[24]\n" + "[4, 20, 100]\n" ] } ], @@ -113,16 +294,19 @@ "print node_counts\n", "files = list(zip(*sorted(zip(node_counts,files)))[1])\n", "files\n", + "effective_epochs_and_loss =[get_effective_epoch_and_loss(open(f).read()) for f in files]\n", + "epoch_lengths = np.array([get_epoch_size(open(f).read()) for f in files])\n", "node_counts = np.array([get_num_gpus(open(f).read()) for f in files])\n", "sync_percentages =np.array([get_sync_percentages(open(f).read()) for f in files] )\n", "execution_times =np.array([get_execution_time(open(f).read()) for f in files])\n", + "# execution_times = [1.0]\n", "losses = np.array([get_loss(open(f).read()) for f in files])\n", "loss_arrays = [get_losses(open(f).read()) for f in files]" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 75, "metadata": { "collapsed": false }, @@ -140,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 135, "metadata": { "collapsed": false }, @@ -148,145 +332,21 @@ { "data": { "text/plain": [ - "array([ 1.04816, 0.50673, 0.23602, 0.17164, 0.044 , 0.01846,\n", - " 0.02425, 0.07086, 0.09969, 0.08962, 0.07535, 0.06142,\n", - " 0.05591, 0.04328, 0.04155, 0.03081, 0.02032, 0.02561,\n", - " 0.02532, 0.0284 , 0.02367, 0.02229, 0.03027, 0.03846,\n", - " 0.03265, 0.02961, 0.02874, 0.02642, 0.02517, 0.025 ,\n", - " 0.00921, 0.0066 , 0.01082, 0.01557, 0.01925, 0.02301,\n", - " 0.02304, 0.02865, 0.02527, 0.02474, 0.0197 , 0.02035,\n", - " 0.01216, 0.0123 , 0.01153, 0.01047, 0.01168, 0.01081,\n", - " 0.00954, 0.01401, 0.0133 , 0.01342, 0.01963, 0.02554,\n", - " 0.02259, 0.0245 , 0.02127, 0.0151 , 0.01591, 0.01684,\n", - " 0.00955, 0.00737, 0.00789, 0.00833, 0.0086 , 0.00934,\n", - " 0.008 , 0.01141, 0.01709, 0.02115, 0.01687, 0.01529,\n", - " 0.01554, 0.0189 , 0.0201 , 0.01489, 0.01229, 0.01499,\n", - " 0.0163 , 0.01706, 0.01684, 0.01251, 0.01564, 0.01953,\n", - " 0.01224, 0.01 , 0.01241, 0.02032, 0.02303, 0.02526,\n", - " 0.0211 , 0.02159, 0.02427, 0.02567, 0.0227 , 0.01473,\n", - " 0.01185, 0.01649, 0.01486, 0.01425, 0.01366, 0.01472,\n", - " 0.01539, 0.01652, 0.01775, 0.00824, 0.009 , 0.01205,\n", - " 0.01131, 0.01181, 0.01002, 0.01029, 0.01673, 0.02227,\n", - " 0.01955, 0.01977, 0.02003, 0.02144, 0.02421, 0.02628,\n", - " 0.00879, 0.00671, 0.00788, 0.00856, 0.01104, 0.01433,\n", - " 0.00918, 0.01297, 0.01917, 0.0205 , 0.01939, 0.02012,\n", - " 0.02601, 0.02989, 0.03163, 0.01821, 0.01749, 0.01772,\n", - " 0.01844, 0.02178, 0.0138 , 0.00715, 0.01114, 0.01614,\n", - " 0.01746, 0.01843, 0.02218, 0.02118, 0.02202, 0.02389,\n", - " 0.01489, 0.01337, 0.01188, 0.01162, 0.00661, 0.00702,\n", - " 0.00745, 0.01256, 0.01604, 0.01596, 0.01663, 0.02029,\n", - " 0.02053, 0.02274, 0.02519, 0.01717, 0.01711, 0.01866,\n", - " 0.01277, 0.01254, 0.00807, 0.00318, 0.00821, 0.01471,\n", - " 0.01518, 0.01532, 0.01388, 0.01509, 0.0159 , 0.01706,\n", - " 0.00685, 0.00792, 0.01146, 0.01458, 0.01866, 0.0194 ,\n", - " 0.02132, 0.02143, 0.02523, 0.02708, 0.02283, 0.02433,\n", - " 0.0211 , 0.0224 , 0.02065, 0.01788, 0.00929, 0.01031,\n", - " 0.00955, 0.01057, 0.01111, 0.00907, 0.01197, 0.01531,\n", - " 0.01402, 0.01537, 0.01722, 0.01651, 0.01623, 0.01745,\n", - " 0.01407, 0.01603, 0.01633, 0.01667, 0.01362, 0.01197,\n", - " 0.01303, 0.01609, 0.01403, 0.01173, 0.01244, 0.01301,\n", - " 0.01147, 0.01222, 0.01194, 0.00741, 0.00676, 0.00858,\n", - " 0.00679, 0.00742, 0.00772, 0.00949, 0.01163, 0.01442,\n", - " 0.0118 , 0.01185, 0.0116 , 0.00998, 0.00949, 0.00898,\n", - " 0.00692, 0.00736, 0.01123, 0.01449, 0.0176 , 0.02064,\n", - " 0.02227, 0.02488, 0.02731, 0.02573, 0.02009, 0.01921,\n", - " 0.01612, 0.01428, 0.01823, 0.01897, 0.01688, 0.01897,\n", - " 0.01593, 0.01784, 0.01968, 0.01895, 0.01713, 0.01414,\n", - " 0.01252, 0.01399, 0.01416, 0.0139 , 0.01545, 0.01264,\n", - " 0.00813, 0.01237, 0.01377, 0.01352, 0.01546, 0.01804,\n", - " 0.01897, 0.01859, 0.01627, 0.01238, 0.01222, 0.01269,\n", - " 0.01065, 0.0082 , 0.01024, 0.01234, 0.0092 , 0.00996,\n", - " 0.00789, 0.00928, 0.01124, 0.01253, 0.0117 , 0.0155 ,\n", - " 0.01731, 0.01777, 0.01871, 0.01825, 0.01719, 0.01816,\n", - " 0.01359, 0.01022, 0.01235, 0.01616, 0.01783, 0.02237,\n", - " 0.02038, 0.01956, 0.01787, 0.01633, 0.01175, 0.01032,\n", - " 0.00903, 0.00886, 0.01097, 0.00908, 0.01057, 0.0129 ,\n", - " 0.01519, 0.01734, 0.01195, 0.01263, 0.01511, 0.01728,\n", - " 0.0125 , 0.01178, 0.01326, 0.01412, 0.0153 , 0.01432,\n", - " 0.00872, 0.01072, 0.01293, 0.01349, 0.01042, 0.00954,\n", - " 0.01316, 0.01913, 0.02055, 0.01444, 0.01506, 0.01873,\n", - " 0.01856, 0.02205, 0.01669, 0.01574, 0.01831, 0.01909,\n", - " 0.01225, 0.01186, 0.0135 , 0.01187, 0.01645, 0.01636,\n", - " 0.01764, 0.02119, 0.02382, 0.02477, 0.02088, 0.02233,\n", - " 0.01276, 0.01427, 0.00863, 0.00889, 0.01219, 0.01422,\n", - " 0.01419, 0.01377, 0.0116 , 0.01341, 0.01562, 0.01946,\n", - " 0.01797, 0.02002, 0.02222, 0.02313, 0.02175, 0.01983,\n", - " 0.01343, 0.01282, 0.01085, 0.01083, 0.00957, 0.01189,\n", - " 0.01154, 0.01372, 0.01439, 0.01486, 0.01312, 0.01532,\n", - " 0.01685, 0.01685, 0.01815, 0.01651, 0.01426, 0.01444,\n", - " 0.01078, 0.01316, 0.00942, 0.00868, 0.00963, 0.01259,\n", - " 0.01156, 0.01316, 0.01156, 0.00928, 0.01273, 0.01548,\n", - " 0.01416, 0.01672, 0.01641, 0.01711, 0.01914, 0.02005,\n", - " 0.01421, 0.01478, 0.01284, 0.00926, 0.01021, 0.01135,\n", - " 0.01046, 0.00885, 0.01046, 0.01085, 0.01136, 0.01341,\n", - " 0.01335, 0.01794, 0.01694, 0.01519, 0.01548, 0.01655,\n", - " 0.01565, 0.01876, 0.01672, 0.01414, 0.0149 , 0.01593,\n", - " 0.01432, 0.01459, 0.0142 , 0.01369, 0.01116, 0.00959,\n", - " 0.01168, 0.01628, 0.01822, 0.01698, 0.01843, 0.02064,\n", - " 0.02327, 0.02276, 0.01851, 0.0158 , 0.01724, 0.01927,\n", - " 0.01469, 0.01532, 0.0099 , 0.01145, 0.01081, 0.01134,\n", - " 0.01419, 0.0195 , 0.02169, 0.02199, 0.02111, 0.02175,\n", - " 0.02103, 0.02286, 0.01475, 0.01652, 0.02084, 0.02156,\n", - " 0.01918, 0.02 , 0.02155, 0.0117 , 0.01052, 0.0114 ,\n", - " 0.00717, 0.00829, 0.00948, 0.0118 , 0.01753, 0.0201 ,\n", - " 0.01797, 0.01866, 0.01845, 0.01971, 0.02038, 0.01982,\n", - " 0.01534, 0.01584, 0.01613, 0.01668, 0.01073, 0.01189,\n", - " 0.01272, 0.01643, 0.01759, 0.01775, 0.01806, 0.01535,\n", - " 0.01642, 0.01686, 0.01551, 0.0142 , 0.01528, 0.01712,\n", - " 0.01977, 0.02154, 0.02471, 0.02318, 0.02011, 0.02283,\n", - " 0.01978, 0.02004, 0.01602, 0.01344, 0.01226, 0.01305,\n", - " 0.00992, 0.0104 , 0.00942, 0.01002, 0.0106 , 0.01396,\n", - " 0.01307, 0.01566, 0.02064, 0.02236, 0.02486, 0.02383,\n", - " 0.02147, 0.02158, 0.01913, 0.0123 , 0.01026, 0.01087,\n", - " 0.01193, 0.01014, 0.00819, 0.01018, 0.0116 , 0.01271,\n", - " 0.01186, 0.01232, 0.00937, 0.01078, 0.00854, 0.00815,\n", - " 0.00942, 0.01446, 0.01882, 0.0209 , 0.01972, 0.0214 ,\n", - " 0.01971, 0.02035, 0.02008, 0.01186, 0.00761, 0.00768,\n", - " 0.0067 , 0.00521, 0.0052 , 0.00456, 0.00606, 0.00605,\n", - " 0.00411, 0.0055 , 0.00907, 0.01308, 0.0161 , 0.01757,\n", - " 0.0149 , 0.01452, 0.01329, 0.01399, 0.01114, 0.00947,\n", - " 0.0115 , 0.01317, 0.014 , 0.01635, 0.01656, 0.01632,\n", - " 0.01956, 0.02112, 0.01707, 0.01909, 0.02521, 0.02495,\n", - " 0.02113, 0.01792, 0.01446, 0.01603, 0.0168 , 0.014 ,\n", - " 0.00572, 0.00666, 0.00767, 0.00917, 0.01245, 0.01354,\n", - " 0.01577, 0.01918, 0.02355, 0.02418, 0.02145, 0.02312,\n", - " 0.01922, 0.02056, 0.01905, 0.01542, 0.01349, 0.01301,\n", - " 0.01185, 0.01212, 0.00662, 0.00624, 0.00935, 0.01149,\n", - " 0.00933, 0.01044, 0.01115, 0.01209, 0.01365, 0.01331,\n", - " 0.01156, 0.01139, 0.01131, 0.013 , 0.01427, 0.01273,\n", - " 0.00812, 0.00803, 0.00904, 0.01081, 0.00836, 0.0089 ,\n", - " 0.01264, 0.01588, 0.01718, 0.0194 , 0.01791, 0.01612,\n", - " 0.01467, 0.01102, 0.00499, 0.00308, 0.00577, 0.00721,\n", - " 0.00873, 0.01101, 0.01232, 0.01494, 0.01712, 0.01915,\n", - " 0.0153 , 0.01596, 0.01585, 0.01247, 0.01412, 0.01264,\n", - " 0.01007, 0.00426, 0.00514, 0.00735, 0.00884, 0.01022,\n", - " 0.0107 , 0.01343, 0.01477, 0.01704, 0.0185 , 0.0171 ,\n", - " 0.01746, 0.0181 , 0.01054, 0.01263, 0.01483, 0.01582,\n", - " 0.01439, 0.01615, 0.01264, 0.01458, 0.01775, 0.01775,\n", - " 0.01582, 0.01464, 0.01613, 0.01627, 0.01873, 0.01709,\n", - " 0.01686, 0.01549, 0.01654, 0.01899, 0.0153 , 0.01168,\n", - " 0.01093, 0.01371, 0.01289, 0.01248, 0.01158, 0.00956,\n", - " 0.00743, 0.00948, 0.00978, 0.0119 , 0.01958, 0.02394,\n", - " 0.02435, 0.02716, 0.02882, 0.02844, 0.02613, 0.01909,\n", - " 0.01031, 0.00965, 0.00814, 0.00791, 0.00856, 0.00924,\n", - " 0.0102 , 0.01234, 0.01362, 0.01243, 0.01478, 0.0115 ,\n", - " 0.0095 , 0.00912, 0.01022, 0.0101 , 0.01257, 0.01402,\n", - " 0.01289, 0.01205, 0.01094, 0.01123, 0.01206, 0.01423,\n", - " 0.01087, 0.01244, 0.01347, 0.01233, 0.0133 , 0.01501])" + "array([ 2899., 2899., 2899.])" ] }, - "execution_count": 19, + "execution_count": 135, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "loss_arrays[-1]" + "epoch_lengths" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 78, "metadata": { "collapsed": false }, @@ -294,52 +354,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+9evlM+zePTEgx9ECYK3sj5PNILFtm1xAIspJe1s1NCSvHm/cKBV8nV4urvBY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\nOaFfZVw2b96MGTNmYOfOnXj22Wf33W+MwW233dbq8T/5yU/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n4LnAnwO/BN4WEX/WetmSJGm6Kn1sdApwMvC/MnPxqNM/Bf5PRFwA3ADcmpmf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\nF0nttMb6LpP4GNWjoz8EnlY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"text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for (i,loss_arr) in enumerate(loss_arrays):\n", - " plt.semilogy(np.array(range(len(loss_arr)))*node_counts[i],loss_arr,label=r\"$N_{{GPU}} = {{{}}}$\".format(node_counts[i]))\n", - "plt.legend(loc=(1,0))" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -357,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 79, "metadata": { "collapsed": false }, @@ -365,18 +391,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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P9OnTB4C///6bp556ioMHD9KtWzceffRRkpKSiI2N5cSJEzz00ENcvnyZ2bNn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\nocJHRERE8oYKHxEREckbKnxEREQkb/w/eVOww8l8UxgAAAAASUVORK5CYII=\n", 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UMwEql9D29G755apMmDCBWrVqUbZsWdq1a8fOfHYoPn78OCNGjKB27dqUKVOG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\n/wztEiVBhY9ImrTlSLwon/FTCDltuO5PLnp/+vSB++6DuXP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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -396,7 +422,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 80, "metadata": { "collapsed": false }, @@ -404,18 +430,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 26, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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QX9NmC/Fp3SpqREREpF7SNqXb3eeZ2QpgKnA1YEW7XgDOTFccIiIiEk3puv0E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\n/S7sMBLm7liKFkxxhw0bqi5cqhrn0q8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JQ4bAunVw771wxBFhRxc/8SpqKrr7LHefvO8L7v4P4LdxikOkUDNnzjz4QVJm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\niIg0BxUVFfTp0wegj7tX5PPcGlMjjebutNu0qda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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -438,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 81, "metadata": { "collapsed": false }, @@ -446,18 +472,18 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 27, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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t+bg4oYuIiEhahS1QZgIXAsuBV7PraoA1IZa1sUUrEkImo8dEpYnymS7Kp4QV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\n9zfN7OfAAHc/K9t+CPA0cA1wI0Ex80t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HKSIihUIFijS6rl3DsOS33w7r//TrB1dcATvsAH36wNVXw6efJh2liEjhWrBgASUlJdx8\n8831PvbRRx+lpKSExx57LA+RxUcFiiSmpCTcQbnhBvjoo9A3ZbPN4LTTwiOgQYPgjjvCgob1MXHi\nxPwELIlQPkXiZ2ZJh7BeKlCkILRqBb/4RRj58/77q9cEOvLIMO/KiSeGBQ2j9FepKJZFgyQS5VOk\naVKBIgWnffvQR+X552Hu3LAW0OzZcMABsM02YZK4l16qe36VCRMmNG7AklfKp0jTpAJFClr37nDh\nhaG/ypw5kMnAjTfCbrvBzjvDxRfDO+8kHaWINEWjRo2ipKSEN954g1/+8pdssskmbLHFFpx33nkA\nvPvuuwwaNIg2bdrQsWNHLr/88hrHf/LJJ5xwwgl06NCBVq1asdtuu621T8mSJUs4/vjj2WSTTdh0\n000ZMmQIn3/++Vpjeu211/jZz37G5ptvTqtWrejduzczZ86M/8M3AhUoUhSqZq2dMAE++CA8Ctp1\nVxg9OixouNdeMH586MsiItIYqvpxDB48GIAxY8aw5557Mnr0aK688koGDBjA1ltvzdixY9lhhx0Y\nMWIEc+bMAWDZsmXst99+TJkyhV/96ldceumlbLLJJhx//PGMGzeuxnUymQxTpkzh2GOPZfTo0bz3\n3nscd9xxa/QjefXVV9lzzz157bXX+OMf/8jll1/ORhttxKBBg7jrrrsa4SsSM3dv0hvQC/Dy8nKX\n4vPll+633OJ+2GHuG2zgXlLiftBB7jfe6P7ZZ0lHJyLl5eWe1p+xo0aNcjPzk08+edW+FStWeKdO\nnbxZs2Z+ySWXrNr/+eefe2lpqQ8ZMsTd3a+88kovKSnxqVOnrmrz3Xff+V577eUbb7yxf/nll+7u\nPn36dDczv+yyy1a1W7lypfft29dLSkp88uTJq/b379/fd9ttN1++fHmNOPfee2/v1q3bqtePPPKI\nl5SU+KOPPrrOzxcld1VtgF4e8+/nDRKsjUQabMMN4aijwvbpp/Cvf8Gf/pRh9uwZnHQSHHpo6Hx7\n+OGhrRSfTCbDjBkzkg5DGkFlZViENN+6d4fS0njOZWaccMIJq16XlJSwxx57cNddd9VYpqFNmzZ0\n69aNt99+G4B7772XDh068Itf/GJVm2bNmnHaaadx9NFH8+ijjzJw4EDuvfdemjdvzkknnVTjmqee\neiqPP/74qn2fffYZDz/8MBdeeCFLliypEeOAAQM4//zz+eCDD+jYsWM8H7wRqECR1Nh8c/jNb6BL\nl2HsuCPcdhtMmxaKl9LSUKQMHhyKlpYtk45Woho2bFjSIUgjmTcPysryf53y8rAER1w6d+5c43Wb\nNm1o2bIlm2222Rr7Fy9eDMDChQvZYYcd1jhXjx49cHcWLFiwql3Hjh0prVVRdevWrcbrN998E3fn\nz3/+M+eee+4a5zUzPv74YxUoIkkaMGAAAMOHh+3tt0OxcuutcMQR0Lo1/PjHoVgZMAC+972EA5Z1\nqsqnpF/37qF4aIzrxKlZs2aR9gFVXQtitzI7B8NZZ53FwQcfvNY2Xbt2zcu180UFiqTedtvBH/4Q\ntnnzwl2VadPgn/+ETTYJE8INHgz9+0Pz5klHK9J0lZbGe2ejkG2zzTa88sora+yfO3cuAF26dFnV\n7qGHHqKysrLGXZR5tZ6FbbfddgA0b96cfv365SnqxqVRPNKkdO8e1v159VV4+eUwx8qTT4bHPh06\nwAknwP33w/LlSUcqImk2cOBAPvzwQ6ZNm7Zq34oVKxg3bhytW7emb9++q9otX76ca665ZlW7lStX\nMm7cuBqjeNq1a8f+++/Pddddx4cffrjG9RYtWpTHT5MfuoMiqTN9+nQGDRq0zjZmYR6VnXeG888P\nxcptt4XtxhvDlPuDBoWZbPv102OgJEXJp0ix+c1vfsN1113H8ccfz/PPP0+XLl24/fbbeeqpp7jq\nqqvYMNur//DDD2fvvffmD3/4A++88w49e/bkzjvvZOnSpWucc8KECey7777svPPOnHjiiWy33XZ8\n9NFHPPXUU7z//vu88MILq9rm61FTnHQHRVJn6tSp9WpvtnpOlddfhxdegJNOgsceW31nZciQMPfK\nN9/kKWipU33zKVII6lrrpmp/y5YtefTRRznmmGO4+eabOeuss/j888+ZNGlSjY7hZsbMmTM55phj\nmDJlCueeey6dOnVi8uTJa5y7R48ePP/88xx22GFMnjyZYcOGcd1119GsWTNGjhwZKb5CYsVQReWT\nmfUCysvLy+nVVB5+SiTu4c7KHXfA7bfDa6/BxhuH2Wx/+lM4+OCwhpCI1K2iooKysjL0M7b4RMld\nVRugzN1jXTir4O+gmNkpZvaOmX1tZk+bWe/1tD/GzF40s6/M7P/MbKKZbbauY0TWpurOyoUXhjWB\nXnkFzjgDXnwRfvITaNcOfv7z0OF2LXdbRUSkAQq6QDGzwcBlwEhgd+Al4H4za1tH+72BycDfgZ7A\nz4AfANc3SsCSWmaw006hg+0rr4TRQH/6E7z1VpgIrl27MHR58mTITnMgIiINUNAFCjAcuM7db3b3\necBJQCUwtI72ewLvuPsEd1/g7k8C1xGKFJHYdOsWCpTy8rBY4cUXw6JFoa/KFlvAQQfB1VfD//1f\n0pGKiBSngi1QzKw5UAY8WLXPQ4eZ2UCfOg57CuhkZodmz9EeOBK4J7/RSiEZMmRIo16vS5fw6OeJ\nJ+D998OihWZw+umw1Vaw554wdiy88UajhpUajZ1PESkMBVugAG2BZkDt9Wk/Ajqs7YDsHZNfAtPM\n7FvgA+AzQHNlNyFJzjzasWMYATRrFnz8Mdx8M2y5JYwaBd//fnhMdO658PzzoROurJ9mkhVpmgq5\nQKk3M+sJXAWMIqxSfDCwLeExjzQRRx11VNIhALDppvCrX8Gdd4bHP3feCXvsAddcA717Q+fOMGwY\nPPAAfPtt0tEWrkLJp4g0rkIuUBYBK4D2tfa3B9acJi/4A/CEu1/u7v919weA3wFDs4976jRw4EAy\nmUyNrU+fPkyfPr1Gu1mzZpHJZNY4/pRTTmHixIk19lVUVJDJZNaYwW/kyJGMGTOmxr6FCxeSyWTW\nmL543LhxjBgxosa+yspKMpkMc+bMqbF/6tSpa70dPnjwYH2OhD9HaWkY+TNpEvTvP5gLL5zOEUfA\n3XeH9YA23XQWW2+dYdo0qL4QaaF9juqKOR/6HI33Ob7++us1ri/F5eGHHwbCv72q340dOnQgk8kw\nfPjwvF23oOdBMbOngWfc/fTsawMWAn9z90vW0v4O4Ft3P7ravj7AHGArd1+jsNE8KJIkd3jpJbjr\nLpg+PQxhbt4c9t8/jAo6/PBwp0WkWGkelOJVlPOgmNlbZvYHM1trX5AYXQ6caGbHmll34FqgFJiU\njeMvZlZ9Or2ZwE/N7CQz2zY77PgqQpFT110XSZna/+MsZGaw225h+PILL8D8+XD55aFw+f3vYZtt\nwvvnnRf6rWQXLG1SiimfIhKfXNfi6QKMBi4ws7sJ84zc7zHfjnH327JznlxAeLTzInCwu3+SbdIB\n6FSt/WQz2wg4BbgU+JwwCugPccYlhW3s2LHss88+SYeRk222Cf1Shg0Lj3r+8x+YMQPGjQsTxnXs\nCIcdFu6s9O8fHh2lXTHnU1arWqVXikfSOcvpEY+ZbQ2cQJiPpBPgwHvAROBGd38vziDzSY940qf2\nsuRpsHw5zJkDM2eG7c03wzT7/fuHYuVHPwpDmtMojflsShYuXEiPHj2orKxMOhTJQWlpKXPnzqVz\nHc+a8/mIp0F9ULJ9Qg4GTgQOA5oTOrb+hzCb693uXtA3pVWgSLFxD+sCzZwZOtk+8QSsWAG9eoVC\n5bDDwmihkkLuAi9NysKFC9fofCvFoW3btnUWJ1DABUqNE5ltARwP/BroSrir8iFwIzDR3efHcqGY\nqUCRYrd4Mdx/fyhY/vMf+OyzMJvtoYeGgmXAAGjTJukoRSSNCq6T7Nq4+8fuPhboAVwBGNAROAd4\n08zuMrPd4rqeiASbbQZHHQW33BImh3v88TDlfnl5WMywbVs44AC49NKw6GEBD9wTEVkltgLFzLYx\nswuA+cDvs7ufBC4C3gQOB541s5/EdU2Rtak9D0NTssEGsM8+8Ne/hkUN58+Hv/0NNtwQ/vxn6NkT\nttsOTjkF7rkHiqFbQFPOZxopnxJVgwoUM9vAzH5qZv8B3gLOBVoD1wC7uPs+7n6eu3cHBhP6p1zQ\n0KBF1mVdz0ubmm22gZNPDn1VFi+Ge+8NfVTuuy/8udlmcMghcNVVhbtWkPKZLsqnRJXrKJ4dCH1N\njgPaER7nVBDmKbnF3df6/zIzux3IuHuLnCOOmfqgSFPkDq+/HgqVe++FRx8N0+1vv30oWA49NDwW\n0uAZEVmXfPZByXUelNcInWC/Bm4CrnX35yMct4Qw0kdEEmQG3bqF7fe/hy+/hIceCp1s77kHJkyA\nFi2gb99QsBxyCPToEY4TEWkMuT7i+R9wOrClu/86YnFCtq0GP4oUmI02gkwGrr4a3n4b5s0L/Via\nNYNzzoEddwyPi048Ee64Az7/POmIRSTtcioW3H0ndx/v7l/EHZBIQ9VewEzqp+ruyu9/Hx4BLV4c\n/jziiDBZ3JFHhpFBe+8NF1wATz8d5mHJF+UzXZRPiSrXtXhKzGxjM6vzcY2ZNc+20R0TaVRnn312\n0iGkSqtW4RHPlVeGYcrz54c7LR06hHWD+vSBdu3CkOYbboCFC+O9vvKZLsqnRJVr8TAc+AzYbx1t\n9su2OTXHa4jkZPz48UmHkGrbbAO/+Q3861+waFGYyfbUU+Hdd+G3vw3v9+gBp50WRg8tXdqw6ymf\n6aJ8SlS5juKZA2zt7l3W024BsMDd++YWXv5pFI9IfD77LHS2vf9+mDULFiwIc7P06QMHHRS2PfYI\n+0Sk+BXiTLI7AK9GaPffbFsRaQI23RR++lO4/np4550wlPmqq8J8K5deGgqVtm1Df5Zrrglzr2hm\nWxFZm1z/H9OGMGR4fZYAm+Z4DREpYmawww5h+93v4Lvv4Lnn4IEHwnbaaWFf585w4IFh69cP2rdP\nOnIRKQS53kH5ANglQrtdgI9zvIZITsaMGZN0CLIWVY96zjsvrBe0eHHoo3LEEfDMM3D00aHj7S67\nwBlnhPlYli5VPtNG+ZSocr2D8hBwvJkNdvdpa2tgZj8HegL/yDU4kVxUFsMCM0Lr1mG15R/9KLz+\n4IPQf+XBB8NcK1dcEYqaDh0q+fJL6N8/FDgtCmYeasmFvj8lqlw7yXYHXiDcgfk7cD1hLR6A7YHf\nACcSZpvdw93/G0u0eaBOsiKFxx3efDMUKw8+CA8/DJ9+Ci1bhsUQ+/ULBUuvXupwK5KkfHaSzalA\nATCzI4HJwNr+P2PAMmBIXXdYCoUKFJHCt3IlvPxyuMPy0ENh7aAvv4SNNw7T8ffrF9YO2mUXKNHM\nSyKNphDX4sHdbzezF4AzgP5Ap+xb7wKzgSvdvUDXRxWRYlJSArvtFrYzzoDly+H558OdlYcegj/9\nCZYtC6OF9tsvFCsHHAA9e6pgESlWDbo56u5vAr+LKRaRWCxatIi2bdsmHYbEZG35bN489Efp02d1\ncfLMM6FgefhhOPPMUMS0bQv7779669lTCx4mTd+fEpX+byGpM3To0KRDkBhFyWfLluHOyahR4fHP\n55/D7NlhZtsPPgjrCu20UxjCfOSRYbXmV1/VHCxJ0PenRNXg7mVmtgGwOWvviwKAu8e8OodI3UaN\nGpV0CBKjXPJZWho60fbvH15/9RU89RQ88kjYhg9ffYdlv/1WbzvtpEdC+abvT4mqIZ1kDwTOBfYE\n6lw0EHB3L9h+9uokK9L0VBUsjz4aCpZnngkFy2abwb77ho63++0Hu+6qUUIi61JwnWTN7DDg30Az\nwoKA7wANXBJMRKRxbLjh6tlrAb7+Gp5+OhQsjz4K55wT+rW0bh2GNfftG7Y99oDvfS/Z2EWailz/\nbzCS0H9lODDe3VfEF5KISONq1Wr1yB+Ab74J0/I/9ljYRo+GP/4x9HXp0ycUK/vuC3vuGYodEYlf\nrk9bdwSecverVJxIoZk4cWLSIUiMkshnixbhzsmf/gT/+U9Ypfm550Kh0qYNjB8f7r5ssgn88Idw\n1llw112VI0JpAAAgAElEQVSwaFGjh1p09P0pUeVaoHwJqOOrFKSKilgfg0rCCiGfG2wQHu+ccQb8\n+9/w8cfw3//CuHHQtSvcdhsMGgTt2oWhzL/9LfzjHzB/vkYK1VYI+ZTikOtU97cQprD/fvwhNS51\nkhWROCxYAHPmhIUQH38c/ve/sH+rrcLdmH32gb33DrPdNmuWbKwicSm4TrLA/wOeM7MxwDnu/l2M\nMYmIFJ1ttgnbMceE159+Ck8+ubpoqZoBd6ONQj+WvfcO2557hn0iUlOuBcoQ4D7gLOCnZvYI8B6w\nci1t3d0vzPE6IiJFafPN4fDDwwZhpNDzz8MTT4Si5aqrwsRyJSVhOHNVwbLXXtC5c6KhixSEXAuU\nUYSVig3YLrvVxQEVKCLSpLVqFUb+7LtveL1yJcybFwqWJ56A++8PnW8Btt46FCp77RWKll13DdP7\nizQlDbmDIlKQMpkMM2bMSDoMiUla81lSEjrU9uwJJ54Y9n38cXgs9MQTYSK5//f/wpDnVq2gd+9Q\nsFStQdSuXbLx5yqt+ZT45VSguPvkuAMRicuwYcOSDkFi1JTyucUWYTTQoEHh9TffQEVFKFqeegom\nT4a//jW817Xr6mKlTx/Yeefi6HzblPIpDZPzVPdpoVE8IlIs3GHhwtUFy1NPwYsvwnffhQnjfvCD\nUKzsuWfYivUuixSPQhzFA6xaKPBHwA+AtsAz7n5j9r0ts/v+p1E+IiINZ7Z6tNBRR4V9lZWh8+3T\nT4eCZeJEuPji8N72268uVvbcMwxx1lT9UixyLlDMbB/gn0AnQmdZJywaeGO2SR/gNuBI4M6GhSki\nImtTWrp6rSAId1kWLAgFS9V2221hiHOLFlBWFma//eEPQ9HSuXMofEQKTU4zyZpZT+A/QEdgHPBz\nQpFS3UygEvhpQwIUqa/p06cnHYLESPmsHzPo0gV+8Qu48spQoHzxRbi78te/hoLkzjvD+126QMeO\n8OMfh7suDz4Y2uaT8ilR5XoH5c9AS2Cgu88CsFoluLt/a2YVwO4NilCknqZOncqgql6GUvSUz4Zr\n2XL1Y54qH30EzzyzehszJhQnZtCjR7jD8oMfhG3nneMb5qx8SlS5TnX/IfC2u+9Vbd9KYJK7D622\n7xZCEbNJHMHmgzrJioiEeVlee211wfLss/Dyy6EDbsuWsPvuqwuWH/wg9G/RoyEpxE6ymwDvRmi3\nIaFfioiIFLCSknDnpEcPOP74sO/rr8MooaqC5e67wwy4AJtuGuZm6d07FCy9e4fHRSJxybVA+Rjo\nGqFdD6IVMiIiUmBatVo9z0qVTz+F555bvd1wA4weHd7bcsvVRUvv3qFD7uabJxO7FL9cC5SHgF+Z\n2QHu/vDaGpjZTwhFzIRcgxMRkcKy+eZwyCFhgzBq6L33ahYtl1wCS5aE97fbDvbYIxQse+wBvXrB\nxhsnF78Uj5xG8QB/Bb4FppvZyWbWoeoNM9vUzIYCE4GvgMsbHqZIdEOGaCWGNFE+C5sZdOoERxwB\nf/kLzJ4NixfD66/DlCmQycD778PIkXDAAdCmzRC6d4df/hKuuCKs9Pzll0l/CilEuU51P8/MjgL+\nAYzPbg4cl90AlgFHufs7cQQqEtWAAQOSDkFipHwWn5IS2GGHsB19dNi3YkVYHHHcuAFssAGUl8O/\n/gXLloUip3v3cIelrCxsu+0GG22U7OeQZDVoqnsz2wYYDhwEdCHckXkPeAC4zN3fiiHGvNIoHhGR\nZCxfDv/7X5gJt7w8bC+9FNYgqipaysrCY6GysjCSqHXrpKOW6gpxFA8A7r4A+H1MsYiISBPSvDns\numvYTjgh7KsqWqoKlvJyuOOO1XdadthhddHSq1coWjbdNNnPIfnRoAJFREQkTtWLlqHZWbW++w7m\nzg3FSkVF+POuu8I6RADbbhsKlepFS4cOdV9DioMKFEmdOXPmsM8++yQdhsRE+UyXXPK5wQZhNtud\nd149R8uKFaEj7gsvhILlhRdqjh7q2HF10bL77mHr0kWTyxWTSAWKmb1N6AR7oLu/k30dlbv79jlF\nJ5KDsWPH6hdaiiif6RJXPps1Wz2xXFVHXHeYPz/cZXnhhfDnDTfAhx+G9zfZJHS+3X331X927x7f\nNP4Sr0idZLPT2AN0d/fXq72OxN1zHc6cd+okmz6VlZWUlpYmHYbERPlMlyTy+cEHYUbcF15Yvb2V\nHcLRogXstFMoWKq2XXbRXC1RJd5JtnaBUcgFh4h+maWL8pkuSeSzY8ewHXro6n1LloS1hqoKlooK\nuPnm0EkXwlpDu+66umjZddcw34seETWegu+DYmanAGcBHYCXgFPd/bl1tP8eMBI4JnvM/wEXuPuk\n/EcrIiLFoE0b2HffsFX59tvQGffFF8Nw5xdfDJPJffZZeH/TTVd34K0qXnr2DHdhJH4FXaCY2WDg\nMuA3wLOEOVfuN7Pvu/uiOg67HWgHDAHeAjqS+4y5IiLSRHzve6uLjyru8O67oWCp2u65Z/WiiRts\nEPqx7LpreDRUdXz79rrb0lA5/eI2s35mdqeZ7buONn2zbfrmHh7Dgevc/WZ3nwecBFQCQ+u45iHA\nvsBAd3/Y3Re6+zPu/lQDYpAiM2LEiKRDkBgpn+lSbPk0g86d4fDD4dxz4fbb4Y03YOlSePJJGDcO\n9t4b3n4bLrggrFHUsWMoUA46CM48EyZPDo+Rli1L+tMUl1zvoPyWMHvsceto8yIwgDDl/WP1vYCZ\nNQfKgIur9rm7m9lsoE8dhx0OPA/8PzP7FWEtoBnAn91d/zSaiM6dOycdgsRI+UyXtORzo43WXOl5\n5Up4551wl+Xll8M2fTpcnl2Rrlkz6NYt3GnZZZcwbHqXXdS3pS45TXVvZu8A77r7Ou+OmNljwFa5\nDDM2s47A+0Afd3+m2v4xQF93X6NIMbP7gP0JU+1fALQFrgEecvcT6riORvGIiEjeLF0K//3v6qLl\n5ZfhlVdWz9nSps3qeV6qtp12CsOiC13io3jWogPwZIR27wJ75HiNXJQAK4Gj3f1LADM7A7jdzH7n\n7t80YiwiIiK0br3m3Zaqvi2vvLK6YHn8cfj738PMuRDurFQVK1WFS/fuTadTbq6dR78C2kdotwXh\nEU8uFgEr1nKd9sCHdRzzAfB+VXGSNRcwYOt1XWzgwIFkMpkaW58+fZg+fXqNdrNmzSKTyaxx/Cmn\nnMLEiRNr7KuoqCCTybBoUc3+vCNHjmTMmDE19i1cuJBMJsO8efNq7B83btwaz2wrKyvJZDLMmTOn\nxv6pU6eudWn6wYMH63Poc+hz6HPocxTQ56jq2/KjH8F7751C//4TeeUV+Oqr8IjowgsraNEiw7ff\nLmLqVPjVr8KooVatRrLFFmN44IHG/xxTp05d9buxQ4cOZDIZhg8fvsYxccn1Ec8DwD5AN3dfWEeb\nzsDrwFPufkBOwZk9DTzj7qdnXxuwEPibu1+ylvYnAlcAW7h7ZXbfj4E7gI3WdgdFj3jSZ968eXTv\n3j3pMCQmyme6KJ+5WbIkPCb673/D3Zbf/jbcUUlaPh/x5HoH5UagBXC3ma3xCCe7bybQPNs2V5cD\nJ5rZsWbWHbgWKAUmZa/zFzObXK39LcCnwE1m1iM7gmgsMFGPd5qOs88+O+kQJEbKZ7oon7lp0yaM\nFvrtb2H8+MIoTvItpz4o7j7VzH4C/Ax4xsxeIsw5ArA9sCvhscq/3f0fuQbn7reZWVtCh9f2hJFB\nB7v7J9kmHYBO1dp/ZWYHAeOA5wjFyjTgz7nGIMVn/PjxSYcgMVI+00X5lKhyesQDYGYlwJ+AM4Da\nfY0/JzxqudjdVzQowjzTIx4REZHcFOIoHtx9JXBRdtjvHqy+k/EuUO7u38YQn4iIiDRBDZ7q3t2X\nA09lNxEREZEG0xo1kjq1hxJKcVM+00X5lKgi3UExs/MABya4++Ls66jc3S/MKTqRHFRWViYdgsRI\n+UwX5VOiitRJ1sxWEgqUHu7+erXXUVYPcHdv1rAw80edZEVERHJTCJ1khxIKkg+yr9ecbk5EREQk\nJpEKFHefVOv15DqaioiIiDRYpE6yZrbCzCZWe32ema25MIJIAai9BocUN+UzXZRPiSrqKB6jZn+T\nUcCg2KMRicHQoUOTDkFipHymi/IpUUUtUL4krEwsUvBGjRqVdAgSI+UzXZRPiSpqJ9mXgQPNbCTw\nTnZfVzM7NsrB7n5zLsGJ5EKjsdJF+UwX5VOiilqgnA/cCYwkjOYB2Du7rYtl26tAERERkciijuJ5\nwMx6AgcS1twZBbwE3JW/0ERERKSpirwWj7u/C9wEYGajgBfd/fw8xSWSs4kTJ3LCCSckHYbERPlM\nF+VTooo6zPhGM6ve9XoIcEN+QhJpmIqKWCczlIQpn+mifEpU9ZnqfpK7D82+XpF9XfRlsKa6FxER\nyU0+p7qPOsx4OdCy2uva86KIiIiIxCZqgfIusK+ZbZPPYEREREQgeoFyC7AV8Hb28Q7Acdkp8Ne3\nfZef0EVERCStohYoo4CzgDnA/Oy+SmBhhO3d2KIViSCT0TJRaaJ8povyKVFFnQdlJXB5dqvqNHt7\nVadZkUIybNiwpEOQGCmf6aJ8SlRR76DUNplwN0Wk4AwYMCDpECRGyme6KJ8SVeSJ2qpz9yFxByIi\nIiJSJepEbcea2V51vLexmbWs472jzOzyhgQoIiIiTU/URzyTgF/X8d5nwIQ63hsAnF7PmEQaZPr0\n6UmHIDFSPtNF+ZSocu2DUp0mbZOCMnXq1KRDkBgpn+mifEpUcRQoIgVl2rRpSYcgMVI+00X5lKhU\noIiIiEjBUYEiIiIiBUcFioiIiBQcFSiSOkOGaJqeNFE+00X5lKjqM1HbIWb2UD3f655DTCINopkq\n00X5TBflU6Iyd19/o7D2Tq7c3Zs14Pi8MrNeQHl5eTm9evVKOhwREZGiUVFRQVlZGUCZu1fEee6o\nd1AOiPOiIiIiIusSdTXjR/MdiIiIiEgVdZKV1JkzRwttp4nymS7Kp0SlAkVSZ+zYsUmHIDFSPtNF\n+ZSoVKBI6tx6661JhyAxUj7TRfmUqFSgSOqUlpYmHYLESPlMF+VTolKBIiIiIgVHBYqIiIgUHBUo\nkjojRoxIOgSJkfKZLsqnRKUCRVKnc+fOSYcgMVI+00X5lKiiTnXfoH9R7r6wIcfnk6a6FxERyU0h\nTHU/H1h/JbN2Xo/riIiIiEQuHB4j9wJFREREpF6irsWzf57jEInNvHnz6N69e9JhSEyUz3RRPiUq\ndZKV1Dn77LOTDkFipHymi/IpUcXSN8TMWgCbAd+4++I4zimSq/HjxycdgsRI+UwX5VOiatAdFDP7\njZm9AHwFvAdcWu29I8zsTjPr2sAYRepFwxjTRflMF+VTosqpQDGzZmb2b+AaoAcwF7BazV4CBgGD\nGxShiIiINDm53kEZBvwYuA/Yxt13rt3A3d8C3gQOzT08MLNTzOwdM/vazJ42s94Rj9vbzJabWazj\nskVERCT/ci1Qjgc+Aga7+0fraPc/YJscr4GZDQYuA0YCuxPuytxvZm3Xc1wbYDIwO9drS/EaM2ZM\n0iFIjJTPdFE+JapcC5RuwDPu/tV62n0FtMvxGgDDgevc/WZ3nwecBFQCQ9dz3LXAFODpBlxbilRl\nZWXSIUiMlM90UT4lqlwLlOVAywjtOgNLc7mAmTUHyoAHq/Z5mJd/NtBnHccNAbYFzs/lulL8zj9f\nqU8T5TNdlE+JKtcC5VWgzMxa19XAzLYAdgNezPEabYFmhEdJ1X0EdKjjmjsAFwPHuPvKHK8rIiIi\nCcu1QPkHsDlwrZl9r/abZtYMmACUEvqC5J2ZlRAe64zMdtCFNUcWiYiISBHItUC5HngEOAp4zcyu\nze7f1cyuAl4Hfgo8QCgacrEIWAG0r7W/PfDhWtq3BvYAxmdH7ywH/gzsZmbfmtn+67rYwIEDyWQy\nNbY+ffowffr0Gu1mzZpFJpNZ4/hTTjmFiRMn1thXUVFBJpNh0aJFNfaPHDlyjY5iCxcuJJPJMG/e\nvBr7x40bx4gRI2rsq6ysJJPJMGfOnBr7p06dypAhQ9aIbfDgwU3qcyxatCgVnwPSkY+Gfo5Fixal\n4nNAOvLR0M9Rdf5i/xxVmtLnmDp16qrfjR06dCCTyTB8+PA1jomLhW4dORxo1pIwwubXQPNab68A\nbgROd/dlOQdn9jShM+7p2dcGLAT+5u6X1GprhDlZqjsFOIBQLM1396/Xco1eQHl5eTm9evXKNVQp\nIJlMhhkzZiQdhsRE+UwX5TNdKioqKCsrAyhz91in9ch5qvts4XGKmY0C9ge6EO7IvAc87O7/F0N8\nlwOTzKwceJYwqqcUmARgZn8BtnT347IdaP9X/WAz+xhY5u5zY4hFisSoUaOSDkFipHymi/IpUTV4\nLR53/wS4PYZY1nbu27JznlxAeLTzInBw9poQOst2yse1pXjpTli6KJ/ponxKVLEsFphP7n41cHUd\n7635wKzm++ej4cYiIiJFJ1KBYmbHNuQi7n5zQ44XERGRpiXqHZRJQC69aS17nAoUaTQTJ07khBNO\nSDoMiYnymS7Kp0QVtUC5gNwKFJFGV1FRoR+AKaJ8povyKVHlPMw4LTTMWEREJDf5HGac60RtIiIi\nInmjAkVEREQKTtRRPOc14Bru7hc24HgRERFpYqJ2kh1F6CSby+J7DqhAkUajqbTTRflMF+VToopa\noKxzQjSRQjJs2LCkQ5AYKZ/ponxKVJEKFHefnO9AROIyYMCApEOQGCmf6aJ8SlTqJCsiIiIFRwWK\niIiIFJwGFShmto+ZXWJm083sQTN7aC3bg3EFKxLF9OnTkw5BYqR8povyKVHlVKBYcCPwKHAmkAH2\nr7XtV+3vIo1m6tSpSYcgMVI+00X5lKhyvYNyEnA8UA4cBNyZ3d8NOJSwuOBK4BJguwZFKFJP06ZN\nSzoEiZHymS7Kp0QVdZhxbccDXwGHuvunZvZLAHd/A3gDuN/M7gWmAU8CC2KIVURERJqIXO+g9ACe\ndPdPs68dwMyaVTVw9zsId1jOalCEIiIi0uTkWqCUAJ9We12Z/XPTWu3eAHbO8RoiIiLSROVaoLwP\nbFntddUjnN1rtfs+8F2O1xDJyZAhmvg4TZTPdFE+JapcC5QKoGe1RzqzCOv0jDWz7mbW2sxGAGXA\nCzHEKRKZZqpMF+UzXZRPicrcvf4HmR0FTAEGufuM7L5bgF+Q7Y+StQLYx92fjSHWvDCzXkB5eXk5\nvXr1SjocERGRolFRUUFZWRlAmbtXxHnunEbxuPtUM7uTmo9vjgNeBgYR+qK8Dowt5OJEREREClOu\nw4xx929qvV4O/DW7iYiIiOQs15lkK8zs9riDEYnDnDlzkg5BYqR8povyKVHl2km2G7A8zkBE4jJ2\n7NikQ5AYKZ/ponxKVLkWKG8Am8cZiEhcbr311qRDkBgpn+mifEpUuRYoE4H9zKx7nMGIxKG0tDTp\nECRGyme6KJ8SVU4FiruPIywI+KiZDTezrmb2vVgjExERkSYrp1E8Zrai6q/ApdkNM1tbc3f3nEcL\niYiISNOT6yOed4GFhCnuF65ne7fhYYpEN2LEiKRDkBgpn+mifEpUuU7U1iXmOERi07lz56RDkBgp\nn+mifEpUOU11nyaa6l5ERCQ3+ZzqPtdHPCIiIiJ50+DOq2a2I7AD0JrQaXYN7n5zQ68jIiIiTUfO\nBYqZHQhcDWy/rmaE1Y1VoEijmTdvHt27a4qetFA+00X5lKhyXYtnD+AeoDNwC/BK9q2/ArcDn2Vf\n3wRc0MAYRerl7LPPTjoEiZHymS7Kp0SV6x2UP2aPPcTdHzCzm4Cd3f0cADPbBLgOOAzYI5ZIRSIa\nP3580iFIjJTPdFE+JapcO8nuBbzg7g+s7U13/xw4FlgJXJTjNURyomGM6aJ8povyKVHlWqBsRlgw\nsMq3AGa2YdUOd/8GeBw4KOfoREREpEnKtUD5BNi41muA7Wq1awW0yfEaIiIi0kTlWqC8CWxb7fWz\nhBE7v63aYWZdgX7A2zlHJ5KDMWPGJB2CxEj5TBflU6LKtUC5F+hmZj2yr/9DWJfnZDN7xsz+BTwH\ntAQmNjxMkegqKyuTDkFipHymi/IpUeU01b2ZdQB+DMxx91ez+3YGbgO6ZZutJBQnJ3kBz6evqe5F\nRERyk8+p7nNdLPBDwjDi6vteAXqYWXdgU+BNd/9kbceLiIiIrEuDp7qvzd3nxX1OERERaVrq1QfF\nzAaa2fVmdp+ZTTezC8xs2/UfKdJ4Fi1alHQIEiPlM12UT4kqcoFiZlOAmcAJwMFABjgHeNXMMvkJ\nT6T+hg4dmnQIEiPlM12UT4kq0iMeMzsBOAr4DvgH8AJh9eLDgD7AzWa2jbsvyVegIlGNGjUq6RAk\nRspnuiifElXUPijHEUblHOruD1bb/5fsOjzHAkcQFgcUSZRGY6WL8pkuyqdEFfURz87A07WKkyoX\nEyZp2zm2qERERKRJi1qgbAy8Vcd7b1V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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -476,16 +502,16 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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AAJCtmq9pcPe7JF0l6WpJP5X0W5LOd/eny6u0SlqWWoWIwujDbmhu5BkX8kSo\nui6EdPevu/vJ7n6Mu3e4+09GPLba3d83ybZfdnfOOcwymzdvzrsEpIg840KeCMVnTyATd955Z94l\nIEXkGRfyRCiaBmSipaUl7xKQIvKMC3kiFE0DAAAIQtMAAACC0DQgE6OHmaC5kWdcyBOhaBqQiba2\ntrxLQIrIMy7kiVA1T4TMAhMhAQCoz0xOhORIAwAACELTAAAAgtA0IBOjP+kNzY0840KeCEXTgExs\n2LAh7xKQIvKMC3kiFE0DMrF169a8S0CKyDMu5IlQNA3IBLd0xYU840KeCEXTAAAAgtA0AACAIDQN\nyERvb2/eJSBF5BkX8kQomgZkolgs5l0CUkSecSFPhGKMNAAAEWGMNAAAyB1NAwAACELTgEwMDQ3l\nXQJSRJ5xIU+EomlAJtasWZN3CUgRecaFPBGKpgGZ2LRpU94lIEXkGRfyRCiaBmSCu2DiQp5xIU+E\nomkAAABBaBoAAEAQmgZkoq+vL+8SkCLyjAt5IhRNAzIxMJDqUDLkjDzjQp4IxRhpAAAiwhhpAACQ\nO5oGAAAQhKYBAAAEoWlAJpIkybsEpIg840KeCEXTgEysW7cu7xKQIvKMC3kiFE0DMtHZ2Zl3CUgR\necaFPBGKpgEAAAShaQAAAEFoGpCJ7du3510CUkSecSFPhKqraTCztWb2iJkdNLMHzOydk6z7YTPb\nYWZPmdlzZvZDM+ME2ixTKBTyLgEpIs+4kCdC1dw0mFmXpOslbZT0DkkPS7rPzBZNsMk5knZIulDS\nSknfl3SPmZ1WV8VoStu2bcu7BKSIPONCnghVz5GGHkm3uPsd7r5b0qclFSWtGW9ld+9x96+6e7+7\n/6e7f1HSf0i6qO6qAQBA5mpqGsxsnqR2Sd+rLPPSJ17dL6kj8DlM0gJJz9Ty2gAAIF+1HmlYJGmu\npP2jlu+X1Br4HOslzZd0V42vDQAAcpTp3RNmdrmkP5H0MXcfyvK1ka/Vq1fnXQJSRJ5xIU+EqrVp\nGJJ0WNKSUcuXSHpysg3N7DJJt6rUMHw/5MVWrVqlJEmqvjo6OsbcHrRjx45xZ6evXbtWfX19VcsG\nBgaUJImGhqp7lo0bN6q3t7dq2eDgoJIk0e7du6uWb9myRevXr69aViwWlSSJdu7cWbW8UCiM+wPZ\n1dU1q/ajs7Mziv2Q4shjuvvR2dkZxX5IceQx3f2oTIRs9v2omE37USgUXn1vbG1tVZIk6unpGbNN\nWqx0SUI8aD37AAAI+ElEQVQNG5g9IOlH7v658vcmaVDS19z9ugm26Zb0TUld7n5vwGuslNTf39+v\nlStX1lQfAACz2cDAgNrb2yWp3d0H0nzuo+rY5gZJt5lZv6QHVbqbokXSbZJkZtdKOsHdryh/f3n5\nsc9K+rGZVY5SHHT3X02regAAkJmamwZ3v6s8k+FqlU5LPCTpfHd/urxKq6RlIza5UqWLJ28uf1Xc\nrglu0wQAAI2nrgsh3f3r7n6yux/j7h3u/pMRj6129/eN+P533H3uOF80DLPI6HN1aG7kGRfyRCg+\newKZ2Lx5c94lIEXkGRfyRCiaBmTizjvvzLsEpIg840KeCEXTgEy0tLTkXQJSRJ5xIU+EomkAAABB\naBoAAEAQmgZkYvQENDQ38owLeSIUTQMy0dbWlncJSBF5xoU8EarmMdJZYIw0AAD1mckx0hxpAAAA\nQWgaAABAEJoGZGL0x8OiuZFnXMgToWgakIkNGzbkXQJSRJ5xIU+EomlAJrZu3Zp3CUgRecaFPBGK\npgGZ4JauuJBnXMgToWgaAABAEJoGAAAQhKYBmejt7c27BKSIPONCnghF04BMFIvFvEtAisgzLuSJ\nUIyRBgAgIoyRBgAAuaNpAAAAQWgakImhoaG8S0CKyDMu5IlQNA3IxJo1a/IuASkiz7iQJ0LRNCAT\nmzZtyrsEpIg840KeCEXTgExwF0xcyDMu5IlQNA0AACAITQMAAAhC04BM9PX15V0CUkSecSFPhKJp\nQCYGBlIdSoackWdcyBOhGCMNAEBEGCMNAAByR9MAAACC0DQAAIAgNA3IRJIkeZeAFJFnXMgToWga\nkIl169blXQJSRJ5xIU+EomlAJjo7O/MuASkiz7iQJ0LRNAAAgCA0DQAAIAhNAzKxffv2vEtAisgz\nLuSJUHU1DWa21sweMbODZvaAmb1zivXPNbN+M3vRzP7dzK6or1w0q97e3rxLQIrIMy7kiVA1Nw1m\n1iXpekkbJb1D0sOS7jOzRROsf7KkeyV9T9Jpkm6S9E0zO6++ktGMFi9enHcJSBF5xoU8EaqeIw09\nkm5x9zvcfbekT0sqSlozwfp/KOnn7r7B3X/m7jdL+uvy8wAAgCZRU9NgZvMktat01ECS5KVPvLpf\nUscEm51Zfnyk+yZZv+EUCoWGeb5atg1Zd6p1Jnt8osfS/vdKG3nW9thsy3M6z5l2nlOtR54z+5y1\nbjeTP6ONkmetRxoWSZoraf+o5fsltU6wTesE67/OzF5b4+vngjeZ2h6bbb+UyDNfzfomQ9MwvmbN\nM3T9Zm8ajsr01cIdLUm7du3Kuw5J0nPPPZfq581P5/lq2TZk3anWmezxiR4bb/mDDz6Y6r/hdJAn\nec7Uc6ad51TrkefMPmet283kz2gty0e8dx49ZdE1stLZhcCVS6cnipIucfe7Ryy/TdJCd//wONv8\ng6R+d//8iGW/L+lGd3/DBK9zuaS/DC4MAACM9nF3/1aaT1jTkQZ3P2Rm/ZLeL+luSTIzK3//tQk2\n+ydJF45a1llePpH7JH1c0h5JL9ZSIwAAs9zRkk5W6b00VTUdaZAkM7tU0m0q3TXxoEp3QXxU0lvd\n/Wkzu1bSCe5+RXn9kyX9i6SvS/pzlRqM/y5plbuPvkASAAA0qJqvaXD3u8ozGa6WtETSQ5LOd/en\ny6u0Slo2Yv09Zva7km6U9FlJj0v6JA0DAADNpeYjDQAAYHbisycAAEAQmgYAABCkKZsGM/ugme02\ns5+Z2SfzrgfTY2bfNrNnzOyuvGvB9JjZSWb2fTP7VzN7yMw+mndNmB4zW2hmPzazATP7ZzP7g7xr\nwvSZ2TFmtsfMNte0XbNd02BmcyX9m6TflvS8pAFJ73b3X+ZaGOpmZudIWiDpCne/NO96UD8za5V0\nvLv/s5ktkdQv6c3ufjDn0lCn8m31r3X3F83sGEn/Kqmd37nNzcyukfQmSY+5+4bQ7ZrxSMO7JP1f\nd3/S3Z+X9LcqzX1Ak3L3/6NSA4gmV/65/Ofy3/dLGpJ0XL5VYTq8pDIv55jyn5ZXPZg+M/t1Sb8h\n6e9q3bYZm4YTJO0d8f1eSSfmVAuACZhZu6Q57r53ypXR0MqnKB6SNCjpOnd/Ju+aMC1flfRfVUfz\nl2nTYGbvNbO7zWyvmR0xs2Scddaa2SNmdtDMHjCzd2ZZI8KRZ1zSzNPMjpN0u6QrZ7puTCytTN39\nOXc/XdIpkj5uZouzqB/V0sizvM3P3P3/VRbVUkPWRxrmqzQM6jOSxlxMYWZdkq6XtFHSOyQ9LOm+\n8jCpiicknTTi+xPLy5C9NPJE40glTzN7jaT/LelP3f1HM100JpXqz2h5iN/Dkt47UwVjUmnkeaak\ny8zs5yodcfgDM/tScAXunsuXpCOSklHLHpB004jvTaUJkhtGLJsr6WeSlko6VtIuSW/Iaz/4ml6e\nIx47V9Jf5b0ffE0/T0kFSf8t733gK51MJR0v6djy3xeq9LEAb897f2b713R/55Yfv0LS5lpet2Gu\naSh/gma7pO9Vlnlpr+6X1DFi2WFJfyzp71W6c+KrzlW8DSc0z/K635W0TdKFZjZoZu/OslZMLTRP\nMztL0sckXWxmPy3fpvf2rOvF1Gr4GV0u6R/N7KeS/kGlN6V/zbJWTK2W37nTUfNnT8ygRSodRdg/\navl+la7yfJW73yvp3ozqQn1qyfO8rIpC3YLydPcfqLF+r2BioZn+WKVD3Whswb9zK9z99lpfpGGO\nNAAAgMbWSE3DkKTDKn1y5khLJD2ZfTmYJvKMC3nGh0zjkkmeDdM0uPshlabHvb+yrDyJ7P2SfphX\nXagPecaFPONDpnHJKs9Mzz2a2XxJv67h+0LfaGanSXrG3R+TdIOk28ysX9KDknoktUi6Lcs6EYY8\n40Ke8SHTuDREnhnfIvLbKt0mcnjU15+PWOczkvZIOijpnySdkfetLXyR52z4Is/4vsg0rq9GyLPp\nPrAKAADko2GuaQAAAI2NpgEAAAShaQAAAEFoGgAAQBCaBgAAEISmAQAABKFpAAAAQWgaAABAEJoG\nAAAQhKYBAAAEoWkAAABBaBoAAEAQmgYAABDk/wNZKamExrfjsgAAAABJRU5ErkJggg==\n", 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CSy+95LsJp5ziT0VMmQIf+1jSUYpIf+U8p8E5d59zbohzbqcuj4rs8+XOudN2sP8crQY5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\nu8XV1dVRXl7eLba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TRcEiIiIFJueiwcymAjcA1cARwJPAcjMb1cMuJwH1wBRgEnAPcIeZ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\nJJT/D/4qo+iCHdmnAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -498,6 +524,15 @@ "plt.ylim(0)\n", "plt.grid()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -520,11 +555,16 @@ "version": "2.7.12" }, "latex_envs": { + "LaTeX_envs_menu_present": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, - "eqNumInitial": 0 + "eqNumInitial": 0, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false } }, "nbformat": 4, diff --git a/examples/performance_analysis.py b/examples/performance_analysis.py index 4729a286..31568581 100644 --- a/examples/performance_analysis.py +++ b/examples/performance_analysis.py @@ -34,7 +34,8 @@ #P_thresh_opt = 0.566#0.566#0.92# analyzer.compute_tradeoffs_and_print_from_training() linestyle="-" -analyzer.compute_tradeoffs_and_plot('test',save_figure=save_figure,plot_string='_test',linestyle=linestyle) +P_thresh_range,missed_range,fp_range = analyzer.compute_tradeoffs_and_plot('test',save_figure=save_figure,plot_string='_test',linestyle=linestyle) +np.savez('test_roc.npz',"P_thresh_range",P_thresh_range,"missed_range",missed_range,"fp_range",fp_range) analyzer.compute_tradeoffs_and_plot('train',save_figure=save_figure,plot_string='_train',linestyle=linestyle) analyzer.summarize_shot_prediction_stats_by_mode(P_thresh_opt,'test') @@ -55,6 +56,8 @@ alarms,disr_alarms,nondisr_alarms = analyzer.gather_first_alarms(P_thresh_opt,'test') analyzer.hist_alarms(disr_alarms,'disruptive alarms, P thresh = {}'.format(P_thresh_opt),save_figure=save_figure,linestyle=linestyle) +np.savez('disruptive_alarms_test.npz',"disr_alarms",disr_alarms,"P_thresh_opt",P_thresh_opt) + print('{} disruptive alarms'.format(len(disr_alarms))) print('{} seconds mean alarm time'.format(np.mean(disr_alarms[disr_alarms > 0]))) print('{} seconds median alarm time'.format(np.median(disr_alarms[disr_alarms > 0]))) diff --git a/examples/slurm.cmd b/examples/slurm.cmd index 16af0d4f..1e22ad99 100644 --- a/examples/slurm.cmd +++ b/examples/slurm.cmd @@ -1,5 +1,5 @@ #!/bin/bash -#SBATCH -t 01:30:00 +#SBATCH -t 01:00:00 #SBATCH -N 3 #SBATCH --ntasks-per-node=4 #SBATCH --ntasks-per-socket=2 @@ -7,21 +7,19 @@ #SBATCH -c 4 #SBATCH --mem-per-cpu=0 -export PYTHONHASHSEED=0 -module load anaconda -source activate pppl +module load anaconda/4.4.0 +source activate PPPL module load cudatoolkit/8.0 module load cudnn/cuda-8.0/6.0 module load openmpi/cuda-8.0/intel-17.0/2.1.0/64 -module load intel/17.0/64/17.0.4.196 intel-mkl/2017.3/4/64 +module load intel/17.0/64/17.0.4.196 #remove checkpoints for a benchmark run -rm /tigress/$USER/model_checkpoints/* -rm /tigress/$USER/results/* -rm /tigress/$USER/csv_logs/* -rm /tigress/$USER/Graph/* -rm /tigress/$USER/normalization/* +rm /scratch/gpfs/$USER/model_checkpoints/* +rm /scratch/gpfs/$USER/results/* +rm /scratch/gpfs/$USER/csv_logs/* +rm /scratch/gpfs/$USER/Graph/* +rm /scratch/gpfs/$USER/normalization/* export OMPI_MCA_btl="tcp,self,sm" - srun python mpi_learn.py diff --git a/examples/tune_hyperparams.py b/examples/tune_hyperparams.py index e8efd61e..56b8eb69 100644 --- a/examples/tune_hyperparams.py +++ b/examples/tune_hyperparams.py @@ -7,8 +7,8 @@ tunables = [] shallow = False -num_nodes = 2 -num_trials = 50 +num_nodes = 1 +num_trials = 20 t_warn = CategoricalHyperparam(['data','T_warning'],[0.256,1.024,10.024]) cut_ends = CategoricalHyperparam(['data','cut_shot_ends'],[False,True]) @@ -34,14 +34,20 @@ lr_decay = CategoricalHyperparam(['model','lr_decay'],[0.97,0.985,1.0]) fac = CategoricalHyperparam(['data','positive_example_penalty'],[1.0,4.0,16.0]) target = CategoricalHyperparam(['target'],['maxhinge','hinge','ttdinv','ttd']) - batch_size = CategoricalHyperparam(['training','batch_size'],[64,256,1024]) - dropout_prob = CategoricalHyperparam(['model','dropout_prob'],[0.1,0.3,0.5]) - conv_filters = CategoricalHyperparam(['model','num_conv_filters'],[5,10]) + #target = CategoricalHyperparam(['target'],['hinge','ttdinv','ttd']) + batch_size = CategoricalHyperparam(['training','batch_size'],[128,256]) + dropout_prob = CategoricalHyperparam(['model','dropout_prob'],[0.01,0.05,0.1]) + conv_filters = CategoricalHyperparam(['model','num_conv_filters'],[128,256]) conv_layers = IntegerHyperparam(['model','num_conv_layers'],2,4) - rnn_layers = IntegerHyperparam(['model','rnn_layers'],1,4) - rnn_size = CategoricalHyperparam(['model','rnn_size'],[100,200,300]) - tunables = [lr,lr_decay,fac,target,batch_size,dropout_prob] - tunables += [conv_filters,conv_layers,rnn_layers,rnn_size] + rnn_layers = IntegerHyperparam(['model','rnn_layers'],1,3) + rnn_size = CategoricalHyperparam(['model','rnn_size'],[128,256]) + dense_size = CategoricalHyperparam(['model','dense_size'],[128,256]) + extra_dense_input = CategoricalHyperparam(['model','extra_dense_input'],[False,True]) + equalize_classes = CategoricalHyperparam(['data','equalize_classes'],[False,True]) + #rnn_length = CategoricalHyperparam(['model','length'],[32,128]) + #tunables = [lr,lr_decay,fac,target,batch_size,dropout_prob] + tunables = [lr,lr_decay,fac,target,batch_size,equalize_classes,dropout_prob] + tunables += [conv_filters,conv_layers,rnn_layers,rnn_size,dense_size,extra_dense_input] tunables += [cut_ends,t_warn] diff --git a/plasma/conf_parser.py b/plasma/conf_parser.py index 15bf4dbe..5a9dde16 100644 --- a/plasma/conf_parser.py +++ b/plasma/conf_parser.py @@ -14,9 +14,8 @@ def parameters(input_file): with open(input_file, 'r') as yaml_file: params = yaml.load(yaml_file) - params['user_name'] = getpass.getuser() - output_path = params['fs_path'] + "/" + params['user_name'] + output_path = params['fs_path'] #+ "/" + params['user_name'] base_path = output_path params['paths']['base_path'] = base_path @@ -93,6 +92,10 @@ def parameters(input_file): params['paths']['shot_files'] = [jet_carbon_wall] params['paths']['shot_files_test'] = [jet_iterlike_wall] params['paths']['use_signals_dict'] = jet_signals_0D + elif params['paths']['data'] == 'jet_data_1D': + params['paths']['shot_files'] = [jet_carbon_wall] + params['paths']['shot_files_test'] = [jet_iterlike_wall] + params['paths']['use_signals_dict'] = jet_signals_1D elif params['paths']['data'] == 'jet_carbon_data': params['paths']['shot_files'] = [jet_carbon_wall] params['paths']['shot_files_test'] = [] @@ -105,6 +108,17 @@ def parameters(input_file): params['paths']['shot_files'] = [jenkins_jet_carbon_wall] params['paths']['shot_files_test'] = [jenkins_jet_iterlike_wall] params['paths']['use_signals_dict'] = jet_signals + elif params['paths']['data'] == 'jet_data_fully_defined': #jet data but with fully defined signals + params['paths']['shot_files'] = [jet_carbon_wall] + params['paths']['shot_files_test'] = [jet_iterlike_wall] + params['paths']['use_signals_dict'] = fully_defined_signals + elif params['paths']['data'] == 'jet_data_fully_defined_0D': #jet data but with fully defined signals + params['paths']['shot_files'] = [jet_carbon_wall] + params['paths']['shot_files_test'] = [jet_iterlike_wall] + params['paths']['use_signals_dict'] = fully_defined_signals_0D + + + elif params['paths']['data'] == 'd3d_data': params['paths']['shot_files'] = [d3d_full] params['paths']['shot_files_test'] = [] @@ -131,11 +145,18 @@ def parameters(input_file): params['paths']['shot_files_test'] = [] params['paths']['use_signals_dict'] = {'q95':q95,'li':li,'ip':ip,'lm':lm,'betan':betan,'energy':energy,'dens':dens,'pradcore':pradcore,'pradedge':pradedge,'pin':pin,'torquein':torquein,'ipdirect':ipdirect,'iptarget':iptarget,'iperr':iperr, 'etemp_profile':etemp_profile ,'edens_profile':edens_profile} - + elif params['paths']['data'] == 'd3d_data_fully_defined': #jet data but with fully defined signals + params['paths']['shot_files'] = [d3d_full] + params['paths']['shot_files_test'] = [] + params['paths']['use_signals_dict'] = fully_defined_signals + elif params['paths']['data'] == 'd3d_data_fully_defined_0D': #jet data but with fully defined signals + params['paths']['shot_files'] = [d3d_full] + params['paths']['shot_files_test'] = [] + params['paths']['use_signals_dict'] = fully_defined_signals_0D #cross-machine elif params['paths']['data'] == 'jet_to_d3d_data': - params['paths']['shot_files'] = [jet_carbon_wall] + params['paths']['shot_files'] = [jet_full] params['paths']['shot_files_test'] = [d3d_full] params['paths']['use_signals_dict'] = fully_defined_signals elif params['paths']['data'] == 'd3d_to_jet_data': @@ -143,13 +164,21 @@ def parameters(input_file): params['paths']['shot_files_test'] = [jet_iterlike_wall] params['paths']['use_signals_dict'] = fully_defined_signals elif params['paths']['data'] == 'jet_to_d3d_data_0D': - params['paths']['shot_files'] = [jet_carbon_wall] + params['paths']['shot_files'] = [jet_full] params['paths']['shot_files_test'] = [d3d_full] params['paths']['use_signals_dict'] = fully_defined_signals_0D elif params['paths']['data'] == 'd3d_to_jet_data_0D': params['paths']['shot_files'] = [d3d_full] params['paths']['shot_files_test'] = [jet_iterlike_wall] params['paths']['use_signals_dict'] = fully_defined_signals_0D + elif params['paths']['data'] == 'jet_to_d3d_data_1D': + params['paths']['shot_files'] = [jet_full] + params['paths']['shot_files_test'] = [d3d_full] + params['paths']['use_signals_dict'] = fully_defined_signals_1D + elif params['paths']['data'] == 'd3d_to_jet_data_1D': + params['paths']['shot_files'] = [d3d_full] + params['paths']['shot_files_test'] = [jet_iterlike_wall] + params['paths']['use_signals_dict'] = fully_defined_signals_1D diff --git a/plasma/models/builder.py b/plasma/models/builder.py index da3274d7..e1913b85 100644 --- a/plasma/models/builder.py +++ b/plasma/models/builder.py @@ -183,7 +183,7 @@ def slicer_output_shape(input_shape,indices): pre_rnn = Dense(dense_size//4,activation='relu',kernel_regularizer=l2(dense_regularization),bias_regularizer=l2(dense_regularization),activity_regularizer=l2(dense_regularization)) (pre_rnn) pre_rnn_model = Model(inputs = pre_rnn_input,outputs=pre_rnn) - pre_rnn_model.summary() + #pre_rnn_model.summary() x_input = Input(batch_shape = batch_input_shape) x_in = TimeDistributed(pre_rnn_model) (x_input) diff --git a/plasma/models/loader.py b/plasma/models/loader.py index d3751701..56ba0021 100644 --- a/plasma/models/loader.py +++ b/plasma/models/loader.py @@ -88,10 +88,15 @@ def training_batch_generator(self,shot_list): yield X[start:end],y[start:end],reset_states_now,num_so_far,num_total epoch += 1 - def fill_training_buffer(self,Xbuff,Ybuff,end_indices,shot): + def fill_training_buffer(self,Xbuff,Ybuff,end_indices,shot,is_first_fill=False): sig,res = self.get_signal_result_from_shot(shot) - sig_len = res.shape[0] length = self.conf['model']['length'] + if is_first_fill:#cut signal to random position + cut_idx = np.random.randint(res.shape[0]-length+1) + sig = sig[cut_idx:] + res = res[cut_idx:] + + sig_len = res.shape[0] sig_len = (sig_len // length)*length #make divisible by lenth assert(sig_len > 0) batch_idx = np.where(end_indices == 0)[0][0] @@ -157,8 +162,10 @@ def training_batch_generator_partial_reset(self,shot_list): num_total = len(shot_list) num_so_far = 0 returned = False + num_steps = 0 warmup_steps = self.conf['training']['batch_generator_warmup_steps'] - is_warmup_period = warmup_steps > 0 + is_warmup_period = num_steps < warmup_steps + is_first_fill = num_steps < batch_size while True: # the list of all shots shot_list.shuffle() @@ -174,11 +181,12 @@ def training_batch_generator_partial_reset(self,shot_list): X,Y = self.return_from_training_buffer(Xbuff,Ybuff,end_indices) yield X,Y,batches_to_reset,num_so_far,num_total,is_warmup_period returned = True - warmup_steps -= 1 - is_warmup_period = warmup_steps > 0 + num_steps += 1 + is_warmup_period = num_steps < warmup_steps + is_first_fill = num_steps < batch_size batches_to_reset[:] = False - Xbuff,Ybuff,batch_idx = self.fill_training_buffer(Xbuff,Ybuff,end_indices,shot) + Xbuff,Ybuff,batch_idx = self.fill_training_buffer(Xbuff,Ybuff,end_indices,shot,is_first_fill) batches_to_reset[batch_idx] = True if returned and not is_warmup_period: num_so_far += 1 diff --git a/plasma/models/mpi_runner.py b/plasma/models/mpi_runner.py index 2fad6bc2..d70d80df 100644 --- a/plasma/models/mpi_runner.py +++ b/plasma/models/mpi_runner.py @@ -20,6 +20,7 @@ import time import datetime import numpy as np +import random from functools import partial import socket @@ -129,9 +130,6 @@ def __init__(self,lr): def get_deltas(self,raw_deltas): - if K.floatx() == "float16": - raw_deltas[:] = map(lambda w: w.astype(np.float32),raw_deltas) - if self.iterations == 0: self.m_list = [np.zeros_like(g) for g in raw_deltas] self.v_list = [np.zeros_like(g) for g in raw_deltas] @@ -149,9 +147,6 @@ def get_deltas(self,raw_deltas): self.iterations += 1 - if K.floatx() == "float16": - deltas[:] = map(lambda w: w.astype(np.float16),deltas) - return deltas @@ -170,7 +165,9 @@ def get_val(self): class MPIModel(): def __init__(self,model,optimizer,comm,batch_iterator,batch_size,num_replicas=None,warmup_steps=1000,lr=0.01,num_batches_minimum=100): - # random.seed(task_index) + random.seed(task_index) + np.random.seed(task_index) + self.start_time = time.time() self.epoch = 0 self.num_so_far = 0 self.num_so_far_accum = 0 @@ -255,10 +252,17 @@ def train_on_batch_and_get_deltas(self,X_batch,Y_batch,verbose=False): weights_after_update = self.model.get_weights() self.model.set_weights(weights_before_update) + + #unscale before subtracting + weights_before_update = multiply_params(weights_before_update,1.0/self.DUMMY_LR) + weights_after_update = multiply_params(weights_after_update,1.0/self.DUMMY_LR) deltas = subtract_params(weights_after_update,weights_before_update) - deltas = multiply_params(deltas,1.0/self.DUMMY_LR) - + + #unscale loss + if conf['model']['loss_scale_factor'] != 1.0: + deltas = multiply_params(deltas,1.0/conf['model']['loss_scale_factor']) + return deltas,loss @@ -466,7 +470,7 @@ def train_epoch(self): loss_averager.add_val(curr_loss) ave_loss = loss_averager.get_val() eta = self.estimate_remaining_time(t0 - t_start,self.num_so_far-self.epoch*num_total,num_total) - write_str = '\r[{}] step: {} [ETA: {:.2f}s] [{:.2f}/{}], loss: {:.5f} [{:.5f}] | '.format(self.task_index,step,eta,1.0*self.num_so_far,num_total,ave_loss,curr_loss) + write_str = '\r[{}] step: {} [ETA: {:.2f}s] [{:.2f}/{}], loss: {:.5f} [{:.5f}] | walltime: {:.4f} | '.format(self.task_index,step,eta,1.0*self.num_so_far,num_total,ave_loss,curr_loss,time.time()-self.start_time) print_unique(write_str + write_str_0) step += 1 else: @@ -640,6 +644,7 @@ def mpi_make_predictions_and_evaluate(conf,shot_list,loader,custom_path=None): def mpi_train(conf,shot_list_train,shot_list_validate,loader, callbacks_list=None): + loader.set_inference_mode(False) conf['num_workers'] = comm.Get_size() diff --git a/plasma/models/targets.py b/plasma/models/targets.py index 0823f459..7f214702 100644 --- a/plasma/models/targets.py +++ b/plasma/models/targets.py @@ -5,6 +5,8 @@ from plasma.utils.evaluation import mae_np,mse_np,binary_crossentropy_np,hinge_np,squared_hinge_np import keras.backend as K +import plasma.conf + #Requirement: larger value must mean disruption more likely. class Target(object): activation = 'linear' @@ -12,7 +14,7 @@ class Target(object): @abc.abstractmethod def loss_np(y_true,y_pred): - return mse_np(y_true,y_pred) + return conf['model']['loss_scale_factor']*mse_np(y_true,y_pred) @abc.abstractmethod def remapper(ttd,T_warning): @@ -29,7 +31,7 @@ class BinaryTarget(Target): @staticmethod def loss_np(y_true,y_pred): - return binary_crossentropy_np(y_true,y_pred) + return conf['model']['loss_scale_factor']*binary_crossentropy_np(y_true,y_pred) @staticmethod def remapper(ttd,T_warning,as_array_of_shots=True): @@ -51,7 +53,7 @@ class TTDTarget(Target): @staticmethod def loss_np(y_true,y_pred): - return mse_np(y_true,y_pred) + return conf['model']['loss_scale_factor']*mse_np(y_true,y_pred) @staticmethod def remapper(ttd,T_warning): @@ -85,7 +87,6 @@ def remapper(ttd,T_warning): def threshold_range(T_warning): return np.logspace(-6,np.log10(T_warning),100) - class TTDLinearTarget(Target): activation = 'linear' @@ -93,7 +94,7 @@ class TTDLinearTarget(Target): @staticmethod def loss_np(y_true,y_pred): - return mse_np(y_true,y_pred) + return conf['model']['loss_scale_factor']*mse_np(y_true,y_pred) @staticmethod @@ -128,7 +129,7 @@ def loss(y_true, y_pred): weight_mask = K.cast(K.greater(weight_mask,0.0),K.floatx()) #positive label! weight_mask = fac*weight_mask + (1 - weight_mask) #return weight_mask*squared_hinge(y_true,y_pred1) - return overall_fac*weight_mask*hinge(y_true,y_pred1) + return conf['model']['loss_scale_factor']*overall_fac*weight_mask*hinge(y_true,y_pred1) @staticmethod def loss_np(y_true, y_pred): @@ -144,7 +145,7 @@ def loss_np(y_true, y_pred): weight_mask = np.greater(y_true,0.0).astype(np.float32) #positive label! weight_mask = fac*weight_mask + (1 - weight_mask) #return np.mean(weight_mask*np.square(np.maximum(1. - y_true * y_pred, 0.)))#, axis=-1) only during training, here we want to completely sum up over all instances - return np.mean(overall_fac*weight_mask*np.maximum(1. - y_true * y_pred, 0.))#, axis=-1) only during training, here we want to completely sum up over all instances + return conf['model']['loss_scale_factor']*np.mean(overall_fac*weight_mask*np.maximum(1. - y_true * y_pred, 0.))#, axis=-1) only during training, here we want to completely sum up over all instances # def _loss_tensor_old(y_true, y_pred): @@ -174,7 +175,7 @@ class HingeTarget(Target): @staticmethod def loss_np(y_true, y_pred): - return hinge_np(y_true,y_pred) + return conf['model']['loss_scale_factor']*hinge_np(y_true,y_pred) #return squared_hinge_np(y_true,y_pred) @staticmethod @@ -188,4 +189,3 @@ def remapper(ttd,T_warning,as_array_of_shots=True): @staticmethod def threshold_range(T_warning): return np.concatenate((np.linspace(-2,-1.06,100),np.linspace(-1.06,-0.96,100),np.linspace(-0.96,2,50))) - diff --git a/plasma/preprocessor/normalize.py b/plasma/preprocessor/normalize.py index 951513ab..f9f23ac0 100644 --- a/plasma/preprocessor/normalize.py +++ b/plasma/preprocessor/normalize.py @@ -418,6 +418,7 @@ def get_individual_shot_file(prepath,shot_num,ext='.txt'): def apply_positivity(shot): for (i,sig) in enumerate(shot.signals): - if sig.is_strictly_positive: - #print ('Applying positivity constraint to {} signal'.format(sig.description)) - shot.signals_dict[sig]=np.clip(shot.signals_dict[sig],0,np.inf) + if hasattr(sig,"is_strictly_positive"): #backwards compatibility when this attribute didn't exist + if sig.is_strictly_positive: + #print ('Applying positivity constraint to {} signal'.format(sig.description)) + shot.signals_dict[sig]=np.clip(shot.signals_dict[sig],0,np.inf) diff --git a/plasma/preprocessor/preprocess.py b/plasma/preprocessor/preprocess.py index be3808c5..1d12f6c3 100644 --- a/plasma/preprocessor/preprocess.py +++ b/plasma/preprocessor/preprocess.py @@ -146,7 +146,7 @@ def save_shotlists(self,shot_list_train,shot_list_validate,shot_list_test): def apply_bleed_in(conf,shot_list_train,shot_list_validate,shot_list_test): - np.random.seed(1) + np.random.seed(2) num = conf['data']['bleed_in'] new_shots = [] if num > 0: @@ -170,13 +170,22 @@ def apply_bleed_in(conf,shot_list_train,shot_list_validate,shot_list_test): print("Sampled {} shots, {} disruptive, {} nondisruptive".format(num_sampled_nd+num_sampled_d,num_sampled_d,num_sampled_nd)) print("Before adding: training shots: {} validation shots: {}".format(len(shot_list_train),len(shot_list_validate))) assert(num_sampled_d == num) - num_to_sample = len(shot_list_bleed) if conf['data']['bleed_in_equalize_sets']:#add bleed-in shots to training and validation set repeatedly + print("Applying equalized bleed in") for shot_list_curr in [shot_list_train,shot_list_validate]: for i in range(len(shot_list_curr)): s = shot_list_bleed.sample_shot() shot_list_curr.append(s) + elif conf['data']['bleed_in_repeat_fac'] > 1: + repeat_fac = conf['data']['bleed_in_repeat_fac'] + print("Applying bleed in with repeat factor {}".format(repeat_fac)) + num_to_sample = int(round(repeat_fac*len(shot_list_bleed))) + for i in range(num_to_sample): + s = shot_list_bleed.sample_shot() + shot_list_train.append(s) + shot_list_validate.append(s) else: #add each shot only once + print("Applying bleed in without repetition") for s in shot_list_bleed: shot_list_train.append(s) shot_list_validate.append(s) diff --git a/plasma/primitives/data.py b/plasma/primitives/data.py index 1354f506..6014803c 100644 --- a/plasma/primitives/data.py +++ b/plasma/primitives/data.py @@ -59,6 +59,7 @@ def load_data_from_txt_safe(self,prepath,shot,dtype='float32'): file_path = self.get_file_path(prepath,shot.machine,shot.number) if not self.is_saved(prepath,shot): print('Signal {}, shot {} was never downloaded'.format(self.description,shot.number)) + print('prepath is ='+file_path) return None,False if os.path.getsize(file_path) == 0: @@ -227,7 +228,7 @@ def load_data(self,prepath,shot,dtype='float32'): for i in range(timesteps): _,order = np.unique(mapping[i,:],return_index=True) #make sure the mapping is ordered and unique if sig[i,order].shape[0] > 2: - f = UnivariateSpline(mapping[i,order],sig[i,order],s=0,k=1,ext=0) + f = UnivariateSpline(mapping[i,order],sig[i,order],s=0,k=1,ext=3) #ext = 0 is extrapolation, ext = 3 is boundary value. sig_interp[i,:] = f(remapping) else: print('Signal {}, shot {} has not enough points for linear interpolation. dfitpack.error: (m>k) failed for hidden m: fpcurf0:m=1'.format(self.description,shot.number)) diff --git a/plasma/utils/batch_jobs.py b/plasma/utils/batch_jobs.py index 56203b97..4ae45732 100644 --- a/plasma/utils/batch_jobs.py +++ b/plasma/utils/batch_jobs.py @@ -112,7 +112,7 @@ def create_slurm_header(num_nodes,use_mpi,idx): assert(num_nodes == 1) lines = [] lines.append('#!/bin/bash\n') - lines.append('#SBATCH -t 06:00:00\n') + lines.append('#SBATCH -t 20:00:00\n') lines.append('#SBATCH -N '+str(num_nodes)+'\n') if use_mpi: lines.append('#SBATCH --ntasks-per-node=4\n') diff --git a/plasma/utils/performance.py b/plasma/utils/performance.py index 43bd6b5a..7bbb9538 100644 --- a/plasma/utils/performance.py +++ b/plasma/utils/performance.py @@ -12,7 +12,7 @@ from plasma.preprocessor.normalize import VarNormalizer as Normalizer from plasma.conf import conf -from plasma.primitives.shots import Shot +from plasma.primitives.shots import Shot,ShotList class PerformanceAnalyzer(): def __init__(self,results_dir=None,shots_dir=None,i = 0,T_min_warn = None,T_max_warn = None, verbose = False,pred_ttd=False,conf=None): @@ -342,8 +342,8 @@ def load_ith_file(self): self.pred_test = dat['y_prime_test'] self.truth_test = dat['y_gold_test'] self.disruptive_test = dat['disruptive_test'] - self.shot_list_test = dat['shot_list_test'][()] - self.shot_list_train = dat['shot_list_train'][()] + self.shot_list_test = ShotList(dat['shot_list_test'][()]) + self.shot_list_train = ShotList(dat['shot_list_train'][()]) self.saved_conf = dat['conf'][()] self.conf['data']['T_warning'] = self.saved_conf['data']['T_warning'] #all files must agree on T_warning due to output of truth vs. normalized shot ttd. for mode in ['test','train']: @@ -551,7 +551,7 @@ def compute_tradeoffs_and_print_from_training(self): def compute_tradeoffs_and_plot(self,mode,save_figure=True,plot_string='',linestyle="-"): correct_range, accuracy_range, fp_range,missed_range,early_alarm_range = self.get_metrics_vs_p_thresh(mode) - self.tradeoff_plot(accuracy_range,missed_range,fp_range,early_alarm_range,save_figure=save_figure,plot_string=plot_string,linestyle=linestyle) + return self.tradeoff_plot(accuracy_range,missed_range,fp_range,early_alarm_range,save_figure=save_figure,plot_string=plot_string,linestyle=linestyle) def get_prediction_type(self,TP,FP,FN,TN,early,late): if TP: @@ -656,78 +656,84 @@ def plot_shot(self,shot,save_fig=True,normalize=True,truth=None,prediction=None, if(shot.previously_saved(self.shots_dir)): shot.restore(self.shots_dir) - t_disrupt = shot.t_disrupt - is_disruptive = shot.is_disruptive - if normalize: - self.normalizer.apply(shot) + if shot.signals_dict is not None: #make sure shot was saved with data + t_disrupt = shot.t_disrupt + is_disruptive = shot.is_disruptive + if normalize: + self.normalizer.apply(shot) - use_signals = self.saved_conf['paths']['use_signals'] - fontsize= 15 - lower_lim = 0 #len(pred) - plt.close() - colors = ["b","k"] - lss = ["-","--"] - f,axarr = plt.subplots(len(use_signals)+1,1,sharex=True,figsize=(10,15))#, squeeze=False) - plt.title(prediction_type) - assert(np.all(shot.ttd.flatten() == truth.flatten())) - xx = range(len(prediction)) #list(reversed(range(len(pred)))) - for i,sig in enumerate(use_signals): - ax = axarr[i] - num_channels = sig.num_channels - sig_arr = shot.signals_dict[sig] - if num_channels == 1: - # if j == 0: - ax.plot(xx,sig_arr[:,0],linewidth=2)#,linestyle=lss[j],color=colors[j]) - # else: - # ax.plot(xx,sig_arr[:,0],linewidth=2)#,linestyle=lss[j],color=colors[j],label = labels[sig]) - ax.plot([],linestyle="none",label = sig.description)#labels[sig]) - if np.min(sig_arr[:,0]) < 0: - ax.set_ylim([-6,6]) - ax.set_yticks([-5,0,5]) + use_signals = self.saved_conf['paths']['use_signals'] + fontsize= 15 + lower_lim = 0 #len(pred) + plt.close() + colors = ["b","k"] + lss = ["-","--"] + f,axarr = plt.subplots(len(use_signals)+1,1,sharex=True,figsize=(10,15))#, squeeze=False) + plt.title(prediction_type) + assert(np.all(shot.ttd.flatten() == truth.flatten())) + xx = range(len(prediction)) #list(reversed(range(len(pred)))) + for i,sig in enumerate(use_signals): + ax = axarr[i] + num_channels = sig.num_channels + sig_arr = shot.signals_dict[sig] + if num_channels == 1: + # if j == 0: + ax.plot(xx,sig_arr[:,0],linewidth=2)#,linestyle=lss[j],color=colors[j]) + # else: + # ax.plot(xx,sig_arr[:,0],linewidth=2)#,linestyle=lss[j],color=colors[j],label = labels[sig]) + ax.plot([],linestyle="none",label = sig.description)#labels[sig]) + if np.min(sig_arr[:,0]) < 0: + ax.set_ylim([-6,6]) + ax.set_yticks([-5,0,5]) + # ax.plot(xx,sig_arr[:,0],linewidth=2)#,linestyle=lss[j],color=colors[j],label = labels[sig]) + ax.plot([],linestyle="none",label = sig.description)#labels[sig]) + if np.min(sig_arr[:,0]) < 0: + ax.set_ylim([-6,6]) + ax.set_yticks([-5,0,5]) + else: + ax.set_ylim([0,8]) + ax.set_yticks([0,5]) + # ax.set_ylabel(labels[sig],size=fontsize) else: - ax.set_ylim([0,8]) - ax.set_yticks([0,5]) - # ax.set_ylabel(labels[sig],size=fontsize) - else: - ax.imshow(sig_arr[:,:].T, aspect='auto', label = sig.description,cmap="inferno" ) - ax.set_ylim([0,num_channels]) - ax.text(lower_lim+200, 45, sig.description, bbox={'facecolor': 'white', 'pad': 10},fontsize=fontsize-5) - ax.set_yticks([0,num_channels/2]) - ax.set_yticklabels(["0","0.5"]) - ax.set_ylabel("$\\rho$",size=fontsize) - ax.legend(loc="best",labelspacing=0.1,fontsize=fontsize,frameon=False) - ax.axvline(len(truth)-self.T_min_warn,color='r',linewidth=0.5) - plt.setp(ax.get_xticklabels(),visible=False) + ax.imshow(sig_arr[:,:].T, aspect='auto', label = sig.description,cmap="inferno" ) + ax.set_ylim([0,num_channels]) + ax.text(lower_lim+200, 45, sig.description, bbox={'facecolor': 'white', 'pad': 10},fontsize=fontsize-5) + ax.set_yticks([0,num_channels/2]) + ax.set_yticklabels(["0","0.5"]) + ax.set_ylabel("$\\rho$",size=fontsize) + ax.legend(loc="best",labelspacing=0.1,fontsize=fontsize,frameon=False) + ax.axvline(len(truth)-self.T_min_warn,color='r',linewidth=0.5) + plt.setp(ax.get_xticklabels(),visible=False) + plt.setp(ax.get_yticklabels(),fontsize=fontsize) + f.subplots_adjust(hspace=0) + #print(sig) + #print('min: {}, max: {}'.format(np.min(sig_arr), np.max(sig_arr))) + ax = axarr[-1] + # ax.semilogy((-truth+0.0001),label='ground truth') + # ax.plot(-prediction+0.0001,'g',label='neural net prediction') + # ax.axhline(-P_thresh_opt,color='k',label='trigger threshold') + # nn = np.min(pred) + ax.plot(xx,truth,'g',label='target',linewidth=2) + # ax.axhline(0.4,linestyle="--",color='k',label='threshold') + ax.plot(xx,prediction,'b',label='RNN output',linewidth=2) + ax.axhline(P_thresh_opt,linestyle="--",color='k',label='threshold') + ax.set_ylim([-2,2]) + ax.set_yticks([-1,0,1]) + # if len(truth)-T_max_warn >= 0: + # ax.axvline(len(truth)-T_max_warn,color='r')#,label='max warning time') + ax.axvline(len(truth)-self.T_min_warn,color='r',linewidth=0.5)#,label='min warning time') + ax.set_xlabel('T [ms]',size=fontsize) + # ax.axvline(2400) + ax.legend(loc = (0.5,0.7),fontsize=fontsize-5,labelspacing=0.1,frameon=False) plt.setp(ax.get_yticklabels(),fontsize=fontsize) - f.subplots_adjust(hspace=0) - #print(sig) - #print('min: {}, max: {}'.format(np.min(sig_arr), np.max(sig_arr))) - ax = axarr[-1] - # ax.semilogy((-truth+0.0001),label='ground truth') - # ax.plot(-prediction+0.0001,'g',label='neural net prediction') - # ax.axhline(-P_thresh_opt,color='k',label='trigger threshold') - # nn = np.min(pred) - ax.plot(xx,truth,'g',label='target',linewidth=2) - # ax.axhline(0.4,linestyle="--",color='k',label='threshold') - ax.plot(xx,prediction,'b',label='RNN output',linewidth=2) - ax.axhline(P_thresh_opt,linestyle="--",color='k',label='threshold') - ax.set_ylim([-2,2]) - ax.set_yticks([-1,0,1]) - # if len(truth)-T_max_warn >= 0: - # ax.axvline(len(truth)-T_max_warn,color='r')#,label='max warning time') - ax.axvline(len(truth)-self.T_min_warn,color='r',linewidth=0.5)#,label='min warning time') - ax.set_xlabel('T [ms]',size=fontsize) - # ax.axvline(2400) - ax.legend(loc = (0.5,0.7),fontsize=fontsize-5,labelspacing=0.1,frameon=False) - plt.setp(ax.get_yticklabels(),fontsize=fontsize) - plt.setp(ax.get_xticklabels(),fontsize=fontsize) - # plt.xlim(0,200) - plt.xlim([lower_lim,len(truth)]) - # plt.savefig("{}.png".format(num),dpi=200,bbox_inches="tight") - if save_fig: - plt.savefig('sig_fig_{}{}.png'.format(shot.number,extra_filename),bbox_inches='tight') - np.savez('sig_{}{}.npz'.format(shot.number,extra_filename),shot=shot,T_min_warn=self.T_min_warn,T_max_warn=self.T_max_warn,prediction=prediction,truth=truth,use_signals=use_signals,P_thresh=P_thresh_opt) - #plt.show() + plt.setp(ax.get_xticklabels(),fontsize=fontsize) + # plt.xlim(0,200) + plt.xlim([lower_lim,len(truth)]) + # plt.savefig("{}.png".format(num),dpi=200,bbox_inches="tight") + if save_fig: + plt.savefig('sig_fig_{}{}.png'.format(shot.number,extra_filename),bbox_inches='tight') + np.savez('sig_{}{}.npz'.format(shot.number,extra_filename),shot=shot,T_min_warn=self.T_min_warn,T_max_warn=self.T_max_warn,prediction=prediction,truth=truth,use_signals=use_signals,P_thresh=P_thresh_opt) + #plt.show() else: print("Shot hasn't been processed") @@ -835,6 +841,7 @@ def tradeoff_plot(self,accuracy_range,missed_range,fp_range,early_alarm_range,sa if save_figure: plt.savefig(title_str + '_roc.png',bbox_inches='tight',dpi=200) print('ROC area ({}) is {}'.format(plot_string,self.roc_from_missed_fp(missed_range,fp_range))) + return P_thresh_range,missed_range,fp_range def get_pred_truth_disr_by_shot(self,shot): if shot in self.shot_list_test: From 1623c5d8f040cd5877f6ef31aaf544f91ba8c253 Mon Sep 17 00:00:00 2001 From: Craig Michoski Date: Thu, 22 Mar 2018 13:24:34 -0500 Subject: [PATCH 2/3] Working on maverick -- cem --- .../d3d/d3d_clear_data_avail_cem.tex | 199 ++++ .../d3d/d3d_clear_data_avail_cem2.txt | 1001 +++++++++++++++++ .../d3d/d3d_disrupt_data_avail_cem.txt | 199 ++++ .../d3d/d3d_disrupt_data_avail_cem2.txt | 1001 +++++++++++++++++ examples/conf.yaml | 14 +- examples/out | 253 +++++ plasma/conf_parser.py | 6 + plasma/primitives/shots.py | 1 + set_envs | 7 + 9 files changed, 2674 insertions(+), 7 deletions(-) create mode 100644 data/shot_lists/d3d/d3d_clear_data_avail_cem.tex create mode 100644 data/shot_lists/d3d/d3d_clear_data_avail_cem2.txt create mode 100644 data/shot_lists/d3d/d3d_disrupt_data_avail_cem.txt create mode 100644 data/shot_lists/d3d/d3d_disrupt_data_avail_cem2.txt create mode 100644 examples/out create mode 100644 set_envs diff --git a/data/shot_lists/d3d/d3d_clear_data_avail_cem.tex b/data/shot_lists/d3d/d3d_clear_data_avail_cem.tex new file mode 100644 index 00000000..bbf6e2ce --- /dev/null +++ b/data/shot_lists/d3d/d3d_clear_data_avail_cem.tex @@ -0,0 +1,199 @@ 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+168443 0.626500 +168445 2.576500 +168446 1.405500 +168447 1.776500 +168448 2.082500 +168449 1.831000 +168483 0.672000 +168485 0.662500 +168486 0.820500 +168489 0.714500 +168492 2.338000 +168493 2.316000 +168494 2.341000 +168495 4.422000 +168501 4.676500 +168502 6.203500 +168503 5.755500 +168505 4.525000 +168506 5.711000 +168507 6.390500 +168508 6.281000 +168509 6.097000 +168510 3.070000 +168511 6.675500 +168512 2.955000 +168513 6.267000 +168514 0.940500 +168515 7.020000 +168517 2.807000 +168518 7.039500 +168520 6.258000 +168521 6.160500 +168522 6.134000 +168527 7.124000 +168528 6.484500 +168531 6.278000 +168532 6.127500 +168533 7.128000 +168538 6.271000 +168539 7.039000 +168540 6.139500 +168541 6.132000 +168542 6.280500 +168543 7.031000 +168545 6.141500 +168546 6.151500 +168547 6.142000 +168549 7.032000 +168550 6.307500 +168552 7.019500 +168553 6.141500 +168555 6.704000 diff --git a/examples/conf.yaml b/examples/conf.yaml index 1f68139f..9caa4f39 100644 --- a/examples/conf.yaml +++ b/examples/conf.yaml @@ -7,10 +7,10 @@ target: 'hinge' #'maxhinge' #'maxhinge' #'binary' #'hinge' num_gpus: 1 #40 paths: - signal_prepath: '/../../../05447/merlo/maverick/plasma_python/signal_data_new/' #/signal_data/jet/ + signal_prepath: '/../../../05447/merlo/maverick/plasma_python/signal_data_new/d3d/d3d/' #/signal_data/jet/ shot_list_dir: '/shot_lists/d3d/' tensorboard_save_path: '/Graph/' - data: 'd3d_data_all' #'d3d_to_jet_data' #'d3d_to_jet_data' # 'jet_to_d3d_data' #jet_data + data: 'd3d_data_cem' #'d3d_to_jet_data' #'d3d_to_jet_data' # 'jet_to_d3d_data' #jet_data specific_signals: ['q95'] #['q95','li','ip','betan','energy','lm','pradcore','pradedge','pradtot','pin','torquein','tmamp1','tmamp2','tmfreq1','tmfreq2','pechin','energydt','ipdirect','etemp_profile','edens_profile'] #if left empty will use all valid signals defined on a machine. Only use if need a custom set executable: "mpi_learn.py" shallow_executable: "learn.py" @@ -19,7 +19,7 @@ data: bleed_in: 0 #how many shots from the test sit to use in training? bleed_in_repeat_fac: 1 #how many times to repeat shots in training and validation? bleed_in_remove_from_test: True - bleed_in_equalize_sets: False + bleed_in_equalize_sets: True signal_to_augment: None #'plasma current' #or None augmentation_mode: 'none' augment_during_training: False @@ -32,7 +32,7 @@ data: plotting: False #train/validate split #how many shots to use - use_shots: 20 #1000 #200000 #1000 #200000 + use_shots: 200000 #1000 #200000 positive_example_penalty: 1.0 #by what factor to upweight positive examples? #normalization timescale dt: 0.001 @@ -111,17 +111,17 @@ training: shuffle_training: True train_frac: 0.75 validation_frac: 0.33 - batch_size: 16 #128 #256 + batch_size: 128 #256 #THIS WAS THE CULPRIT FOR NO TRAINING! Lower than 1000 performs very poorly max_patch_length: 100000 #How many shots are we loading at once? - num_shots_at_once: 100 + num_shots_at_once: 200 num_epochs: 10 use_mock_data: False data_parallel: False hyperparam_tuning: False batch_generator_warmup_steps: 0 - num_batches_minimum: 20 #minimum number of batches per epoch + num_batches_minimum: 200 #minimum number of batches per epoch ranking_difficulty_fac: 1.0 #how much to upweight incorrectly classified shots during training callbacks: list: ['earlystop'] diff --git a/examples/out b/examples/out new file mode 100644 index 00000000..54f7ca2d --- /dev/null +++ b/examples/out @@ -0,0 +1,253 @@ +I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally +I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally +I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally +I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally +I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally +all signals (determines which signals are downloaded and preprocessed): +dict_values([Normalized Beta, Locked mode amplitude, stored energy time derivative, plasma current, plasma current target, Radiated Power, Plasma density, q95 safety factor, Input Beam Torque, plasma current direction, Electron temperature profile, Radiated Power Edge, plasma current error, Radiated Power Core, internal inductance, Input Power (beam for d3d), stored energy, Electron density profile]) +Selected signals (determines which signals training is run on): +[q95 safety factor] +{'callbacks': {'list': ['earlystop'], + 'metrics': ['val_loss', 'val_roc', 'train_loss'], + 'mode': 'max', + 'monitor': 'val_roc', + 'patience': 5, + 'write_grads': False}, + 'data': {'T_max': 1000.0, + 'T_min_warn': 30, + 'T_warning': 1.024, + 'augment_during_training': False, + 'augmentation_mode': 'none', + 'bleed_in': 0, + 'bleed_in_equalize_sets': False, + 'bleed_in_remove_from_test': True, + 'bleed_in_repeat_fac': 1, + 'current_end_thresh': 10000, + 'current_index': 0, + 'current_thresh': 750000, + 'cut_shot_ends': True, + 'dt': 0.001, + 'equalize_classes': False, + 'floatx': 'float32', + 'norm_stat_range': 100.0, + 'normalizer': 'var', + 'plotting': False, + 'positive_example_penalty': 1.0, + 'recompute': False, + 'recompute_normalization': False, + 'signal_to_augment': 'None', + 'target': , + 'use_shots': 200, + 'window_decay': 2, + 'window_size': 10}, + 'env': {'name': 'frnn', 'type': 'anaconda'}, + 'fs_path': '/work/00004/michoski/maverick', + 'model': {'backend': 'tensorflow', + 'clipnorm': 10.0, + 'dense_regularization': 0.001, + 'dense_size': 128, + 'dropout_prob': 0.1, + 'extra_dense_input': False, + 'ignore_timesteps': 100, + 'length': 128, + 'loss_scale_factor': 1.0, + 'lr': 2e-05, + 'lr_decay': 0.97, + 'num_conv_filters': 128, + 'num_conv_layers': 3, + 'optimizer': 'adam', + 'pool_size': 2, + 'pred_batch_size': 128, + 'pred_length': 200, + 'regularization': 0.001, + 'return_sequences': True, + 'rnn_layers': 2, + 'rnn_size': 200, + 'rnn_type': 'LSTM', + 'shallow': False, + 'shallow_model': {'C': 1.0, + 'final_hidden_layer_size': 10, + 'kernel': 'rbf', + 'learning_rate': 0.1, + 'learning_rate_mlp': 0.0001, + 'max_depth': 3, + 'mlp_regularization': 0.0001, + 'n_estimators': 100, + 'num_hidden_layers': 3, + 'num_samples': 1000000, + 'scale_pos_weight': 10.0, + 'skip_train': False, + 'type': 'xgboost'}, + 'size_conv_filters': 3, + 'skip': 1, + 'stateful': True, + 'use_batch_norm': False, + 'use_bidirectional': False, + 'warmup_steps': 0}, + 'num_gpus': 40, + 'paths': {'all_machines': [d3d], + 'all_signals': [Normalized Beta, + Locked mode amplitude, + stored energy time derivative, + plasma current, + plasma current target, + Radiated Power, + Plasma density, + q95 safety factor, + Input Beam Torque, + plasma current direction, + Radiated Power Edge, + plasma current error, + Radiated Power Core, + internal inductance, + Input Power (beam for d3d), + stored energy, + Electron temperature profile, + Electron density profile], + 'all_signals_dict': {'betan': Normalized Beta, + 'dens': Plasma density, + 'edens_profile': Electron density profile, + 'energy': stored energy, + 'energydt': stored energy time derivative, + 'etemp_profile': Electron temperature profile, + 'ip': plasma current, + 'ipdirect': plasma current direction, + 'iperr': plasma current error, + 'iptarget': plasma current target, + 'li': internal inductance, + 'lm': Locked mode amplitude, + 'pin': Input Power (beam for d3d), + 'pradcore': Radiated Power Core, + 'pradedge': Radiated Power Edge, + 'pradtot': Radiated Power, + 'q95': q95 safety factor, + 'torquein': Input Beam Torque}, + 'base_path': '/work/00004/michoski/maverick', + 'csvlog_save_path': '/work/00004/michoski/maverick/csv_logs/', + 'data': 'd3d_data_all', + 'executable': 'mpi_learn.py', + 'global_normalizer_path': '/work/00004/michoski/maverick/normalization/normalization_signal_group_3236450206765786377241194018831785328.npz', + 'model_save_path': '/work/00004/michoski/maverick/model_checkpoints/', + 'normalizer_path': '/work/00004/michoski/maverick/normalization/normalization_signal_group_3236450206765786377241194018831785328.npz', + 'output_path': '/work/00004/michoski/maverick', + 'processed_prepath': '/work/00004/michoski/maverick/processed_shots/signal_group_3236450206765786377241194018831785328/', + 'results_prepath': '/work/00004/michoski/maverick/results/', + 'saved_shotlist_path': '/work/00004/michoski/maverick/processed_shotlists/d3d_data_all/shot_lists_signal_group_3236450206765786377241194018831785328.npz', + 'shallow_executable': 'learn.py', + 'shot_files': [machine: d3d +d3d data since shot 125500], + 'shot_files_all': [machine: d3d +d3d data since shot 125500], + 'shot_files_test': [], + 'shot_list_dir': '/work/00004/michoski/maverick/shot_lists/d3d/', + 'signal_prepath': '/work/00004/michoski/maverick/../../../05447/merlo/maverick/plasma_python/signal_data_new/', + 'specific_signals': ['q95'], + 'tensorboard_save_path': '/work/00004/michoski/maverick/Graph/', + 'use_signals': [q95 safety factor], + 'use_signals_dict': {'betan': Normalized Beta, + 'dens': Plasma density, + 'edens_profile': Electron density profile, + 'energy': stored energy, + 'etemp_profile': Electron temperature profile, + 'ip': plasma current, + 'ipdirect': plasma current direction, + 'iperr': plasma current error, + 'iptarget': plasma current target, + 'li': internal inductance, + 'lm': Locked mode amplitude, + 'pin': Input Power (beam for d3d), + 'pradcore': Radiated Power Core, + 'pradedge': Radiated Power Edge, + 'q95': q95 safety factor, + 'torquein': Input Beam Torque}}, + 'target': 'hinge', + 'training': {'as_array_of_shots': True, + 'batch_generator_warmup_steps': 0, + 'batch_size': 64, + 'data_parallel': False, + 'hyperparam_tuning': False, + 'max_patch_length': 100000, + 'num_batches_minimum': 200, + 'num_epochs': 10, + 'num_shots_at_once': 100, + 'ranking_difficulty_fac': 1.0, + 'shuffle_training': True, + 'train_frac': 0.75, + 'use_mock_data': False, + 'validation_frac': 0.33}, + 'user_name': 'michoski'} +shots already processed. +validate: 98 shots, 23 disruptive +training: 200 shots, 46 disruptive +testing: 99 shots, 15 disruptive +...done +normalizationMachine d3d: +loaded normalization data from {d3d: 397} shots ( {d3d: 84} disruptive ) +Machine: d3d: +Var Normalizer. +stds: [ 1.40912863e+00 0.00000000e+00 4.92695381e-04 2.76347908e+05 + 2.17596701e-01 1.24422188e+00 1.12670149e+00 1.82833458e+03 + 4.64327935e-04 6.80558316e-01 3.05198138e-02 3.81909883e-01 + 1.00950409e+00 3.93063968e-01 9.28953115e-01 1.51406147e+00] + +...done +Training on 200 shots, testing on 99 shots +Using TensorFlow backend. +I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: +name: Tesla K40m +major: 3 minor: 5 memoryClockRate (GHz) 0.745 +pciBusID 0000:08:00.0 +Total memory: 11.17GiB +Free memory: 11.10GiB +I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 +I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y +I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K40m, pci bus id: 0000:08:00.0) +I tensorflow/compiler/xla/service/platform_util.cc:58] platform CUDA present with 1 visible devices +I tensorflow/compiler/xla/service/platform_util.cc:58] platform Host present with 20 visible devices +I tensorflow/compiler/xla/service/service.cc:180] XLA service executing computations on platform Host. Devices: +I tensorflow/compiler/xla/service/service.cc:187] StreamExecutor device (0): , +I tensorflow/compiler/xla/service/platform_util.cc:58] platform CUDA present with 1 visible devices +I tensorflow/compiler/xla/service/platform_util.cc:58] platform Host present with 20 visible devices +I tensorflow/compiler/xla/service/service.cc:180] XLA service executing computations on platform CUDA. Devices: +I tensorflow/compiler/xla/service/service.cc:187] StreamExecutor device (0): Tesla K40m, Compute Capability 3.5 +validate: 98 shots, 23 disruptive +training: 200 shots, 46 disruptive +Build model...Compile model...done +no previous checkpoint found +10 epochs left to go + +Epoch 1/10 +Process Process-1: +Traceback (most recent call last): + File "/opt/apps/gcc4_9/python3/3.5.2/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap + self.run() + File "/opt/apps/gcc4_9/python3/3.5.2/lib/python3.5/multiprocessing/process.py", line 93, in run + self._target(*self._args, **self._kwargs) + File "/home/00004/michoski/.local/lib/python3.5/site-packages/plasma-1.0.0-py3.5.egg/plasma/models/runner.py", line 88, in train + K.set_value(train_model.optimizer.lr, lr*lr_decay**(e)) +NameError: name 'lr' is not defined + +During handling of the above exception, another exception occurred: + +Traceback (most recent call last): + File "/opt/apps/gcc4_9/python3/3.5.2/lib/python3.5/multiprocessing/process.py", line 252, in _bootstrap + util._exit_function() + File "/opt/apps/gcc4_9/python3/3.5.2/lib/python3.5/multiprocessing/util.py", line 311, in _exit_function + p.join() + File "/opt/apps/gcc4_9/python3/3.5.2/lib/python3.5/multiprocessing/process.py", line 121, in join + res = self._popen.wait(timeout) + File "/opt/apps/gcc4_9/python3/3.5.2/lib/python3.5/multiprocessing/popen_fork.py", line 51, in wait + return self.poll(os.WNOHANG if timeout == 0.0 else 0) + File "/opt/apps/gcc4_9/python3/3.5.2/lib/python3.5/multiprocessing/popen_fork.py", line 29, in poll + pid, sts = os.waitpid(self.pid, flag) +KeyboardInterrupt +Traceback (most recent call last): + File "learn.py", line 96, in + p.join() + File "/opt/apps/gcc4_9/python3/3.5.2/lib/python3.5/multiprocessing/process.py", line 121, in join + res = self._popen.wait(timeout) + File "/opt/apps/gcc4_9/python3/3.5.2/lib/python3.5/multiprocessing/popen_fork.py", line 51, in wait + return self.poll(os.WNOHANG if timeout == 0.0 else 0) + File "/opt/apps/gcc4_9/python3/3.5.2/lib/python3.5/multiprocessing/popen_fork.py", line 29, in poll + pid, sts = os.waitpid(self.pid, flag) +KeyboardInterrupt diff --git a/plasma/conf_parser.py b/plasma/conf_parser.py index 5a9dde16..dc8420d5 100644 --- a/plasma/conf_parser.py +++ b/plasma/conf_parser.py @@ -80,6 +80,8 @@ def parameters(input_file): d3d_100 = ShotListFiles(d3d,params['paths']['shot_list_dir'],['d3d_clear_100.txt','d3d_disrupt_100.txt'],'d3d data 100 ND and D shots') d3d_full = ShotListFiles(d3d,params['paths']['shot_list_dir'],['d3d_clear_data_avail.txt','d3d_disrupt_data_avail.txt'],'d3d data since shot 125500') d3d_jenkins = ShotListFiles(d3d,params['paths']['shot_list_dir'],['jenkins_d3d_clear.txt','jenkins_d3d_disrupt.txt'],'Subset of d3d data for Jenkins test') + d3d_cem = ShotListFiles(d3d,params['paths']['shot_list_dir'],['d3d_clear_data_avail_cem2.txt','d3d_disrupt_data_avail_cem2.txt'],'d3d data since shot 125500') + d3d_jb_full = ShotListFiles(d3d,params['paths']['shot_list_dir'],['shotlist_JaysonBarr_clear.txt','shotlist_JaysonBarr_disrupt.txt'],'d3d shots since 160000-170000') nstx_full = ShotListFiles(nstx,params['paths']['shot_list_dir'],['disrupt_nstx.txt'],'nstx shots (all are disruptive') @@ -153,6 +155,10 @@ def parameters(input_file): params['paths']['shot_files'] = [d3d_full] params['paths']['shot_files_test'] = [] params['paths']['use_signals_dict'] = fully_defined_signals_0D + elif params['paths']['data'] == 'd3d_data_cem': + params['paths']['shot_files'] = [d3d_cem] + params['paths']['shot_files_test'] = [] + params['paths']['use_signals_dict'] = d3d_signals #cross-machine elif params['paths']['data'] == 'jet_to_d3d_data': diff --git a/plasma/primitives/shots.py b/plasma/primitives/shots.py index e2865d72..10135015 100644 --- a/plasma/primitives/shots.py +++ b/plasma/primitives/shots.py @@ -237,6 +237,7 @@ def __getitem__(self,key): def random_sublist(self,num): num = min(num,len(self)) shots_picked = np.random.choice(self.shots,size=num,replace=False) + print("shots_picked =", num) return ShotList(shots_picked) def sublists(self,num,do_shuffle=True,equal_size=False): diff --git a/set_envs b/set_envs new file mode 100644 index 00000000..59e4a5d3 --- /dev/null +++ b/set_envs @@ -0,0 +1,7 @@ +module load gcc/4.9.3 +module load python3/3.5.2 +module load cuda/8.0 +module load cudnn/5.1 +module load tensorflow-gpu/1.0.0 +module load mvapich2 +module load git \ No newline at end of file From 6556b412c74b9d4d68e26e9d54e44150da37ea52 Mon Sep 17 00:00:00 2001 From: Craig Michoski Date: Thu, 22 Mar 2018 16:09:38 -0500 Subject: [PATCH 3/3] cerm --- plasma/conf_parser.py | 16 ++++++++-------- plasma/models/mpi_runner.py | 6 ++---- plasma/models/targets.py | 9 +++++++-- setup.py | 2 +- 4 files changed, 18 insertions(+), 15 deletions(-) diff --git a/plasma/conf_parser.py b/plasma/conf_parser.py index dc8420d5..389c8517 100644 --- a/plasma/conf_parser.py +++ b/plasma/conf_parser.py @@ -1,4 +1,3 @@ -import plasma.models.targets as t from plasma.primitives.shots import ShotListFiles from data.signals import * @@ -10,6 +9,7 @@ def parameters(input_file): """Parse yaml file of configuration parameters.""" + from plasma.models.targets import HingeTarget, MaxHingeTarget, BinaryTarget, TTDTarget, TTDInvTarget, TTDLinearTarget with open(input_file, 'r') as yaml_file: params = yaml.load(yaml_file) @@ -42,18 +42,18 @@ def parameters(input_file): #ensure shallow model has +1 -1 target. if params['model']['shallow'] or params['target'] == 'hinge': - params['data']['target'] = t.HingeTarget + params['data']['target'] = HingeTarget elif params['target'] == 'maxhinge': - t.MaxHingeTarget.fac = params['data']['positive_example_penalty'] - params['data']['target'] = t.MaxHingeTarget + MaxHingeTarget.fac = params['data']['positive_example_penalty'] + params['data']['target'] = MaxHingeTarget elif params['target'] == 'binary': - params['data']['target'] = t.BinaryTarget + params['data']['target'] = BinaryTarget elif params['target'] == 'ttd': - params['data']['target'] = t.TTDTarget + params['data']['target'] = TTDTarget elif params['target'] == 'ttdinv': - params['data']['target'] = t.TTDInvTarget + params['data']['target'] = TTDInvTarget elif params['target'] == 'ttdlinear': - params['data']['target'] = t.TTDLinearTarget + params['data']['target'] = TTDLinearTarget else: print('Unkown type of target. Exiting') exit(1) diff --git a/plasma/models/mpi_runner.py b/plasma/models/mpi_runner.py index d70d80df..3f5bdf3b 100644 --- a/plasma/models/mpi_runner.py +++ b/plasma/models/mpi_runner.py @@ -185,6 +185,7 @@ def __init__(self,model,optimizer,comm,batch_iterator,batch_size,num_replicas=No self.num_workers = comm.Get_size() self.task_index = comm.Get_rank() self.history = cbks.History() + self.model.stop_training = False if num_replicas is None or num_replicas < 1 or num_replicas > self.num_workers: self.num_replicas = self.num_workers else: @@ -736,7 +737,6 @@ def mpi_train(conf,shot_list_train,shot_list_validate,loader, callbacks_list=Non epoch_logs['train_loss'] = ave_loss best_so_far = cmp_fn(epoch_logs[conf['callbacks']['monitor']],best_so_far) - stop_training = False if task_index == 0: print('=========Summary======== for epoch{}'.format(step)) print('Training Loss numpy: {:.3e}'.format(ave_loss)) @@ -747,8 +747,6 @@ def mpi_train(conf,shot_list_train,shot_list_validate,loader, callbacks_list=Non print('Training ROC: {:.4f}'.format(roc_area_train)) callbacks.on_epoch_end(int(round(e)), epoch_logs) - if hasattr(mpi_model.model,'stop_training'): - stop_training = mpi_model.model.stop_training if best_so_far != epoch_logs[conf['callbacks']['monitor']]: #only save model weights if quantity we are tracking is improving print("Not saving model weights") specific_builder.delete_model_weights(train_model,int(round(e))) @@ -759,7 +757,7 @@ def mpi_train(conf,shot_list_train,shot_list_validate,loader, callbacks_list=Non val_steps = 1 tensorboard.on_epoch_end(val_generator,val_steps,int(round(e)),epoch_logs) - stop_training = comm.bcast(stop_training,root=0) + stop_training = comm.bcast(mpi_model.model.stop_training,root=0) if stop_training: print("Stopping training due to early stopping") break diff --git a/plasma/models/targets.py b/plasma/models/targets.py index 7f214702..7aa3df6c 100644 --- a/plasma/models/targets.py +++ b/plasma/models/targets.py @@ -5,8 +5,6 @@ from plasma.utils.evaluation import mae_np,mse_np,binary_crossentropy_np,hinge_np,squared_hinge_np import keras.backend as K -import plasma.conf - #Requirement: larger value must mean disruption more likely. class Target(object): activation = 'linear' @@ -14,6 +12,7 @@ class Target(object): @abc.abstractmethod def loss_np(y_true,y_pred): + from plasma.conf import conf return conf['model']['loss_scale_factor']*mse_np(y_true,y_pred) @abc.abstractmethod @@ -31,6 +30,7 @@ class BinaryTarget(Target): @staticmethod def loss_np(y_true,y_pred): + from plasma.conf import conf return conf['model']['loss_scale_factor']*binary_crossentropy_np(y_true,y_pred) @staticmethod @@ -53,6 +53,7 @@ class TTDTarget(Target): @staticmethod def loss_np(y_true,y_pred): + from plasma.conf import conf return conf['model']['loss_scale_factor']*mse_np(y_true,y_pred) @staticmethod @@ -94,6 +95,7 @@ class TTDLinearTarget(Target): @staticmethod def loss_np(y_true,y_pred): + from plasma.conf import conf return conf['model']['loss_scale_factor']*mse_np(y_true,y_pred) @@ -118,6 +120,7 @@ class MaxHingeTarget(Target): @staticmethod def loss(y_true, y_pred): + from plasma.conf import conf fac = MaxHingeTarget.fac #overall_fac = np.prod(np.array(K.shape(y_pred)[1:]).astype(np.float32)) overall_fac = K.prod(K.cast(K.shape(y_pred)[1:],K.floatx())) @@ -133,6 +136,7 @@ def loss(y_true, y_pred): @staticmethod def loss_np(y_true, y_pred): + from plasma.conf import conf fac = MaxHingeTarget.fac #print(y_pred.shape) overall_fac = np.prod(np.array(y_pred.shape).astype(np.float32)) @@ -175,6 +179,7 @@ class HingeTarget(Target): @staticmethod def loss_np(y_true, y_pred): + from plasma.conf import conf return conf['model']['loss_scale_factor']*hinge_np(y_true,y_pred) #return squared_hinge_np(y_true,y_pred) diff --git a/setup.py b/setup.py index 59e70b94..ab3be99f 100644 --- a/setup.py +++ b/setup.py @@ -25,7 +25,7 @@ download_url = "https://github.com/PPPLDeepLearning/plasma-python", #license = "Apache Software License v2", test_suite = "tests", - install_requires = ['keras==2.0.6','pathos','matplotlib==2.0.2','hyperopt','mpi4py','xgboost'], + install_requires = ['keras>2.0.8','pathos','matplotlib==2.0.2','hyperopt','mpi4py','xgboost'], tests_require = [], classifiers = ["Development Status :: 3 - Alpha", "Environment :: Console",