{ "cells": [ { "cell_type": "markdown", "id": "e196b80d-a45b-4fb6-b7a1-f2c3ae9e636b", "metadata": { "tags": [] }, "source": [ "# Model Building, Training and Deployment" ] }, { "cell_type": "code", "execution_count": 12, "id": "184a2340-d928-4e9c-965a-483efaac5d9e", "metadata": { "tags": [] }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import sagemaker_datawrangler\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "794b74d8-d1cc-470f-8d11-8ddacbb5993e", "metadata": { "tags": [] }, "source": [ "## Load the processed data " ] }, { "cell_type": "code", "execution_count": 13, "id": "1af48e9c-ed17-407d-ba14-15ddfa7b5418", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Download Parquet from S3 locally, or use awswrangler to read pandas df directly from S3\n", "processed_dataset = \"s3://fcc-spark-example/subset-boston-data.parquet/\"" ] }, { "cell_type": "code", "execution_count": 14, "id": "14824a2e-f361-4ae7-9d70-cd747f730075", "metadata": { "tags": [] }, "outputs": [], "source": [ "comp_pd_df = pd.read_parquet(processed_dataset)" ] }, { "cell_type": "code", "execution_count": 15, "id": "e2e772fd-30aa-4761-ba42-0b617161286b", "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "32298b9b8e884a5ab8a953c43576d626" }, "text/plain": [ " no2_avg date temp_max temp_min wind_avg pressure_sea_level \\\n", "0 0.015557 2020-01-07 41.35 30.60 4.30 1015.00 \n", "1 0.011425 2020-01-08 44.25 33.35 4.65 1009.45 \n", "2 0.005458 2020-01-09 40.40 22.10 7.45 1029.80 \n", "3 0.016883 2020-01-10 51.00 24.90 5.30 1034.60 \n", "4 0.006466 2020-01-11 67.85 49.25 7.25 1023.60 \n", "... ... ... ... ... ... ... \n", "1230 0.004851 2023-06-08 65.55 55.85 6.10 1004.35 \n", "1231 0.008181 2023-06-09 63.75 54.25 4.80 1006.30 \n", "1232 0.007668 2023-06-10 65.20 53.70 4.20 1006.90 \n", "1233 0.004565 2023-06-11 74.85 56.70 6.30 1011.25 \n", "1234 0.005963 2023-06-12 80.10 66.00 8.00 1011.80 \n", "\n", " pressure_station \n", "0 507.25 \n", "1 504.45 \n", "2 514.60 \n", "3 517.05 \n", "4 511.60 \n", "... ... \n", "1230 501.70 \n", "1231 502.65 \n", "1232 502.90 \n", "1233 505.10 \n", "1234 10.90 \n", "\n", "[1235 rows x 7 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "comp_pd_df" ] }, { "cell_type": "markdown", "id": "3dac2ada-5925-4388-a885-cd6b11c00cc4", "metadata": { "tags": [] }, "source": [ "## Spliting the data for training and testing " ] }, { "cell_type": "code", "execution_count": 17, "id": "6daba743-0dd9-4195-a3e8-3994b5eb9e5b", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1071, 5) (1071, 1)\n", "(163, 5) (163, 1)\n", " temp_max temp_min wind_avg pressure_sea_level pressure_station\n", "2020-01-08 44.25 33.35 4.65 1009.45 504.45\n", "2020-01-09 40.40 22.10 7.45 1029.80 514.60\n", "2020-01-10 51.00 24.90 5.30 1034.60 517.05\n", "2020-01-11 67.85 49.25 7.25 1023.60 511.60\n", "2020-01-12 71.95 51.25 9.85 1012.05 505.60\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import warnings\n", "warnings.simplefilter(action='ignore', category=FutureWarning)\n", "\n", "comp_pd_df = comp_pd_df.set_index('date')\n", "comp_pd_df.index.name = None\n", "\n", "# Drop the 1st row \n", "aq_df = comp_pd_df.iloc[1:].copy()\n", "aq_df.index = pd.to_datetime(aq_df.index)\n", "\n", "# Dropping the NaN \n", "aq_df = aq_df.dropna()\n", "\n", "# Use the data from years 2020 up to 2022 as training, and the year 2023 as our candidate year for testing and validating our model.\n", "aq_train_df = aq_df[aq_df.index.year < 2023]\n", "aq_test_df = aq_df[aq_df.index.year == 2023]\n", "\n", "# Input Feature \n", "x_train = aq_train_df.drop('no2_avg',axis=1)\n", "x_test = aq_test_df.drop('no2_avg',axis=1)\n", "\n", "# Class Labels \n", "y_train = aq_train_df[[\"no2_avg\"]]\n", "y_test = aq_test_df[[\"no2_avg\"]]\n", "\n", "print(x_train.shape, y_train.shape)\n", "print(x_test.shape, y_test.shape)\n", "print(x_train.head())" ] }, { "cell_type": "markdown", "id": "edbf09e8-c660-4659-8a98-91f62d091c10", "metadata": { "tags": [] }, "source": [ "## Few helper functions" ] }, { "cell_type": "code", "execution_count": 18, "id": "82af81d2-78dd-465a-8f5a-258c7084de2e", "metadata": { "tags": [] }, "outputs": [], "source": [ "from math import sqrt\n", "from sklearn.metrics import mean_squared_error, r2_score, explained_variance_score\n", "\n", "# sMAPE is used in KDD Air Quality challenge: https://biendata.com/competition/kdd_2018/evaluation/ \n", "def smape(actual, predicted):\n", " dividend= np.abs(np.array(actual) - np.array(predicted))\n", " denominator = np.array(actual) + np.array(predicted)\n", " \n", " return 2 * np.mean(np.divide(dividend, denominator, out=np.zeros_like(dividend), where=denominator!=0, casting='unsafe'))\n", "\n", "def print_metrics(y_test, y_pred):\n", " print(\"RMSE: %.4f\" % sqrt(mean_squared_error(y_test, y_pred)))\n", " print('Variance score: %.4f' % r2_score(y_test, y_pred))\n", " print('Explained variance score: %.4f' % explained_variance_score(y_test, y_pred))\n", " forecast_err = np.array(y_test) - np.array(y_pred)\n", " print('Forecast bias: %.4f' % (np.sum(forecast_err) * 1.0/len(y_pred) ))\n", " print('sMAPE: %.4f' % smape(y_test, y_pred))" ] }, { "cell_type": "markdown", "id": "e17ab3fa-cf8b-4206-ba09-61eecb7d72d2", "metadata": { "tags": [] }, "source": [ "## Training the model \n", "\n", "In the section below, we create a new training job using the Linear Learner algorithm. Once that job completes, we deploy an endpoint and run some validation tests against it. \n", "\n", "**NOTE**: You only need to create this training job and deploy it once. You can use the same endpoint, even in future runs of this notebook, without re-training or re-deploying. " ] }, { "cell_type": "code", "execution_count": 19, "id": "1dd74da4-2cc7-4fc9-85f5-77aa1e6ff2ec", "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2023-06-14 20:45:23 Starting - Starting the training job...\n", "2023-06-14 20:45:37 Starting - Preparing the instances for training...\n", "2023-06-14 20:46:31 Downloading - Downloading input data...\n", "2023-06-14 20:46:57 Training - Downloading the training image......\n", "2023-06-14 20:47:52 Training - Training image download completed. Training in progress..\u001b[34mDocker entrypoint called with argument(s): train\u001b[0m\n", "\u001b[34mRunning default environment configuration script\u001b[0m\n", "\u001b[34m[06/14/2023 20:47:59 INFO 140522871928640] Reading default configuration from /opt/amazon/lib/python3.7/site-packages/algorithm/resources/default-input.json: {'mini_batch_size': '1000', 'epochs': '15', 'feature_dim': 'auto', 'use_bias': 'true', 'binary_classifier_model_selection_criteria': 'accuracy', 'f_beta': '1.0', 'target_recall': '0.8', 'target_precision': '0.8', 'num_models': 'auto', 'num_calibration_samples': '10000000', 'init_method': 'uniform', 'init_scale': '0.07', 'init_sigma': '0.01', 'init_bias': '0.0', 'optimizer': 'auto', 'loss': 'auto', 'margin': '1.0', 'quantile': '0.5', 'loss_insensitivity': '0.01', 'huber_delta': '1.0', 'num_classes': '1', 'accuracy_top_k': '3', 'wd': 'auto', 'l1': 'auto', 'momentum': 'auto', 'learning_rate': 'auto', 'beta_1': 'auto', 'beta_2': 'auto', 'bias_lr_mult': 'auto', 'bias_wd_mult': 'auto', 'use_lr_scheduler': 'true', 'lr_scheduler_step': 'auto', 'lr_scheduler_factor': 'auto', 'lr_scheduler_minimum_lr': 'auto', 'positive_example_weight_mult': '1.0', 'balance_multiclass_weights': 'false', 'normalize_data': 'true', 'normalize_label': 'auto', 'unbias_data': 'auto', 'unbias_label': 'auto', 'num_point_for_scaler': '10000', '_kvstore': 'auto', '_num_gpus': 'auto', '_num_kv_servers': 'auto', '_log_level': 'info', '_tuning_objective_metric': '', 'early_stopping_patience': '3', 'early_stopping_tolerance': '0.001', '_enable_profiler': 'false'}\u001b[0m\n", "\u001b[34m[06/14/2023 20:47:59 INFO 140522871928640] Merging with provided configuration from /opt/ml/input/config/hyperparameters.json: {'feature_dim': '5', 'mini_batch_size': '1000', 'normalize_data': 'True', 'normalize_label': 'True', 'predictor_type': 'regressor'}\u001b[0m\n", "\u001b[34m[06/14/2023 20:47:59 INFO 140522871928640] Final configuration: {'mini_batch_size': '1000', 'epochs': '15', 'feature_dim': '5', 'use_bias': 'true', 'binary_classifier_model_selection_criteria': 'accuracy', 'f_beta': '1.0', 'target_recall': '0.8', 'target_precision': '0.8', 'num_models': 'auto', 'num_calibration_samples': '10000000', 'init_method': 'uniform', 'init_scale': '0.07', 'init_sigma': '0.01', 'init_bias': '0.0', 'optimizer': 'auto', 'loss': 'auto', 'margin': '1.0', 'quantile': '0.5', 'loss_insensitivity': '0.01', 'huber_delta': '1.0', 'num_classes': '1', 'accuracy_top_k': '3', 'wd': 'auto', 'l1': 'auto', 'momentum': 'auto', 'learning_rate': 'auto', 'beta_1': 'auto', 'beta_2': 'auto', 'bias_lr_mult': 'auto', 'bias_wd_mult': 'auto', 'use_lr_scheduler': 'true', 'lr_scheduler_step': 'auto', 'lr_scheduler_factor': 'auto', 'lr_scheduler_minimum_lr': 'auto', 'positive_example_weight_mult': '1.0', 'balance_multiclass_weights': 'false', 'normalize_data': 'True', 'normalize_label': 'True', 'unbias_data': 'auto', 'unbias_label': 'auto', 'num_point_for_scaler': '10000', '_kvstore': 'auto', '_num_gpus': 'auto', '_num_kv_servers': 'auto', '_log_level': 'info', '_tuning_objective_metric': '', 'early_stopping_patience': '3', 'early_stopping_tolerance': '0.001', '_enable_profiler': 'false', 'predictor_type': 'regressor'}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 WARNING 140522871928640] Loggers have already been setup.\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Final configuration: {'mini_batch_size': '1000', 'epochs': '15', 'feature_dim': '5', 'use_bias': 'true', 'binary_classifier_model_selection_criteria': 'accuracy', 'f_beta': '1.0', 'target_recall': '0.8', 'target_precision': '0.8', 'num_models': 'auto', 'num_calibration_samples': '10000000', 'init_method': 'uniform', 'init_scale': '0.07', 'init_sigma': '0.01', 'init_bias': '0.0', 'optimizer': 'auto', 'loss': 'auto', 'margin': '1.0', 'quantile': '0.5', 'loss_insensitivity': '0.01', 'huber_delta': '1.0', 'num_classes': '1', 'accuracy_top_k': '3', 'wd': 'auto', 'l1': 'auto', 'momentum': 'auto', 'learning_rate': 'auto', 'beta_1': 'auto', 'beta_2': 'auto', 'bias_lr_mult': 'auto', 'bias_wd_mult': 'auto', 'use_lr_scheduler': 'true', 'lr_scheduler_step': 'auto', 'lr_scheduler_factor': 'auto', 'lr_scheduler_minimum_lr': 'auto', 'positive_example_weight_mult': '1.0', 'balance_multiclass_weights': 'false', 'normalize_data': 'True', 'normalize_label': 'True', 'unbias_data': 'auto', 'unbias_label': 'auto', 'num_point_for_scaler': '10000', '_kvstore': 'auto', '_num_gpus': 'auto', '_num_kv_servers': 'auto', '_log_level': 'info', '_tuning_objective_metric': '', 'early_stopping_patience': '3', 'early_stopping_tolerance': '0.001', '_enable_profiler': 'false', 'predictor_type': 'regressor'}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 WARNING 140522871928640] Loggers have already been setup.\u001b[0m\n", "\u001b[34mProcess 7 is a worker.\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Using default worker.\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Checkpoint loading and saving are disabled.\u001b[0m\n", "\u001b[34m[2023-06-14 20:48:02.140] [tensorio] [warning] TensorIO is already initialized; ignoring the initialization routine.\u001b[0m\n", "\u001b[34m[2023-06-14 20:48:02.152] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 0, \"duration\": 14, \"num_examples\": 1, \"num_bytes\": 64000}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Create Store: local\u001b[0m\n", "\u001b[34m[2023-06-14 20:48:02.165] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 1, \"duration\": 12, \"num_examples\": 2, \"num_bytes\": 68544}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Scaler algorithm parameters\n", " \u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Scaling model computed with parameters:\n", " {'stdev_label': \u001b[0m\n", "\u001b[34m[0.00439276]\u001b[0m\n", "\u001b[34m, 'stdev_weight': \u001b[0m\n", "\u001b[34m[ 17.315563 16.48217 2.943069 7.619689 107.631516]\u001b[0m\n", "\u001b[34m, 'mean_label': \u001b[0m\n", "\u001b[34m[0.00712981]\u001b[0m\n", "\u001b[34m, 'mean_weight': \u001b[0m\n", "\u001b[34m[ 63.2551 47.22725 5.8719 1015.7822 503.70264]\u001b[0m\n", "\u001b[34m}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] nvidia-smi: took 0.032 seconds to run.\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] nvidia-smi identified 0 GPUs.\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Number of GPUs being used: 0\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.2714105, \"EndTime\": 1686775682.2714422, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"Meta\": \"init_train_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 2071.0, \"count\": 1, \"min\": 2071, \"max\": 2071}, \"Total Batches Seen\": {\"sum\": 3.0, \"count\": 1, \"min\": 3, \"max\": 3}, \"Max Records Seen Between Resets\": {\"sum\": 1071.0, \"count\": 1, \"min\": 1071, \"max\": 1071}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Number of Records Since Last Reset\": {\"sum\": 0.0, \"count\": 1, \"min\": 0, \"max\": 0}, \"Number of Batches Since Last Reset\": {\"sum\": 0.0, \"count\": 1, \"min\": 0, \"max\": 0}}}\u001b[0m\n", "\u001b[34m[2023-06-14 20:48:02.316] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 4, \"duration\": 44, \"num_examples\": 2, \"num_bytes\": 68544}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.316056, \"EndTime\": 1686775682.3161302, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0221836547851562, \"count\": 1, \"min\": 1.0221836547851562, \"max\": 1.0221836547851562}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3162742, \"EndTime\": 1686775682.3162887, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 1}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0017330932617188, \"count\": 1, \"min\": 1.0017330932617188, \"max\": 1.0017330932617188}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3163774, \"EndTime\": 1686775682.3163877, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 2}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0297357177734374, \"count\": 1, \"min\": 1.0297357177734374, \"max\": 1.0297357177734374}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.316453, \"EndTime\": 1686775682.3164623, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 3}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0046986694335938, \"count\": 1, \"min\": 1.0046986694335938, \"max\": 1.0046986694335938}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3165479, \"EndTime\": 1686775682.3165593, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0761766357421876, \"count\": 1, \"min\": 1.0761766357421876, \"max\": 1.0761766357421876}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3166254, \"EndTime\": 1686775682.3166342, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.1338963623046876, \"count\": 1, \"min\": 1.1338963623046876, \"max\": 1.1338963623046876}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3167067, \"EndTime\": 1686775682.316716, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 6}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.1349073486328125, \"count\": 1, \"min\": 1.1349073486328125, \"max\": 1.1349073486328125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3167863, \"EndTime\": 1686775682.3167956, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 7}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9341780395507813, \"count\": 1, \"min\": 0.9341780395507813, \"max\": 0.9341780395507813}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3168614, \"EndTime\": 1686775682.3168705, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 8}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8852998657226563, \"count\": 1, \"min\": 0.8852998657226563, \"max\": 0.8852998657226563}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3169262, \"EndTime\": 1686775682.3169348, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 9}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.99037548828125, \"count\": 1, \"min\": 0.99037548828125, \"max\": 0.99037548828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3169937, \"EndTime\": 1686775682.3170028, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 10}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.10408984375, \"count\": 1, \"min\": 1.10408984375, \"max\": 1.10408984375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.317061, \"EndTime\": 1686775682.3170688, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": 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{\"StartTime\": 1686775682.31727, \"EndTime\": 1686775682.3172784, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 14}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.116240234375, \"count\": 1, \"min\": 1.116240234375, \"max\": 1.116240234375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3173451, \"EndTime\": 1686775682.3173537, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 15}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9400440063476563, \"count\": 1, \"min\": 0.9400440063476563, \"max\": 0.9400440063476563}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3174162, \"EndTime\": 1686775682.3174245, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"model\": 16}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 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#early_stopping_criteria_metric: host=algo-1, epoch=0, criteria=mse_objective, value=0.8852998657226563\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Epoch 0: Loss improved. Updating best model\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saving model for epoch: 0\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saved checkpoint to \"/tmp/tmpbebpabte/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #progress_metric: host=algo-1, completed 6.666666666666667 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.2717326, \"EndTime\": 1686775682.3279157, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 0, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 3142.0, \"count\": 1, \"min\": 3142, \"max\": 3142}, \"Total Batches Seen\": {\"sum\": 5.0, \"count\": 1, \"min\": 5, \"max\": 5}, \"Max Records Seen Between Resets\": {\"sum\": 1071.0, \"count\": 1, \"min\": 1071, \"max\": 1071}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 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1686775682.3468702, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.1853551025390625, \"count\": 1, \"min\": 1.1853551025390625, \"max\": 1.1853551025390625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.346892, \"EndTime\": 1686775682.3468974, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.2336414794921875, \"count\": 1, \"min\": 1.2336414794921875, \"max\": 1.2336414794921875}}}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, epoch=1, train mse_objective =1.0060579223632813\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #early_stopping_criteria_metric: host=algo-1, epoch=1, criteria=mse_objective, value=0.7475651245117187\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Epoch 1: Loss improved. Updating best model\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saving model for epoch: 1\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saved checkpoint to \"/tmp/tmp07mly4jc/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #progress_metric: host=algo-1, completed 13.333333333333334 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.32813, \"EndTime\": 1686775682.3631196, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 1, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 4213.0, \"count\": 1, \"min\": 4213, \"max\": 4213}, \"Total Batches Seen\": {\"sum\": 7.0, \"count\": 1, \"min\": 7, \"max\": 7}, \"Max Records Seen Between Resets\": {\"sum\": 1071.0, \"count\": 1, \"min\": 1071, \"max\": 1071}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 4.0, \"count\": 1, \"min\": 4, \"max\": 4}, \"Number of Records Since Last Reset\": {\"sum\": 1071.0, \"count\": 1, \"min\": 1071, \"max\": 1071}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #throughput_metric: host=algo-1, train throughput=30281.231607188598 records/second\u001b[0m\n", "\u001b[34m[2023-06-14 20:48:02.388] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 8, \"duration\": 19, \"num_examples\": 2, \"num_bytes\": 68544}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3883982, \"EndTime\": 1686775682.3884788, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9905108642578125, \"count\": 1, \"min\": 0.9905108642578125, \"max\": 0.9905108642578125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.388896, \"EndTime\": 1686775682.3889365, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 1}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9697520751953125, \"count\": 1, \"min\": 0.9697520751953125, \"max\": 0.9697520751953125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3891943, \"EndTime\": 1686775682.3892155, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 2}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9988036499023437, \"count\": 1, \"min\": 0.9988036499023437, \"max\": 0.9988036499023437}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3894608, \"EndTime\": 1686775682.3894765, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 3}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9725399169921874, \"count\": 1, \"min\": 0.9725399169921874, \"max\": 0.9725399169921874}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3897462, \"EndTime\": 1686775682.3897643, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.7680731811523438, \"count\": 1, \"min\": 0.7680731811523438, \"max\": 0.7680731811523438}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.390016, \"EndTime\": 1686775682.3900344, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.806511474609375, \"count\": 1, \"min\": 0.806511474609375, \"max\": 0.806511474609375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3902814, \"EndTime\": 1686775682.3903003, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 6}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.174260986328125, \"count\": 1, \"min\": 1.174260986328125, \"max\": 1.174260986328125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3905706, \"EndTime\": 1686775682.390594, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 7}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9782020874023437, \"count\": 1, \"min\": 0.9782020874023437, \"max\": 0.9782020874023437}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3906584, \"EndTime\": 1686775682.3907397, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 8}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8612511596679687, \"count\": 1, \"min\": 0.8612511596679687, \"max\": 0.8612511596679687}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3908057, \"EndTime\": 1686775682.3908222, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 9}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9583413696289063, \"count\": 1, \"min\": 0.9583413696289063, \"max\": 0.9583413696289063}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.390889, \"EndTime\": 1686775682.3909223, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 10}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.068509765625, \"count\": 1, \"min\": 1.068509765625, \"max\": 1.068509765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3909645, \"EndTime\": 1686775682.390974, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 11}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9904548950195312, \"count\": 1, \"min\": 0.9904548950195312, \"max\": 0.9904548950195312}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3910522, \"EndTime\": 1686775682.3910644, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 12}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9665740356445313, \"count\": 1, \"min\": 0.9665740356445313, \"max\": 0.9665740356445313}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.391123, \"EndTime\": 1686775682.391133, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 13}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.094591796875, \"count\": 1, \"min\": 1.094591796875, \"max\": 1.094591796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.391165, \"EndTime\": 1686775682.391173, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 14}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.79725927734375, \"count\": 1, \"min\": 0.79725927734375, \"max\": 0.79725927734375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3912022, \"EndTime\": 1686775682.3912113, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 15}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9526909790039062, \"count\": 1, \"min\": 0.9526909790039062, \"max\": 0.9526909790039062}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.391237, \"EndTime\": 1686775682.3912451, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 16}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0188262939453125, \"count\": 1, \"min\": 1.0188262939453125, \"max\": 1.0188262939453125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.391274, \"EndTime\": 1686775682.391282, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 17}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.040380859375, \"count\": 1, \"min\": 1.040380859375, \"max\": 1.040380859375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3913114, \"EndTime\": 1686775682.39132, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 18}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.06257666015625, \"count\": 1, \"min\": 1.06257666015625, \"max\": 1.06257666015625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3913507, \"EndTime\": 1686775682.3913593, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 19}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9720366821289063, \"count\": 1, \"min\": 0.9720366821289063, \"max\": 0.9720366821289063}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.391383, \"EndTime\": 1686775682.3914008, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 20}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9800789794921875, \"count\": 1, \"min\": 0.9800789794921875, \"max\": 0.9800789794921875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.391428, \"EndTime\": 1686775682.3914363, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 21}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.7420567626953125, \"count\": 1, \"min\": 0.7420567626953125, \"max\": 0.7420567626953125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3914676, \"EndTime\": 1686775682.3914752, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 22}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8369196166992188, \"count\": 1, \"min\": 0.8369196166992188, \"max\": 0.8369196166992188}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3915083, \"EndTime\": 1686775682.3915164, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 23}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0585985107421876, \"count\": 1, \"min\": 1.0585985107421876, \"max\": 1.0585985107421876}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3915396, \"EndTime\": 1686775682.391557, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 24}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.97812890625, \"count\": 1, \"min\": 0.97812890625, \"max\": 0.97812890625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3916197, \"EndTime\": 1686775682.391629, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 25}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9628043212890625, \"count\": 1, \"min\": 0.9628043212890625, \"max\": 0.9628043212890625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3916867, \"EndTime\": 1686775682.3916955, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 26}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.95672900390625, \"count\": 1, \"min\": 0.95672900390625, \"max\": 0.95672900390625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3917522, \"EndTime\": 1686775682.391762, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0154259643554688, \"count\": 1, \"min\": 1.0154259643554688, \"max\": 1.0154259643554688}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3918242, \"EndTime\": 1686775682.391835, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.4585963134765625, \"count\": 1, \"min\": 1.4585963134765625, \"max\": 1.4585963134765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3918927, \"EndTime\": 1686775682.3919027, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.1031575927734374, \"count\": 1, \"min\": 1.1031575927734374, \"max\": 1.1031575927734374}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3919566, \"EndTime\": 1686775682.3919659, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.1859615478515626, \"count\": 1, \"min\": 1.1859615478515626, \"max\": 1.1859615478515626}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3920279, \"EndTime\": 1686775682.3920383, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.4575228271484375, \"count\": 1, \"min\": 1.4575228271484375, \"max\": 1.4575228271484375}}}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, epoch=2, train mse_objective =0.9905108642578125\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #early_stopping_criteria_metric: host=algo-1, epoch=2, criteria=mse_objective, value=0.7420567626953125\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Epoch 2: Loss improved. Updating best model\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saving model for epoch: 2\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saved checkpoint to \"/tmp/tmp6osgzt6b/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #progress_metric: host=algo-1, completed 20.0 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.3639219, \"EndTime\": 1686775682.4016776, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 2, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 5284.0, \"count\": 1, \"min\": 5284, \"max\": 5284}, \"Total Batches Seen\": {\"sum\": 9.0, \"count\": 1, \"min\": 9, \"max\": 9}, \"Max Records Seen Between Resets\": {\"sum\": 1071.0, \"count\": 1, \"min\": 1071, \"max\": 1071}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 5.0, \"count\": 1, \"min\": 5, \"max\": 5}, \"Number of Records Since Last Reset\": {\"sum\": 1071.0, \"count\": 1, \"min\": 1071, \"max\": 1071}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #throughput_metric: host=algo-1, train throughput=28230.00524116261 records/second\u001b[0m\n", "\u001b[34m[2023-06-14 20:48:02.421] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 10, \"duration\": 19, \"num_examples\": 2, \"num_bytes\": 68544}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.421685, \"EndTime\": 1686775682.4217732, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.97465673828125, \"count\": 1, \"min\": 0.97465673828125, \"max\": 0.97465673828125}}}\u001b[0m\n", 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{\"train_mse_objective\": {\"sum\": 0.9566934204101563, \"count\": 1, \"min\": 0.9566934204101563, \"max\": 0.9566934204101563}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4224877, \"EndTime\": 1686775682.4224963, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8342539672851562, \"count\": 1, \"min\": 0.8342539672851562, \"max\": 0.8342539672851562}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.422566, \"EndTime\": 1686775682.4225757, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.7825014038085938, \"count\": 1, \"min\": 0.7825014038085938, \"max\": 0.7825014038085938}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4226305, \"EndTime\": 1686775682.4226394, \"Dimensions\": 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{\"StartTime\": 1686775682.4242685, \"EndTime\": 1686775682.4242785, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0258807373046874, \"count\": 1, \"min\": 1.0258807373046874, \"max\": 1.0258807373046874}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.424346, \"EndTime\": 1686775682.4243553, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9605823364257813, \"count\": 1, \"min\": 0.9605823364257813, \"max\": 0.9605823364257813}}}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, epoch=3, train mse_objective =0.97465673828125\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #early_stopping_criteria_metric: host=algo-1, epoch=3, criteria=mse_objective, value=0.664224609375\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Epoch 3: Loss improved. Updating best model\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saving model for epoch: 3\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saved checkpoint to \"/tmp/tmpvyffkmcm/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #progress_metric: host=algo-1, completed 26.666666666666668 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4020026, \"EndTime\": 1686775682.4337852, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 3, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 6355.0, \"count\": 1, \"min\": 6355, \"max\": 6355}, \"Total Batches Seen\": {\"sum\": 11.0, \"count\": 1, \"min\": 11, \"max\": 11}, \"Max Records Seen Between Resets\": {\"sum\": 1071.0, \"count\": 1, \"min\": 1071, \"max\": 1071}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, 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\"training\", \"epoch\": 4, \"model\": 19}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9396033325195312, \"count\": 1, \"min\": 0.9396033325195312, \"max\": 0.9396033325195312}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4557335, \"EndTime\": 1686775682.4557388, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 20}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.7545194091796875, \"count\": 1, \"min\": 0.7545194091796875, \"max\": 0.7545194091796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4557624, \"EndTime\": 1686775682.4557679, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 21}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.6577925415039062, \"count\": 1, \"min\": 0.6577925415039062, \"max\": 0.6577925415039062}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4557931, \"EndTime\": 1686775682.4557989, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 22}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.6481115112304687, \"count\": 1, \"min\": 0.6481115112304687, \"max\": 0.6481115112304687}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4558203, \"EndTime\": 1686775682.4558256, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 23}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8002721557617187, \"count\": 1, \"min\": 0.8002721557617187, \"max\": 0.8002721557617187}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4558465, \"EndTime\": 1686775682.455852, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 24}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.978783203125, \"count\": 1, \"min\": 0.978783203125, \"max\": 0.978783203125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.455877, \"EndTime\": 1686775682.4558825, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 25}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.97126953125, \"count\": 1, \"min\": 0.97126953125, \"max\": 0.97126953125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4559035, \"EndTime\": 1686775682.4559088, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 26}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9603385009765625, \"count\": 1, \"min\": 0.9603385009765625, \"max\": 0.9603385009765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4559305, \"EndTime\": 1686775682.4559355, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0040426635742188, \"count\": 1, \"min\": 1.0040426635742188, \"max\": 1.0040426635742188}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.455957, \"EndTime\": 1686775682.4559622, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.137970947265625, \"count\": 1, \"min\": 1.137970947265625, \"max\": 1.137970947265625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4559832, \"EndTime\": 1686775682.4559884, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0475438232421874, \"count\": 1, \"min\": 1.0475438232421874, \"max\": 1.0475438232421874}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4560132, \"EndTime\": 1686775682.456019, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9947923583984375, \"count\": 1, \"min\": 0.9947923583984375, \"max\": 0.9947923583984375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4560401, \"EndTime\": 1686775682.4560459, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.026781982421875, \"count\": 1, \"min\": 1.026781982421875, \"max\": 1.026781982421875}}}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, epoch=4, train mse_objective =0.9596456909179687\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #early_stopping_criteria_metric: host=algo-1, epoch=4, criteria=mse_objective, value=0.6481115112304687\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Epoch 4: Loss improved. Updating best model\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saving model for epoch: 4\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saved checkpoint to \"/tmp/tmpo1m4e8l0/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #progress_metric: host=algo-1, completed 33.333333333333336 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4339921, \"EndTime\": 1686775682.4641032, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 4, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 7426.0, \"count\": 1, \"min\": 7426, \"max\": 7426}, \"Total Batches Seen\": {\"sum\": 13.0, \"count\": 1, \"min\": 13, \"max\": 13}, \"Max Records Seen Between Resets\": {\"sum\": 1071.0, \"count\": 1, \"min\": 1071, \"max\": 1071}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 7.0, \"count\": 1, \"min\": 7, \"max\": 7}, \"Number of Records Since Last Reset\": {\"sum\": 1071.0, \"count\": 1, \"min\": 1071, \"max\": 1071}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #throughput_metric: host=algo-1, train throughput=35409.339155933216 records/second\u001b[0m\n", "\u001b[34m[2023-06-14 20:48:02.491] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 14, \"duration\": 27, \"num_examples\": 2, \"num_bytes\": 68544}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4919455, \"EndTime\": 1686775682.4920099, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9453318481445312, \"count\": 1, \"min\": 0.9453318481445312, \"max\": 0.9453318481445312}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4921064, \"EndTime\": 1686775682.4921165, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 1}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9235821533203125, \"count\": 1, \"min\": 0.9235821533203125, \"max\": 0.9235821533203125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4921634, \"EndTime\": 1686775682.4921706, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 2}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.953370849609375, \"count\": 1, \"min\": 0.953370849609375, \"max\": 0.953370849609375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4922144, \"EndTime\": 1686775682.4922214, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 3}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9268721923828125, \"count\": 1, \"min\": 0.9268721923828125, \"max\": 0.9268721923828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4922643, \"EndTime\": 1686775682.492271, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.773339111328125, \"count\": 1, \"min\": 0.773339111328125, \"max\": 0.773339111328125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4923208, \"EndTime\": 1686775682.4923284, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.80880908203125, \"count\": 1, \"min\": 0.80880908203125, \"max\": 0.80880908203125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4923725, \"EndTime\": 1686775682.492379, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 6}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.048414306640625, \"count\": 1, \"min\": 1.048414306640625, \"max\": 1.048414306640625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4924202, \"EndTime\": 1686775682.4924266, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 7}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8782147216796875, \"count\": 1, \"min\": 0.8782147216796875, \"max\": 0.8782147216796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4924755, \"EndTime\": 1686775682.492483, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 8}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8275795288085938, \"count\": 1, \"min\": 0.8275795288085938, \"max\": 0.8275795288085938}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4925585, \"EndTime\": 1686775682.4925673, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 9}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.913042236328125, \"count\": 1, \"min\": 0.913042236328125, \"max\": 0.913042236328125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4926105, \"EndTime\": 1686775682.4926171, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 10}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.016889892578125, \"count\": 1, \"min\": 1.016889892578125, \"max\": 1.016889892578125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4926608, \"EndTime\": 1686775682.4926672, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 11}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9420863037109375, \"count\": 1, \"min\": 0.9420863037109375, \"max\": 0.9420863037109375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4927084, \"EndTime\": 1686775682.4927146, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 12}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8867086791992187, \"count\": 1, \"min\": 0.8867086791992187, \"max\": 0.8867086791992187}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4927588, \"EndTime\": 1686775682.4927654, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 13}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9733970336914063, \"count\": 1, \"min\": 0.9733970336914063, \"max\": 0.9733970336914063}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4928064, \"EndTime\": 1686775682.4928126, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 14}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.7901372680664063, \"count\": 1, \"min\": 0.7901372680664063, \"max\": 0.7901372680664063}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4928527, \"EndTime\": 1686775682.492859, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 15}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8763417358398438, \"count\": 1, \"min\": 0.8763417358398438, \"max\": 0.8763417358398438}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4929016, \"EndTime\": 1686775682.492908, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 16}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9699887084960938, \"count\": 1, \"min\": 0.9699887084960938, \"max\": 0.9699887084960938}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4929485, \"EndTime\": 1686775682.492955, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 17}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.992428466796875, \"count\": 1, \"min\": 0.992428466796875, \"max\": 0.992428466796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4930024, \"EndTime\": 1686775682.4930098, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 18}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.01025, \"count\": 1, \"min\": 1.01025, \"max\": 1.01025}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4930508, \"EndTime\": 1686775682.493057, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 19}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9248118286132813, \"count\": 1, \"min\": 0.9248118286132813, \"max\": 0.9248118286132813}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.493105, \"EndTime\": 1686775682.4931114, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 20}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8261681518554688, \"count\": 1, \"min\": 0.8261681518554688, \"max\": 0.8261681518554688}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.493152, \"EndTime\": 1686775682.493158, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 21}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.6961256103515625, \"count\": 1, \"min\": 0.6961256103515625, \"max\": 0.6961256103515625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.493205, \"EndTime\": 1686775682.4932125, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 22}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.7402147827148438, \"count\": 1, \"min\": 0.7402147827148438, \"max\": 0.7402147827148438}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4932532, \"EndTime\": 1686775682.4932592, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 23}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8579925537109375, \"count\": 1, \"min\": 0.8579925537109375, \"max\": 0.8579925537109375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4933102, \"EndTime\": 1686775682.4933178, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 24}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9779578857421874, \"count\": 1, \"min\": 0.9779578857421874, \"max\": 0.9779578857421874}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4933589, \"EndTime\": 1686775682.4933648, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 25}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9760250244140625, \"count\": 1, \"min\": 0.9760250244140625, \"max\": 0.9760250244140625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.493414, \"EndTime\": 1686775682.493421, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 26}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9629715576171874, \"count\": 1, \"min\": 0.9629715576171874, \"max\": 0.9629715576171874}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4934669, \"EndTime\": 1686775682.4934733, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9994073486328126, \"count\": 1, \"min\": 0.9994073486328126, \"max\": 0.9994073486328126}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4935138, \"EndTime\": 1686775682.4935198, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.1921080322265625, \"count\": 1, \"min\": 1.1921080322265625, \"max\": 1.1921080322265625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.493563, \"EndTime\": 1686775682.4935694, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.1444920654296875, \"count\": 1, \"min\": 1.1444920654296875, \"max\": 1.1444920654296875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.4936187, \"EndTime\": 1686775682.4936264, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.08440185546875, \"count\": 1, \"min\": 1.08440185546875, \"max\": 1.08440185546875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.493668, \"EndTime\": 1686775682.4936745, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.09065771484375, \"count\": 1, \"min\": 1.09065771484375, \"max\": 1.09065771484375}}}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, epoch=5, train mse_objective =0.9453318481445312\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #early_stopping_criteria_metric: host=algo-1, epoch=5, criteria=mse_objective, value=0.6961256103515625\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saving model for epoch: 5\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saved checkpoint to \"/tmp/tmp7934rs_j/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #progress_metric: host=algo-1, completed 40.0 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.464354, \"EndTime\": 1686775682.5012136, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 5, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 8497.0, \"count\": 1, \"min\": 8497, \"max\": 8497}, \"Total Batches Seen\": {\"sum\": 15.0, \"count\": 1, \"min\": 15, \"max\": 15}, \"Max Records Seen Between Resets\": {\"sum\": 1071.0, \"count\": 1, \"min\": 1071, \"max\": 1071}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 8.0, \"count\": 1, \"min\": 8, \"max\": 8}, \"Number of Records Since Last Reset\": {\"sum\": 1071.0, \"count\": 1, \"min\": 1071, \"max\": 1071}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #throughput_metric: host=algo-1, train throughput=28950.469397093417 records/second\u001b[0m\n", "\u001b[34m[2023-06-14 20:48:02.520] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 16, \"duration\": 19, \"num_examples\": 2, \"num_bytes\": 68544}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5209537, \"EndTime\": 1686775682.5210161, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9310816040039063, \"count\": 1, \"min\": 0.9310816040039063, \"max\": 0.9310816040039063}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5210886, \"EndTime\": 1686775682.5210981, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 1}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.908891357421875, \"count\": 1, \"min\": 0.908891357421875, \"max\": 0.908891357421875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5211248, \"EndTime\": 1686775682.5211313, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 2}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.938753173828125, \"count\": 1, \"min\": 0.938753173828125, \"max\": 0.938753173828125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5211549, \"EndTime\": 1686775682.5211608, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 3}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9125347290039062, \"count\": 1, \"min\": 0.9125347290039062, \"max\": 0.9125347290039062}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5211837, \"EndTime\": 1686775682.5211895, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8437225952148437, \"count\": 1, \"min\": 0.8437225952148437, \"max\": 0.8437225952148437}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5212116, \"EndTime\": 1686775682.521217, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8259671020507813, \"count\": 1, \"min\": 0.8259671020507813, \"max\": 0.8259671020507813}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5212417, \"EndTime\": 1686775682.5212471, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 6}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8553734741210938, \"count\": 1, \"min\": 0.8553734741210938, \"max\": 0.8553734741210938}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5212693, \"EndTime\": 1686775682.5212746, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 7}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.7271370849609375, \"count\": 1, \"min\": 0.7271370849609375, \"max\": 0.7271370849609375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5212965, \"EndTime\": 1686775682.5213022, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 8}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.81710888671875, \"count\": 1, \"min\": 0.81710888671875, \"max\": 0.81710888671875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5213294, \"EndTime\": 1686775682.5213356, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 9}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8989347534179688, \"count\": 1, \"min\": 0.8989347534179688, \"max\": 0.8989347534179688}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5213604, \"EndTime\": 1686775682.5213661, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 10}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0003717041015625, \"count\": 1, \"min\": 1.0003717041015625, \"max\": 1.0003717041015625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5213885, \"EndTime\": 1686775682.5213943, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 11}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9267559204101562, \"count\": 1, \"min\": 0.9267559204101562, \"max\": 0.9267559204101562}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.521416, \"EndTime\": 1686775682.5214217, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 12}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.7510884399414063, \"count\": 1, \"min\": 0.7510884399414063, \"max\": 0.7510884399414063}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5214434, \"EndTime\": 1686775682.5214489, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 13}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8058598022460938, \"count\": 1, \"min\": 0.8058598022460938, \"max\": 0.8058598022460938}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5214736, \"EndTime\": 1686775682.5214794, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 14}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8390768432617187, \"count\": 1, \"min\": 0.8390768432617187, \"max\": 0.8390768432617187}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5215015, \"EndTime\": 1686775682.5215068, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 15}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.7280487670898438, \"count\": 1, \"min\": 0.7280487670898438, \"max\": 0.7280487670898438}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5215285, \"EndTime\": 1686775682.5215342, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 16}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9546195068359375, \"count\": 1, \"min\": 0.9546195068359375, \"max\": 0.9546195068359375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.521556, \"EndTime\": 1686775682.5215616, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 17}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9772722778320313, \"count\": 1, \"min\": 0.9772722778320313, \"max\": 0.9772722778320313}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5215852, \"EndTime\": 1686775682.5215905, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 18}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.99382861328125, \"count\": 1, \"min\": 0.99382861328125, \"max\": 0.99382861328125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5216124, \"EndTime\": 1686775682.5216177, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 19}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9096521606445312, \"count\": 1, \"min\": 0.9096521606445312, \"max\": 0.9096521606445312}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5216396, \"EndTime\": 1686775682.521645, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 20}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.7180503540039063, \"count\": 1, \"min\": 0.7180503540039063, \"max\": 0.7180503540039063}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.521669, \"EndTime\": 1686775682.5216744, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 21}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.773458984375, \"count\": 1, \"min\": 0.773458984375, \"max\": 0.773458984375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.521696, \"EndTime\": 1686775682.5217016, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 22}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.7085059814453125, \"count\": 1, \"min\": 0.7085059814453125, \"max\": 0.7085059814453125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.521723, \"EndTime\": 1686775682.5217288, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 23}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.69880712890625, \"count\": 1, \"min\": 0.69880712890625, \"max\": 0.69880712890625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.521752, \"EndTime\": 1686775682.5217576, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 24}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9765272216796875, \"count\": 1, \"min\": 0.9765272216796875, \"max\": 0.9765272216796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5217795, \"EndTime\": 1686775682.521785, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 25}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9800650634765625, \"count\": 1, \"min\": 0.9800650634765625, \"max\": 0.9800650634765625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5218065, \"EndTime\": 1686775682.521812, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 26}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.965568603515625, \"count\": 1, \"min\": 0.965568603515625, \"max\": 0.965568603515625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5218358, \"EndTime\": 1686775682.5218415, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.99485888671875, \"count\": 1, \"min\": 0.99485888671875, \"max\": 0.99485888671875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5218627, \"EndTime\": 1686775682.521868, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.024142822265625, \"count\": 1, \"min\": 1.024142822265625, \"max\": 1.024142822265625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5218894, \"EndTime\": 1686775682.5218952, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0862781982421874, \"count\": 1, \"min\": 1.0862781982421874, \"max\": 1.0862781982421874}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5219188, \"EndTime\": 1686775682.5219243, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.024538818359375, \"count\": 1, \"min\": 1.024538818359375, \"max\": 1.024538818359375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5219464, \"EndTime\": 1686775682.5219517, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9883281860351563, \"count\": 1, \"min\": 0.9883281860351563, \"max\": 0.9883281860351563}}}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, epoch=6, train mse_objective =0.9310816040039063\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #early_stopping_criteria_metric: host=algo-1, epoch=6, criteria=mse_objective, value=0.69880712890625\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saving model for epoch: 6\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saved checkpoint to \"/tmp/tmp92elofyn/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #progress_metric: host=algo-1, completed 46.666666666666664 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5014715, \"EndTime\": 1686775682.5333478, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 6, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 9568.0, \"count\": 1, \"min\": 9568, \"max\": 9568}, \"Total Batches Seen\": {\"sum\": 17.0, \"count\": 1, \"min\": 17, \"max\": 17}, \"Max Records Seen Between Resets\": {\"sum\": 1071.0, \"count\": 1, \"min\": 1071, \"max\": 1071}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 9.0, \"count\": 1, \"min\": 9, \"max\": 9}, \"Number of Records Since Last Reset\": {\"sum\": 1071.0, \"count\": 1, \"min\": 1071, \"max\": 1071}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #throughput_metric: host=algo-1, train throughput=33489.391911134306 records/second\u001b[0m\n", "\u001b[34m[2023-06-14 20:48:02.550] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 18, \"duration\": 17, \"num_examples\": 2, \"num_bytes\": 68544}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5506957, \"EndTime\": 1686775682.5507567, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 0}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9173878784179688, \"count\": 1, \"min\": 0.9173878784179688, \"max\": 0.9173878784179688}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5508235, \"EndTime\": 1686775682.550833, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 1}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8949188232421875, \"count\": 1, \"min\": 0.8949188232421875, \"max\": 0.8949188232421875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5508597, \"EndTime\": 1686775682.5508664, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 2}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9247484130859375, \"count\": 1, \"min\": 0.9247484130859375, \"max\": 0.9247484130859375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5508902, \"EndTime\": 1686775682.5508962, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 3}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8986099243164063, \"count\": 1, \"min\": 0.8986099243164063, \"max\": 0.8986099243164063}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5509188, \"EndTime\": 1686775682.5509245, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 4}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.7554178466796875, \"count\": 1, \"min\": 0.7554178466796875, \"max\": 0.7554178466796875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5509465, \"EndTime\": 1686775682.5509522, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"model\": 5}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.7331316528320313, \"count\": 1, \"min\": 0.7331316528320313, \"max\": 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INFO 140522871928640] #early_stopping_criteria_metric: host=algo-1, epoch=7, criteria=mse_objective, value=0.6582151489257813\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saving model for epoch: 7\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saved checkpoint to \"/tmp/tmpihmdbvf6/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #progress_metric: host=algo-1, completed 53.333333333333336 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5335622, \"EndTime\": 1686775682.5605636, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 7, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 10639.0, \"count\": 1, \"min\": 10639, \"max\": 10639}, \"Total Batches Seen\": {\"sum\": 19.0, \"count\": 1, \"min\": 19, \"max\": 19}, \"Max Records Seen Between Resets\": {\"sum\": 1071.0, \"count\": 1, 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16}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9256754760742187, \"count\": 1, \"min\": 0.9256754760742187, \"max\": 0.9256754760742187}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5841098, \"EndTime\": 1686775682.584116, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 17}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9490548095703125, \"count\": 1, \"min\": 0.9490548095703125, \"max\": 0.9490548095703125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5841677, \"EndTime\": 1686775682.5841753, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 18}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9622835083007812, \"count\": 1, \"min\": 0.9622835083007812, \"max\": 0.9622835083007812}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5842187, \"EndTime\": 1686775682.5842247, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 19}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.881699951171875, \"count\": 1, \"min\": 0.881699951171875, \"max\": 0.881699951171875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.584268, \"EndTime\": 1686775682.5842743, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 20}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.8024757080078125, \"count\": 1, \"min\": 0.8024757080078125, \"max\": 0.8024757080078125}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5843172, \"EndTime\": 1686775682.5843236, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 21}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.7414646606445312, \"count\": 1, \"min\": 0.7414646606445312, \"max\": 0.7414646606445312}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5843656, \"EndTime\": 1686775682.5843718, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 22}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.7593012084960937, \"count\": 1, \"min\": 0.7593012084960937, \"max\": 0.7593012084960937}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5844176, \"EndTime\": 1686775682.584424, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 23}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.77842919921875, \"count\": 1, \"min\": 0.77842919921875, \"max\": 0.77842919921875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.584464, \"EndTime\": 1686775682.5844703, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 24}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.975521728515625, \"count\": 1, \"min\": 0.975521728515625, \"max\": 0.975521728515625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.58455, \"EndTime\": 1686775682.5845592, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 25}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9882509155273438, \"count\": 1, \"min\": 0.9882509155273438, \"max\": 0.9882509155273438}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.584605, \"EndTime\": 1686775682.5846114, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 26}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.9727536010742187, \"count\": 1, \"min\": 0.9727536010742187, \"max\": 0.9727536010742187}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5846615, \"EndTime\": 1686775682.5846689, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 27}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 0.987942138671875, \"count\": 1, \"min\": 0.987942138671875, \"max\": 0.987942138671875}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5847163, \"EndTime\": 1686775682.5847228, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 28}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.114265869140625, \"count\": 1, \"min\": 1.114265869140625, \"max\": 1.114265869140625}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.584766, \"EndTime\": 1686775682.584772, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 29}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.0097518310546876, \"count\": 1, \"min\": 1.0097518310546876, \"max\": 1.0097518310546876}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.584822, \"EndTime\": 1686775682.5848298, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 30}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.06188427734375, \"count\": 1, \"min\": 1.06188427734375, \"max\": 1.06188427734375}}}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5848756, \"EndTime\": 1686775682.584882, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"model\": 31}, \"Metrics\": {\"train_mse_objective\": {\"sum\": 1.184638671875, \"count\": 1, \"min\": 1.184638671875, \"max\": 1.184638671875}}}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, epoch=8, train mse_objective =0.9044035034179687\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #early_stopping_criteria_metric: host=algo-1, epoch=8, criteria=mse_objective, value=0.6714547119140625\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saving model for epoch: 8\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saved checkpoint to \"/tmp/tmpqnqq79z6/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Early stop condition met. Stopping training.\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #progress_metric: host=algo-1, completed 100 % epochs\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.5608544, \"EndTime\": 1686775682.5920916, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"epoch\": 8, \"Meta\": \"training_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 11710.0, \"count\": 1, \"min\": 11710, \"max\": 11710}, \"Total Batches Seen\": {\"sum\": 21.0, \"count\": 1, \"min\": 21, \"max\": 21}, \"Max Records Seen Between Resets\": {\"sum\": 1071.0, \"count\": 1, \"min\": 1071, \"max\": 1071}, \"Max Batches Seen Between Resets\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}, \"Reset Count\": {\"sum\": 11.0, \"count\": 1, \"min\": 11, \"max\": 11}, \"Number of Records Since Last Reset\": {\"sum\": 1071.0, \"count\": 1, \"min\": 1071, \"max\": 1071}, \"Number of Batches Since Last Reset\": {\"sum\": 2.0, \"count\": 1, \"min\": 2, \"max\": 2}}}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #throughput_metric: host=algo-1, train throughput=34124.12324521422 records/second\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 WARNING 140522871928640] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 WARNING 140522871928640] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n", "\u001b[34m[2023-06-14 20:48:02.595] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 22, \"duration\": 2, \"num_examples\": 1, \"num_bytes\": 64000}\u001b[0m\n", "\u001b[34m[2023-06-14 20:48:02.601] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/train\", \"epoch\": 24, \"duration\": 4, \"num_examples\": 2, \"num_bytes\": 68544}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #train_score (algo-1) : ('mse_objective', 1.402099597912167e-05)\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #train_score (algo-1) : ('mse', 1.402099597912167e-05)\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #train_score (algo-1) : ('absolute_loss', 0.0029783833817162322)\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #train_score (algo-1) : ('rmse', 0.0037444620413514236)\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #train_score (algo-1) : ('r2', 0.27792141957110017)\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #train_score (algo-1) : ('mae', 0.002978383368998592)\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, train mse_objective =1.402099597912167e-05\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, train mse =1.402099597912167e-05\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, train absolute_loss =0.0029783833817162322\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, train rmse =0.0037444620413514236\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, train r2 =0.27792141957110017\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, train mae =0.002978383368998592\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Best model found for hyperparameters: {\"optimizer\": \"adam\", \"learning_rate\": 0.1, \"wd\": 1, \"l1\": 0.0, \"lr_scheduler_step\": 10, \"lr_scheduler_factor\": 0.99, \"lr_scheduler_minimum_lr\": 0.0001}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] Saved checkpoint to \"/tmp/tmp5yfliyfo/mx-mod-0000.params\"\u001b[0m\n", "\u001b[34m[2023-06-14 20:48:02.606] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/test\", \"epoch\": 0, \"duration\": 465, \"num_examples\": 1, \"num_bytes\": 10432}\u001b[0m\n", "\u001b[34m[2023-06-14 20:48:02.608] [tensorio] [info] epoch_stats={\"data_pipeline\": \"/opt/ml/input/data/test\", \"epoch\": 1, \"duration\": 1, \"num_examples\": 1, \"num_bytes\": 10432}\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.606149, \"EndTime\": 1686775682.6092098, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\", \"Meta\": \"test_data_iter\"}, \"Metrics\": {\"Total Records Seen\": {\"sum\": 163.0, \"count\": 1, \"min\": 163, \"max\": 163}, \"Total Batches Seen\": {\"sum\": 1.0, \"count\": 1, \"min\": 1, \"max\": 1}, \"Max Records Seen Between Resets\": {\"sum\": 163.0, \"count\": 1, \"min\": 163, \"max\": 163}, \"Max Batches Seen Between Resets\": {\"sum\": 1.0, \"count\": 1, \"min\": 1, \"max\": 1}, \"Reset Count\": {\"sum\": 1.0, \"count\": 1, \"min\": 1, \"max\": 1}, \"Number of Records Since Last Reset\": {\"sum\": 163.0, \"count\": 1, \"min\": 163, \"max\": 163}, \"Number of Batches Since Last Reset\": {\"sum\": 1.0, \"count\": 1, \"min\": 1, \"max\": 1}}}\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #test_score (algo-1) : ('mse_objective', 7.457159848118121e-06)\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #test_score (algo-1) : ('mse', 7.457159848118121e-06)\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #test_score (algo-1) : ('absolute_loss', 0.0021702433290657092)\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #test_score (algo-1) : ('rmse', 0.00273078008051145)\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #test_score (algo-1) : ('r2', 0.3824632561538417)\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #test_score (algo-1) : ('mae', 0.00217024328407083)\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, test mse_objective =7.457159848118121e-06\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, test mse =7.457159848118121e-06\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, test absolute_loss =0.0021702433290657092\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, test rmse =0.00273078008051145\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, test r2 =0.3824632561538417\u001b[0m\n", "\u001b[34m[06/14/2023 20:48:02 INFO 140522871928640] #quality_metric: host=algo-1, test mae =0.00217024328407083\u001b[0m\n", "\u001b[34m#metrics {\"StartTime\": 1686775682.1381984, \"EndTime\": 1686775682.6149552, \"Dimensions\": {\"Algorithm\": \"Linear Learner\", \"Host\": \"algo-1\", \"Operation\": \"training\"}, \"Metrics\": {\"initialize.time\": {\"sum\": 130.52105903625488, \"count\": 1, \"min\": 130.52105903625488, \"max\": 130.52105903625488}, \"epochs\": {\"sum\": 15.0, \"count\": 1, \"min\": 15, \"max\": 15}, \"check_early_stopping.time\": {\"sum\": 6.097078323364258, \"count\": 9, \"min\": 0.19359588623046875, \"max\": 1.2798309326171875}, \"update.time\": {\"sum\": 282.00221061706543, \"count\": 9, \"min\": 24.399518966674805, \"max\": 53.28536033630371}, \"finalize.time\": {\"sum\": 11.105537414550781, \"count\": 1, \"min\": 11.105537414550781, \"max\": 11.105537414550781}, \"setuptime\": {\"sum\": 1.966238021850586, \"count\": 1, \"min\": 1.966238021850586, \"max\": 1.966238021850586}, \"totaltime\": {\"sum\": 585.95871925354, \"count\": 1, \"min\": 585.95871925354, \"max\": 585.95871925354}}}\u001b[0m\n", "\n", "2023-06-14 20:48:18 Uploading - Uploading generated training model\n", "2023-06-14 20:48:18 Completed - Training job completed\n", "Training seconds: 107\n", "Billable seconds: 48\n", "Managed Spot Training savings: 55.1%\n" ] } ], "source": [ "import boto3\n", "from sagemaker import get_execution_role, LinearLearner\n", "\n", "# Setting the bucket name\n", "bucket = 'fcc-spark-example'\n", "data_location = f's3://{bucket}/aq-linearlearner/data/train'\n", "output_location = f's3://{bucket}/aq-linearlearner/output'\n", "\n", "\n", "# This is used to run the LinearLearner training job\n", "role = get_execution_role()\n", "\n", "llearner = LinearLearner(role=role,\n", " predictor_type='regressor',\n", " normalize_data=True,\n", " normalize_label=True,\n", " instance_count=1,\n", " use_spot_instances = True,\n", " max_run= 1800,\n", " max_wait = 3600,\n", " instance_type='ml.c5.xlarge',\n", " output_path=output_location,\n", " data_location=data_location)\n", "\n", "# Starting the training \n", "llearner.fit([\n", " llearner.record_set(x_train.values.astype('float32'), y_train.values[:, 0].astype('float32'), channel='train'),\n", " llearner.record_set(x_test.values.astype('float32'), y_test.values[:, 0].astype('float32'), channel='test')\n", "])" ] }, { "cell_type": "markdown", "id": "ddb5ad19-3e05-494a-9fb5-1d686cf22fc1", "metadata": { "tags": [] }, "source": [ "## Deploying the model (`endpoint`)" ] }, { "cell_type": "code", "execution_count": 20, "id": "b8a6689e-161d-40a0-8c97-e8449de78ecd", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------------------!" ] } ], "source": [ "llearner_predictor = llearner.deploy(initial_instance_count=1,\n", " instance_type='ml.t2.medium')" ] }, { "cell_type": "code", "execution_count": 21, "id": "400296c8-74dc-40e7-81a4-64adab383bd6", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE: 0.0027\n", "Variance score: 0.3825\n", "Explained variance score: 0.3828\n", "Forecast bias: 0.0001\n", "sMAPE: 0.2961\n" ] } ], "source": [ "result = llearner_predictor.predict(x_test.values.astype('float32'))\n", "y_sm_pred = [r.label[\"score\"].float32_tensor.values[0] for r in result]\n", "y_sm_test = y_test.values[:, 0].astype('float32')\n", "print_metrics(y_sm_test, y_sm_pred)" ] }, { "cell_type": "code", "execution_count": 25, "id": "f402ba65-0b82-4283-8421-98f7ed9475fb", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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P8VDWjh07sHDhQk56vOjs7MSyZctw4oknora2FrW1tRgaGkJbWxsA4Etf+hKSySTmzJmDb3zjG/j973/P1aLxhlR8KghXAUNJfCQkJCQmDoIxor5UYrtlwqZzitcjY9t21mvxuKMqWRbpHPDEE09g2rRpruXC4TAAIBqN5t32ZZddhqNHj2L16tWYNWsWwuEwzjnnHGQyGQDAjBkz8Oabb2LDhg146qmnsHz5ctx2223YtGlTTjI1VpDEp4IQCxjKrC4JCQmJCQRFKTrkNFFw/PHHIxQK4bnnnsNXv/pVAICu69i2bRuuvvrqnJ875ZRTEA6H0dbWhkWLFvkuc9ppp+HBBx+Eruu+ROXZZ5/F3XffjQsvvBAAcODAAXR1dbmWiUaj+OxnP4vPfvaz+Na3voWTTz4Zr732Gj74wQ8iFArBNMdnHpTEp4IwZR0fCQkJCYlRQjwexz/+4z/iO9/5DhoaGjBz5kzceuutSCQSuOKKK3J+rrq6Gtdccw1WrFgBy7Lw4Q9/GAMDA9i8eTOqqqpw6aWX4tvf/jb+67/+C5dccgmuu+461NbW4oUXXsCZZ56Jk046Cccffzx+8YtfYMGCBRgYGMB3vvMdl0q0du1amKaJs846C7FYDL/4xS8QjUYxa9YsAKSOzzPPPINLLrkE4XAYTU1NY3acpMengjBl5WYJCQkJiVHELbfcgqVLl+JrX/saPvjBD+Ltt9/Gk08+ifr6+ryf+8EPfoB//dd/xc0334y5c+figgsuwOOPP47W1lYAQGNjI55++mkMDQ1h0aJFmD9/Pn7+859z9ef+++9Hb28vTj/9dHzta1/jKfUMdXV1+PnPf44PfehDOO200/A///M/ePzxx9HY2AiAmK337duH4447DpMmTRqjo0Og2LYw+77HMTAwgNraWvT396OmpmbMt/fRVRuxt2sYAPDBmXV4bPmHCnxCQkJCQmK0kUqlsHfvXrS2tiISiVR6OBJ5kOtclTJ/S8WngnBXbrYqOBIJCQkJCYn3BiTxqSDclZtld3YJCQkJCYmxhiQ+FYTs1SUhISEhITG+kMSngpAFDCUkJCQkJMYXkvhUEKbl+Hqk4iMhISEhITH2kMSnghBDXbppQzelwVlCQkKiUpBJzhMfo3GOJPGpIETiA0jVR0JCQqISYLVoEokKdGOXKAnsHI2kzYWs3FxBmB7mmsqYqImMb88SCQkJifc6NE1DXV0dOjs7AQCxWCyrt5VEZWHbNhKJBDo7O1FXVwdN08pelyQ+FYRX8ZFtKyQkJCQqg+bmZgDg5EdiYqKuro6fq3IhiU8FIUNdEhISEhMDiqKgpaUFkydPhq7rlR6OhA+CweCIlB4GSXwqBNu2wXhPVTiAobQhFR8JCQmJCkPTtFGZXCUmLqS5uUIQ1Z6qMOGfslGphISEhITE2KIs4nP33XfzBmHz58/Hs88+m3f5TZs2Yf78+YhEIpgzZw7WrFnjen/nzp1YunQpZs+eDUVRsHr16qx1sPe8P9/61rf4MpdddlnW+2effXY5uzjmEIsXVkUI8ZGKj4SEhISExNiiZOLzyCOP4Oqrr8b111+P7du3Y+HChViyZAna2tp8l9+7dy8uvPBCLFy4ENu3b8f3vvc9XHXVVXj00Uf5MolEAnPmzMEtt9yS07S0detWtLe3858NGzYAAL70pS+5lvvkJz/pWm7dunWl7uK4QFR84lTxkR4fCQkJCQmJsUXJHp877rgDV1xxBa688koAwOrVq/Hkk0/innvuwc0335y1/Jo1azBz5kyu4sydOxfbtm3DqlWrsHTpUgDAGWecgTPOOAMAcO211/pud9KkSa7/b7nlFhx33HFYtGiR6/VwODxix/d4QExlr2bERzYqlZCQkJCQGFOUpPhkMhm89NJLWLx4sev1xYsXY/Pmzb6f2bJlS9byF1xwAbZt21a2cz6TyeCXv/wlLr/88qxaCxs3bsTkyZNx4okn4hvf+Ebe1MR0Oo2BgQHXz3jBNAXiE2HERyo+EhISEhISY4mSiE9XVxdM08SUKVNcr0+ZMgUdHR2+n+no6PBd3jAMdHV1lThcgj/84Q/o6+vDZZdd5np9yZIl+NWvfoWnn34at99+O7Zu3YqPfexjSKfTvuu5+eabUVtby39mzJhR1njKgaj4sFBXQoa6JCQkJCQkxhRlpbN7VRbbtvNWufRb3u/1YnHfffdhyZIlmDp1quv1iy++mP89b948LFiwALNmzcITTzyBL3zhC1nrue6667By5Ur+/8DAwLiRH+bx0VQFsRBJnUxJxUdCQkJCQmJMURLxaWpqgqZpWepOZ2dnlqrD0Nzc7Lt8IBBAY2NjicMF9u/fj6eeegqPPfZYwWVbWlowa9Ys7N692/f9cDiMcDhc8hhGA5z4KAqiQUJ8pLlZQkJCQkJibFFSqCsUCmH+/Pk8o4phw4YNOPfcc30/c84552Qtv379eixYsKCsJmMPPPAAJk+ejE996lMFl+3u7saBAwfQ0tJS8nbGGqLiE6WKj0xnl5CQkJCQGFuUnM6+cuVK3Hvvvbj//vuxa9curFixAm1tbVi2bBkAEj76+te/zpdftmwZ9u/fj5UrV2LXrl24//77cd999+Gaa67hy2QyGezYsQM7duxAJpPBoUOHsGPHDrz99tuubVuWhQceeACXXnopAgG3WDU0NIRrrrkGW7Zswb59+7Bx40Z85jOfQVNTEy666KJSd3PMYYjERyo+EhISEhIS44KSPT4XX3wxuru7ceONN6K9vR3z5s3DunXrMGvWLABAe3u7q6ZPa2sr1q1bhxUrVuCuu+7C1KlTceedd/JUdgA4fPgwTj/9dP7/qlWrsGrVKixatAgbN27krz/11FNoa2vD5ZdfnjUuTdPw2muv4aGHHkJfXx9aWlrw0Y9+FI888giqq6tL3c0xh5/HR2Z1SUhISEhIjC0U27btwou9NzAwMIDa2lr09/ejpqZmTLf1ZscgLlj9DBrjIVy75GR853ev4ryTJmHt/3fmmG5XQkJCQkLiWEMp87fs1VUhMMVHVRXEQrJlhYSEhISExHhAEp8KgRGfgKogGiKnQTYplZCQkJCQGFtI4lMhsAKGqqIgGpSKj4SEhISExHhAEp8KwbQsAEBAc9LZpblZQkJCQkJibCGJT4VgmE4BQ57VJUNdEhISEhISYwpJfCoEFupy1fGRio+EhISEhMSYQhKfCsGvcnNSN2FZsrqAhISEhITEWEESnwrB9KncDABpw6rUkCQkJCQkJI55SOJTIYjp7BGB+CQyRqWGJCEhISEhccxDEp8KQSxgqKkKwgFyKqTBWUJCQkJCYuwgiU+FICo+AGRKu4SEhISExDhAEp8KgXVnVxVCfGKyQ7uEhISEhMSYQxKfCsGi6ewBjRCfiFR8JhT6Ehn85OndONibqPRQJCQkJCRGEZL4VAisgCFXfCjxSUjFZ0Lg0ZcPYdX6t3D3xj2VHoqEhISExChCEp8KgRUw5B4fGupKScVnQmAwpQMADvRIxUdCQkLiWIIkPhWCU8eHnIJoSDYqnUjQTVJPqXMgXeGRSEhISEiMJiTxqRAc4kP+jwZlOvtEAgtFdgykKjwSCQkJCYnRhCQ+FYKTzk5OQYAyIMOUlZsnAnRKfPqTOlKSjEpISEgcM5DEp0IQCxgCQJD+NmSvrgkBXSCgMtwlISEhcexAEp8KwVvAkHl9JPGZGDAsh/jIcJeEhITEsQNJfCoEbwFDRoBMSXwmBFioCwCOSOIjISEhccxAEp8KwfKks7NChrr0+EwIiOdBEh8JCQmJYweS+FQILGtI06TiMxFhSMVHQkJC4piEJD4VAitgqLFQlyY9PhMJbsVHmpslJCQkjhVI4lMhmNQ8q6luxUems08MyFCXhISExLEJSXwqBDavah6Pj1R8JgbE8yCJj4SEhMSxA0l8KgSm+GSls5uS+EwEeENdti3Pi4SEhMSxAEl8KgTDU8AwIAsYTiiIBDSpmxhMGxUcjYSEhITEaEESnwrBsvzT2U1LenwmArxlBY70y3CXhISExLEASXwqhFwFDGWoa2JA95wHmdklISEhcWxAEp8KIauAoWxZMaHAWlYEqRInDc4SEhISxwYk8akQsgoY8qwuGeqaCGCKz9S6KADZr0tCQkLiWIEkPhWCt4ChJkNdEwrM4zONEp9OSXwkJCQkjglI4lMhsNYUjPAEZahrQoER0On1hPhIj4+EhITEsQFJfCoEw0N8NJnOPqHAFJ/p9TEAMtQlISEhcaygLOJz9913o7W1FZFIBPPnz8ezzz6bd/lNmzZh/vz5iEQimDNnDtasWeN6f+fOnVi6dClmz54NRVGwevXqrHXccMMNUBTF9dPc3OxaxrZt3HDDDZg6dSqi0SjOO+887Ny5s5xdHHOMVzr7251D+PjtG/GH7YdGdb3HOmSoS0JCQuLYRMnE55FHHsHVV1+N66+/Htu3b8fChQuxZMkStLW1+S6/d+9eXHjhhVi4cCG2b9+O733ve7jqqqvw6KOP8mUSiQTmzJmDW265JYvMiHjf+96H9vZ2/vPaa6+53r/11ltxxx134Cc/+Qm2bt2K5uZmnH/++RgcHCx1N8cc2QUMyanwplGPFM+/3YU9R4ex7rX2UV3vsQ52flioq3MwzcmqhISEhMS7FyUTnzvuuANXXHEFrrzySsydOxerV6/GjBkzcM899/guv2bNGsycOROrV6/G3LlzceWVV+Lyyy/HqlWr+DJnnHEGbrvtNlxyySUIh8M5tx0IBNDc3Mx/Jk2axN+zbRurV6/G9ddfjy984QuYN28eHnzwQSQSCfz6178udTfHHF7Fh4W6zFGeXNOGOSbrPdbBFJ+pdVEoCiFC3cOZCo9KQkJCQmKkKIn4ZDIZvPTSS1i8eLHr9cWLF2Pz5s2+n9myZUvW8hdccAG2bdsGXddLGuzu3bsxdepUtLa24pJLLsE777zD39u7dy86Ojpc2wqHw1i0aFHOsaXTaQwMDLh+xguOx4ecAlYvZrS7s2cMsj5dEp+iYds2V97CQRVNVYSMy1o+EhISEu9+lER8urq6YJompkyZ4np9ypQp6Ojo8P1MR0eH7/KGYaCrq6vobZ911ll46KGH8OSTT+LnP/85Ojo6cO6556K7u5tvh6272LHdfPPNqK2t5T8zZswoejwjBStgqNEzMFbm5jQlPrIVRvEQ1bGQpmJKDSE+nYOS+EhISEi821GWuVmhtWcYbNvOeq3Q8n6v58OSJUuwdOlSnHrqqfjEJz6BJ554AgDw4IMPlj226667Dv39/fznwIEDRY9npOAFDLniQ36PfqjLcm1PojBE8hnQVEyiis/RwdJS2tc+vxdLfvxsyZ8bKWzbxm1P/h2PvXxwXLcrISEh8W5AScSnqakJmqZlKSidnZ1ZSgtDc3Oz7/KBQACNjY0lDtdBPB7Hqaeeit27d/PtAChpbOFwGDU1Na6f8QKv4+MpYOhtjjlSsFCXTJMvHhnhHARUBeGARl8v7Rg+tv0QdrUPYNu+nlEdXyG09SRw11/34IdP7BrX7UpISEi8G1AS8QmFQpg/fz42bNjgen3Dhg0499xzfT9zzjnnZC2/fv16LFiwAMFgsMThOkin09i1axdaWloAAK2trWhubnZtK5PJYNOmTTnHVknwys2sgKE2tubmiUJ8bNvGXX99G8/tLj7MOd4Q1bGgpvK2IqVmdQ2nDQBAip6D8UIiY7q2LyEhISHhoORQ18qVK3Hvvffi/vvvx65du7BixQq0tbVh2bJlAEj46Otf/zpfftmyZdi/fz9WrlyJXbt24f7778d9992Ha665hi+TyWSwY8cO7NixA5lMBocOHcKOHTvw9ttv82WuueYabNq0CXv37sWLL76IL37xixgYGMCll14KgIS4rr76atx00034/e9/j9dffx2XXXYZYrEYvvrVr5Z9gMYK2QUMxyadPa2zUNfE8PjsPDyA2558Ezf+aWLWVwKcY6Uq5PwwVa5U8sgICDsH4wVG3NKGxcPKEhISEhIEgVI/cPHFF6O7uxs33ngj2tvbMW/ePKxbtw6zZs0CALS3t7tq+rS2tmLdunVYsWIF7rrrLkydOhV33nknli5dypc5fPgwTj/9dP7/qlWrsGrVKixatAgbN24EABw8eBBf+cpX0NXVhUmTJuHss8/GCy+8wLcLAN/97neRTCaxfPly9Pb24qyzzsL69etRXV1d8oEZa2QVMByrdHaTmZsnxgQ4mCIqxHB6fFWQUsBCXQHqu3LOTWkEhikuzGc1XtCFceqmjVCgeC+dhISExLGOkokPACxfvhzLly/3fW/t2rVZry1atAgvv/xyzvXNnj274JPpww8/XHBciqLghhtuwA033FBw2Uojq4DhGHVnZ2rDaHuHygUbx0TuQs8UkxAlPuVk3Nm2zRWflD6+JE8M1aUNE6GA7EwjISEhwSDviBVCLsVn9NPZJ1YBQ058JnCWGSNljIwGyvD4ZEyLn8vxVnzEsOZ4b1tCQkJiokMSnwqBTa6ap2WFOcqEgBcwnCBE492QZZYxGCkl50Qtw+OTEEJ56XE2N4vFKiXxkZCQkHBDEp8Kgc1N3u7s+miHuoyJ5fHJmBPLbO0HRkpZpl05/qvhjJNRlRp3c7OzvYwkPhISEhIuSOJTIWQpPmOWzj6xPDVMeSpGPdFNC1c+uBX/93evjvo4ntzZge//4TVfYsDGGOQeH/K7FMVnuJKKj8fjIyEhISHhQBKfCoGFtFiqtNidfTRTkDMTrI6PY24uPJ6te3vw1K5OPLLtwKgbhFc/tRu/fKENL+3vzXrPMP09Pu8axUcgueOdSi8hISEx0SGJT4XgLWDIwimAEwYbDUy0lhW6kF5fiOD9z987+d8DydIa2hZCvuKCXPFR3VldpRAft8dnfMmHKT0+EhISEjkhiU+FYHoKGDJVARjdsNREC3WJoaVCqs9fBeLTmxhd4sNCQLpfqIt5fGj9G6bKlav4pMc5nV2GuiQkJCRyQxKfCsHMSmd3TsVoqjNs0p04io8zjnxj2ts1jHe6hvn/vYnMqI6DhZ/8yBcbV0D11vEpnjwmxFBXBdPZpblZQkJCwg1JfCqE7JYVStZ7o4GM4KmZCO0LxEKK+YjE04LaAwB9Y6X4+GSXsddGlNUlhrrGW/GRoS4JCQmJnJDEp0KwvKEukfiMUqq3bduuiW8i+JtdxCeP4vP034+4/u8bRcXHtm2u+PjVN3KID1V8yjA3i4pPZQsYylCXhISEhAhJfCoEr+KjqgoY9xmtlHaSISb+X/mn/4xZ2OMzmNLx4js9AID5s+oBAH2jaG52jcHnmPBQFyM+ZRQwFBWfiraskFldEhISEi5I4lMheM3NgDPRjlaoK+OZ1CdCEUO3udl/Un5udxcMy8acpjjeP70OwOh6fEQFJm+oyxOGLFfxGW+fjVgE03sNSEhISLzXIYlPheBNZweEfl2jZET2eksmgsG5mFAXS2P/2MmTUR8LAgD6R9HjIyowvqEuy13AsJw+asOZyqWzS8VHQkJCIjck8akALMsJQbEwClBe9lA+eCfciZDSrhtCVlcOIrFtHwlzfeTESaiLhwCMsuKj51edvAUMNUqASmlSmkiLBQzHO9QlPT4SEhISuSCJTwVgCsYbMY09ONqhriziM7EUHzMHEUtSotAQD6EuShSf0azjI5KBYszNxSg+e7uGkRRUnkoqPjKrS0JCQiI3JPGpAESviMB7HMVntEJdE5D4iJ6TXB3j2f6HAirqY0TxGd1QVyGPj7vGUqEChs+8dRQfu30j/u2Pr/PX3FldlVN8ZB0fCQkJCTck8akAxAnUpfiMeqjL6/Gp/CRYjMeHkaOAqqAuxhSf0TQ3O8fFbwzstWDAW8DQf7xrN++DbQNvtA/w14bSblVpPI3l7srNlT/nEhISEhMJkvhUAOIEKpqbWb2YYzvUJXp8/CdlTjw0lROfvqQ+agUY0wUUHzauoKediF9orr0/iY1vEjO2WGRR9PgA46v6uJqUSo+PhISEhAuS+FQAZg7iw5pijpY6kBXqmmhZXTn2k03cAU1BHQ11ZQyLe39GCnc6e/YYuOKkFW5S+rttB3lhSBfxybjHOp4d2mVWl4SEhERuSOJTAbg8Pg7v4RPsaBUazAp1TYCsLlcdHx/SYdu24LFREQ9pvHXEaLWtcKez5y5gGPQUMPQSH8uy8ci2A/z/obTB909sUgqMr/IiQ10SEhISuSGJTwUgFi9UfNLZR0vxyQp1TTjFJ3tSFvc9pKlQFEf1GS2fT7pAEUXD06srl8dn855uHOxNojoSADuN/bTCdCLtJjrjqby4Q12S+EhISEiIkMSnAvArXggI6ezvkawuv/GIagXz1rCU9rFRfPxCXe7u7IEcvbqY2vP5D0xDLR9jBhnD4vvJyFNqPD0+LsVHenwkJCQkREjiUwGYdGISixcChbOHSoVXZZgILStcBQx9qyY7Y2aEg6W0jxbxKdSygis+Aab4ZHuvhtIGnny9AwBw8RkznDEmdVc9H6ZWjafio5tS8ZGQkJDIBUl8KgCm+ASyFJ/c2UPlIG16Q12VnwQLFTAUyRAze9eOckq7qPj4prOzlhVqbo9Pz1AGGdNCLKRh3rRarvj0Dme4vyekqagOBwCMLwExZAFDCQkJiZyQxKcCYBM+S19ncMzNY9SrawIoPoUKGDJypqkKVJUpPrRf1yh1aBfJgF8Tz4y3ZYWPEseUqYBnjH1JnRcvjIU1hIMagPFtW6HLAoYSEhISOSGJTwXAJlBvqCsw1unsEyCry6345G4QKqphLIzUOzxa5mZR8clnbs7t8WF/s2XqeDgug2FqbI6HAgjTIojiuegcTI0pEZIeHwkJCYnckMSnAhCzukSwCXa00tknZlaXoJr4tYsw3KQDcEJdfaOk+KRcTUrzVG7OUnyyvUHsPafCtM5DXbGQJhAfQkA6B1P48C1/xeVrt47KvvjBldUl6/hISEhIuCCJTwWQk/iMcjr7RMzq0o38io9YvJChXlBTRgOiCuIXCnJaZriblIqCWZbiE3UM2CyVPRYOIMJDXeTD7xwdRsa0sLdreFT2xQ+yjo+EhIREbkjiUwHkJj7kdOijRnwmuMcnTzq72MNstDu0F6v4MPKlKn6Kj/sc1seddHam+MR9FJ9h2spitHxcfhDHmZGhLgkJCQkXJPGpAHIRH2Z2Nscs1FX5p393k9LcWV0hQfGpG3XFp8AYKHEIFeHxYe/VCrWGmMcnFgoI5mayziFKfEYrc88PhlR8JCQkJHJCEp8KoFCoa9Tq+EywUJdp2RCH4G9udvfJAhz/zFgUMMz41RLiio871CUeP4OHw9y1hnoTGZ7VFQ9riGQpPmbWukYb3nT20WruKiEhIXEsQBKfCsAskNU1ZsSnwuZmrwLlF+5hHiBfj88odWgvpPjoWens2dl2huUOybEx9ie9ig8lPlzx0bPWNdrw7pNfyr6EhITEexWS+FQAuVpWjLa52evvGMvwSjHwTsC+BQw9xQMBR/ExLRuDaSPrM6UiXWST0lCeJqXeUFedUGSRKT5VYQ3hAA110XMxNA6Kj5dQynCXhISEhANJfCoAwzNpMox2Ort3whtLQ20x8O6Xr+JjZis+kaCGCFVO+oZHHu5KuVpW5Am3USLKvFeuAoY50tlTuoVuWm8oFgrwcTPFZ5h7fMbH3AwcG0UM+xM6Pv1fz2LNpj2VHsqY4K0jg/je719De3+y0kORkDjmURbxufvuu9Ha2opIJIL58+fj2Wefzbv8pk2bMH/+fEQiEcyZMwdr1qxxvb9z504sXboUs2fPhqIoWL16ddY6br75Zpxxxhmorq7G5MmT8fnPfx5vvvmma5nLLrsMiqK4fs4+++xydnFMkatX16ins+vuCbzSvbq8xMc3nZ3X0HFfmk64a+QGZ1Hx8Svq6JAvt8fHt4AhVaaqwgG+3OE+MnnFBcWHkdChlEN8xsp74w1pHguKz/YDvXj90AB+//KhSg9lTPDQln349Ytt+MP2w5UeioTEMY+Sic8jjzyCq6++Gtdffz22b9+OhQsXYsmSJWhra/Ndfu/evbjwwguxcOFCbN++Hd/73vdw1VVX4dFHH+XLJBIJzJkzB7fccguam5t917Np0yZ861vfwgsvvIANGzbAMAwsXrwYw8Pueiif/OQn0d7ezn/WrVtX6i6OOXKGurTyPD65JlAWWorTflF6hUNdYoNSwH88Oq+a7D42taOY0p4uoPhkhboE4sOOte4xqCuKwlWfQ5T4uBUfGurKOKG6sQp3eQmmt3XJuxHsPB2rfqUEbWybzIw8lCshIZEfgVI/cMcdd+CKK67AlVdeCQBYvXo1nnzySdxzzz24+eabs5Zfs2YNZs6cyVWcuXPnYtu2bVi1ahWWLl0KADjjjDNwxhlnAACuvfZa3+3+5S9/cf3/wAMPYPLkyXjppZfwkY98hL8eDodzkqeJgoJZXSXc3O/ZuAcPbdmH3/3juZhWF3W9xzKJ4iEN/UmdK02VQpbHxzfMlF3HBxjdIoaFPD66p46PqMxZNqApjj9JDMnVRoPoGsqgvS8FgCg+7FwzsjUseJRMywbNdh9VeAnVsaD4sHDdsRC284ND7GQGnoTEWKMkxSeTyeCll17C4sWLXa8vXrwYmzdv9v3Mli1bspa/4IILsG3bNuh6+U/v/f39AICGhgbX6xs3bsTkyZNx4okn4hvf+AY6OztzriOdTmNgYMD1Mx7IWcenjHT2p3YdQXt/Ci/t7816j4W6YlTxqXQ6u5dk+BcPzCYUwOimtKdcWV25fUa8ZYUwFhYacwotZmefMYIXc/XqoopPauwVH7ZPbGjHAllg5+RYVXxYNuNo+fskJCRyoyTi09XVBdM0MWXKFNfrU6ZMQUdHh+9nOjo6fJc3DANdXV0lDpfAtm2sXLkSH/7whzFv3jz++pIlS/CrX/0KTz/9NG6//XZs3boVH/vYx5BOp33Xc/PNN6O2tpb/zJgxo6zxlIrcvbpoqKuEpz52o/Rresme9OMhIitUuklpNvHJnVHl9fg4RQxHIdRVMKvL06RUOE/s3DlZXWL2Wci1nngou2XFkKj4jNHTPQshxkOE8B4Tio95rCs+kvhISIwXSg51AcTPIMK27azXCi3v93qx+Pa3v41XX30Vzz33nOv1iy++mP89b948LFiwALNmzcITTzyBL3zhC1nrue6667By5Ur+/8DAwLiQH4f4uCf3cgoYsonAz8fB3ouFJqjikyejyuvxqRfSxUcKVx0f6tsRr0XdQ2pEgsqOoVPHR6wwHXRtJxbWEE55Chi6PD6jP8kRHxL5Ox4OYDBtHBMd2vVjnPhkJPGRkBg3lER8mpqaoGlalrrT2dmZpeowNDc3+y4fCATQ2NhY4nCBf/qnf8If//hHPPPMM5g+fXreZVtaWjBr1izs3r3b9/1wOIxwOFzyGEYK02fSBMTWCMXf/NgEnPRVfKjHJ0wVn0p7fLzm5rwFDL2Kj9MLayQwTCuLAOqmjVBAID5M8aHnR/QbWYz4eNLZAYecMcSFAoZc8RnLUJdlQded9bPznqtDu2XZUNXyHj7GG+y6OFZDXey76f2OSEhIjD5KCnWFQiHMnz8fGzZscL2+YcMGnHvuub6fOeecc7KWX79+PRYsWIBgMOj7GT/Yto1vf/vbeOyxx/D000+jtbW14Ge6u7tx4MABtLS0FL2d8QCb8NQc6ezlhbqyJwQe6qIen0qns5dWwNDr8XGqN48EfmEfUXkRFRNGvsShGJ5QVzBPqCsW0hDh6ezulhXiukYNv/4yQnedjiokAJAUe8CfLPzwT29g/g83vGvqxrB9MC274tfxWECGuiQkxg8lp7OvXLkS9957L+6//37s2rULK1asQFtbG5YtWwaAhI++/vWv8+WXLVuG/fv3Y+XKldi1axfuv/9+3Hfffbjmmmv4MplMBjt27MCOHTuQyWRw6NAh7NixA2+//TZf5lvf+hZ++ctf4te//jWqq6vR0dGBjo4OJJPkxj00NIRrrrkGW7Zswb59+7Bx40Z85jOfQVNTEy666KKyD9BYgKWzexUfrYyWFexJ2M/j4w11VfqmqnsLKubrzu5VfEYpnd3vOInKk3iMWLhNURRXSrv4GS1PqCseFlpWGBYyhuUiIaPu8dn3LNSBg/iQ+jrfPtl29j4/u7sLvQkdf9vbM7pjGCPkOkfHCiTxkZAYP5Ts8bn44ovR3d2NG2+8Ee3t7Zg3bx7WrVuHWbNmAQDa29tdNX1aW1uxbt06rFixAnfddRemTp2KO++8k6eyA8Dhw4dx+umn8/9XrVqFVatWYdGiRdi4cSMA4J577gEAnHfeea7xPPDAA7jsssugaRpee+01PPTQQ+jr60NLSws++tGP4pFHHkF1dXWpuzmmMH3CJIAz0Zbi/dBzhLrEkA4zN1f6STmrgKFvDR1/jw9TUwZGSfEJaaqvr0IknaKao6kKTMsWFJ/scdZFsxUf3rJCN12p7GRbozjJWSZgkDT6c9WdeNI6E9Fg7lAX2/eDve8SxUcgzWnD4qbxYwUsjV0SHwmJsUdZ5ubly5dj+fLlvu+tXbs267VFixbh5Zdfzrm+2bNnF6xiW+j9aDSKJ598Mu8yEwVsvs+Zzj4KoS5RWWDp7JVuWeENufhWTfYJIQFOAcORenyY4hMOqjBtEjYRj7eoSomKXEBVkIFD1vwUH9HjE1AVhAOqU8DQsFwZXcAoE1HdITAfUnciqCmcHPiF95gZ/kBPYvTGMIYQCUEpBuee4Qz+76Ov4ssLZuD8U/x9iBMBjPDLOj4SEmMP2aurAmBqQZbiM4JQlzerS5wcHMWn0uns7v3y20/eLkL1Nzf3J3VuMC4HjASEAxpXa8RJlWWVKYr7/PBGpbYnnV319/jEQhoURXFaVujZxGdUPT66Q2COVw+jRe3LqiEkgpHQA73vQuJTgiry59fbseGNI3jg+b1jMaxRAw91HaNZaxISEwmS+FQA7L49GgUMGZnwhrrYBK9R5QHw99SMJ4pJZ88V6mKKj2W72z6UCnZcIkGVE01XqIvVEVJVV4q75sm48zYyBdweH+avYcc+ZZjZxGc0n+4z7tYtH9J2IkS37aeQsPDXuzHUVQo52N9NiJ03zDjRoMtQl4TEuEESnwrA9Jk0gdLT2W3b5hOw17TLJrZwQIVGw0aVbllRTAFDb7sIhkhQ4ySifwQGZx7qCqh8G35d173b99ZYMn1M2PUexQcAwjTcZNtA77A7TDeqHh/dTWDOVnYKio8P8aH7ebgvWXHvVzEQQ0ClKD77uggh9JLOcvHS/h788oX9o95gVtbxkZAYP0jiUwHwdPasXl1MgSjupiqmXmd7fJwJPqiWbpoeC2QEtQXIofhw03D2pSmGu8qFo/hofBsuNSFH5Wiv/8qvgGEkqHKVxav4AMRvImJ0PT7ukNWZ9ms5iY9t23yfddPGkYHU6I1jjFCux4cpPqwJ6Ehx7aOv4ft/eB1vHRkq6/O2beP29W/iv3e4u8zr0uMjITFukMSnArB8Jk0AWSnThSASJG+oixGhUEAtK4Q2FmDjZdlG/r26/IkH4IS7RkJ8RMWHbUMch5GjcjQjpZZtu5YTw5WKonCDM1d8BOLTnaX4jH6oK1M1HRlbQwu6MNlsB+Dj//KoCu8Gg7NIfIptwWFZNvb3kOMyWqEuVkdqIFXeNbi7cwj/9fTb+I8ndrleN2SoS0Ji3CCJTwWQq4AhT2cv8uanCwpOVqjLZeItvQfYWIDd1J0WGrnTrL2kEHDSxUfSr8ut+GQfb93w7w7P/s0uYOhJu6djZH2yiMGZfLhryN0zbiyyuvRIA7bbJwAA5gxsA5BNdLzE4cC7wOdTjuLTOZjmDwCJjDkq4Sn2PSuXoLBrwKtAyVCXhMT4QRKfCiB3AcPSlBndyP0UnOHER1R8Kp3VRbYfDeVuoWH4eGcYakZB8Um7PD401OWT1RUM+Cs+2QUM/bPPWAkBti0gO9Q1ulldRNkwtSg2m+8DAMyixMdbx8dLHA6+CzK7xDEX6/HZ1+0Yvg3LHpVmrWkhRFgOmD9N3AfbtmVWl4TEOEISnwrA9KkBAzjhnbJCXRmv4kP+DwVUrkpU2sTqVJLOE+rKEWoChH5dyfJr+aQEJcyvRYiY1SXC6/HJqfjQMbISAgB4PZ3uIQ/xGc2n+wwhL4YWxWaLEJ+W/u0AsklxluLTM/EVH9H7Uiw52N/tznQbqc9H9EaVe+5YqCxjWFyBEr160uMjITH2kMSnAmCKT6509mLlbnG5lJEr1KVyVWKiFDBkRMBv8shlLgZGx+PDFB/RiOxOZ8+f1eUoPv61mFhmFwvnAeBtK7yhrtFVfAh5MbQI9tnNAIBIuhsqrKw6Pu9GxUcvS/Fx79dIfT5pHxN8qRDDtIZHPSR/S8VHQmKsIYlPBcAmT++k6Z1cC8FFfHIUMAwHNCFNfqJ4fPKZm/N5fCjxET0+72wC/ngVkB4sagxpH8VH90mVzvL45Cpg6CFo759RBwB439Qa/horYji2WV1E3TC0KPpQRcYMCzUYzhkGZXg31PIpx+Mz2oqPGDIsN2wsqpU8s84SCZUkPhISY42yWlZIjAxGLuKjlabMiMuldCKds6J7vCdVQBUm+JHfVDOGBUXxV2QKjtfwZHX57Gdexccvnf2Z24B9zwKzPwyc9uWCYxAVHyery6eAYcC9fW+NJb90dgD4ypkzsfiUKWisCvPXWPr+mHp8aKhLV6PQEUBCiSFmJ9CgDPqEupwwaMaw0N6fhG5aZZ3T8UI5xGdfl0fxGUHhS8BdAbvcRAGRtGcMC/GwW82SxEdCYuwxce90xzBypbOPRPEB3FI8u0mHA2qWMbdc2LaNr933Is65+X8wWEY6b5a5OV/LCh+Pj9OvS9j2YAf93V7UGLjHR6jj425SSj1GBfqoGTlCXQBcpAdwFB/v/o5qCxGdEZ8IAGBQrQUA1GEop+LTUhtBOKDCsoH2voldy0f0vqSLIAe2bXNzMyOeIw11ibWyyiUoIml3OrKLoS571IsjSkhIuCGJTwWQs4ChTyXhfPDefMVwV0aY4Etdby68erAfL+7tQddQBrs7Sy/gxsJITh2f7MnDUVKK9Pgkusjvoc6ixpD2qdzsDnX5V472klIjh7nZD+GAN2xGfo9qeQFGfDRCfIY0EmprUAaz6viI/q/p9VEAE79nV0ZQW4oxNx8dSiORMaEqwAmTqwEAw+niQ109wxnc+Pgb+HvHAH9NVHxGw+PjZIi596fSXjwJiWMdkvhUAIUUn2L9A94bpFjEUJzcSl1vLvx+u1Nt9uhgOs+S/vB6fPxaaDAlJRTw8fhQ4zAnPqYBJHvJ38USH5/Kze6sLv/K0V6Pj5Ejnd0PzMzNwNLyxyLUlVEZ8SGKT70ymBUaEv1fMxpiACa+wdnPh5UPrGLz1Looz7RLlBDq+tOrh3H/83vxs03v8NdE5ax8j4+f4uMlPjLcJSExlpDEpwLIVcCQTaLF9tTyZkWJUnxaqNzM1JORKAyGaeFPrx7m/3szlIoBr9xMM5503wKGhRWfvgT1yiS6nTeHjhQ1Bnfl5mzvU67K0V6DOE9n9wl1eeFVfOrGgvhQxSejEOIzTIlPA/J7fLjiM8FT2kv1+LAeXbMb47yY5HAJ5mamDg2kHLIkKqrl1/ERzM0+oS7yvyQ+EhJjCUl8KoBcBQy5CbnIp0nvk68r1CX06hqNlhXPvd2FLqEOTddg6bV0shSffFldfnV8KGEYzphkXSzMBZSs+ISDmtAbLTtVOru4pJs86j4tK3LBS3xqqXJljuYEp7sVn0SgDgBRfHLV8QkHVMyoJ4rPRA91lUp8mOIzqzGGWJhcb4kSPD7sOhTDWy7FZ4R1fADH7O8lOqU0YZWQkCgdkvhUALkKGJaadp431MW7s/u3ZigVf6BhLkYIjg6Vbob1FjD0M3I63pnclZsBGu4aFolPOYpPdhYdD3V5s7rUHIpPEZlQ3lBX7RiGulJU8UlS4kMUH3+PD1F8WKhrYis+pVZuZsZml+JTAvHR6blJucLHQlZXGecuY1iulHr2cOLdH+nxkZAYW0jiUwE4BQy9k6szEftldqR0d7+h7FBX9tPpaDQpHU4beHInIRafef9UAOUpPt4ChgDgHZKeQ3EBCFGsjpBJjBCfo86byR7ALJxpJqodfoTQyBHC8h5DPQd59UOW4kOJj0hwU7qJ7W293P9VMpjiA5JRlgw6Hp987UxmNLBQ18RWfDKjoPiUEupi14T4MDHSrC5v4U1ubvbsj2xbISExtpDEpwIwC5ibgWxC8PeOAZx2w3r84E9OV+e8oS5686y3ejDp2e/jVOWdsj0+G944gqRuYlZjDOefMgUAyZopFd5Ql/gaQ77u7IAnpV30+ACwhzoLpgKneB0fIZ1dONg81OXZvpZVwDA3QfMiLBC9kKYi5tOd/j+fegsX3b0ZT7xWXFp+FijxSdFQVypYD4BkdYntEQCRFGtc8SENPUdW4G8sUYq5WUxln90URxVVfEoxNxtc8ckuEUHGUw7xcT8ssH3yPpBIj4+ExNhCEp8KIFc6uyb4WrxZI4+/chgZ08KOA738Na8k7r1Jq7DwyV3XofrVB/APgT+VXcfnyZ2kVs7nPjANk6qJolCWudlTwBDIfdPPRXxYhs6AN9QF4Ie/3YTz//OZvIqAK9utBHMzOzfMl8PT7osJdQmKTzys8XWJRPQQDTWV7bVhoS6q+KSCdQCAepCK1n6KSTigoi4aBPPYD6ZGVudmrGBatuvaLaT49CZ0vi8zG2K8YWwp6ezsmhB74LkqN5fxEOGqPwVnP7xETnp8JCTGFpL4VAC50tnFxpjeG+tzu8kkn3FN0vlDXf+o/REtfS8DAKJIl52Cu5dmyHxwZh2aaHG+rhGks0cFxcebwZavgCEgKD7JjDvUBWDfvnfwdudQ3tRs/3T27GPqrc8T8IS6jFJCXQLRq4oEBL9Q9kTq7aReNHQ38cmEHMUH8C9uGQqoUFWFE9FSFJHxRJb5twDx6Rkm12ZNJIBIUOMNY0vx+PDzYYihrpFldXmJT66O7NLjIyExtpDEpwJgBERVFSDRA/g0LRWVkN7hDF491A/APTF6JwTRjzB96HWsCPyO/x+CDstGyR4S27a5GjG9PsoVn+GMWfJE6efx8WawOR6bHIpPlNbySejurC4AjegDkL8nk386u1A5N0cBRS2nubk0j088FPD1XLFzWfbTPiU+SYURnzoAQJ0yDA2m67oRFR/Aaag60l5W5aCY8FqpxIcpn4xgc8WnpFCX5VoXMPI6Pn1Jf8VHprNLSIwvJPGpANg9s6XzWeDWVmDjzQDcCpCoQmze0824kTtkkSvUZaTxjaM3IaBYSEUmAQBCCrnpl2pwHkgaGKRPytPqYoiHNN4CwNfgfPAl4JdLgaNvZb3FbughTc3ZnoM9aedUfGJM8dGBYerxCZHKvE0g5DDfZCoqPn7p7Ozp20tovB6fXN3Z/eBSfMIBrjSJ+84IV1mKj2UCBsmyS9rE46OHawGQsdVhyHXdiMZ3gITfgPFXfF5u68VpN6zH3Rvfzrucl+gUIgaijwsAqvj+lRLqIufDryio+H4p6Eu4vy8O8ZHmZgmJ8YQkPhUAe1qs79lBXnj+TmC4G6qq8HYG4qT43NtOSCfjuvnmCHV1vI7JZgf67DjenPd/AAAhMOJT2k31YB9REhrjIURDGhRF4apPVkq7bQNPrADefgp49ZGsdbHJQsw0E/fBtm2uABUyN7uyuqacAgCYpBDiU6ri46rcnCNNnZcaMEtPZxcVn6qIv+LjVzemaOhOKnrSJopYIBAEonUAaGaXXzsT2kOMhbpK8cCMBl450IeMaeHl/b15l/OSjEKqGCMrbL9iZaSzs/NhWja/Rt2hrtLJyYBX8clRuVl6fCQkxhaS+FQA7D4ezvSRP4wk8LefAnBCLGxStG0bz+52Qjoi8cnp8aHZTgftSbDjTQBIqEtcb7E4KIS5GJjP56hX8dn3HND+Ch1odi8vR01RfVUP07K5spUrhMSKGLpCXZPnAgAmKX0A8hMfsYChX5PSXB6j7HT24hUfMbQXDwd81S4e6irnaV93PE1JUOKjqkCsEUB29WaxgS0bEzD+oS6mUHrT7b3I14w333qZ0hYvI5QnZvqx79VICxgWH+qSHh8JibGEJD4VADO1hvR+58UXfwqkh5yGovTmt7874SouJ958s0Nd9MZO+1f12XEEgiT0EWSKT4k3VebvmeZDfLIyu7b8xPnbh/iwJ1m34pOttgC5s6WY4jOYSDl9uqbMA+AoPknd/8nesmw+2UQCKt+G7kNAshQfT4d7s4AXSYRL8RE8Pm7Cxcy0ZRCfDDGfIxgDi5QFNIUTH28tn2yPT2VCXUyZKUT2srKeChIfGupi+0dDXUNlKD7iOF3p7GVkSOY0N8teXRIS4wpJfCoARj5CTPEBgFQf8NJaQVkgN79n3yaqxkzaTDJ/qIv+z4gPqqGFCPFxPD4lhrq44hPjr/FQl5jZ1bUbeOsvzv+Z7Mwqh1QoXNHxUz2A3PVxWDq7xVPZFWDSSQAcj0+uJ3txAg0HNYT8ChjydHb39rOalNJxayWam91ZXSLpG4VQVzDqHj9TfLyhLoGAAiLxGV/FJ+2jpPihdHMzDXWFshWfQnWeGMQHBOa7SrnS2ctXfEKUVOf0+EjiIzGO2HGgD/+zq7jK98cKJPGpACx68w2m+8gLcz9Dfm/5CWIquWmzifW53cTH8rGTJwMgk5bNO4TnyOpK9gBgig8hKaFyFR/q8ZlWV0Dx2XIX+a2R97gKQWGYFi/KSMzN2WEmcWy5vDOsbYWapMbmWANQ3QLACXUlc0zgonHYpfiIykuOrC5vOxFex6eMUJfmCWcCTo2jEYW6gnH3+GMNAEgtH5e5mTWwpfsfL6PA32igaMXHW3m6WHMz9TAx87Zp2UUraqKik/QNdZWu+LAGpezBIVeT0rKuAQmJMvEPv9iGKx/aVlZttncrJPGpALiBNkNDNWcvJ5P3YDs+qmwjy5g2DNPC5j1kgv/E3Cn8894bZhX1aHhDXb2oRoApPpT4lFrE0M/jk6X4DHcBr/yG/P3Br9NBuomP7iE13vRwwEltV5Xc3hmWzq5RcodYE1BFSGGNkkQE6ZzEJ0XVFE1VEBAyy1zp7LmyulQnBGnbds7q235wKT5hzV/t4orPCEJdoZi7DpFL8RFDpMwDQ8YVrZDiw85TISJTqiLClBmWfcjMzUDx+yg+VHCPjz7CUBdVfJqyiI93/6THR2J8YNs2OgfTsO3slirHMiTxqQB4AUOm+FRNAWZ9CADQAjKhG5aFjoEUBlMGQpqKD86q459nkyO7cbL+VVyKTziKTzBEFR8a6ipVRj/Ul+3xmVRFyAd/QtjzV5JOPWUecML55DXdTXzEyY2Ym90hPTK2wtWQWTp7JEMVn/gkIFwDQyX72aT0I5Ejnd1p3ErWz0I9LtUpR1aZWHTQ5UUqyuMjprMHc2R1jcDjI4S6XMcwh8fHUXyYIlKhOj7pAVwb+A2mZ97Ju1jGKE0R8aaza6rCSVCxmV2iouNnwi4n5ZxNLJMZ8ZGhLokKI21YPKGk3Mr+70ZI4lMBGJaNEHRoBg1RxBqAUBwAEFdTfBmWXlwdCXDZHnBumOypNJfi02dXIxhi5mZy0y3l4h5KG9yQ6Rfq4v26Un3kd+NxQKiKDtKr+IjER3EpKAxcrcijorCsrlp7gLwQbwQUBUMBEtaZjL6Cig+bEHkGnaj45CBfIlkxXSbsYkJd7pYVfpWbR5bVxczNcU7cAqriyeoS6tGYbgLopLOPb6hrXu/TWBZ4HF/N/C7vct4MuoLEx3OeASecV2wRQ7GwJgt1ienspXrlLMvmxIcppmLRygjS+LD6GgIwJPGRGDeI98r30nUniU8FYFk26kCznhQVCNdy4hMDIROmZfObdCysQVUVH1MkmYCZ74XdmG1ubo4jGHZndZUio7OMrtpoENWRIH+d9+ti6expSkLC1UCImqBzEJ+gpkBRFKddhFWYdIiIhQhxaFDoNmMkXX+AEp8mpd/tVTEN4M0/A8PdWYoPr9zsQ0ByFTC0bNt1gyingKHG/U3Z+16WuZkZyUMxd68xQfHJuBQfp2UF4HhgchHGsUJ1phMAELXy9ydjx5u1nihEfJIZFupyjjvv0F5krSK34pPt8Sk1HDWYMviTdZbiY9j4duAP+GXoZnxRe0bW8ZEYN4jqeLlNrN+NkMSnAjAsG/W0hxKi9YCqAkFCGBjx0U2LP4Gzp1U2UXmbG/JQF500bR7qqkIoTJSaACyosEpSfFjPK1HtARzFJ6mbZIxpui+h6tyKDw1XRDQb+OM/4RP6Rr6fznHxDzOJUBQFdbEgmsAUH1KZulepA0BS2pNi9eO3/gz85hJg/fVZSoBfHR/25a9JHgIe+d/AQeK5EhuLisew5AKGOer4GCPx+LhCXVTx0byKj9vj88/aozjz8Y8DQ0cRLVENGS3EjD4AgGbn3y673pmymS7k8eHn2d0qBCjewK37eXxG0LKij3Zmj4U0vh9cubUszFQICZymdPHvioTEWCMpfB/K7eX4boQkPhWAadmoV6jiEyVKBVNKooqg+NCnU+bB4MSHdQj3hLrYE7vCFZ8qhKjHByCqj7c3Vj4wf49obGbjYeGRo4Nph/iEqzmB8xIfNubT1XeAlx/CV1O/4fvJkCuV3IuaaNBRfGiBxh5OfPpcX2YMHCa/j76Zpfh4ayYBjvoz4+DjwK7HgW0PkGUFsiI+7Rch+PhkdeX2+Iw81CVmdTHFZ8gd6tItXKi9iMjgfqBtC1dSxtvjEzf7AACand9Uya4d9j3QhcxGP3g9PuJni1Z8/AoYjuDpmIWM66JBgXA7xTDjICHuKNLvqZCDRGXB1FHgvWWql8SnAjBtG7Us1EVTjhF0h7oM0+ZPp6zOCgt1hd7+C3DrHJw8+AIAMdRlAaYBJU3q2fSjioe6ACAMvUTFJ9vYzMDDXUNpIE33JVzNQ3awdMBwKjuzm3kt9TCFbaZsOePJiGoFw5t/BvY+49p2XTSIRh7qIpP7UbuOjAv97gmcEbDBdqdicRGKT4Rl3NFjKaagixldilJqVldASI3PzrQqL6tLDHWJWV3k2qpWktDTTqpqxrT4dYZEV8WalFYx4gMzb/Ncnr1IlU3bzl+BnBHcqBjqKrFDuzury8fcXCI5YRldNdEgf4DhSQqGjSqFfNeiyEjiIzFuEBVQaW4ugLvvvhutra2IRCKYP38+nn322bzLb9q0CfPnz0ckEsGcOXOwZs0a1/s7d+7E0qVLMXv2bCiKgtWrV5e1Xdu2ccMNN2Dq1KmIRqM477zzsHPnznJ2cUxhmrkVnwhEczO5KJmiEw6qUGGh+YUfAolufHjwzwCcUFfaMIGUUw06GaiBooX4/0EYJT2pHvIpXsjQRDO7shQfRnwAV2YXu5nXaGTCDdrZZmuu+LBMqUQP8PD/An59CWA6qkBtNIhGT6irw6wl41I8xIeFgYaOIJ0mRCzL4+Py2tCMJ058yHliXMy0bcdAXISxGSAEqakqjEhQRVN12NdUzXt1FdGtPAu8jo8nqytcCws0syndyxdP6yYiCiM+3RUrYFhjkXMYhJHX16J7lE0gvzKW1Ecj1OVXx8f0fb8YMGNzXSyYRbh100IVKPFR0rmPRZHFFwHgL6+34297e0oao8R7D6LH571EuEsmPo888giuvvpqXH/99di+fTsWLlyIJUuWoK2tzXf5vXv34sILL8TChQuxfft2fO9738NVV12FRx99lC+TSCQwZ84c3HLLLWhubi57u7feeivuuOMO/OQnP8HWrVvR3NyM888/H4ODg6Xu5pjCtG3UexUfZm62CfExLQvDdCJiT+QhTcUF6lZEBvcBAI5P7wRgo1oMddEw14AdhaYFAUUBKPkJwSgpjpvL4wN4FR/B3KwF+fbEcBf7UlUrZP+CvHeYqLZ4CEXvPsA2CYHq3ceXq4uF0MA8UjTUddisIeNSPFldjBTYFpAgxSB5qIsTkOwneU58aOsNjfUWM22ng3wRqewA8SX99h/OxmP/+CFqbnZ7fEzL5sUdyzK2CgUMnVYaCqCqSAbIcdFSziToUnyGuyvWnZ1l5oWKJD6MoAH5iY9fqIubm4ut42OJig/L6sq+VosFK15YFw1lefUMq4hQ13A3sPo04MnrXS+nDRN/7xhwhf66h9L4x1+9jCse3PqeeoqXKB2pTPnh23czSiY+d9xxB6644gpceeWVmDt3LlavXo0ZM2bgnnvu8V1+zZo1mDlzJlavXo25c+fiyiuvxOWXX45Vq1bxZc444wzcdtttuOSSSxAOh33XU2i7tm1j9erVuP766/GFL3wB8+bNw4MPPohEIoFf//rXpe7mmMKwbNSJ5maAh7qY4qObNhLM3BxmoS4F/xB4nK+n3urFdOWoE+oyLF61ud+uQoilwFMiElSMkpqU5vL4AGJKe8at+ACO6iMQHybrx1UyATDFx+2vETKSAGDgkLPBrt38z/qI4ihmNKvroE5M1ZPQj4TYq0to4KkOdgDINjeLPc/Y8Qml3YpPQPDlsGW+oG4Edm9AMZgzqQqnTK1xrYvtr7dnV8mTlRDq0rkaRfYtGawj/1PiY9s20oaJKGgYMtGFaLA0/8uowLJQZwuKTx4iw96LBjXuqcpHlBhRcddPoorPiOr4FC5gOJw28N87DmEg5fYtcY9PLOhkZ/ISBp5Ql5+5+eDfgP424NXful6+6Yld+OTqZ7HxraP8tQGaQTaYMnC4L+ldk4QEh6jySnNzDmQyGbz00ktYvHix6/XFixdj8+bNvp/ZsmVL1vIXXHABtm3bBl0vrlJkMdvdu3cvOjo6XMuEw2EsWrQo59jS6TQGBgZcP+MByxIUH0Z8WKjLdszNQx5z8+n2TnxAfQemGgaaTgQAzFfe4qEu07JhDJPCfr2oQm2Uhga44qMXzeqTGRNdQ2RyzEt8vKEuwDezi4UG4jTEEoABBZa7gKHhnrS5MRkAuh3i0xIkE70F4mOxbRttaUp8lD4k0z6hLgDBRDsZpifU9V3jp8CfVrrHwIkP2TfNVcDQwiT04kb7buDRK7KOTSFoHo+Pl4yWbHDmio+Tzs5Us3SIXF+sL5xh2QjbGagK3eZwl5DOPn6Kj53qQ0ChfqRCxEdoHOtVS/zAiEo0JHp8WOZaceTOm9XlNbXnUnwe3LIP//zwDtz/3F7X68zjUxsLIhRwN6n1hrp8FZ8hkvWF4U7n+wZgVzv5+0CPQ/DFY/P20exmwe92vH6oHx9btRF/fq290kN51yOplx++fTejJOLT1dUF0zQxZcoU1+tTpkxBR0eH72c6Ojp8lzcMA11dXb6fKWe77HcpY7v55ptRW1vLf2bMmFHUeEYKQ8zq8pibwzZTfCweemBZN0uTJDzYNusLwAmE4M1Xd6Mq7NTY0QcJ8emzq3g4Sgx1mUWyeqb2VIUDvCO6CHeoy0N8fDK7GKGI01AXG48rs4mls7PH+v6Dzga7ROJDjt2wWgOoGhIZE5028fhEFB2aLoQ2hTGEEqQRn6j4NKIfX8Z6YNt9QKKHPsnbXCFBhqzLpfiYNhqZYpfqB3Rnn4pBwFO80TAtzFPewa2Bn2IyekdIfNgxpAQhVAcACNPQXdqwEIVjdCYeH6qG6MU38RwpMgOd/O+AYubdZ6aABANqllriB293dsD5DhVtbhauy0wmjUyXm8jkeoDY00mut+6hjOt1J6srxCtms322jAwiCnk/ihwenyHneKHnHf4nq56eq3nxns5jj/hseuso3ukaxp9f97+vSxSPpFR8ioc3k8W27bzZLX7L+70+GtstZWzXXXcd+vv7+c+BAwdKGk85YNkrTqjLbW4Oc4+P7fb4dO7C6emtMG0Fb7Z+HZhxJgBggfoW4mENbBeZ4tOHKq7KIOAQn2JZvejv8Tt+LsUnI2R1Ab6hLl6ETph0Qx6zte5RK9yKz9v8z8kqOXZ9CgkdDaUNpBDGsE3GFNH7nAlcUHzCSTJ5iOnsU5VuZxtDR2CYhBioJh1negiwba74WDYJdcUhhBAEQ3kxYN4gFtLKmBauDKzDlwOb8Bltc+lFDF2hLvcxzISJ4hOmik/GEPw9gMvcbNtuH8tYQiQ+xXp8QprKw7ele3xKU3zE6/Ij7Q8gevfp+IzqKMe5ykIcGWDJCe73+2kdH2JupueGedoM53uSM6tr2J/4sH55ria0wrHZc9RdVuJYACOvZRX7lHAhIT0+hdHU1ARN07IUlM7OziylhaG5udl3+UAggMbGxlHbLjNFlzK2cDiMmpoa189Ygz1J5jI3M+KTldX1Ginr/5Q1H72R6cB0QnxOUtoQMYd5SwtrmLWrEIiPEOoq1j+Sz98DOIpP72DCURzCNa59EbO62I05BlHx0f0VnwIeH6a2dNlke4MpcpyGEeXb4Dd/weMTTTHi4yg+0xRBdRw6At200QBBMbJNwEi5WmyYluVSrkomPt5O76aNSSDrqFOGS09p91N82DYo8YnqfQDIZMFqRQEAhrsQFZSR8SpiaAw6x71QqIsTn4CKECMN+YgPfS/iqphNDdxFKj4isZmReAMA8I+BxwEwX5b/96iDEh9vfzGe1SWks7N9CIrER0n7r3voiPM3JT4p3cQg3R/RF+RSfI7BUBe7L44XST+WkXS1YZHExxehUAjz58/Hhg1uQ+eGDRtw7rnn+n7mnHPOyVp+/fr1WLBgAYLB7BBKudttbW1Fc3Oza5lMJoNNmzblHFslYHLFx5POTkNdITsDFRYMoXJzLKyRYnoAnjDPIinPNS04rEyGptio7XmNp+6yqs29EENd5HdQKb4P0JEBMjlOqY34vs/S2VPDwqTPvD2+ig/Z7yg8oS6PsRfIQXwSXTxjrc4m2+w0yfaG2BOgGqXbSDtPMgLxiaUJ8WHHihAfxxSKoU7opuVU1WZID2UVMIxDJD59KAXeAoaGafOCjNVIjJD42HQbZB8ten1FaZXkjGE5xmYAMNNQjWFe82a82laYg85xD8LI+/TOjkdQU7KKePqBKT7uOj7FV6c2LduVOV5lkO/UKep+fFDZLSyTPVF09DuhahEs1FUrFDBkxMet+OTy+AjXaTchPrxJMICM6Rw/kRS+cwwSH+Z9TJVT+kHCBVflZpnOnhsrV67Evffei/vvvx+7du3CihUr0NbWhmXLlgEg4aOvf/3rfPlly5Zh//79WLlyJXbt2oX7778f9913H6655hq+TCaTwY4dO7Bjxw5kMhkcOnQIO3bswNtvv130dhVFwdVXX42bbroJv//97/H666/jsssuQywWw1e/+tWyD9Bow7SJh6QuS/FxauXEkIJh2Xzybkq1AV1vwkAAf7VO5zf9V3ESAKD66MvOTZ6Sg367CpN8Ql3FKj5DVEVhxmkv6qJknQGd7ocW5tvJF+qKiKEuxa348HYLqgJYFjDQ7qwbALrI9VClE7Wg06xGMmNikGbQZBRC0uJKynmSyTjEpzrD0tlpk1JNwTRvqMuynVR5hvRAVgFDloUDoIxQl1vxyZgWV7GqlcQIQ13CMQSgxhnxIWPMCnUBQMJJaR8vxcca9hKfwoqPaG7O1R3dtm3/Oj48Zb/wsfUSj2paaBEALg1sEJZzf5eG0gYn4d5Ql2huZqFWrmSZDjmJIuOvZvkoPl1DYoFQf8WnayiDvoTbb/RuB1d8yin2KeFCwtWk9L2j+PjPanlw8cUXo7u7GzfeeCPa29sxb948rFu3DrNmzQIAtLe3u2rrtLa2Yt26dVixYgXuuusuTJ06FXfeeSeWLl3Klzl8+DBOP/10/v+qVauwatUqLFq0CBs3bixquwDw3e9+F8lkEsuXL0dvby/OOussrF+/HtXV1SUfmLGCadqoQYJntHDFJxABoACwEUWahLroJDSt438AAHuqPojBVIzfGF/GSfgknkW8cxsiwY8AABSazt5nx33NzbnScL1gxuqqkP8lUh0JQFHAs1G4vwfwNzfTm3HUdgiD1+PjarA5fJRUf1ZUYPoCYP/zJLNrxhkIHSRei9ftVpw3lOYkTdeigEWII3+SETw+1TolPkzxUd2hLnvwCExrDupVD/HJDEFTibrkeHzKD3UxxYcdE8M0UU/DazVIjoriw1SFYJSYvkMmWSZtWIgqHjP2cDfPgBrVlPYXf0ZM41/7PVAz1bNN57iHFBOZPE/vfsQnV7+ujGlxtSbso/gMFRHqEsm4Agu1Vh///0LtBfxA/9/oQi0My0JIeHZkag+QHepi5LwmEoRlu9uThEznGo0paRiGzxgFooiePQAcf4+4Lu/fAPH5zJ8VwrECdl8sq9inhAvuUNd7h0iWTHwAYPny5Vi+fLnve2vXrs16bdGiRXj55Zdzrm/27NlFZZPk2y5AVJ8bbrgBN9xwQ8F1VQqmbaOWhrnsYAxKkIaSFIUoJZkhxJQ07dVFvuCNB54EAPy9/jygy7mxvWSdCChA9MjLiFKuoab7AAC9qPb3+BQpZw7xGkL+l4iqKqgOBxBP05u9SHx80tnZmCO21+OTnY0S0BQnzFXVDEw6mRCfrt1AshfKoZcAAM+Yp+ErwxnH56DFAB2II+Ub6opaw4gh5ZACTcFUgfhYtM5PtuIz5Cg+pg1D6K0EgKtsxcJrbraSfQgqZLzVSqLsrC47GHV6dVEvTChGfFAhi0yuaW+oCwASXYiHSJhwVENdrz4MHP07sO954LQvud5SEt2u/3U9tyrBnkTDYlZXjmMk+j58KzcXQexEyb8OQ9BAFVarFaepe3Gx9lfcZX4+6wlZJD7eSYQtGwqo/Lzz/TLdBmTF9ChyetIpEgoQ9Sc95Ap1eZvQithzdAjzZ9Xn3uF3GRxz83tnoh4rJN+jio/s1TXOMCyLG5sVpvYwCB3addNCIm2iGd2IHX0FgIJ3GhYBcJ523zCnYciOQM0M4gSVpH5rlPj023E0VdOnPFbAEMUXMPS2y/BDbSyIasVH8cnj8RGJTzjL3CxURGbEp2Yq0HQC+bt7N/DOJsC20KbNQDsa0TWY5uZmM0CPn+Lv8QGAKUovVw00VckyNwPI9vhk3B4fw7JHxdzM9tcW1I+qUhUfi5ivAcDQHCM6S2ePxIniw5S2tGFmh7qGu5xeVqMZ6kr2kd+Z7MrpWtJNfAw9nbUMQyl1fJjvQ1Wc3nYASgrliRMA6wmnh2pxv7EEAPC/Ak9Bg5nliWDGZrIO5z1L6O/m2gfabDVsuYmPaniKDrJU9kDEUYh796JLUHzE7WUrPseWz2dYenxGDaLi816q8i2JzzjDsiDU8PE8hVHCEEUahklCXYu1beS9GWdBj5IqxWmd3DBTpoJXrOMAAKdYxP8SEBSfxjjz+JDfoRIqN3s7w/uhNhoUQl1CRhzzK/n06gpbQqhLcZub2d+hgOKkstdOAxop8el6G9hDwn67YmcAALqHnVCXxYljijzJ2LZDfCJ1AIBmpYdnBil6Ag2KMCnQCcaV1QUA6UHBkGzBMEeazu72+IjqRzUSLgn/pf09eO1gnvULoTxDdYgPI1fRKnJeYkhCNy1iblayPT4sFDSqig9TwtI+xEdooQEARiYP8TEc4uM1BnshprKLZRjYdZzIFK5VJKo1kxTqjQo3Yp11FgaVKkxVenCKsj/rCfmIi/iIFcmd9QU1xTHvg5CfiOUmOpqZg/jEJwMNc8jf3Xvc5mZXHR/3uFhtoWMFQzyrSxKfkcKdzv7eUdAk8RlnGJaFOjaxehUfSnziSgpDaQOWDXxS3Urem/tpbsrNmBbPPNlpzwYAzDHfQQAGQgYNo0Xq+JMlNJI9FyzF3Oxpl+GH2mjQMfmGq4T9yB3qCmWFuoRCcWIPLFa8sGYa0HQ8+btnD/A2IT5t9ecAIObNoTTxT9hBdvzS5ElGIAVoJARxChzFx1UgEYBCJ5jsrK5BV38tw7JQpYxGVhc5JmrCUXyqlQRXOBIZA1/9+Yv46r0v5L4pCYqWzkzgEIkPUXziSGE4bWQXMARoh/ZRVnxs2yGE6WzFIZj2EJ88io/j8VGyjMFesFCXmMoOOH2+TMsuqKiJvrMmWmYgFW5ABkF0a5MBkKxM7xja+53rzduGhCGoqXwf2HtRy61KZhEfVsOnajK/jtHzjsfcnB0yro+R7/2xpvgw/6FMZx85XKEuqfhIjBVMsWpz1KP4CKGugaSOKiRwpvp38t7Jn3bJ/Iww7LSIubtV34NaCFWKqwRSRSfEEPSi09nZBJg31BUNogqshk+hUBczcjo3+Wxzs+jxoYpPzTSgdgbZBzNDQmBaGP2TSR2jrqG0Y1hljV6Zx0ckPg1kwiCKD50U+0nByk67DgCgpnoQhIEmRnx4s9Uhh/jYNlV8RhDq8nh83IpPEml6M+pL6EgbFgZTBq8DkwV2jANRGJajcLBQVzBKFJ+IomMwkfLP6hp2ihiOmuKTHiQ1kACnwCWDbSPsIT6mkbt9jauOT4F0dr+qzYBjbgYKV28WvyNNVPFJBknNsZRGrrFqJLLU045+57i6CnMaouKjuhUfw0LE9hAfw3N+WEZXlaD49OwpaG6e20LOfVtPGZmCExhMjU4b41dp/FiFy9wsFR+JsYKL+MS8ig8hPlGkMZDSMUXpJdlfkVqgodVFfNiN/w2q+EzL7OFKRb8dQ2O1kx7vmJvNohWf4QLmZgCojYZQBR9zM8/qEvoHmRZUWAjazlNqdgFDISNJ9PiomvOkCwCzzkVdLbmpdw9lMEBDXSpTzFhWFwu1BSIkZAbi8WHF/dBHiM/r1mzYKtnPRvQ75uZa2sJErONDm4i6Ql3My1IknKwuUgtG9LsEFRMGPW5it/TeXCnJjNyFnOKFpDE73ceQo8QND/bRrC46YYboOUt0OZWNRyurSzR8i8ZcgChorEmtTU3jeUJdrDhfSCtsbuap7CG34qOpCjc7F0ppZ9ehojgen6Eg+a6mNHI8q5Vk1kRxJIfHRxfOi6Yq/Ie9F7PdCk/A8oa6aEZXfBIn8OjZ66njk21unl4fRVU4ANOy0dbtJlfvVoj3Pst+bxlyxwLv1XR2SXzGGaZl5w510VBNTEmjP6k7Cg71p/BUXsPkT5Hv2C2wAxFErCTer5D6Hn12FZqqnbCHU8dHL/riHirG3OwKdflldTlP+rppuao2k/F4Cxg6IQ2H+BDCgsbjnQ8e/3GesdYlpLOrEbLdKDM3M1IQjALVLQDc5mYW6jpoT4IRIf6pSUq/E+qqn833Qyw6OGJzs+ooM5YNBDzqB1ufeFPqGc6hiPBU9rjQrkL4WgdCyNDkzdRQv1vxqZtJfg93IRZkdW5GKdQlhv+8oS4a2kvaIQyCkGTTq3II8DM35wpXOYpPdoi2iretKE7xqQoFeKirT60jYwlQ4oNE1nepvd+f+IjjZ2AELqWbiMFNdIKWp9wAV3ymCIrPOziaw+PDw8oBFcdNIveUYyXc5b0+U8eQklUJpN6j6eyS+IwzTLsYxSeF/qSOGoURH+LTCPuEuqAGoEw+BQBwrvY6AFK1mVVWBuBkdSnFNSk1TIvHz4s3NxcIdRl2VoglpLhbaPDO4gqc4oVUqeGZXQBw3MfRSPeveyjDSZoWIWPg6ewCKWDEh4S6GPEhis9huxGZ6CQAwGSl1yGm9bRGVHrAFZ4yLGtkoS7NIT6GZTkNUSlsagZ2Ex9H8UlkDDz8tzZSG4Yd42BUaFDq7q2WUsh1lUoMkJYVnPhQRSvRzRWfYgr8FQVRBfOGumh18W7UQKekzMyTzs7NzYFizM3M45N9a+PVmwuoWuw6jIcDPNTVQ9uj6CzUpSSySjF0D4tZVtk96MQsM6Y6DqUN5zvE3jO9dZYEj09DK/l7sB1Gyv1g4f07qKk4bhIhasdKzy5vHSZpcC4ftm27iKTM6pIYMximULU5h7mZeHwM1DD/TLQOgEB8aHYOQG+gLacBAD6k7gRAqzaLio9YwLAIxUds5FjI3MzT2UMi8WFZXYLx1vT0t2Lj8ancXGv1OsULq0gPNjSdSH5XTwUmz+UZa2JWVzBKxhBDitwQWagtGOUF9PwUn0N2EzJU8ZmjtCNA67agjhGfIbA5i3t8RmBuZiQKIDeboIf4KDQ0JN6UROLziy37ce1jr+E/ntjlCnU5TV7dX2vWyiMz3O/O6mKKT6Lb6V4+WoqPK9TlMYvT9P0euxo6aI+5vHV8nGu9kMeHeVm85mbAMTgX8vgwQhMKqJikknPRyYhPkFxjNUi4yEbnYNrV5kJ8jxNSwXfEmq0mMqa7CjiAoO1VfGioq2oyeVii3sBZitO41BXqEhWfyZT4HCNd2r2kNS0NzmUjbViwXNesJD4SYwTLtp1QilfxEbKS+pM6ahVPqEt42uWTgaoCzacCAJoV2qBU7MwOuOr4FMPq2cRAsmgKZHX5Kj7Zoa6MmW2qDUN3KVDsi1dLW1KgqhnQqOJ08qeAkz8NnP/vgKLwGkU9wxn00c7X4RjZbjxPqGsy+sDtH9TjQ4gPUXxOovWQEKoCYo18P1gBQ9OkWV3edPYSTJaaKio+NoJpdwFE1UfxET0++6hf48mdHTBZGCkYF5q8uhWfjEqIqJ4cRNov1JUeQFWAbGvUzM1FhLp67BroNjm/llGY+ISKaFnBxh/1IT5OSnuhUJdTBLKJenzaDUJ8DEp8qpWka6Lo6E/6rgMQurAL552VVBgWFB87QIqZRuy0+3vKQl1xklHGwl2zFachs1+T0pCmYmYDOfcHez2+oUrCNNyJByXAq/gcS6bt8YZXLZPmZokxg2HZqGPenSzFxwl1JXXT8fhQxUf0N3AjcEAFmt/vWk2vV/GhdXzCnkrJuZDX2Lx/C/Dq/wOQx+OTo2WFn8dHnCDY2Goy9EYvtjkIVwOX/Ao47csAgIZYCIpCPDKdNLslRBUf3qSUmZtDcaBqMiwoCComIplecvOlPqJDdhNSEUJyTlQIGUKswUnRF8zNhuVTudm2ssM5eSBOgKZpI5whik/KJunHaoYpPv6hLpbN05vQsbedqgHBqBMqVN1f60zAIT6uJqXVLYBCCEKdTcjW8JiEuvwVn25Uw1Qo8fFWKxYgVj0Oa/kVH7GOjxfxIg3cvO2HoqCRenwO6bRlCVU2q5B0ZW6xjC6WQu6Xzu7y+AQcozW7lmxKbLIalQ4Lig/ADc4i8fEzN4c0FbVRMp7BIrvSjznSg8CPTwP+oxm4ZSZw9znA358o+uNZHh+p+JQNb1hbprNLjBlMy3Y6s2cpPk46OwDUKKz4HvP40Do+hifUNeUU2HAm034IDUoBVx0fowg5k9fw8evT9bvLgceuBHreQW006GQ3+Xl8LAOgT/K6NzwEmtXlCgkwxYdK+Mzf44OApqI+RlQfJrZEaLG+OFJI6oZb8dGC6AatYpzuBIY6ANuEAQ1HUYdkiIS6TlCoqTrW6OxTxl3HxzYyvMUERwmZXaqqgNXW000T4QxRfPbbUwAAmk4VH2Gy6hWIj5jN89YBeqxCMXfLDwGsorWVGiQeH3YeQnGuatXahGwlxyPUJSg+pkquTTuP4lNS5Wb6elj0+GQSgG3zcF5BxYcS8GothQgliW1pQnxMSnyqkXAVJmRVm2dQhcXPtB8KZBOfobThPDzEieoYQcYhPplhh1Rz4kN8PjOVI9zL5zY3Ow9FVRHWoyx3uYBxxaGXncSFVD/Q+Qbw4pqiP+4NU0qPT/nwEh+p+EiMGcxM2vHFeOv4sMrN1IORK6tLDHUFVBUIxTFYNZuvptf2hrpKq9zMnoizMrpMAxikpuOed1DnallRk7UfZLDkpq2b2YXzcnVnr0rTybwmN/EBgMa4u/FijLZniCnU3MyNv2QyOmITohlJHOFhrm5tEiyoSIYIAYgqdAKONTq+JU/lZkWoSM1VuzIzu6z0MAIWOS6M+ASoQpIQbuo9CX/is7edHqtg3F0OQIBFQ6h2etCd1RWMceJTbZPxj1o6e75Q1zBJ3++xa2AXQXxEs26xlZt5qOvAVuBHs4Gn/s0xNxdKZ6cEnKWyD9thdKbI+mx6nVcrCY/iQ74HM+qpuuZTx0cMQbL9EENdSjU5/zEl5XyeVW0OxpwQMiVAjcogWmqpfytHFlk1/Q4zH1zFcZTWJTv+E8BFPyV/Mw9TERjyenxkv66ykR3qkoqPxBjBpBktJlROaDgEczMAx+OTJ9TFXhusm8tX02dX8awnAFzx8dbNyYWcVZtTfQDo5/sPoUbw+KQ1sW5QUCj+N8zH7GdudhV6ozfseNon1OUDcR8DquJ4fLwFDCnxabcJ0QwPH+LGZlaJd4gqPhyxRleoixEfywY0RuaUsOMDKrOIoTXkpHazQooBgxwz0W/DFB/btnmoS1EAI+1kdek+XhIAsCmBs9PD7ialoTgQJ/tdbfSRbY7WE7SogFk6IKarJ5xQFyc+ZuGsLtHjk6s7e9Ib6nrxHsBMA3v+6vTrKmRuputussk+dNm13EfGVMBqJFxPyEM9HQhBx/QGQkR0y+LF9XQfQsr2I5VKIaIQNUapIsQnKio+vF3FJHCZkJLtOmUIU+sirmMEOEQr5FJ8jIlR7K9zF/ndfBr3Jro6zxdAdqhLKj7lIkvxkaEuibGCRZ92h5UqwOPF4KGuXIqP4G/wPkUmGt7HV2OE69xP/dTjE/TUzcmFnB6fhJB9NHAI1SGNE59BK+Jelqk+NLPLz+NDzM3ZBQzjKepdKKD4iKpWVSQAhU7wEUVHJpNxZTyZlo2/WyR9u+rF/wTaNgMAugOE+AzTyrwcsUaXSdtFJijxSWsxHoYsPbOLro+FfVDNa9oEDZ90dqr4DNG2EwDw0ZMmCyQm5nh8PIoPOxeKPuTO6gpGOXGLGiQ0VYgUFA1vx3ox3DXshLoYKbeKqNwcDCgFzc1pMZ092Qfs+hN5o/9g0ensjKg0oA8ASbvnXhJ6vquVpKOyHH0T/7FnKW4P3sMVH9t20oPZWAOudHa6H0mhuCNVciJIO0SGp7JPcZajIfJ6DGJqHSVavh4fhau2ulm4Vce44Oib5PfkuTy0h0Q3abZbBLLT2SfAPr1L4X3IkXV8JMYMVoJOMFp19ptCywUAQh2fOgCObyGtm0KmCH2tySE+ijeExltWFBnqytWuQmitgP5DUM0UqSwNoC+L+Lgzu3SfrK4QdJdPgis+SaFdRR64iE844AqxWZmEY24OEv/LPcZn8YY1C2riKLDtfgBAb5Ckyw8HPX6rWIPj8TFS0ODcJBQaitK1uEB8SlN8NBb2EFK7EwoZf5j2W3NVbqYFDFl/pqpwAJ8/fZpDJvNkdSlUuVL1IXdWVzDGFZ+o3ke36TMBtb8C/PlaN/EtBC8RFIlPwtlnhSmDORQf27Zd5uBQsebmgAa8/ihRewAg2YO6ANlGIY8PezhoACElXXYtf0+J0FAXhFDX4e1QYeFT6os4LuIQGfZd0wUiwsBKU5hJclzSCPFwcUwRzM1iuwoGqvjUK0OYRomPu0mpo/iIPj0vaRh32DZwlCo+k06m+6EAsIu+tqTHZ/Tg9fPJdHaJsUOS9v7xIz4ec3NWqEu46fPME3YDnewQn2C1R71goS6lOMVnKJfikxQVn4Oc1Fi2gj4j6LsvLNSlGzZi3lCXYmQVMGzAAKJJqvhMnot8ED0+VeEAEAjDpllKSmbYZW5OGxYSiOCKzDWwhafnvhCZUBJKFCmhu7lL8QGgCb4ehalYgRg/N+V6fFifrh67BjqtChw2ybZEEkKUHpOHuZqqQvjoSZNQpZLJvCutClldbuKj0sKOmp5ARje4okjMzYT4hGhKfSLjExJ5/sckZPTGfxe/g16zt5j1RlXPbtRACTDi46/4iDdjV6+uQh6fkAbs+LXrvUkWIVzFenzqLbIPIvHRojTRQDFg0evLpuRVVWyceOQvfFlGzvJVbjZT1EekRIWszozg8RHaVTBQxacOQ5haSx44DMuGRb9LacPZnqo6qk/FfT5DnUQJVFRSkFQLOAkeRYa7sur4TAQV610KaW6WGD+kyQSZ9lV86I2PTkw1PNSVXbnZ+xSpVU/BenM+XrVaYdXOdK834DQpLc7czLK6PB4fj+LDnuKHEEF/yjOZ8OrNYqiLKQ1xPh7d4/E5Vd1L/mk8AYgIhmkfiG05aiJBQFFgccI15DI3s+PVjkbgkl+T/l0AusMk/GWYFvpUQSmLNZJWH1Qt03Rn4mZ/62Koq8x+XQr3u9QgE2TEhyk+7mPa130UXYNksm2qCqM6EkRLjBy//YOOwuANdQUo8Qmaw7ANgXwK5uZQugcNGMC9gVthbPmpe7CM1CVLUHzo8TBtSsKY4qMnuRLXY4vEx1/xYedNg4no5tsxpfdlAIV7dU1K7wcObSPp+rSGU6NJwkaJQk1KqXJWyzw+cK5DLVINi2ZQKnSfUgPOpF2/5zFnPXSMfuns7G87RdehxPjDgiudXWxXwUAVn6BiYlrM2RdGsMQ6PoCj3I674pPqBw5uc/5nxub62STMCjiEjoX0CkAqPqMH9l1hCrH0+EiMGRRKfFjPHxeCjrlZhYUaljHlyeqybOeiZaGuSFDDN/X/g89mfoiG6rh7vSUXMCTrzuvx6T/IJ8QhRLO7h3PiQybxjOjxoU95YRhZBQxPpf3GMPUDBcfpUnyoidMOMj9LwmVuFg2yyvQFwKWPAxfcjLaq0wGQp/xu1DkrZ6ZlGiZihmMAUOnEbQTKD3Wx86YmmeJTDZMWx4taTPFxbvLHKYcwec37cNKW7wAAr9NUS8M3fUbQ3etMQDBGJu6QmXDVVkIwCsTJfmrJbvwgeD8+pu2A+jdPerFOz5s3OysXLJMT/A40uD9L1REdGgYRRYASHyWH4sPO25nq3xF85ma8b8cPyOs5Q13k9ZPa/0heOGExN9HW64REFCIAvKyCj+ITDgaRpgUhVVphO9nnTNpq5xs4RWsj66HfNcNP8WGp7fT7kVBinAxElbSzf7yGj6P4pJQwr/k0LewUAmTnX2zxATjfjcHxVnyeuAa49+OOz4oRn0mCksuJT1dRq/RWFj8me3XteRpY9113QsAYgCVPVEfItSSzuiTGDBq9WbLS9y4IWV3VrF0FkJXVBQjVlelrUa7OKO7ihYCrZUVJdXzyeXyMJNBHbvDDdhR9iVzEh4a6TAtxFmKhHiRSx8ddwJArPlNPLzhOUfFhT7UK3W7QTMJmk3wo5irjDwCYcSZwznKujmRMC0csZ4LjxIeGu1QhVMNIkBGIOZl5JZqbmeKj0nYVPXYNL44XtVh3duemfo76BhTbwOTurWTfqb+pySCT7mGrPmcBw3CMKoZWEqpJJkpTDZOu9zTUpRzahk9pfwMA2N6qugb9v9gijQIJPGyz6tdU8RFq+AAKtCA9h1auUBfNsKKp5fHBvVBg5Q11aTAx+9Dj5IXT/xf3irHCmIX6kfHWKSYJ/4nEJxJUkVLJNabSeksGzcwzaPuNpdpzZJcNtwIT1BSigP72UlzQS8Jw7LpKqVH+4BNFxjFv+yg+3cMZ9IJcK/WKQ2S922PFHuOVUHxsG9jzP+Tv10jBU57RNekkZzlOfIoLdbF0dtZ+5Jg0N//1JuBvPwXefmpMN+MQH3J9SHOzxJiBVeU18hCfsKKjgba1MAMxx6MjPDGyGwALdUUEUuRqUAq4FJ9SKjdnmZu9oQ6aoZFX8eFZXUKTUk583GZrw7RxqsoUnyKIT9yd1QU4Rt64kiIGZ8AV6vKqIYz4GKaNw4YQWuOKD3lNETK7AqOh+LAQZdLxu7AaMTHbnc6uKMCJCkm/r850IoI0IT6Wifo0KQa3x5iS09wcjlNCZSdhp8kx4SFBam4WQ02K4SE+jAh5CxHmAs3oGkYEfTYrCUA/O+woXACgBanik4P4MOWjTiVjUK0MpqA3t+JjWJir7EckdRQI1wInXADUTgcAVNFswUL9yNg1WeNDfMIBDSnaqJSZ3DVKXl+IfAQA8GnleahwSk5kxFDXjl8Bb/wBi4+uhQILKg2bptW4o/ggnV3HJ+6Ym7sG0/y4aqkefl2yz3gVH17LZzyLGPbtdx6U3n6KqBdiRhdDicSH3ZtYKQtXy4otdwGPfC2nX+xdA6as05IbY7YZGjWokYqPxFgjyJ4SQ7U+bzq1cJoVcvFbQmHAgKZypYCFQZh8Lpbob/IqPjScUGwBw9yKj5f4kCe4QduH+NB92bxrP3qGyROsN9QVUtyVm6vMHkxVekgV6ubTCo5TrOPDnlqUEDOIu4lP2qv4UDDiOJDS0W4K54RlxrFaPpkhqIz4mGS9RiA+YnNzgCs+1VAoiYrbZP1sgp5cHcaJqnMTnKUcIb3KBg5Ds3VkbA1vp2udHlMexSdCCzvGkUI6SQ3pGs3Cizn1izpByN6IiQ9VvwYQxzDodlioi3maKPEJhsj7ak7Fh+xTneZ4k2arR3IrPhkTpzHVcNoHybVfS3xc0SQpvpko2LKCrLuapvh3Cx6fcFBFWqPhT0p8Aimy3AvV5wPRBkxRenGuupOTbe65UW0yOQMI2hk0oxcBTnyirlCXblpENWHER8jq6hpKo5cRykRvluHb2w2+IuZm0duTGQLe2eTO6GIok/g00IceV5PS5+8Edv2RVId+N4N9z1iF6zGCV/HRpeIjMVZgxMcK+yg+gTBshZySFpCnJdtT5JDdzBg5CfgQH1e7CqDkUJej+HjNzZT40MwpdJKY/RCiGMhSfMiNeceeQ/jEHZswnDGcAobUnOk1Wx+nvw0ASNcd5xCOPIiHA7xCbzUPdZHPxZQ0bKE7u+7jswCc49c5mMZR2tLCjtRylY1ndqUHOVkJW0w1iY/A3Ey2G0hRxceugRolE2wYGcDI8BvTtNqI00MMwGzlCDnHPUQdO2BPxtFhk0/Y3pYVQbreuJKCkSITrc1INuv2rQawOnYVAEA1M4B4E2SG6KIVnz4AQL8dx5BNTawsTMbS9ymZCITItaoUMDfXCh3MZyp5iI9hYh73iVHVkCo+4WFSJqFQrSLdtBFGBhHqtToqhroCGtIBN/EJ0ZYjyfAk4JTPAgA+or7qEB861g8kngd69/J1zVY7EKQZfGkt7lRuR4YoWpkhJ8woEJ+jg2n0gl6XyR6nmjXdnpjVBQgen/EMdXHyQa/Fl9a6M7oYmOJYoseH+ftc5mZqIyjWKD1hwYnP4THdDLu/SMVHYswRohk7rhYPDIoCi/ZVYp3WFaYosM+zHj8pp4M6QDwjk6vDCGkqptdHXZ9x6vgU26Q0h7mZhbrYE1s3ISpDfooPv4mn0TOcgWXDaVmRI9R1kkWJz6TCag8DU314WE6shaQ7ik+Wx4eCTQ6dAykcpZWTFRbmAnyrN4eo4mOOyNxM1hWkik8vqqFFhWsiPcC9KHNr0mhQHH/NLKWDqHqU+Oy3p6BrKJ2zZQU7JnGkELJpQ0xGfLQg8P/9GfjmRuyJnep8RlR92HEs1uNDQ129VhWGQK/FtNvj023XIBJUoQVoY1bbf1Jm562W9a0DIX756vhwxYcZ5CnxCQy1Q4GFYb+UfQGGZaGR1vAxlQAG4CQLhIMqMkzxMQYBy0JYJ+deD9cDNTSshgRXXlhBxI90PezazmylA0GD7Fda84a6LIcMBGOuGlWvHOxHH1XMkOjxUXzc13pFFJ9DVPGhTYXxJm1EKmZ0AY7iM1RsVhf5TmQRH8t0rtMi1zUhYerOd2+MiQ8LdXHFRxIfibFChFbl9So5DMx7wUJdqof4sJR29uQj+n5+deVZePgfzkZdzOvxEZqUjijURWP2LZSY0PCEv8eH7EccKVx0+jRSPl+lxCfmKD5ixdmTrD0AgMzk9xccIwMz+VZF3ApNHGnnRhiKZcn/DIw4dg6m8aI1F9sDpwFnLRP2g4W6HMUnwhSfUJVgbi6xgKGqIASdG6W77WrEI2EM2ST0YyX7OfGZF2p3fdar+Oy3p6AnkeFP+pqnjg8rxBhT0qjiBQ+FFiOT5wLNpyIYFiYk0eBcalYXDXX1I45hm4W6GPFxPD7RoAaVllrIFepiBKdGID4zlSM5KzdbmZSjjjHFp2YqAAWKmUYjBmHZ+eu/GKaNJoWWnQg3AkID4HBA5fWWgvoQkO6HapPzZEbqgSDZ34gQxtVNCx9U3sLM4deJ+jpvKQBCfFgGn67FuLk5oujQDd0hPkI4smsojcdePuhSfNg17c3qYq9XC20rxgWmTopeAsCHrnY/5IlhLmAEHh8a6mLn0VUZvPgWGBMO4n6MueJDjiXL6jJlqEtirBClio8a9VF8ANhU8WlhxCdW53qfPcWxJx8xrHHClGp8cKanajMg1PEpMtTlV7nZspw2BM2nupYfQgR9OUJdMSWNb330ODz3fz+Klgh9OmOKj6eA4Sk2IT5mc/HE57Pvn4pZjTGc1UrTppnHR0lBZU9OwRgyJjWDexQf5oc5MpBCAhGsar4NOOsfnAXYTVtQfCI2WS8JddWR9zODpIkrQ3oQeOVh4MWfusNGfLsKrwxsQMMA4oiHA7xtRXq4jy97vE2y50z6dZ2lHCGEjxOfZlIUlxY39JqbxUKMbEJ3ER+KaDiENE2T5sTHspzqx+WEuuANdVHigxpEghpUam7WbIMX4BPBCI6Y5Tgrj+Iz09iHkGISEkK9PdCCQDWp0D1VoUUM85AA3bR5g9JMxF0MNBzQoNN6S0FjkId/B+0oIY60PlSEhavoPnwzQBWP918CzDgbANCqdPDwrx6Iu5QQO53i6hgrOQAAD23ZT/qt1bJ2D4Li4/UUVUrxObKThEcjtYToHP8J5z0v8WFp+kWEugzT4kQnS/FxFcgcHeKj57jGxhQsXAcQ4jOG/dVYSZSaKM3qkoqPxFghapEvqJJD8bG54tNNl3MTGYf4uM3NecGyuhQTllm47kXCL9SV6gNseiPwEp88oa4YUoiGAphcHYFKZX2Xx4d92QY7MEXphWkrsD3rz4fLP9yKTd/5KGY0xDzbTbuJj+cpmCEYYGZxss/1XrVMMDd7iY8dqnIXWUwPkBT//3cZcNvxwO//Afjzd4HdT2aNO6ApaKSZe4NqLQAF8XCAe2ISg46RfKq+DwDwN/sUAECreoSUL+glr3eFSbp2O+0Q7jU3IxDmpIkRHyXsqfUEkiKcBN1/RnzEkFeJoa4+OMTHohWKxVBXNKghEHT6yPmRGabUVXmIj25aWUTJsmyczMnzB5ymngAPd80OsArVub8HhmXx46RH3M1rieJDjdnGEFeweu0q4rNzFSF0WlZ8RH2VrOCMK4HGOWQsSgfitNedEYhz0gQAZmbYIQNUFUlmTPxiyz4AwPyTjyPvJXs40c0Y5Jg4IU/yetV4Kz4szDVtPulHePKnnPe81diZ4qMPu2tM+UCs2lzPiQ9TfIRrcxRCXTsO9GHevz2JNZv2jHhdJUF8uDDTpbWJKREJTx2f0TA3b9vXg/k/2IDfvXQQ2PU48OBnxly5KgeS+IwnbBsxmrGjxev8F/GEuriHhMJrbvZO5L7QhMk8TxdsgNw82QRUJfT54WpPqAqob3V9ZgixLOJjaGTCiyspxIIaUUMMd1ZXWEyvP7wDAPC2PQ1apLCxOSco8alVhp3wSTDqTikWEPSQhIa4h/i4zM20ZpJIfLSgs0yqD9j0I2Dn78m+0s7jOLIza5gBVUUDVRUGVEKeqsKkqB8AJAbI8Y4GNdQNEu/Tk8YHAVBSrCe54jMUnQkA6BggyozX3AxFQYoW3WsCJT6hbMUnFgogxYgPIzy6k02F9GBxT6As1GU7oS4jydLZnT5d4aDGPT5BJRfxodeiQHxqlCTqMZi1fNqwME/x+HsYKPGZFSTfq3wkQDdt7vGxok4PtxBtAWHS8x0yhp3QHapJY1QW6oIQ6jJMp01I9VSggZCWWUonD+HpgSpAVZFWKPnRE45yQUNd/++lA+hN6JjREMWpx88m73kUH/GYZCk+40Z8qLF52gLy+4Tzne+Cl/iEqhzCV0D1GRLC+yx8xwsYjnKoa+veHqQNC5v3dBdeeDThVVUHx440OObm0VN8nn+7G93DGTz23A7gD98C9j4DvP7YiNc72pDEZzyhJxEEVWpiPiEp8gYAYBKdFOH1+NAsJhaOyprk/CAQn1xeCgYxBBAXs7qYvyfWQFoAKEJNITuKjGG5MizSqmPUjIU1p2Eo4CpgaNnkSd2iN8vX7dYsMlIS6KTUCMFzE4rnMTe7j19OxUcIdbG0fFYl2sns6gXeWk/+XnofsOj/kr+7s58aNSHU1a+Qz8dCAQza5PwnBwnxiQVVRPt2AwBesE7BgB2FChs48CKZHBUNBjXUdlDFx08FzKjsuiLHRfUlPhqSdh7FB3bBp3Iy+D4AJJ2dKT6219yMGkSDKi9gGILhm6nFQhssxZ/Bz+BMjM2EDGrTPuheESU+01VCfPI1KjVMixMSW1Bcmb+O1eAKm6LiU00UnwDZ34iS4aTN3SYkCtTOgKUEEFZ0HK+QlGWTXku6SjMyMwnnOxdvgmnZuPdZQuqu/PAcaFVUiRI8PmIrG8C5Dnw9Pjt+Azz0+bFJ/T4oKD4A+X58/h7g4/8KTJnHF8sYFn7xwn4YURrKK0B8Etx7qPEsVp7Onhld4tOTIA+IQ6lxrgnElFGGMVRLnFAXzeqy7Lym/2KQ0Mk5+uTRtbx6O/oP5P5AhSCJz3iCGmANW0UomkPVCHlCEJ6QGKvGymTfUkJdAKBY+RUfdnMMB1R3zycmuUYbSHPBqmb+1rBCbvai6pNSmOKTJjdmllquaNw3E4IBgEjzNi1u9oY1szgylwuUODKPBhQV0EIF09kZGr3FH2k1ZdHcXEXDE5wUsXP0ziaSShuqAuZ+Fmg6nrxOs99c21UVnKCSSa9boaE/TcWwQsavU4/PrFAf1MwADFvFO3YL9tu0gi+r6lo3A3W0RQnz+HiblAK0oSqcUJfmUy4gHtIcxYcRH28V52LCXZT49NlVPHRnpweJ6ZV+B3rsakRDGu/OHoDpS3zYeYvToo42VT/8UtqTyWFe6FGb5imASf0+07jHJ1+oy0Yt7ZMnZlWyhw4zRMsOmG7FJxrUuE8nIjQaVXWBtAWjgBbAcIwQMZatZ1LfkK4yxSfpTODxJuw40Ie2ngRqIgF8acF0Hi5Gopdf07rprmjt1PEhE5vL47PxZuCdvwL3LQZeuKdoL8lzu7tw5//s9vVjASDnt+st8vf0Bc7rp30JWPh/XOHH9W904F/+eycOZui1WCANXUy6YAVbfRWfoZETn95hcp8c9zYfXsVnDGv5JDx1fICR9+tKpE2coBzEV7X/cV7sk8TnvQ1qXBtEDFExjCQii/h4Ql0snb0Uj4+qwlbJ9goqPn7GZkBQfOjTWe00/pZNVRaR+CRBs62QgqIojlIQivOCiqpiIwAThmXBpjH6flQVt0+5QI8fq3yNYAxQFD4hhEtWfCjxSbMChjbiLDOKEx96jlhp/jnnkX1szE18NAVYopIWES8G5tOxqEgoZPxGghCEk1XyxLcPLcggiP02JZxv0xtLfSvPbGP3LC+ZA6iHBMAkpQ8AoPp4fKKhAFL0vOUkPsUYnMWsLlrAUM046ogFFX2oQiSguaqK+2VaEeJjI0oVH4WGsGYpnVnmU7vjdQQVE912DVd4OOj/U2xCfPIpPrppoZa2glCjYtVmclxZa5FIluKjCsTHaTSqUMXHVEOkTQiARNUs1zYtTnyowVlPuDw+rE7WzMYYYiGhq3lmEFGNTGBE8WFFLBVecJN9l3kdn4HDpLIyQDIz/3It8HshkzEPbnh8J+7Y8BZeOdjnv8Dh7QBsoG6WU6MnB9p6yDllNZ0KKTW8zEYowElo2s/jk+53h2jLQE/FiI9X8Wn3X64U9O5zHjwFpDx1fAAU1csxH4YzBr4f+CUCioU+jV6jUvF5j4M+7Q7YMVfBQRGKl/jkqOPDy9IXqY7YdIJRCyg+w7lS2VkNH3bDrXGIj0LJgdivK0EnPNZpnoe6QnFeVwgQavlQJWHYjoyQ+JAJZBILdVEFqFAdH4Ysj49gbg6oCsLQEVRoKwmmBjHi0/kG+X3iBXRlxMSKZE+WSXGGuR/Hqe0wlSCeVwnxCWgKErQPlJkkN8ATaGp2m0Z8PPuY4sO21TAnq0VJ0EfxsSjxaeLHJZq1TDysIcVCXSzEZXgmkGKIj08BQ00f4hN5OlgLGyoiIc1VXDOX4hNDGhroey0fAADMUjuyllc7SAr139Xj3MZmgF+vkywyueZVfEwbNVTxUYWQdDjIiA+ZqCOWoPjwUJeYzk4mEVYJ29Ac83KyerZrm0zxYcsoHo8PCyOzgp3kmiP7WA+nH57fdc5DXWwSb9tCfjefBiy5jazn1YeB/sLqAvuOZ2VxMhzeTn57Q40+YAolbwlSgPgMuUJdVPHxy+oqYl2F0MtCXePd0T5L8RlhqKvz78CPPwA8eqXrZdu2s+r4ACPPZJvZ+zcs0l5FxtZwfeb/Iy9K4vPehk0nhEHkJj7wei+8oa4CE3dOULKhFVB8hgp1ZueKj/NEzQrviYrPsE22F0OKyOguxUckPmSCYA1FE4hk16EpBfT4hRXH2Aw4qb65enUxZCk+grlZUxWn7QacvmBecorjz6efjTsE0ePzmT/8LADgUOO5GKDkIKgpSGlse4T4tNJU9vbQbADAPrvZtR5CfNyVuv0UH4sZcilpYzVjRMRCgeysLt3zpFhUqIv4k/oFj49mZYBB0isrGSRkIhrUSNgUQDBHqCtj2k4qu6IBU0hm2yylM2v54BFCfHZrx2ePiYa66qxehJHJ26/LsCzUUMUnWOWYmyMB8p216XmPWsP8e9ELSnyEUBe75hSTXDOW6hCfTO1s1zaZasqJj5EUPD6NPKTD7xuqxq+7eqpuiokJ4n2BKT5JnVb3bnuBvDHrXOCsbzoNUFn6fB4wpSxnOYBB2lS1frb/+wIc4sMUnwIen4wY6qKKD6/j41FKRkh8uocd4jNSFaQksP0IUzI4cGhkvpujuwDYwJvrnHMDcj9k+yUqPiM1OJ80RK6tR82PYJNOjezJ3uJrgI0TJPEZR+iJPgBE8XG6qbuhFqn4MBRPfMjFrdl63i8Su6HN0TqBR78BdLxO3mA34Wi24qPRcIAf8QnAJJlkjPgEY+SmTdtehGhml8K6uAuTQ1nwHj/6f07Fx0Oysjw+QqgroCq87krSDiEQoORQDEc2nwbUtAgrpGnHnnDX6UObAADvTPo4r1UTUJ3O3wptZjvTJMSnK0bWs8+a4loPGubwYm58n3xUwCwlMZe5OYv4lKj4mDpX9/rsKqdXF8DbNQwH6gCQTudOqQWD11oSkTEsVLPihZEarqLNUo5khcaiXa8BAPYGT0AWYg3ceNys9ORVfHTT8fgE4g7xYYqPTa+JsJ3mZI4rPkL1ZZbVpVHFxwyIxMedGckItkmzIVVD9PhM4mnb4YBw36DfxVqQSSVj2r7XufgQM5w2gf1U8Zl5Dl0PVbUKpE5blg01M4hpOJq73xnL/ozW+78vgBGfTpN+xwqGupwwPCOAXPHxTqwjVXyGHWV8XFUf9v2adCIA4NCBd/CFezbn9lQVAg9x2aSPGUUq43x34uEAF0hHmtLekibf8XfCJ2EIMSTZg9wEU30k8RlHMMPqAOKubuoisuqr5EhnZyg21OVuW5H7S8S+5MuG7gJe+y3w7O3kDW+oS/D4hGPZis+gJRCIjFCjgykorKgiCwlQZYFlg5UNoVgfAD4R5TI3e/+viwVd/zuVm4egKuCVj4cQcerliKocC3Mx+Pl8jr6FlvReZGwNexo+wlsaBDUVGRqSCmQGocDCtMw+AMBgDZnM9/sqPm6y5mduhrc3nE8Bw5ivudmj+BR6chN6lg0iBgMBpFhRRFp3aEirA8AUH2Zuzu3xqWGKT7iGl1KYpPTD8GTAhAcJSewIz84el6JwlXKq0p0/q8tyPD6heHZWF0JC7SZK5nrtavKd5lldOie0KlN8As61bdTN4X+n7CDvUm9SxSea6XJKT8SaeOoxC/GQ1xnxocUWhawu8T4RCqh87EMD3cAR+jDDiA/7TjPSkgPJjIG1oVvxdPj/INi9K8dCJRCfIUJ8OookPkyNjoUCToadZROC6VUiR1DLx7RsVyivMsTnJABAdaYT29v60EWPVckQv79v/Df/k2VfBVQFoYDKM2lHqvhM14l3rPk4klxw0KIRgglmcC6L+Nx9991obW1FJBLB/Pnz8eyzz+ZdftOmTZg/fz4ikQjmzJmDNWvWZC3z6KOP4pRTTkE4HMYpp5yC3//+9673Z8+eDUVRsn6+9a1v8WUuu+yyrPfPPvvscnZxTGBSxWdIifmGIwBAFbJtTCWQNUGVr/g4JtJ80u1w2sAHlbdwauol8kL7DvI74SU+0/l643EyWbsUH0NF2qZPmi7iE3eNJ0xDXQpVCVjaddnwKhsFPD5iBllVOOB+ogYEwmCjSknzgnPDdtT5rEhOT1js/jwnPrud13aRG9Dz1jwk1CquDAQ1hfeBChlDOE15B2E7BQRjMOtmAwCOopa3NQEUoH52UaGurNpIPsQnHg4gZXvMzVkeH09IwQs68aW1Klj09sKrN3PiQ4gDIT6EFIVyZXUZFqpZg9JIDRCtQ79CzonWu99Z0DR4+w9dJCYiGPFBd37Fx3DIViBezx8umMoQCIa4osn2t4eHuoQihPQYBkxa6VsTSH3tNGRssr4hRPl1aVJyVJMiShKCcSAUyw51AVzxqbHIZKkLdXy81znzcVhtLwKwiXJWTdVDRlKS+RWfdNtWLFDfQlgxMKftd/4LlaH4HNbptVkgGyvBEy8013FIGVa2EjkCxac/qbuS3AbHM6WdEx9S4bpGSSKOZN4WK3khlp/Y/zwnhIxIM88Yu5eNiPgk+9Bkk8jAKe8/C5Ggin0GJT7vdsXnkUcewdVXX43rr78e27dvx8KFC7FkyRK0tbX5Lr93715ceOGFWLhwIbZv347vfe97uOqqq/Doo4/yZbZs2YKLL74YX/va1/DKK6/ga1/7Gr785S/jxRdf5Mts3boV7e3t/GfDhg0AgC996Uuu7X3yk590Lbdu3bpSd3HMYNGn4aSS7a9gEENd6UBNlkmzbOJDM6lCipHXwDacNrAiINzUet4hT/FiOjsATJpL4vjHfQy1tA6E2KE9mTG4wdlNfOiE62qjYUJlxEcboeLjndAZ8aH7HPYcL/HJOMvYDBDFiNYsiitJVNFQ1zAijrLCiE+s0aldwsCJj+DxoU9ef7bOhGHZTiaOpiJD+0CFzSF8VqMhiZOWoLZKJDtULaiZBgQjWeE5PxUwEPUoPjlCXdkFDEtMZ6cZXakAIR/xkOZ0aKfEh9UtCguKTzCPuZkZjZnvoV0loUSt3+l0LhIyqwDxmaZ0uT0qG/4VePyfeUp30BhGQKFjidRxPwlTGYKawluLMPTS9HwIqo5Cj51msrpPznvBYBAHbNJxfciOcvWQqUJ1GZrNQ9tVsFBXVCQ+9CGkxhYUnxxJD8znEzhI/T1M7QGEUFd+xSe44xf87xM6/wIYPokSRRKflG7yjCnH41OsuTng8jqmddNRItl3cQTEp2fYvV9lZ3a1vwo89e+l+VvYdVzdwiuENys9uYmPZeY3pYuKj23xcBdLZWeWC+arLKaJdU4c/TsA4LDdgNr6RpzZ2ohDNs3se7cTnzvuuANXXHEFrrzySsydOxerV6/GjBkzcM899/guv2bNGsycOROrV6/G3LlzceWVV+Lyyy/HqlWr+DKrV6/G+eefj+uuuw4nn3wyrrvuOnz84x/H6tWr+TKTJk1Cc3Mz//nTn/6E4447DosWLXJtLxwOu5ZraGjARIGVJBk1Ca0690LChKQHs5fzKhLFhroUIXsmn+JT1/USFmqvw4TmkJz2V7LT2UMx4J9eBr7yMCc+ouKTyJiOv0MfFrK66NOdEHoz9SQU2g5DH7HiU1qoS1RH6v2Ij6LwWj7VSHFz8xCijgl7ziLSlfucb/N0ZQ6R+FgW+d3xGixo2GDOh2nZwtgUXhwvbg/h04z4zPsiJ2XV4QBU2vIADSTsEwsFEBM8Y1ktKwAEvb3hfMzNVWE/c7M3nb24UBe7xptrI851QIlPLyU+0aDGK/rmalmRMW1B8SGfOxKYSj7TLyg+NGMyYYcRDLkVMA6hlg83N6cHged/DLy0lmfQhE3y1G2pQSAYJdlncL57AU3lhSYZnPT8AFFqAdic+BBlwxY8PuGAir00bDmMCP8eM+JTr1PFh9YtSus+oS76/YxbZLLUTQvpHIoPa1sRbSclFFzEp5hQV3oQsbf+AICE5mJGv28rlmKJD1N7AOAoy+pKdPn2tWNgZLU6aEN9ez3qA+S7SBQfShhoVeyRhLpYRhdD2T3OnrkVeO4O4PVH3a9v/q/s1xhY+DZcjf4QaedBiE8OhfLpHwD/eQrw5l/83+feSvp93/kHAE7xQkZ82H1xRHV8Okn48y1rBuJhDU3xkEN83s2hrkwmg5deegmLF7vl/MWLF2Pz5s2+n9myZUvW8hdccAG2bdsGXdfzLpNrnZlMBr/85S9x+eWXkxoxAjZu3IjJkyfjxBNPxDe+8Q10dub+AqTTaQwMDLh+xhT05pzWqnIvI0xIZrg26+3yFR+HaOh55Mxz2n4GANg55bNA60Ly4uHt2R4fgJqUFU58+oQbxnDGRJKFA/xCXQGHiFkpR441tByTVrEIhMiExUC3ly4i1NXg9fcw0PBjDElU0Uk4YYedY187HVi5E1i4MvuzdTMBNUAUlMHDwI5fAwDeqTkDvaghHgXB48OIzyT0YYrSh1SgGjj+45z4NFWHgSZifOSkCnCFu/wKQAZjXuLjl84e4Onstm/lZhQ2N9OJL6GS/WipjWY1Ku0FeS8ackJdQcVwarIIyBiWk9VF+6IdDRDFJzwo3EzpxDeQL2OyjpQEmKYcdXp19QlKNVUJIgbZRyNUCygKJxvsd1BVeGsRAOiz4zDhpFjz6ss0xBVkio+gBgU1lWfoiaEutkwDIz60l1VK9wl10VT7KpPse9ql+HiITziAEHRUd79KXnApPoz45Al1vf4YNCOBPVYL1prUx0avZQ7bLp74CJ4Vdj3AtvKSLxaePLX7L8Cvv4yVVJlO6aajRNKHgULFEPPBq/gMlBvqovd7V5i7621g/feBx1f4f4Z9v8LV6FLIQ2aL0uOrhgIAdv2J/G5/xf99pvic8jnye//zwNDR7FAXfYgbSTq7TUtsvGVPRzSkIR4OHBuKT1dXF0zTxJQp7sySKVOmoKOjw/czHR0dvssbhoGurq68y+Ra5x/+8Af09fXhsssuc72+ZMkS/OpXv8LTTz+N22+/HVu3bsXHPvYxpNP+xrCbb74ZtbW1/GfGjBk59300oNAS3ulAHuIjKD5+xKfcdHaFhxTM3IpP+ys4buglZGwNr825ktdMwb5nAYs+9Qi9ixgY8REl4WTGQB8oyek/6M7qAhzFR9Fh0XLzSTvEezeNBFZAeBrnio9/r65QIcUH4CpSHClevHBYVHzyQQs6qb1H3+STxWuTP03H5aSVBlQFRsit8rVN/gQQCOP90+sQC2n40PGNpNHlOd8GPnQVX04Md/ldE+FY4VCXqPgYaXq+vFldRYa6hlRH8eGhLopuGtpwZXXBzNmri2d10YrfQwEyqWppYZIUa2TlSBxAnaD4sFCXi/iQ+1HUFIgPnMkhl+LTY9OChvTp2WCZiRly3wna9BgKZDMUULHHJspVr13Nzxnr1Re26T2LFgFkoS4/j0+MEh+xV1c28QniVOUdUlYgPsnJNgSKy+p6+UEAwCPmefid+RHy2u71bl9OehCwTfc6c0BUfAwEYIbryD95QlRMpWtKEaVvtkLSs1NiqIvVziqi23su9I5WqMug+9gjhGRZVet0v3+oUCA+B01yDJuRI9Q11OmQqlzHjWV1TZ5L7ue2Bfz9cU78Y17FZwQeH+sIUXx229MQDwUQC2s4zInPwbLXOxYoy9zsVVls2856rdDy3tdLWed9992HJUuWYOrUqa7XL774YnzqU5/CvHnz8JnPfAZ//vOf8dZbb+GJJ57wXc91112H/v5+/nPgwNiyUoU+lbLYrS9Ej4qf4pMVqikyq4srLHpuVn+UfClftk+EXTsDmErL/u97nq4j6jthsu6+4pNRImNiq0UMetjzV5+sLkfxsemXU5T8RwJbPIbc3Ey+6PkUn8ZcxIcanGNIcuIzZEeKP/aNNL16631E9YnWY28jCdGK/c2CAZXXc2E4PIN0tp7REMP2fz0fP/jcPJIuf8F/ODd5eBQfH0IWjHquJZ9QVzigQlfIesy0J9TFCFlBc3MfAGBQIfvRIoa6KLpoFk+0SI+PV/FJ0+9PICP0Y6PEh1RFz6/4TFW6kUzTa9VH8YlRs7AVZgTN7fEJaG7FhykWzAtkUMVHNcm4meIjfrdDmoo/mufiHuMzuNO4yDlnXo8aJT4sNOF68KHqa8wg+64LWV3eB6TqSABnqKQtDGae7fYOxgooPh2vA4degqUE8aj5EbxtT8ee0EnkYYhVKwcctSYQ8VUURXQOuh9GMxHWrysP8WGhLoOMk9UvSovm5lEIdfV4Q13lZnWxxAAa4iUrf8f5O9WPLHDiU4N30uQ7m9PjwwpRArlrMHGLQQw48ZPk74MvOQUxQx5z8wg8PspRQnzetGYgGtRQFRIUn8F2UupigqAk4tPU1ARN07KUmM7OzizFhqG5udl3+UAggMbGxrzL+K1z//79eOqpp3DllVdmvedFS0sLZs2ahd27d/u+Hw6HUVNT4/oZS2hU2cj4eHc4xInPWxgPTi0RvnipWV1KHo/PEDkHR+x6YoZseT95nYU7mL/HA5YxIj4ZJTImnrFOI//sedr5QvOsLif0ZlN1IWGHff0ppcIWM7sKZXWpRSg+Yab4JHioaxjR4sfKnq7fpAT81C/zCr9JkfioKgLBCE//7rTrMNjiZCWGA1rOh4GmAooPvL25fCYmRVG4x8RkT4rs3LP2A4U8PlTSZj6eyTURDNlu4tNpUwO3kNWVi/hkTCGrixKRdJCs24/45KuKjuqpsBUNYcVAOEUnWD/iQxUfprhyczP97oV8FB9Vcfx2Ji9CSCa+AFVvFNHcHFAxjCh+ZHwFb9izEWTXpfe8xJjikzurK2oIik+eUNcMhe7z5FNc7xlMbckVZnrlNwCAQ80fRTfIMXkq9HHynhjuYsTJRxX24qiX+ITpZ/KEqHg6e4ZM8vUg58kd6qIPA4luwCyPsGQrPmVO2Ezx6d3n9ELrEZIcvMTH1Pn3bQBR7M2Q671Z6eEeLxf2C1aQXAoX+x4H404YsL/NMTcHqemdmZvLVXyGu6BS8nU4OBOqqiAWDqALNdCVIFGaxrDvWKkoaZYJhUKYP38+z6hi2LBhA84991zfz5xzzjlZy69fvx4LFixAMBjMu4zfOh944AFMnjwZn/rUpwqOt7u7GwcOHEBLS0vBZccDAVqUzsyVdQK4FJWa+knZbxeoQ5MTgrk5J6unxdg67TpS9CzW4K7AmqOjfI1PqCuRMfCSdSJ0LUaeRg7QDD2e1cXS2Q2nXcUoKT6ulHa6PRbq8h4/V1aXt2ozAy3WeFxqp2BuLkXx8VQSPv1/8xuNqPgENFJTg2UMPWGehXgkx5g8KOTxyZXmnwU68TIVjoe6qkgGUsFQVwcpIviORm6yVWGN1yZiOKKT/6Ne4uMb6rKzFB89SH6HdEF9SjGPT+4aWdAC0OPkXlDLsqZYzypAUHxoQ1RKCFgIKxJwno7FrK5emzQoZaTUpKSeEZ8QIz4hIdSV6zr0KnHM42P4ZXWRB5Gw0QeApbP7X+dVkQCvTeQlJv/83/vIuIe74QvqH9nXsJC/9JRC781HXnPIcBmp7AzJECM+uUNULJ09kiLL1NJstnTGuYegfhZIKw+7YHp+LrCqzewYlh/qot+djNOuxZXdScPCHIJ/rm1IRbtNjkmL0uP73cD+552/cyllzOMTinHFE31t/FiyUBd7iCvb3EyNzW3WJH6vqQprsKGiW6P3jglkcC758XrlypW49957cf/992PXrl1YsWIF2trasGwZaXJ33XXX4etf/zpfftmyZdi/fz9WrlyJXbt24f7778d9992Ha665hi/zz//8z1i/fj1+9KMf4e9//zt+9KMf4amnnsLVV1/t2rZlWXjggQdw6aWXOlVzKYaGhnDNNddgy5Yt2LdvHzZu3IjPfOYzaGpqwkUXXVTqbo4JgjqV0PMRH2FCCldl30BGWseH98bywxCJmRPiQ2+wzOcD5HyS472AhPLuiYwJHQEcbTqLLMTYvjerS9EBpvggkrO+USlQBNWMPX0XU8cnp+Jz2sUAgDMHn8Jk2uQzYUf8CwX6QSQ+zacCLadxf1BKMPQGVAXhgIrd1jQYtorfmR/hT2QFNyGM3VeJEo5JRgkDOdQqpkrY3gKGdALOa242dd5D7C3MBkAmataHCgAQqcWwofL3RI+P31NtxjBR4/H4ZKjiE9JzKD65Ql0AzBqS0t6g0/L9Ph6fOA112RGm+JDxMsUnoKoYFHxLvIYP2wa95lT69B6yyASoeEJdItj3WCRHZDB5FB8aogrrAwBI1Wau+HizusIBXuFZJCYp3cTfjtDrONWPzoHsZpas+GZHaCZ/6bAed0gavW+MhPgkaDXvfMSHhbqCKaIKVdnDCMCAkRSuyUito0yXGe5iis/0BnIuys7qMoR9pIUuXX6fLOJDiXwgin29GRyhxGeK0ptt/E/2OVX1gTyKj5DVRbMa0X8I6QzZR28dn7LNzTSV/S17OmmiC6dieKdK7x0TyOBc8ixz8cUXY/Xq1bjxxhvxgQ98AM888wzWrVuHWbNIt+H29nZXTZ/W1lasW7cOGzduxAc+8AH84Ac/wJ133omlS5fyZc4991w8/PDDeOCBB3Daaadh7dq1eOSRR3DWWWe5tv3UU0+hra0Nl19+eda4NE3Da6+9hs997nM48cQTcemll+LEE0/Eli1bUF2dJ7Q0XjAyCNAbIPMO+EJ8Mvf06QL8iE+xHh+hcnMuOVNUfFj3eObzAQqGugDnJsGk1N6pC90L86wuMp4wdF68kIS6Rq74iJ3H0wolPkVUbs7p8Wn9CNB4PCJWAh9XXwZA1Kmie4qJxOf0r9HtuhWfoEYKboaDKr6tX4UlmVuw0251pannQ1O1o/j4XhNC5WY9T60kNvEq3gKGnPjkUXy63iLVhsM12G+R5cNBDbZIfISGmxFB8VEVG7qRHVIgio9QwBCAQUNQYWOI1DEBHOKDGFdmfEFv/pPMI8Rr6BPqqrLJPjLiM2cSGf/sRnJdeev49Npu4mMz4kO9PcyorIadz6iq4rrW2TlTvEoc/c7lS2dXbRM1SCBj2r6VmwHyHa1TGPGpc8aeyKAfZP80WLjqgU3uqtbpIeLPANAecKq1JzKGowJmER9n/bnAsrrYMRhWC3vIhtIGgjAQSDkhuToMw0xR4qMGSAiZjavMzK4e2oh1VgM5FwMjVXwAQnj0lHvy94a62ENFpAb7uxNc8WlSBqAzzx3Dgb8BsJ05ItHtfBdEiIpPdQs5RpYOhZ4zx+MzQnMzS2W3Z/B7FptDOsCIz8QxOBf3OOnB8uXLsXz5ct/31q5dm/XaokWL8PLLL+dd5xe/+EV88YtfzLvM4sWLc/aZikajePJJn7oSEwXCF1qJ5CFiWoj0sbJNf49PVh2fYhUfJ6RgWDYx2R19Czjpk84yTPFBvdPfZ+oHnPdj/opPOKAhFFCRMSwMpHTUxoKc+AxNXwRsFwfMsrocszWEBqVeYlcOxOrXGTWCGPJ1Zy9C8VEUYP5lwPrv8yafQ3aUl3kviOpmoOkkMjGcSgpuavSzjAQwlSakaehBDXpo5hNX3gqgMS6GuvwUH4cMGnn6oWksFMlu2owAsckkn+LTTlOlp8xDspd8T6NBjZAuJiTEm5DsElJphbGaenaWi8vcTAmPK9sx1U+uS2ZutmOoz+XxAaA1kAe0qehCergPEdHXIhIfBXxSuWbxifjSgumY00TbiXg9Pqh2ee+44mOmYVk2IiD7pXoSA4KaCsMy+d+Am7QD4IQz6af4BCPk+6QnUKcMIWOYwnWeXcCQ9R8TFZneYR0ZBJGww4gpabR3HMa//GEnbv8y9fexVivxSegxnfEPp01yXffu5Q9MpSg+XVTxmdEQw96uYSQUum4/wy8Aw7SQ0i00w/1+vTIIi9W+CVWR7yoj6QUqQecCU3xmNcYBHB25xwcgx6l3HwBh/hLauwBw1fDZ1zWMPlRhyI6gSkkhNNgGQOhBx8JcJy0BXnmYrDfRA1R57BHc4xMjDYFrpgF9+xEePgygxqnjM9IChryGz3Q+d7Dfh8Bq+bT5frQSGPksI1EcuBQfRTiUx7ehKE5YohjFp1iiwENLBkzLIg1If3Oxq/6DLSg+rNIrNzgDORUfwOnwy+LhSfrUqDbO4f2VyAA8vbpgcMXHVQ15BBAbcqZAtpPJ8SQsHs+cHh8AeP9XoSvO+wklArXYsSoK8I2ngW+9yMkj28+k7q606zWvR0PFPZtMqhZDXT7jCsZ4Cwldy018WOVw1Vu5mU0mmUEgV5Nb6u9By2muSsOq0C7DjjU6k3jISWcHANObOg+fJqUAAoGQY5hmky1XfOJuVcSDQAOr5dOF5NF97jdpuKDKJttTYnXkM5qK4yZVcQ+Pt45PD/X48H2kBvGAmYJuWQhT4qN5wljitceJjzdrMiud3bNvVPWpxyD0PIpPVTiAOubxEe4rrPbWoFpD1zOETW8JhIERn8bjXUpQxrRgxmnyCVd8+uiY8hMf27Z5qGt2I9nfQVb6IgfxSdBrhoWaGRogEB+mpLNrtczqzYz4zKSKT1lZXbadrfiIGV1AbsUnXI393QkACvaCZC5XDe5xL8uMza0fcY633/7yrK44bNtGKk5Uu3iCWA9iWaGuMhQf26Zd4Fmoiyo+9KFtv0nnjXdzqEuiTNB47mC+AmsMjLVXZ5uys7wBxU6+guKjm7aTYnmEeDKgJ3m6/VHR4xOtd4hLnmyNGp7ZRZ6OXHUijv+4sAPZio9C5diEHR4Vj4/oZ0nS9Gw+IXiehGOhAP7XWTPx1bNm5lZ8ACDeiJ115/F/U0os97J+CFe5FDMWJkvr7id+bxpyrNC1QiEqPr4qoKLwEFdWp3YBARqOYW0WeFYXU3xsK7uaM0MHVXyaT3UpFAGharQVbeS8KRLUiPRO4av4GCaqWKiLTmyhgIo+Gp7hky0rYGjH3AZgD9R6ovhMV47C6KbGZnHisG1UUy+MEvGfwIOamj/URTP2NDMF3bQRVcgkr3nUHDfxIdeDGA5DqIqbzVM8nd2zbzThoF4Zcnt8vMQnpDheKVHxoWGdYdo/rU4Z4g8tAFzEZzjjDqUYUfokX6LHZyDpGNln0fBhIeLD/D0tWrbiY7PwK1N6RxDqyhgWBum2ZjFSVk6oyzLId4Whd687owvIT3x6CGE5GibXa7VIfDIJ4DCNoMw61yF6fintguLz220H8Ph+WlWcGo29lZvzVfXPiaEjQLIXFkhtKm+oa59O73vvZnOzRJlIFXdjBgBc9FPg82uASSdmvTXSys1h6DBNoUIqy2qhak/KDmIAMcfjA9CO44pb/fGA+XwGPB6fWCgAHCcSH+bxIZNDSDF4n67Ry+pyJg9WiZiHALTsY/8fF52Kmy46teBqX570ef53UhlZTzFH8aGhLrrf3vMbKzLUVRsN8nXmyjYLUQLSUJtdH4ohHKXhHIuFuujvWCNI/Af+4S7bFojPaU6dkKDmqiGkRxzVMBoklb9ZiwfLp6CbaiaEvllk/EFNxYDNJkqm+PQBKFC5GXC1rTB79pHXpn6Q/DbTsFIDPCSk5shiDGiKj7nZOW+c+Fhp6IaFKFN8vMRHy1Z8AuIygsLqrbvCQR9G6jCUt0lpnSKQVY/HB3BKBNRhCAndhMUmQFHx8SgfqQglGIOlEZ+jQ+Saqo0GUUerpfex0CFTbzxgxGd60P1+gzIIhREf9sAzglAXU8BUBZhWT85xWaEub3Pfnr1ORhcj+1nEh+ybEazGkQFClhO1JLxVOyyYog9uJcSqZhpQNyu3wmXqgEXHHorh1YP9POxk0/u+t1dXWebmLlIuZjA6FWmE+NzBQl17dHo99B/MrRaPMyTxGS8UKcUDAKYvAD7wFd+3sio3lxjqCsKAnep3Kqz2uonPEbsesVDAHca54Gbgmt3ArHOQC05Kuw7btt3pkq0LaYw55NwUhYKKKu2qnUS4eCKXD4KiMUxDIrwfVqB8YnWo+gPYYp6CXrsKB7VphT+QB5qH+DiKj+Zaptg6Taqq4FOnteDk5mrMqPdXoxT6RJxP8QlGqOJjG6QOClN3glHHIO2X0t7XRq5xNQh70kmucFY47ig+epic/4Cq8H1mLUb8iE9YJ9uylAD3h8XDGvoY8WGKj+Dx8YYLXaidDgsKokoGwSPUfDZ5Ls9Q0gc7edq3GvMniF7FJyvURVWagJWGblqIgio+njCWX6jLRY7ijl+DpbNnkTpqwK5WEnkVn2pa82bYjnD1F3Am+kywDgBVUGzwbvBsUkPTCVmKTzLMFJ/SPD6seOGk6jBXB/osSiRzKD6shk+L5iY+9RgEdKb40OtzBIoPK15YHwvx8P1Q2sjpLc0J0d/DxsJCwVPeR37nSGcfpmHUulgQqTqSGFGfEIgPKw3CClHGWfFHj+IjdmYPxtE5mMZBm1xT0xUa1mXNa0eSzk6P82CQrJs9rLGoQYfdCBsKYKZH1Dx2NFGWuVmiDPAbc7Sw4pMHXuJTtCeG3uxCMKAKWRFc8aE3r07UOcZmBlXNNs15IBYxTBsW2PcnFtKAcAT4+h9J2ITdnDTH46PRyXXYjoxKAUMx1JWgig/v1TUCYhXQVHxdvxYAUBUbmeLDJqZUnlBXLJi7YKEffnzJ6fmrqLNQgE/1bYZQVJh4jaQT6gpEyXFND/hn3rCb+uSTkUHAFc6KxB0CkaL1WsQJ3FKDgJn0Jz4GmdTMUDVUul9V4SD6WWgky+NTQPEJhNGj1KPJ7kG8nU4gdTOJl6ZvGNZgJ+8GH8hh5g+oCrr+//bePF6Oqk4bf6p6qd7vvmVfCFlIBAwIQWLYERgdBIYICjoD/gYBATOMI4O+IDPvAL6IGUcR31FQR1CcAUbeMQooEhEQWaIkCIRAIIHk5uauvdze+/z+OEudqq7e+97c5Tyfz/3ce7urq6tPV9V5zvN9vt8vaUGKeEA8AcQQoMUYxQb03PDkU8gWCHwa+1weq7dKVjf53y7JD8X9PYUCEYSmqEYRI7EBpJHJF6SQri3UVaDjOIoQ/AUiFjY81JX3tQJRCB9QIp2nYVauUnQchvGMlUjEPB3oBYoVnxLjxsH9PV0hQ6Q+D+cqEB+mJPfq1ufbtRj0DA8R2RSfOiZZ3qerLegV97RsniCdK1S2KMjgio/LSwl7atQMT815P/VWllB8OAlc2BFENLwEANCRfIc2cNV14N0X6PbzPkB/i89rIz48o0t3A24vBmJpxFkl5aMiMVy4eB5OWk5JIj//cvUoPqz+U9xFr3P+nfo9LugakCVuFILdcCUO0LImnJgeQijFZ7JgUXzqJz4NNynVstDknjxC8TFr+ITsxKcKhA1T8RmXVob8IsD8Y6kRz3Y8BrJw5cysrmYXMEwU6HGVyuqqBbquIQs3snA37EWy1/HhBFY+vpKtF8qgLFHihLBU8UIAfpn4ZJNmqMvjNycWp5R2OcyVMW+efo8LgYipABzM031YPDFM8SEOxMebZ4qP1MMs5HNjlEgen4LZsiBKguXT2QEMumlzUN84K2IohQvIyF4YGp1kXcFWx9e7dA0J+LEx82X8cPm3QKBb3pPXQnITGuriWV32cbcoPuxvj9dAntDvkLBQV0rqzF1072D7DGhpi+JjJ/i8g/sYCQqjMGCGughTabpc7FrM5Kh3IxMDNB1oW4zxtFXxibt5vRxGfPh9pco+XV1hQ6gCQ3lGfNJRxw7tY0lK0Lo1RhZaqfelTYuJULlYVDUQ6hpJ0PdpD9CQDb+cam5UyhUft8+smMw9P3NZaLWEx2cwS++NizoCSIXmI03ctAjm2F4aKnrvRbaftfR3KaInV20GcDCawl6m+ERS+/HV89aIPov8flaXuZl5i8b0VgBmUURN00TYK8dD3OMlimROMhTxmSxI5stmER+XrlVfS0bqiWRp7hh9jzbL44oPaas6hVqG7PHhYS6vWy99fNzcrOXgyrFeXbX0vyoHaYKJFWzm5kYUH+mzNJp9Zn+9k+JTpLw1iiqIT9DwIMlUMmTHzVVjpVAXV3wkY7OLhbPCkVax2befpzf7Dx3eKR7jxKeQLyY+vgInPma4LGy4TcUnNUqPh00qUQQqEvcRj7UVTi4yD3nWGoIMU09Ljuhw+5zrbWkaDUH+iRyGtzQ6Afu90nnFlB2PCHVx4mPL6pLORf631+1CkmUiEtGuwiQCRfcOpt75heLDKjfbF0isavwoCVoK8o0yxUdnKk2Hzqqop/Omv6d1IeD2iiah/Bwd0ZmyMz5I/STVenwsoS76XQ3luDmfULJlAyceHWDvwcJF7YjBzUNdnByH2PebGHAkUUXY8gXgp5cChTyGE/TY2oIe6LqGEDu+mosYcsXH7bNmtRoRs5+YPZ2dEZ8DaXr9LWwPwOvxYjdhSS4HX6cK/fggoHtoMVTA9ILZiY/Up4sQgoPxNPpJO4imF4WdGkpnZ0rTqMZKcEj+UB724iFulKoOPslQxGeyIEnxjYS65JtlTZOvVLnZJYe6QOhKgik+B0mrqdLUANnjk7R1/nWEVFDRzYjPOJrTq0sOdcXybuTyZuitEcVHJnFVE84q9gVI6eySctDIeeKIKkJdQalDOzWa0oF7N07McXUyN++XFB/J2AwALeEQxkgABaLh2YMG2gIe3Hj2SvFSU/EpXlX78rx9hFXxGROKz4i4ttLEgzS8loKaThgzrNmSf/WTd7HlLVZ+gU32UQSglTkXOUHnqc5WxYeOr5ekkcnlEWBZXXbC6XEwN3tcuiA+eT9TfKQil0XnnYeHulLI5kp3Z9eYn2QUIcTT5jhzxccdoiSrTZcUH8nfA0AoPl2sWOaoFqY1xwBq3uVG2loUH3avGcu6RPjbKdzFFZ+2Art3ddPzp02LwZPjig87J8K9rFBfThRfLInRvcAfvgP8+WfAgVcwzBUfluHp1IewKsjEp10iPu1LTHN5CcVnX4q+58KOIAy3jl2ENeMefN0Mc/WuMUOnIqvLRiqkjK6R8SyyeYIc3Ga2sFRXxyWIT/2KD689Jidk8MVbytvmfIyHCIr4TBYk82Ujio/sJahJvZCIj1tWfAC6ipA8PtVWC5ZhVXzoDTJYjkDJx5PnxKc5BQx5qCtNPBjPWuXbRszT1kq7jR2nXdlyixW/5PGp43soC16OwKE+FEfIcCPFiY/U6+jkf/0DDmbZ43biMz4MRFlV1t7VRcX2WvxefDZ7HT6X/RwG0YL/9ZFV6JB6ixF2LjiFuvwFPqmZ6ktIVnySo9KigioqoQrEJ+4ziU/B145tB/LYk2ambqb4iPTqEuDnAlci5GtaYxOSt5BGLiNl99gVH/Zda5o58XhculDcsr4y7SrETpjio6UtTUqLriOmxoyRoGUS54qPN8KID0vlT2TylowuQohQfLoZ8UlkCqZfg7UsEJ4WAPtGk3jg+T1FKdK8anNXyBCTZCKTE0Ztp8yuaDILgCDCOrPzRqvtiMGds4W6dJfZnkHujO6EN39t/j24UxDBtgAnPsV9CKsCDxG7Davi077EvP5SY9YsJ3YeD2boe3dHDBhuF94kLJHi4OtmmGveMebrSoW6JMVnIJZin8sDjYUJMWYSn4YqNzMVZ4jQ8ZfvW3wOSPKWJKW6yE8ylLl5siA1UbTI4jXCkgJbC0lwy8TH1rxv5B2Lxydch+LDbxDRZFbcIMt6VGTFJ08NtM1qWcGJTxJejGfzlq7fjSk+1jBjI3DZ1AT+uS3m5maHuo6/gp4HR3+y5CZBw4UkMWjmOvNsFKAjCxeGsl5afN4e6trJKqa3LQJ8LUhm6STLsxdb/B48U1gNADhpeRfOPcqWEcezjPK2TBgw4uOCSGUHKPExs7pMxSdKaMZkJVKaDMwRfycCc4BRYIiwju+jtMhcDCGnlwrw9+ATopypyYsQekgGBb7qBoTpmcMrqTzmfs12GFm/vXihw/XEPT5II5snUlaX7fxkYZUxhCwF+fhEH2DEp4Vlf42ncxbiIycsdIcpsUtk8jSsFNtvEh9/G7gx5pofb8ML74wgZHhwzvtMsnnQIatrPJ0HWiM0POWg+ERTWUSQgJswciwpPv15PsFL31nbQlo7Z/QdAB8sHjeON6Tm2INvYDhBw1Bc8QmJPoS1enxKKD4dS02CV8jSUDL3JAqPDx3fsM+DIXcGuwpc8dlpEiXu7wGECb6cx2eApcd3h31A63xgDyyKT0PmZkZmBgqc+Jj3LW6bSAjioxSf2QW5iWIDio98Q6sn1OXRcvCkR63PyYoPaavLVBuRJOGqQl2uYuKTaFKTUnQtx0BgGX6ePx7JTB7pPD0eTWvMmyMfWrM9PpyQyRNotcULq0b7EuCMf6ahgBIIGW6kbYoP/V9DtMCkdVnx2fs88D+fp3+vOhcAikJdXreONXNb0Bny4p/PXV1swGahLhEqYSCEIEh4tWHJ4+MzFR+SHBH+uRgCgoCXQyo0T/w97KFjMchkem6UjWkVFB97qEv6rnRWodlACnnWgDcLN20ZIIF/5/JiRtM0bC5ciHtzZ2J8Di0fwc3NjmUwpKwuwOxgbs/+lBUfXhMnXyAihBRspcpNiDVotSs+CYksdbIq4Yl0zjyXWMsCHubaPZjAC+/Q93xjwKoQOoW6rIqPU6grhy5ubPa1AhFW0VhLIZhnCpHcA5GrGuUUn3wWeGur+b+j4mOtT1Y1hLnZrvgspd8ZDxHKn5VdVwNZ871pqIstFAZeMyvtz3VQfFJj1K/JIfXpkksImF3azYKC3GKQdQp1pcaAPz1QunAp8/jwxAVZ6ed+uxjL+JoqxEcpPpOFatNtK0DTNNEXq6Zwi0Q0vBmeGbGAsv6hXeKEpA1K6wl1FWd1lff4mOZmDwt1JeCrvhJ1OXj8+OkxP8Ydj+3ExkxehLo8Lr2m9HA7ZJWmURO2nfiIrC6pwGLTQ11VIORzYx8nPuN04uKen9E8C0/xrK7BXcD9F9KU92VnAKd8GYBzsb2HrjwB6VzB0Xis8bYVNnNzvkAQYn26dCk8FzTMrC6SHIEmLSoq+XsAIB8yFSde12QIViNzXCuv+PCJIp5yIj5UhTFIBvEMnSzSmgE7JTN9PdZz4Wn9GDyeORqnEuaPYNeTY7aaxwx1ASYRK7o3CI+PGeqKJrNCQAi3U+ITIONwI4dkKmmShs5l4pr2e1ziWk+k81Ko63X6mxGf/972nnjrvcPmhJnNF0StnK6wwUtiIpUtgBgt9H+HcgnRZNZsVxHqAYwWFDQXdJJHV57VETJkxWcR/c2zVp2w9w9WI/XQGxhmXcvbQw2GumTFJ9xH77/5NF18aBoleclhOi8wEseJz8GMRHw8Ot4ifchDhyvNiV8LVY44fK1mf8fxISDC1DXRmT0gyGZ32DDDgNUoPoQAP/kE8PZT9HiP/6z1+UJBLJD25xgJN+R7GCOOzPiszM2zDZLHp1HTKl/N1RS2kVpWeDPM48M7rzPDXB4ujCBUdX8oGbIJ0CxeWM7jQydRHzLwsirB46RJig/MHldyqMto1JdjyeqaGI+PXHyv2qrNzUTQcItq1zm2kuOek2GeeZOO0ZXl/RfSm96co4EL7hWKRjLDa86Yx+9x6aWzrdw81GWdXLJ5gjCrOKxLbS88Lh0pN/NzpEYti4pqFB8jEMJBpvDsylDfEw91cSQqKD782nNSfHihQi8yKDDFJ6MZsIPvw37Oc9LCjcqm4uPk8bEqPglmQC7l8RklZqiLqxthww1PsA28MncrEnCN7aXmYE8QCPeJ8HXQcImFUSKdA0JM8RliRmh/Gwgh+O8/msTn3REz3DecyIAQGipuC3gtmYs5rtiUMDd3YZT+E+oGdB05oxUA0FFgk6k91AWUV3x2sTAXDxsN7sIoqyrN+/bxc7b2rC5J8dF14IPXAMvONFPZucFZzuziyiWrCh7xeWC4XUjDiwO6lIk4d60IJwKg+3fK7BKKT1B4fLoisuIje3zo/orS2V/6ASU9gKnqyUiOiIzK/Wl2LsoeHzZ+o5haio8iPpMEwtPZG1R8AJP41BRukZqCejOj9DFeqp9lPsQ8HSDQ61J8eJXTqKT4lPf40BtLK0y/SNNaVsC8+JKZnFS1uTm1d4DGQ112j49IZ5YmwXqy6xpF0GtmdeXjdCWXIvS7HcwwUpGJAXt/T3sP+duBi39qWW0LM26V5xFXfDSb4pPM5kVndjdrGMqR89L/9VxK1JGJkoAIuVb6jNww+kKCKhZDxKr4JPRw0etk8O+fkwh5MaMbNCToQwaETT7liE9R/z1RU4URn1INSgFLHR/AbO1QpPjwUBfMdHZevLA16KGGYBZqatHi8EdZQ82OpYCmCUIV8LrFZJbI5IAwm5D5d+dvx0t7RliTTYp3R5LS3/TxnrABl67BcOvgl1LOzc6hEh4fofiw8FreR0mrzjueS5l/QvEZdVZ8CCEovPEr+s8xl9Fway4JI0nvhdzjY+9BWDVkxQcATvkS8Imfmn42e1gvlxGviRI/3Gxs+DnytmaGZy1hLg6nfl1SVtdATPb4MOLD6wLBvB9ZjOjRfcBjXzb/d2oyyt/P14IxNkTWUBe9Loa5Z04Rn1mEQl40AI2SYMOKj5MpsiKkLCqPID5HWzYZc9EbSV0eHz+T5bMFRJNsdViFx4dXii1AQxoeccNpFMI0mcmXLOpWK5qZzl4U6nLozt70dPYq4NI1ZDV6sy6wmxRPrz6Y5lldceCtJ+nfy04vqsSaFB6f6sZbYyRYt3l84qmcaKyp+62KjMsXFoX+eDgjimBVoa6A4cIXs5fjztDf4f/FaEuAYViJTkKvEOqynUsyKfGwthM+ZJBnoS5H4lMi1OUVq29OfCpndXHFJy7V0LKAm5tJCEOsOvGozc/CKy63IYb2sR30sa7lAGBpQSN8ObLiw+FvxUMvUbXnxMOo6bY/mhIhlDcP0ut9SRdrn6JpguCnhYrnQHySWXTJoS4ABXvTZJn4tC6iv2P7Hb0pX3/4KegHttNWCoefKUJH8/I0O7Gt4XR2SfFxgp34ZOQFoB9hnxuapolF7luQEgLmOREfbnCWiI/Umf1gVA51MRKVHRdEpKiODyHA/2yiKhQPMzs1GWXvRwKdgqDLio+o01Tg5SeGq6utNMFQxGcykEmg4O9EmrhpefsGlQd+U6tJwRAFA7Pw59jFxrJwOIZZQbJ6CufJYYwDTFYtq1iwGwKf2BLEhxW9EUv2RyPgpCGZzZu1TRro0wVYyU6j6ex24sRDZzI5q6eQZDOQ4/VUWFYXT2/v54pPOmaaQhdvKHp92YnaATpbBeskZzbHBBBLZxFhio/FuAog6POaKe1sVR8lAVFBvByCXjfeJn24e/RY8PBOHi6kWL8qABh3lVd87GTFEupiHdZ9yCCXYuZm3dquApCuY7vi4y6l+Dh5fFh4gSk+3LNjIGedYCTFZ88wHVOh+HDiw/w5bVocy0Z+Sx9jDYa54hM0JMUnnTeLBTLkjFb8z8tUNfn/PrQEXreOfIFg/xi9J+wepOOxuNMMJfKJMu1yJj6EEERlczN/T1bnSEAmPoF2s6Chw4TteoumsR8IrqCkgdUqWqrtg9dlqt4i1JWulfjYFB875JR2QIS58m4/8jB9VLyul8jsAqwZXRxOmV0WxYceT3fYoPdeUcuHXjtFlZt3/xbY+QuqhJ37bfqYU5NRpvgUpO9Cnj/4+A0w4zNIobhH2SGAIj6TAV8E731mO5anfwDd7bU2AK0D/GKoyQjMiE8LEvDylNBAh5n9AGAIrfThOhQft0sXrzvAbnJllSOXVdkZhw+bP36UpYBfI+CkK9lExcc9gYqPl5Eyt8usdl2P16oZyLvozVpjkyUPdcVZw1dE95l9h5aUJj7VKlaax2ygm5HMlfFUDmFGjGWCDrBaPjylvUbFh9+Y5TIHAJBwm4X3khWIj/37kxUfNyM+Hi0vylhk9eKVPydPRcSHe3xydJKx10WyQKrczGEgg8P/8yTg+2fTB7JJUSpglATxzhAlH6biw8giU1BW67sxJ/UmNcwefiYAq+Jjqb0TthKf16NujCWz6IkY+OBhnZjXSv0qe1mI662DVNlY0mUSH1HkzsWrcVuJTypLCzN286rNnPgErMQnpftFlho0razPZ02K1sN5WjuKPtB5OABKfLrChkiCCEsh/CLkc6W7jVet+IzS38zYzMN9/Dzmi+Q/FZYA0Gj9Ik5yZDjV8nHI6uqOsGuYh7vuuxDY8gV0J1nFcn79De6kvw8/EzjsNPreuWRxPzD2fjkedtTsJTnouRLLaoAxdXw+ivhMEtK5PACtrjCSHaVWimXBwgk+jV7AxOWlxsg2k/gcBL3x1+st4T4frviUDXXZbgi+YBgrep1bBNQDXivJEupqkFRZFZ8GiY/tu5PN0vzG0fR09ipRYKtU3tqEh7rizHSJ6Lt05dZxmCmbSyg7UTtAZyTYjZxoJgvQVXa4hOIT8rkxxn0DvDs08Vdlbi5F7OOuVvMzuGoNdUlGbqnDOiePjooPy+ArVo/ovpNZSjaEguak8DKPjwc5uEG3n6sNwhN7F9jzLA1xsWMgmgtx+LF3OIl8gRSlbvNQ1/kuZmZdsE48lpCKkoYMOdRlJT5vxuj4n7KiBy5dw9w2es5wn085xSehOxMfTjpEny4WWtWCJvEhmguX3/cKTrztCfSzhZdY1Dn4fObkaEjr0dH5dHwF8dmP45eY+y0Z6sokgO+sB/79FOfQTUXFxxbqYgQ5YyM+/F7/Sm4e8Kn/B3z8fuf9OYW6WFZXWvMJ3yUvPonjr6RkaXwQ+MN3cO4fPoEWxM10dp5Z52+lcwcvWyAVPaTvR0lM2qBjFvC6LZmzlnOlVBf5QwBFfCYJTpku9aK+UJeVaBAfKzQmKT79pBVA/WnU/GLtH6Ori7KKhU3xiUj9nJoBv4dldWXyUp+uRlUac7wnyuMDmMTnUIW6CCu052b9nXhdnzhsHemXnOT4+rKhGQfoorimtdhkPJVFCMyfYeubFZYVH4YoghWrNgPFodww+39MIj7pCoqPXT20pLN7fCgw/5GL1czKOyg+pRYwEVsKdbqs4iMRCKb6iDEDqAFd9NBqhcelI5MvYP9YUgp1ccWHLnzmaWxiWnG22M04C/UEDMnjk8nTBYxUamCkQI+nk6WDz2ujxOzd4XHkCwRvM9Pz0i6TWIr98RICtnR20aBUH6UPsEnYFZL6vXlD+N2bQ4ilc3jqDaZ6iJT2t2FHO6Fj8l6+Bdv2jJqhLn0fTlreJbYzCxjaiM/z3wMG/kyVT4f0+4qKj2hbwT4TU3wyTPUyQ11mhl9h4YnWYogyAk4eHzrWY3n6XQS9LvPcP+JcYNOrwCf+C/CG4CJZ9GrDyPNQF6/VxRccIgXeFjZkoa60ly+areeo5VzhCp1SfGYPeEpqUxQfkQFUe6iLI8OyYsTNAcD+PF2FNEp8hlijv2p6dXFo3mCJDeuDnNXVjM7sgK2AYZM9PpaGlew4D1Woi1cY1li2DPf4JIht9erg7wFkc3OVoS632UBXDnUlE1G4NG5aKVZ8Rm3Vlaut42NXIlfPpef9qGaG00S6fAnYyxFYSIlGjfoA4GalI3IuG2lE6VBXRKqCDgApdv463jtcXlEMj4e7QppEfAZ3CWOz5m/DfEZE9gyNi1BXq98a6hJYbhIfWfER1Xg5GZAKYg4y4sNX+vPbTcVn32gSmVwBXreOOa3mePDPxStWFyk+ySy8yJoZoExlckuKT1Iz9/fSHkb0SoS6stksOkDfY4C04vdvDWGfm07s3dooPjTfVA0jUn0ygUwCePpfpf9tlcyB6hUfns7OiEZSp2PAybjcoihTrqqyU6iLeXzGsnRfIszF4fJYkhPCGDfNzYL4sOuglREfe2YXL3nhocTHvqgIyOeKIj6zD7yacaOp7ICZ+VNTLRm3zVPjZheepPi8l+PEp74Jl69SeNi7msrNAhNFfGRzc8NkRfJxTKDiwyeNalKzJwS2nlK8jo9V8dGAxesdX54S5QyqHG9Wudmj5SyKTy5BzdU5zVN0TKESik81Y2ZvBfK+efS8H4ZJfNLu8mFX+7VnJ3kpFh40WAZlzlU8ARollNsWP/eVVBHq0jSzlg8zOIdlxWdol6n4+FqxsINOrG8PjWOENeTkGUw8rAUAb+kLLeqCRfExTDW1UCCWcNdgju6fKyVC8RlJ4k3m71nUEbAQf06kYsSZ+Iwls+hkRAW6RyhTsuIzVjDH98V3OPFZRH/bQl2JkQNwaQQFomEYETy3ewi/eTuJfkL32zJubu8Y6nr+e9a08bQT8ank8Wm1flamGo1rAcv7yn6ZdK4K4iMfF8vqGmbEhzeXLQIjNyEtaZqb7cSnguITdzsvmi2hLqZKPfz0y7j5kVeY/ePQQBGfSYLpe2h8yEUabB1ZXRxjvJKm5PHZmy1uMlcLeId2jvJZXba09SYTH76KLBBTpm5U8WlmAcNSWV0AcOM5K3HlSUtx5LzWht6jXvCWCxwpeGG4dSQgTd5zjirZhbtswT0nSMU1ZeKTTzCPkStiLdgGrvhYz5mqPT6yH8elYUUfPe8HpVo+3GtRCqV8ORxpjXnqsnRiyzsQH044hLmYgZeGEIoPu3cYpcbTY01pt4S6ht4wwyn+NizsoGP2znBCeHzsWV0AsBXHWt7CovhI1/V41prZNZBjjWINTny44jPu6O8BzPuEILKpqMU0HE3Jqezd5rkgmZsPZswx3HkgTsNjom3FO5b9JYdp1tkQwsjDhZf2jOKxVw7gTbknFkNIInn5AilWe4Dyio+nWOkDUOzxYcQnAU58mGKoa+LjliUKjh4fqvgMpisRH3reh5GUFB/2mSoqPlS94f64olCXhfhQYj04sA8//sOehpNNGoEiPpOEWjNdysH0BtQf6hpmkjTaFgGBTpBQL95rkPjYwwy1KT7lJ5paIY+z6EDdxPBU44qPrYChRMpOWdGDL3x4RcPZf/WCt1zgSMGLw7pDINCR1tmNvIS/B6hD3eR95GweH8KUirSnOOxUyuNTTahL1zVxbi5oDwiFZSBP3ydK/HC5yxOoInOzzbuXYeFBXjqi4BDqOn1VD249bw3+/szllscjtkyiZCXPlMjsopOtJdQlKz7+NqH4vDM4Lq6LNpvHBwAeLbzf8hZyVpfPYxYdHE9LmV2629JuAYAIre2PprDzAFURlnRZr3Ueehzl5vlC1lJ7Z2w8i6DGiITkJ5IVqrE8JZb8u9y2Z8TMXEpHzTEAkB6lxGdEa0NnyEAmV8DWnQfxJpGIT/wgsO1HCGfNsEw8lTPVnrbFwhBt6V3HIUJdlRSfUcs+eNVmPn5yLZ90tpziw4hPJm6OHfP4HMzQ8e2uRHwsig/zLQnFp7i/FwCh+ER1+nnsi13Z40MYUW3Xouhr8TXUPqhRKOIzSZgQ4lOL6qC7zMZ4AAaYJA23AVz5eyQu+y3yoM/XU8cHKCY+5dPZbRNLkxUft0sXRIebI5tZudnVcFaXXfE5dDcBO1yG9btIEi8O76E3QNHDqhzxqTGryyQ+OWTy0qqWZ0RJ9XU4LFldAHJExziMqhQfwLxBL+0KiZvzuwX6PkMkUnFRIZeS8Lr1IpKaZgULeQPNgsMEaLhduOgDC0Q4iIMrp7wQaKqSWmyr5RO2EJ83RT0m+M1Q1zvD48VZXe2LQaBhT6ELL2QWgkgqiVzHR9M0MWZxuYihvw1xtl2I1VPqDFG1kBDgqTfoJLnErvjwtgZZ068kh7uiqZxQsywKiqT4xOHHvDY/Tl1B/SovvTNCCSFXoySfTz5Kic+Yqw3HLTHJ0wEvUzX+9ADwr+8DfnYVvI9/URCPaCoLvHgv3Wb935nkxVHx4aGuKrO6YrTfGPetyecxL/FRNtRlRMzFLVd9WFbXgSQnPiWOhYe6MG6ms3My57UrPlJWFyHCrzPKIgj2hAz+f75ARMp7O2LobSlxLJMERXwmCbVmupSDqONTa0E+6eb7blq62Ya6MM6652q2Ogy1IGKbdILlQl2aZlV9mkx8AJN4jbGVbaO9uizp7A23rLB7fKbOpeg2rOpEmik+APDdwOXA+uuBRR8q+Xp+rldN8qVQl3xz13lGlFHstwkZHoviE0UAgFaV4gOYN+QlXSFB9F/KLsZLi/8WX8ldWjGUKfvFnLw3vFJzqMCJT4mQhwN4qGvMFuoqmRFqq+UT0SXikx23dE7noa43D8bFWIusrvYlSF70MC7J3oBcQbN8F7LiA8Di8zELCrYJXxIPEWmaJoW76HHJNXwAU/EZz+bN7D0pU2osmTXrFMnExwjTrvegxvsPLGrH+xdS1epFbnB2SGkvxFiLE3enNXV93ir6R2yfWQPn7d8hzM6V8ZEDwDBr5bHyL8w2LY4en0qKDyc+UZoO/x6tK/SatgQALNmJIrOrHPHRNCmzi5Z34J9h/zh9fUnFx2cqPqJlRSmPT2oMz7/2Nvt7lPZzg9nyxa74yP8n2QKmTYuhr6X662EiMHXutjMcNa+Cy8Cop44PYFFZ9qSsjHtciuHXK0HajaUVQ2byTcHTfOLD318oPk0tYNi8fQGNp9o3Ex6fNRSRgqn4PJL9AHDql2ljxBKoWd1k56VXsxIfT4aFiXzFXqKQ1KEdMI2x1RIfU/EJikk8niF4ftHf4snC0UWKnB3y807XdIZ5fHj4ySnUVQr2UBcPcZRUUG0enxYeFuJ493l2oK2Y1+aHppmTqFvXLFXXjWUn4R1CFRx+TwCsHh/AzNaJp3PA/GNpJuDCExBP02OWv4f57VZFa0mn9fzyyynPdiUE1Ovk0zKWzwoA0DTR9TsOP45d3I5jFtFz5Y97Rql64ZDSriUo8Rn3duD4xabiM/99JwEdy2gvrI0/ogrK+BCWG1TV0N5j49i5nIYFOSkoG+qqoPiAUN/MwdcAANsKtIWKPH5c4a9oBubZdbF+oJAXx/Begp6r3ZEK5maUMTcbIREK/dIPfoFX9o2Znda9YcRzrPaY7Rx16Zq4D4yzAqEdSvGZPTB9D40POV+htfirk/UFJIXlvbTfkqLJuy83km5vDzNU3JfsO5pIxYcRn8bT2ZtXwHAqKz4en7PHB6ClCkiparUMNRv55VCXhfiwVb/s62AI+9xmywpQxcfr1quu/H3Oml4s7AjgQ4d3md3G5Ya2tSg+DsQna+vNRUqZXB0gsrpEOnuF8bRndcmKD2Bm+vjbYLhdmCOttlsDXstCx6Vr4n0SUu0aOasLsGXrtC0C/mE3smfdKdQ+mUxxxYe+n8fMImMQik86Z5YtkIiPRfGx+c9iukl8PrC4Hcu6wwgbbiQyebx+ICaltJuKj2ucpnynjA4c1h3Cyr4IusIGTly1CPjcC8Bnfg2s/AjQdyQAYK1OO8979r/IPhAzfrMwUD4VxZ2PvY5n3pSMxZUUH4/PJEVvPkF/ty3Gexn6XUYcFJ+yoS4AiDCPUlRSrADsjTPiUzLUZSo+JdPZAaH6zNUGsXd43Dyvgh2CJDsltIiFhYu+D1V8FPGZFRB1fJqg+Fy6bhFu+sgqXLpuUW0vlIjGCAnjvVHzBpkUJ24jxMfZ2FYS7gkOdbGxHp0A4tNoAUNN05pqlm4mDL/1u9C9AfSw1WIqWxCr/1KotVcXV3zcNnOzL0eJjxaorPhESXWp7BxXn7IMW//+ZPREfOLGTAgQYxN8RcVH+r6crumiFhWlVv4OiJRMZy+v+IhQF7gR2Nrmg6/Yuc8HKM4oA8zrVlZ8xm2Kj6UwHQB4/JbzImghPub72f09gOnxGS+l+KSyksfHSnwSLERPvCEs6QzCpWs4akErAObzEZldb4vXeJOU+GR8XdA0DQ9feQJ+c/1JaLGPxbwPAABWE5rlFTq4jT4+nxEfFuraNzCIbzyxC/+y5VXztZU8PoD5WXexLvHzjhWL0Zo9PoCV+LCMLgIN/UlOfMorPmGMI5cnQD5L21NIzwEQZvG52iBdTHIvUaBThEKdqvWLcgWsTlZIS2FO8NDe7xTxmSSYtU0aJz7tQS/++oOLa+9kLqWQjyCE90ZM4pMow9irhZzOrmlVrPgnWPERoS5m4mxmY9FmKDQum0F2qsDwWb8Ljy+AgNctJviheNrpZQLJWs/1EoqPjxmDXU7Ex0HxqdbYbIdMXLgfrNK54rYoPsXb5uwtKmwTdjnIBQwJIUJBK5nObuvQHuL9zeYcbd2OVQvmPh9AMjZLsPTiYuB/80msqIghzFo3hlu3nM/zJeKz2BbmAiTFJ5NzJD5jyRz8ItRlVc6GvLTZZrBzoVCu1nKfzzsjQDv1zGD4TfEaX5pO2LkANUL7PC6LQiXAuqAvz74GHQW0jrzMHueKD/0sqQQ9Vl4Xie68QqgLMD8ra/hL5h0jxlBeRPK6bfzaSGby+NHv38H+MZuyxxuPxvYLxYcqjRq8Lt30ctnBFJ8QksgWCtbQnUR8CGtPM1cbpMZ7ofh0CfO7vUYWYJLkKALIsQSauUayaLvJxNS5285wNNPcXDdsis+7I7LiYzUv1gP5YvV7XJW9QhbFp7np7IDpHWhWqMvdxAKGgNUg3WhdoGbCF7B+F15GhDrD9PwZjGfKvr7mc93FW1ZYKzcH8/QG7A21F70kZLiRhlc0UI2SoPPkVQV0XROTLz9XKn2/cqjTiZBkdRuhqIX4MHNzrkBJjzme1WV1BXkdnznWlHQnxcdpMhSKT1pSfNLWhZGlPgsDr5dlV37lUJfd2Czvs6zHx8ncDODXc6/EZzPXwjjyY+KxtbLBmbWiwOheIMt6CGaoN4UEu4uOxYL5VPGZl3kLR2m74MknaXirawV9nik+uWTU8vnpgxUKGAJSVhg9zzO9a5Fj5mL5XObZqdzj88if3sOX/nsH/uruZ3EwJi1CInPp7+h7gvjkXfS7lhuvFh8HD3UxxYcTH7ff4gtNBen+52kHqf8sIYe6Ss8fnCSPJrMYJpRI9bgdDOGTiKlzt53haKa5uW6wCaYAHVEELKEuwdib5PGpSjmyKD7VTwzVgheq42pWowbiZoa67PuoFFqZTASDVuLDQ18dQXoTHyyj+OQLRJCXqsO6Oj1XZMUnXyAIEXpz9IY7il5iuHV4XJpQfWLwV21sdgKfyHltm0qKnqwIOX3OYsWneo+P3+MSxCuazFUOHdrq+JjEx6b4sIl2kSXU5aD4eK2KTyZXEN+pMDdzsiKRIz7x2wmohfg4hbpkjw8nPlJWVzSZhR9c8bG+/oqzP4ATPnoZ/uq4JeIx3ux473ASBX8nUzQIzcjKjMNXoGneWsTaYLUIkblAuA8u5HGp+zH62Nz309IggFisEZbVFU/nTP9bLYoP226shdYF0jSrTYATa25yPxCl19+7I0l85ocviPMDEab4RPeLUFeWFc7kvdMcIZmbcwXi7O8BkPDR/c9zDHWVnj/4tbV7MCGITytxMIRPIhTxmSQ0s3Jz3WBEI+1pAYGOd0dMA9x4tnHiI3ssqtrPBHt87MfQzMrNjZqbAevk2oz9NQv+oPWGF2AKEL95DpVRfMRNGLVkdbFQl5YTq9pEJodW0AnKFylWfDRNQ1Dy+URJdcULS4HfnM0MwFqyuhxCXbZKzVoNxF7TNMnnkzWzuqqs3Bwk7LruOMyqpLJQ14J281prDTooPsJzQ4lMUvLu8PBliKd4S+GwOE9lt30P7UGv+G6W9TiEugxpgWJTfPIFglg6J9QsO4Hsa/HjkuMXWkzt8nmQyObpOAC0mCNL9U4RD3zB1qJjsUDTRFjrHP05+hhTgQAIYqCxOj75AhHqXHWKj0R8+o5CLEvPo5DhttSFspubZWXpj3tHsemnf6StQ8KyuZleO1lWcLTFgeCabyCZm/NSqMuwflejXpo1RkNdWSnU1VmU9SeDn09vHjSJj548tP26FPGZJDSzgGHdYBdhnq38ZI8Pz9qoaEguA5oKT/+uivi4JjbU5bMTnyZ6fBpNZ7fvr1H/UTMRCgSQJ5J5lxGhzhD9vsp5fGTiU3U9KKlyMy/aF0/l0KLRm7dX6slkOU7D9Pk04vEBzMnXDHVVUHyk552UGHs3dr0GxQcwFxEjiYxQW0orPnQM/IwcBDjx8UXMSd8TFNf/gmoVn7RJQgF6/fDFQ0AuYMgQK6H4aJqG/3PBkfjSOStxWHdxFW6+r6QD8eFmX18Jc7MTDLcuFimJtEx83gBYDZ+DpBVhfxUeSUZ0PBo7r7m/BxD3LFfWDNvE0lnqkq9G8eEd2gFA8vfY66GZdXzoMfDtPnR4FzwuDVu292PLjv2m4pNNiIKIac1a0doRdnMzL8hoU3yGGfHp0sYwPj5uVXxsWX8yeBh510Acw2D7PMSNSqfO3XaGY0oQHxav1Vipd9njM94E87Uu1QSpTvGZYHOzbaybWbm5KYrPFPX4GB6X6MgOACEW+uoIcY9PaeIjjLgO1YxLQipgOJSg+44n04hwk65DOjtAJ9i3CvRm/zbpbUjx4ZMv71heUx0fh2wre28uzVsb8eET1QHJw1Ha42MqPl5k4QEjI0bYnPSlSTZkuAWJLZ/VlbP8lic1uYcVh1B8jOJ9fnh1Ly5fv6Tocfn9MvkCcrw9CSM+nAiHdGYcroJAcjUQAK0rxH0+Q28CcUp8BtBa3fkiEx2A1vjhYIqIJ28q54l0HshLiigjm/tGk3it3wzfAbAqPpaMLutxeUsoPicd3oXzjqaG410DcXoP5fsc2gUASLGyCi3+Mp+VeXx8WhYoZKR2FdbCocP5EBKENd9NvGdRfKpJZ3/rYBzDvB+e3FPsEGDq3G1nOKaGuZmetHwFPZTICBlbpCPWaRDl4KuV6jw+kxzqamoBw+Z6fKZSqEvTNNFyAQDCIaviM5ioHOqqiUDLxIeF0cajw+bz8spYQtjnxj/lLsGn9f+NJwtHNqT4iLo07HqoHOqSPD4On7VgIz66Udv5zUNdA1GzGGGlys0BLW1tUOoNmZO+raHsStaYdX5bsYIisrq44pMuDmNYChgyOBUvrAby+KXdnPjQyZcrcCGdnXNVhgxDgvjkgY6l9MHBNwTxOUhaqzPD9x2JvEa3O+CZBwQlvxmr42NIxCeeyplqDyAUn0vv+QM+8m+/E41aARQRn7hDRhdQnM7OCVLI5xZlJkT4mRucB2ntoSRrLGxXkSzwmsqOL58o6fGJpnLYTehC4x9GbgJGWPuKQEfZdHY+zulcASPTWfG56667sHjxYvh8PqxduxZPPfVU2e23bt2KtWvXwufzYcmSJbj77ruLtnnwwQexatUqGIaBVatW4eGHH7Y8f/PNN0PTNMtPb2+vZRtCCG6++WbMmTMHfr8fJ510El555ZV6PmLTMSXMzUxhcYc7xcn43ii9aIXi0+Dx8Yu2qolPVnwmoHKz3+u8cqoX1pYVja8ZPBaPz9Rag2QkxScSoau0Dk58YuVCXYzgV1lIEIAlnX2IkapMjN4Yx2HNLJERMtxIwI8nxxcD0Gqq42OHnSRXDnXJWV3F29oVH7dRm3mfT1Q8a8frKqOgsWvHj7TZoNQboibcbtaGgac6M9x63hrc9Yn3W1o2cNgVH3sqO2ArYMgQTzmHuirBy4zqAJDUeYd2pvikbMSnyuw4y/F1cMVnFxCnHp+DpKU6ouzxYzRCs7je9K6wPscUnyBM4hNLZ01/DzTA5cVALIVdA3Fk8wS/3NFvvp4Tn/AcoGWuCGHZx8+wVW4WBMlwi2uSK6Xiex6i6fsJQq+tsqEulxvETcfVR8ZBUrYGpQzRVBZfyV6KA6QV8wr7gDTLvJM8Pk7p7PIimHt8ph3xeeCBB3DdddfhxhtvxLZt27B+/XqcddZZ2LNnj+P2u3fvxtlnn43169dj27Zt+Md//Edcc801ePDBB8U2zz77LDZu3IhLLrkEf/rTn3DJJZfgwgsvxHPPPWfZ1xFHHIH9+/eLn+3bt1ue/+pXv4o777wT3/zmN/H888+jt7cXp59+OmKxQ+sgB5pbublusAlGC7RjTiu9Me8fo6sTUaDMIUZbC0zFpxriwyYH3WMlQU1CsxWf2ZLVBQAZKSupNcIVH2ZuLqP4JBtRfLS88A9l4lTxSejFnhCOkG3iaiTUZZ9sKjYptfTqKv6sRPJ2ZIkLrhrPb57SPsCIjxO5EpDq+IS54sMnrRXnAOfcCXz4NstL5rUFcPaaPkcyFbAVJ0yki8MYRQUMIXl86vge+L7HbcSHKz6BEnV8SkEUzUvlTMUnOQwy8GcAVPGplij3zz8LAPC0sd76BBvjIFIAaDaXRfFx+wBNw6v7zfnn8T9LxKfvKJrRuPIjAEySZydk/LvnJve4NM5m+JkrPszgzEJd8QIPdZUneUT4fJIgqRKKTzKL58kKnJ7+P/hx4TT6oDeMrL9LZGM6Kz7mY9OW+Nx555247LLLcPnll2PlypXYvHkz5s+fj29/+9uO2999991YsGABNm/ejJUrV+Lyyy/H3/zN3+COO+4Q22zevBmnn346brjhBqxYsQI33HADTj31VGzevNmyL7fbjd7eXvHT1dUlniOEYPPmzbjxxhtx3nnnYfXq1fjBD36A8fFx3H///bV+zKaDs/VmFDCsGzxmG+qVjKostCBaVjQW6uKTT03p7BMQ5gKK1atmKj7NICruKWpuBszKw2niQXuIp8RWNjebBL9OxYedj3nWByjpKkN8bGSlMXOzdV+V0tnl79/pmiZSU9IUvDX7y/gCYiBGJ9GySqzHNDeH7MRHdwHHXgZ0Hlb1e3PSwA2r4w6KT8ChgGG9ig9gTpgJTnyYz4S37ajF3AyYpDiRztH7Cw8BvfMsAOrxqZag7Vt5GY5KfQdPu2x+H2ZudmsFGKDHGU/nijK6/rzP9PZs2ztq1t6ZcxTwhd3AWbcDgGPxQsBsrsxN7uZ2HlFiQlyTnPiwysuxPL22IpXaG0nEp5B2Vnw4CY0iiBsyf4PM3z4NXP4rjBek+m0O14JF8ZmOoa5MJoMXX3wRZ5xxhuXxM844A88884zja5599tmi7c8880y88MILyGazZbex7/ONN97AnDlzsHjxYnz84x/HW2+9JZ7bvXs3+vv7LfsxDAMbNmwoeWzpdBrRaNTyM1FINimU1BBO+BztrH3UxWbYgl0wZkn6BhUffy2KD/ORTEBGF1B8ETbepFR3/LteTNWsLsCsQ5OEV/RW6mC/R8azyOYLGB3PWLK4ALldRQ2fRyI+sTStW5Mfp921U57izuwc9gmioXT2olBXLYpP8WeVPT4peGtWG02PD70+yxJJofikzKrNRmnCWAnVKD7C3FxFAcOq3pPtL8bbkGTHgVxGTLY+UiPxsXuQmOqjsTTqIbRWfS/2ez0YRbjoXJfvW5xwJtK5ooyuV/eb8wohwK9fPWDuwxcBT4WVCY0Mex0fuV5SkQrLiQ9DtFBlX0dmcA5p4yiUUnxSOev/4WVA9woxt7l1zfE8lxcVwtw8nYjP4OAg8vk8enqshZ96enrQ39/v+Jr+/n7H7XO5HAYHB8tuI+/zuOOOww9/+EM8+uij+Pd//3f09/fjhBNOwNDQkNgHf121x3brrbeipaVF/MyfP7/SENQFQghSuSlgbu5YSjtrB9rFJMYvmHIFqGpBF+sHU1U7DW5uniDFp9l1fJod6rJmdU2tUBf3qKThFaSgLeAFP8yz/vUpHHXL4/jLbz6NfMFsWpqsJ3uRFTD0gr52OJEBkpT4ZMoQn4lUfCq2rJC+L6drmnhM4pMk3pqJLSc+B5i5uSyRlLK6ihSfOhC01ehxMq46pbM3Qnz4vmNE8kaloyL8YxBGJqoNddmPj/t8GBKejsqV5Rn8Xjr2STvx0XVkXfR4gho9vpiT4sOID68o/SuZ+EgoZQ6XPT6EEMs48/vsKFuMiFo+DKM5eh6VNTcD0HgtHySFsVw2PQOm+sbBSWlCqtrsNKbydTqmScSnQrPjiURdM4H9wxFCyp5ETtvbH6+0z7POOgvnn38+1qxZg9NOOw0///nPAQA/+MEP6j62G264AWNjY+Jn7969JT9DI8jmiZgcDinxkcAJCjeqcsm6kV5dAHDZiYvxDx9egYs+sKDyxu4JDnU1WfFpdhbWVFZ8uGKR0cxS97quoSdCH981QGt9vH4ghm17RsTram5QClgKGAI0/Koz42TO21LyZXbiU2/LCqDYlFkplOmpkNUFKdSVhFHz+cL9J3yVXV7xodePT8uK2kdNUXxsWV3yGMnp7Px+HiuTzl71e+ZgTripMTa5EngKtYa6bOZrntbPkDKca0M5gY+9XMiRI63T4+GEM57KAVlGPt0+JDN5vHWQXivXnkrJ11NvDFoKP3KUDHVJ6eypbEHMJSHDjVZpMTKSyJi1fBhGstUpPppQfJIgJbO6rMSHEyHezqRURrBcBsEV6qSes/O/B5AKTVcnEDXdKTo7O+FyuYoUlIGBgSKlhaO3t9dxe7fbjY6OjrLblNonAASDQaxZswZvvPGG2AdAlZ++PvPLL7cfwzBgGGUqazYJBUJw3tFzkczmD22oS4Jd8Uk2oXIzAPREfPjsSUur23jCFR/nG0i9kEWZpig+U7RyM2B6VOwViG/5y9X47c6DOGZRG7Zs349HXzmAX+7oxzGLaG2oVKUqw06QQl0AMJhIw5MeBQDkjTLExzZBNJLVFTLsoa7qPT6GUwabx+rxCdSp+HCUzZKTyECXxjJtjNJKWSXYG5A6KT5yP7F4Ooewz1OyZUU1KGpbkYkBqTFEkxo8yENnamC1io+Zzs4IRqdV8cn6qyc+/FwuUnwAJDU/QgAiehrIF3t8Xj8QQ4HQxID1yzoxt9WP90aTeOqNQZx5hDUruRTx8YoChgWaNQYaHeMKS3vQi8F4BoPxDLpb51peG81XkdUFmNWbkYRWgviMlVB8+BiX8q/K50Nna5h6zg4xaroavV4v1q5di8cff9zy+OOPP44TTjjB8TXr1q0r2v6xxx7DMcccA4/HU3abUvsEqD/n1VdfFSRn8eLF6O3ttewnk8lg69atZfczGfB5XLhz41H49ifXTpku3B02o6pTHH/CMcGKT7NDXZqmiRBHMxQaS6hriik+PFRjr0B8+qoe/NO5q/GXR83Fx1jxtF/s6Ber/rrKNrCsLh0EOgoYimfgzlC5nfiKO7NzhJsY6rKf9zVldTmFoezm5lqJT4nMHueD8YOAHm83RtkLGgh1eU01B5BDGVIdH69bjD/vHVWqDk01CEgKEvebIDWGaCorepABqPpeEbQTnw5zMTZMQvD7auidxu4jRR4fAAnQ/SyOMP+NLauLG5tX9kWgaRpOX0UX4b/6c3G4SxQwtClmch0f2UDOlVhhcE6kab0mqT7aOCs4WNHIzc3N2ji0TKmsLiux5Wok94ny5Ac75PtwX0uZStaTiJrvtps2bcJ3v/td3HPPPXj11Vfx+c9/Hnv27MEVV1wBgIaPLr30UrH9FVdcgXfeeQebNm3Cq6++invuuQff+973cP3114ttrr32Wjz22GO4/fbb8dprr+H222/Hr371K1x33XVim+uvvx5bt27F7t278dxzz+GCCy5ANBrFpz71KQB0UrruuuvwL//yL3j44YexY8cOfPrTn0YgEMDFF19c7/jMWHTa0iCb0Z29ZnBzoK/0qr4R2FWHZpAVrvTM5AKGAKAxFYGUKbm/4fAu+D0uvDeaxCvsBl9X2QapWS3N7ErDm2XKRYnihYD1Zu7WtYZKRdhVispNSst7fDSLx6f2UJe90m5ZBU3TUGBEq1tjYccmeHw44eETnr3URQ+bxLgPqRHFh6tJ8XTOVKvSUYzJDUp1d8maTnYU1RlqXUjLZoC1q6iBnPGxz+YJ9dFIiLJ08UUhYh6/ID4G/ryfnser5tDPdAYjPk/uPFj0PpVDXXlzG2mMeUr7cCJDpSDJ4JyEgbDPXfl+xUNdSIreY6VCXbzpLA918e+fh8HtkM+H3khtFcwnCjWfoRs3bsTQ0BBuueUW7N+/H6tXr8aWLVuwcOFCAMD+/fstNX0WL16MLVu24POf/zy+9a1vYc6cOfjGN76B888/X2xzwgkn4Cc/+Qm+9KUv4ctf/jKWLl2KBx54AMcdd5zY5t1338VFF12EwcFBdHV14fjjj8fvf/978b4A8IUvfAHJZBJXXnklRkZGcNxxx+Gxxx5DOFz/TWCmolPK6iKEmE1KG6zjUxOO+Bhw8DVg7V9PyO7t0msz1Da3riGN5resmGoenwU97cA+oKu9tOLi97pw0vIu/GJHP365ox9HzIngJeb3qUl9kSYzLyti6MvRVacrUNyglEO+oYZ97qrNqk4oMjdXmCjkUJgjKZEqDCcbSGfnqKSgEU8AyI2jWxulDzTB48O9G28wP9fiTmv2ZW/Eh10DcRyIplAoEEt9mVoxp5VOiLsHE5LiE0U02SJ6kFXr7wEcQl26C2hfAgy+zooXVn+M8tinsnnLtTqWp/fRecG8+X4i1GUqPqv66Gc6jDVpHYynUSgQSx2laMmsLtPj4zTGZoaulNk1shsAMA6jcpgLsCk+nPiY4dJsviAUwPntAbzWHxOhLl5rqidcSvExj3WqKD51xTWuvPJKXHnllY7Pff/73y96bMOGDXjppZfK7vOCCy7ABRdcUPL5n/zkJxWPS9M03Hzzzbj55psrbjvbwVcJ6VwBg/GMMNhPaqgr1A38xdcnbPd29aoZZEUXik8z1CM5PX5qKT4tEarC8eKFpfDh1b34xY5+/GLHfvREDDz1xiC8bh3nv39u2ddZoJs3Zg9yGIynEcjTCcMdLBPq8snEp/4wF+CQzl5DHR8nUiI3JU3CqDud3XyP8q/nlXebQXzk3lnJTB67BigJXdFr3Wc3a5fQH00JdQioT/FZPZdOsjveGwMWcMUnhrFkDl1c8amh0aulZQVH5zJKfGqo4QNQxUXTaBJSMpsX51q+QDCSMwAX0OeT6/hQBYS4DbzWT8eOEx9OaAkB4pmcheCWyuryuuj5lckVHKs7C7+mvZYPqNpYKaOLfkju8RmH7hDqkjO65jKSyhUgrvjw88EOr1uH16Ujky+gd4oQn6m1zFSYNAS8bkEM9gybJdenivm6GbAbQg1X45+NE5RmEBV5X42oFRMCvrqu0Erk5BXd8Lg0vHkwgVv+h1bFveGsFVjWU8PEq+sipd2NPIbiGQQLdNXpCRW3VOCQs4caqeEDOBUwbMzj4/Z4kCXMG0I8NZ8vPo/LolBWpfgA6IBzg8laICul298bQzZPEDLcIsTB0ctCGwfGUkKJ8Li0upIIVs+lRPvNg3FkPUxZYuns/hqLFwLm9ykXWETv+wAAbxd6ayLKmqaJ+2IqY4a6hhMZxFn6faeXEZ+Uqfgk8m6MZ/Iw3DoWd7LMO49LkOCYVBcnmy+IxICiUJeD4iMfv6jlwxUfqT3JOHw1KT7d2ig0VoXaQnykEFtbgL4fD4HyWlOlQl2AGSZVxEfhkIOrPu+OUOLj8+hN8a5MFei6ZiFyHnfjn6077IOmlTby1QKXq3lG6aZj1V8Ch38YWPupsptFfB6ceBjNkMnmCdYv68Sn1i2q/f2Yz8er0Q7tYdBVpy9SJtTls4a6GkHQbm6ulNUlXSdOiwWPS0eS9TtLwajrupJX6hXN4iy0pmvFk1at4Ct0ACJ0ubw3XETO+UR3IJp2NN3Wgu6wDz0RAwUCDGbptRWPDiOTKyDYSKhLLrq37ir8x7yb8N382bU3UnXI7DoYSyPGzM0Bls4ekxSfkQwdwxW9YYuCyDPiZBVFJkEle3Vl84hLDUo52mVzM2BRfKoPdVGi3KexwoKablHYeFgr4vcI/5nw+LDq4t3h0qTmE8ctxLolHXjfvInxc9aKSYxrKEw1dAQN7B1OYs8QJT72m/9MgN/rEjerRnt1AcB3P3UMDkRTTVm5cB/JVOvTBQBoXwxc/EBVm561ug+/ef0g2gIe3PFXR5ZuplkOLg+QZdWbozH4WAsAf6Sr5EsCHpcIQTQa6rJ722pRfIwSxCcNL4AkMrpRFxmI+N0iY6Yy8bEpcw0QH4COR2a8gBffocTHHuYCTOLTH0011KeLY/WcFhyIDmBfyoM+AAdZgdsVnR4gitpCXfY6PgBghPA740NI4EBRRmAl+ByIz0AshQShx8Q7tCckj89gin7n3NjMEfZ5MBjPWMgOz+gKeF1FYVZLVle6tLm5qF8XWKjLX8VnZb6qLk1qVyGds5zkhH1uEYbloS5T8Sm9GLz+zOWVj2ESMfNmOoWqwSVSHuo6pH3EJgh8paZrzUkZn98ewPz22jptlwL3+ExJxacGnPf+uRhKZLB+WWdZubssmM/Hgxyi8WHAAPJEQzDSWvoluoaQ141YOtew4uNx6fC6ddFssdF0drdLQ4p4AQ3IaPWNiVXxKX+OaF7bOdkg8Ql63Rgdz+KlssSHTnQD0ZSk+NRPQFfPbcGvXxvAO3EX1gIYG6HEZ0137cSHh1bimZyliK1TqKga8HujXMTwYCyNBOh36y3Qe+h4Jo9CNgUdwEFWx3BFr5X4iOKUDoqP03lsreNT7PEx21bwDu31KD6279cWKuUkp8XvEedlNJlFPJ0TY9pd77V/CDC977gKDYGHazjxmYmKD/cxTUVyYdYEmoKKTw1wu3R89qSlwqdRF6QihmHQ6sNRBBHwlr9p85V9rSt4x31J+6hUwLDF74Hf40JX2Nm47JVCXVm9vrCobHAuW8AQgN5sxYddN7zA6Yq+Ys8QVz0HYmmzs3gD38Madv7sGmOhnfgoAGBlJ9tnDfW++HdJiFmPCChPMMpBeHzkUFc8jTgLdblzCfF4Nk0Zz1iWvoabgTk46eLFCK3HVXy+y5WbBcGUs7pEo1Km+LTQ+lp5uJBEbeZm83/nGj4RvwctAbq/sWQWA8zYHDLcDVVOn2xMnyNVaDpMjw+9UGei4sNv4FOlcKQM7vFpRsPTaQ+W0t7iBbIsnTaKENoqhM34zbbRUBdAz5VhNn9VCnX5vS787OoPsoyf4m3dLg0pTny0OomPNLlVCnVphp341G9uBopbeCx3UHy6QgY0jVZvfoeFyxsKdTHi80ZUBzyAN5+A161jUYSNbw2Kj9/jgq4BBULDT9zs7JQVVe3+ACuJGoimkWDmZlcmITKXcpkkDACjzOPTZUvzNj0+xaEup+PixCeTLwivTcgh1DWeyWM8k0Mg0gec+r/w0z+NovCuLohKWRQpPs5VmyM+SfFJ5UTxyu4SqexTFeqOO4vBVwr7xijxsRcomwngZK4Z/p5mY6YoPk0BU3w6/BD9puJ6qNwrAEiKT4OhLsA6mVSjEB7eE8bCDmcVwuPSBfGxt/2oFnKIolKoq0jx8VYeu3KQ0/vntvodVQO3Sxeq8Zus1k8jq/6eiIHOkIEY882EMY61C9rgKfAGpdWHmDVNM8lOWiYY9YW6fF4Hc3M8jTgLdSETE+dijik+w4z42NO8eWXmWKq6UJfsIRtmCpy8Xchwi4WdUH3W/x3+n3EOgCraVQCAN4QCpPtQieKFLX6PxZw9ECufyj5VMfVmA4VJQydj6byGj98z8wRAvlKbkoqPMDdPvWObdHDi49PQqtFJdFyvHK7hE5i97k09CFpCXY2RUY9Lx58KS5ElLrztWlzXPiyhrkrmZokUFFyG2Q6mTsj1vJz8PRw8pX0Xa8TZiOKjaRrWzI0gRuhnCWvj+OBhHWbTzxoUH8ChejOkthA1h7qKO7QfjKaFuRnpuHi/PDve8QL9/vgCk0MQBwdzsxPBlBdtnPjIXipN09AZ9FqeB6wqTUXoOlKaNL5FoS6e1eUWRCpXILTgJMqnsk9FqDvuLAa/WDhmouLDb+BTkfhwVWEq+o8mHS76PbX7gBbm8Um6K4dr/vqERThtZTdOXdnd8CHIBS8bJaMel4b/nfsEjk5/B+94D6trH/KEVbG+lmRuznsar1Qv3wtW9JXeHzc4c8WnUa/V6rktiDLfTAhJrFvaCWRY/LEGxQcort6cyRWQzjnXyqkEfh9JZayKT0IoPibxKWSpCpKGB20BT9G9R3h8qlR8PC5NJFjxzC07wRS9F7nBGdYU9GqQ1KXxLRPq8ntcYmHAq3or4qMwbdBhq0UzqX26Jgn+KWxudqlQlwmm+LQaGlqY4pPxVCY+J6/oxnc/dWzZGiLVwhrqalzxATTEEaj73JPTkMs2KQUshSaJ0ViYC7ArPqW/Bz7hJRghaNTgunpuC+JMRQlqaRw5JygpPrURn6Ctlk88XbpWTiU4pbMfjJnmZqTNUFchw4gP8RT5ewA5q0tSfNKliY+mmUUhhxmxsR9/e9CW0g5Tpakq1AUgKYeWvfZQl2lu1jRNkKldB+i1qjw+CtMG3BTHMantKiYJgWng8Zlq7SoOCQTxMRWfbBXEp5mQz/9GDecycaqXRNVTwBAAPIHGi8TJHp9qQl0cjYS6AJrZFYP5WdzZeOOhLtZOgyssfk9xrZxKsBcwTGbyiKdzwtyM7DjCXvY95+jxpuFMfMLCHGwqPqPjlLCUIim8lk+B2RIiRYqPtXpzoUAEmaqqjg+AVBnFx06i+O+3BhnxUYqPwnRBW8ALec6d0YrPFAx1mYrP1Du2SYfI6iJoZebmnLd1Ug8hJIV3mqP4FP9dC2pJZ5cVH63BjC7AzOryusx2C06whzgaVXz6WnyIBANIErYoS8eALGupU2+oK8WJT32p7ADg9zKPD1O2RpOUYMhkocPDiAwrYJiGx1GJNAsAmooP9+a0B52VE3u4zE4wucmc9+uKpXPCu1mVxwdA2iUpPqU8Pux9+e9snr5JqQalUxXqjjuL4dI1IZECM1Px4Ss1YwqSC7ciPiZYAcOI11R8Cr7JLW/PJ3tXE3qnuZtBfCzp7BX24S29Wq8HXPFZ1hMqq4702CqYN5pdp2kaPnn8QtNvko6axMdepLECRKiLNSptiPjY6vjwMJXhCwAafa7dQ8mLJoiPt4Tiw1PrTcVnSBAfZ1O6vf+ZnWCKRqVsP5yoGG69slrIUFbxSVn9QnbfkPL4KEwryBkHM1HxmdJ1fFg4ZUq2rJhssFBXxJM3O4z7S3dmnwjwyaQZoUdLqKvOc6+lzqyuZhCfpV109b9uSekmsUBxm4JGKjdzbDr9cLS30/5vSEUbCHXRMeNZXaJWTh01n+weHwsRYOPd7qaERy8w4kM86HLo6WdWPi5WfOz2Aw478bEXm+V+Td7iZKxGfw8AZNySsiedQ4QQcawtJYjPdEtnn3lLfIWa0Bn24vUD9O+ZSHz87AYxFQ3EnqncpHSywUJdi/74Nbj0UQBAJrJoUg8h2EQjvNzk1Fuvx8dfS1aX86RVL05b1YNf/90GLKjQnqXZHh8BHq5rJNTls2Z1ccXH7o+pBvaWFVE5YyobBlKjaHWlAXih581QV7WKzzDz5nSUVHzM7z9kuIv64dk9PrUamwEgYwl1meHSdK6ATJ5mwwnFRyKPYZ972kULptfRKjQdVsVn5p0OJyztwKKOAM5a3XeoD6UIKqtLAlN8XOlRJIkX/zv3CazqWDGph8BDXc1Q4GSVp14i1eKn6dAEVRCKJis+gKn6lEOL3wPDrYs08aa1LeCfIV2/4hO0pbPHy2ROVYLd3GxRVDQ6TmGdEh9XgZKPUsSHk4Z0roB0joXh2LHZa/5wyIq10xh32jq015rKDgAZt7PHh+9L18zFgUyopluYC1DEZ9ZDllbtHapnApZ2hfDk3598qA/DEeuXdWFlXwR/8b45lTee6WidDwAg8z6Ac97aiLcKffjXST4fzVBXExQfiTzVWxPI49LxyNUngpAqyFOTPT7VQtM09ER8ot9fMypoAxDdwpEak+r4VN+rCzBrChWFuuogZ8UeH6k4IOHEJwUgDHdBNjcXExmZxMZSOWSZmuLWtZIZWHKoy4kE8/v4cCIDQkhdoa6shfiYf8vqFve+ycc53VLZAUV8Zj06pRh0oEoTnEJzcFh3CL+4dv2hPoypgZNuAFacA63vKET/5QkgnmneJFolzIa2TVB8dFnxqX9/8yuEmsw3kUNdk1sGoHciiI/BjO1NVHxGx2uoZGyDvWWFWdfGDeQZ8QGt3+MhTPEhzuZml64hZLgRT+cQTWZF/6+2oLekqV5uW+FE3LgpOpsniKZylhYT1SJbwuPjtC95DKej4qPMBbMcnZLiE2yWTK2gUCtcHmDuWkB3YQkLsVTylzQbrQF6LTSjWa+uayKUOSk1pA6R4gOYxlZdq8KLVC18ssenOcRn7wglZ3PbatsPIIW6nDw+TB0JakloKMAD+n4Fl7ck8YgIn0/ONDaX8PcAVsXHiVz6PC5BiIbiaanScvX39Jxc8dsh1CWTHflzTTdjM6AUn1kPOaY8E7uzK0w/fOvi92PvyDgO657cCfx9c1twxYalWLuwOdlkbl1DvkAmx7w+AR6fasENziHD3XAZAAH+GVLRus3N9lAX7yC/qERj2XIwQ100LGUhAyl6rIHCOLwwM7XCwVDJ8Qj7PMBYykJ8SqWyA1aPTylVbU6rDzsPxLFjX7SuUFfO41y5mWd0yeEt2TvU04Sq6ZMNRXxmOWSPjz1FUkHhUKArbDiGCCYauq7hi2c1z1DtAhp1AAAAGb9JREFUdVHT76QQH90FuH1ALjX5xIfV8qm143lZ8HDd+CBAWJuIehWfVA6EEEF8FnbUriT6i0JdkuKTpITBR5IwYLaMiIRLm8PNRqXZijV8AJvHp4Qyf/qqHuw8EMcjf9wnwra1mJt5j7eM7oPXZb6Hc6jLfF6FuhSmHWSPj1J8FBSaB7drkrP2eEr7pIe6TMWnaeChrtgB8zFvbUqNHOo6GEsjmc1D14B5bXUQn6JQl1TXxksJjlEYhwFKEnJER3ukNPGRG5Xy/lvlQ12yx8eZzHz0yLkAgK07B0RYrxbikwjMQ4p4cMBYbHl8zMEbZc3qmn6hLkV8Zjm6IwZaAx50hozm3rgUFGY5uNIzaXWajv0MsPQUoGf15LwfwwcWtaMj6MVJK7qat1NO3uL99LfuFrWeqkVYquOze5Bmhs1t89dVzFQuYFgoEFPx8bmFx8ebH4eh0cdLpbJzyI1KK7WrACpndQHA8t4wVvSGkc0TbNszCqDGUJevHevTm/HthV+3PG6v2mz/uxkNgicbaqab5TDcLmy5Zj1ckhlTQUGhcUw68Tn5hsl5Hxt6W3x4/sbTiorqNQTDpvjU6O8BTMWnQICdB2IAgIXttft7AKsans4VrHVymB/GnUsIxacS8ZEVH95RvVTVZsBmbi6zQP3oUXPw2i9fF//XksHm1jUcRBvGYSUyZjac+b6tfg86gl7kCUFPy/RTfBTxUcCc1tqzHBQUFMpDVOZ2z/wFRVNJDwDwPm25+jK6AGt5jlf2RQHU5+8BAJ9EPJLZvLUyMlOntEwcbV5qfi7Vp4vD9PjUntVVrpjlR943B1+ViE8tig8PzeZ4C3gGs52GIW2r43+uoTWmjEoNdKcgVKhLQUFBYQLAlZ5JSWefabD7lOpQfHRWLwcAduwbA1BfRhdAJ3r+PSbSOVFpOeIz09mRjqPNQz1AaeJcvJCDKz7RVLaqrK5KdXw45rcHLFmJLYFaiA/9fLm8lfiUMl/3tfin7aJZXZEKCgoKEwA+kTSj6emsg70IYx3EBwCCrPr3zv44gPoVHwDweej3eTCeBmHcIOxzAwHWUHX4TbR7yvfp4pAblQ6xxqLVhroqtS/5y6PMSvC11PHx6FzxKVge520wOssc33SDIj4KCgoKEwCvCHWp22zN8NmJT33KAvf58CabizrrU3wAs5fhQJRWaDbcOjU9zzsWCHYD40M4hTwHgBEfh87sHNx4PTKeEVWgy5mbvVV6fADg7DV9CPvcmNvqrylhhRP1rE3x4Q1Uyx3fdIPy+CgoKChMANyTbW6eSfAEAM1Vdw0fDjtJaKQaODc4H4hSBUT4Z1xu4MiNwDP/hg3pJwFUofiw1/LaQrpGDcOlYK3cXD581RkysOWa9fC69ZoKSrodFJ9UNo8ES+Evp0hNN6grUkFBQWEC4JnsOj4zCZpm9fnUWMOHQ27D0xvxibT0esBfe4ApPpYaOUd9km5D6HN53Sj7XlzxGWRhrraAt6xB3FLHp4rw1fz2QM2FBbm5WVZ8uL/H49IqKk3TCYr4KCgoKEwAzlnThyVdQRy7qP1QH8r0hBzuajDUBTTm7wEAP/P49HPiIxOQ7hW01xyHq3xYyJ5mXs7YDFRXublRuFlj3byU1SX8R0Gjee1IpgBmDoVTUFBQmEK4ZN0iXLJu0aE+jOkL2eBcp7lZVinqzeji4KGuAXuoi+PoTwLvvQgA0Dzl1Ra76bgS8fFOAvHhymQub4a6hhKVawxNRyjFR0FBQUFh6sFosuLT2ajiUybUBQCrz0dWY53qKxEf22s7yxihATPUFfC6JqzQrJO5eSheOdV+OkIRHwUFBQWFqQdf44qP7Iept2qzOBw78bGbjH0tGF5wJgCgva0N5WC4dYv3q2Koi4XZJrKtkFM6+7BIZZ85GV1AncTnrrvuwuLFi+Hz+bB27Vo89dRTZbffunUr1q5dC5/PhyVLluDuu+8u2ubBBx/EqlWrYBgGVq1ahYcfftjy/K233opjjz0W4XAY3d3dOPfcc/H6669btvn0pz8NTdMsP8cff3w9H1FBQUFB4VBCNjfXS3ya6vGhxIennztVRe45959ROOI8LD3rmrL70jTNQpwqEZ9l3SF0BL048bDOWg+7anAlKacUn2I88MADuO6663DjjTdi27ZtWL9+Pc466yzs2bPHcfvdu3fj7LPPxvr167Ft2zb84z/+I6655ho8+OCDYptnn30WGzduxCWXXII//elPuOSSS3DhhRfiueeeE9ts3boVV111FX7/+9/j8ccfRy6XwxlnnIFEImF5vw9/+MPYv3+/+NmyZUutH1FBQUFB4VCjGaEuqcdWw8THa83S4m0nLGhbCP2v7gX63ldxf2FJjarkoWkNePHcP56Kr114ZHUHWwdEqKsw8z0+Netmd955Jy677DJcfvnlAIDNmzfj0Ucfxbe//W3ceuutRdvffffdWLBgATZv3gwAWLlyJV544QXccccdOP/888U+Tj/9dNxwA22yd8MNN2Dr1q3YvHkzfvzjHwMAfvnLX1r2e++996K7uxsvvvgiPvShD4nHDcNAb29vrR9LQUFBQWEqoSmhLqqqdIa8FevfVILflp5eSwNQJ8g+n2oUFfcE14Piobd83imra2YRn5pGMpPJ4MUXX8QZZ5xhefyMM87AM8884/iaZ599tmj7M888Ey+88AKy2WzZbUrtEwDGxmjvlfZ2a6rok08+ie7ubhx++OH4zGc+g4GBgZL7SKfTiEajlh8FBQUFhSkASx2f+ogP75e1rDtcYcvKsNflqaUBqBNkxWcqhJJ4OntWSmc3G6jOYo/P4OAg8vk8enp6LI/39PSgv7/f8TX9/f2O2+dyOQwODpbdptQ+CSHYtGkTTjzxRKxevVo8ftZZZ+G+++7DE088ga997Wt4/vnnccoppyCdTjvu59Zbb0VLS4v4mT9/fvkBUFBQUFCYHDQh1PXBwzpx+/lr8M8fW1154wooDnU1qPhIitFUIBb888VTORDWjGyQe3xme6gLQFEhI0JI2eJGTtvbH69ln1dffTVefvll/O53v7M8vnHjRvH36tWrccwxx2DhwoX4+c9/jvPOO69oPzfccAM2bdok/o9Go4r8KCgoKEwF+FrMv+sMdbl0DRuPXdCUw2l2qGuqKT5zW/1w6RqS2TwORNPobfEJxadzChCzZqIm4tPZ2QmXy1WkxAwMDBQpNhy9vb2O27vdbnR0dJTdxmmfn/vc5/DII4/gt7/9LebNm1f2ePv6+rBw4UK88cYbjs8bhgHDmFlfqIKCgsKMgCWrqz7Fp5koIj5O5uYaIBOntkBjJKoZ8Lp1LGgPYPdgAm8ejCPidyOZpX26ZpriU1Ooy+v1Yu3atXj88cctjz/++OM44YQTHF+zbt26ou0fe+wxHHPMMfB4PGW3kfdJCMHVV1+Nhx56CE888QQWL15c8XiHhoawd+9e9PX1VfX5FBQUFBSmCCyhrsZq8DQDPm+zPT709W0Bz4Qbl6vF0i46zm8djItUdsOtW7LjZgJqHu1Nmzbhu9/9Lu655x68+uqr+PznP489e/bgiiuuAEDDR5deeqnY/oorrsA777yDTZs24dVXX8U999yD733ve7j++uvFNtdeey0ee+wx3H777Xjttddw++2341e/+hWuu+46sc1VV12FH/3oR7j//vsRDofR39+P/v5+JJNJAEA8Hsf111+PZ599Fm+//TaefPJJfOQjH0FnZyc+9rGP1Ts+CgoKCgqHAk3o1dVM2BWfRosJcsVoKoS5OJZ0hQAAbx5MmKnsQe+M6tMF1OHx2bhxI4aGhnDLLbdg//79WL16NbZs2YKFCxcCAPbv32+p6bN48WJs2bIFn//85/Gtb30Lc+bMwTe+8Q2Ryg4AJ5xwAn7yk5/gS1/6Er785S9j6dKleOCBB3DccceJbb797W8DAE466STL8dx777349Kc/DZfLhe3bt+OHP/whRkdH0dfXh5NPPhkPPPAAwuHGHf0KCgoKCpOIKRzqChnuhlUarhh1TKGqyEs6qeLz5sG4qNo8lY6vWaiLsl555ZW48sorHZ/7/ve/X/TYhg0b8NJLL5Xd5wUXXIALLrig5PPcEF0Kfr8fjz76aNltFBQUFBSmCZrQpLSZ8HtNotNomAsATlrejdNX9eCv1pb3qk4muOLz1sGEmdE1hRSpZkF1Z1dQUFBQmHrwtQD+NiCXAfyth/po4PeY02XY1/jU2R704t8vPabh/TQT3OOzbyyJ90aojWSmVW0GFPFRUFBQUJiK0F3AX/8SKGSnRqhLMvg2WsNnqqI96EWL34OxZBYvvjMCYOY1KAVUd3YFBQUFhamK7hVA75pDfRQArB6fZoS6piI0TcMSpvq8tIcSn5kY6lLER0FBQUFBoQJk4tNo8cKpjKXM5zOeoTV8ZlqfLkARHwUFBQUFhYrwSebmRosXTmVwxYdjJnp8FPFRUFBQUFCoAK9Lh87K2cxkxWdJZ8jy/1ToI9ZsKOKjoKCgoKBQAZqmiXDXTPX4AGZmF4fy+CgoKCgoKMxS8MyumZrVBQALOgJw6WalZhXqUlBQUFBQmKXwMcUn0oQ6PlMVhtuF+W20fIDf40LAO/M+qyI+CgoKCgoKVaA7TP0uc9sOfV2hiQSv4DwT1R5AFTBUUFBQUFCoCl+78Ci83h/DEXNaDvWhTCiWdAbxBGZmKjugiI+CgoKCgkJVWNwZxOLOYOUNpzlW9tE+aTNV2VLER0FBQUFBQUHgL47sQyKTw0mHdx/qQ5kQKOKjoKCgoKCgIGC4Xbh03aJDfRgTBmVuVlBQUFBQUJg1UMRHQUFBQUFBYdZAER8FBQUFBQWFWQNFfBQUFBQUFBRmDRTxUVBQUFBQUJg1UMRHQUFBQUFBYdZAER8FBQUFBQWFWQNFfBQUFBQUFBRmDRTxUVBQUFBQUJg1UMRHQUFBQUFBYdZAER8FBQUFBQWFWQNFfBQUFBQUFBRmDRTxUVBQUFBQUJg1UN3ZJRBCAADRaPQQH4mCgoKCgoJCteDzNp/Hy0ERHwmxWAwAMH/+/EN8JAoKCgoKCgq1IhaLoaWlpew2GqmGHs0SFAoF7Nu3D+FwGJqmNXXf0WgU8+fPx969exGJRJq679kMNa71QY3bxEGNbfOgxnJiMBPHlRCCWCyGOXPmQNfLu3iU4iNB13XMmzdvQt8jEonMmBNtKkGNa31Q4zZxUGPbPKixnBjMtHGtpPRwKHOzgoKCgoKCwqyBIj4KCgoKCgoKswaK+EwSDMPATTfdBMMwDvWhzCioca0PatwmDmpsmwc1lhOD2T6uytysoKCgoKCgMGugFB8FBQUFBQWFWQNFfBQUFBQUFBRmDRTxUVBQUFBQUJg1UMRHQUFBQUFBYdZgVhOfW2+9FcceeyzC4TC6u7tx7rnn4vXXX7dsQwjBzTffjDlz5sDv9+Okk07CK6+8Ip4fHh7G5z73OSxfvhyBQAALFizANddcg7GxMct+PvrRj2LBggXw+Xzo6+vDJZdcgn379lU8xu3bt2PDhg3w+/2YO3cubrnlFksvkv379+Piiy/G8uXLoes6rrvuusYGpQmYCeP60EMP4fTTT0dXVxcikQjWrVuHRx99tMGRKY+ZMG6/+93v8MEPfhAdHR3w+/1YsWIFvv71rzc4Mo1hJoyrjKeffhputxtHHXVU7YPRBMyE8XzyySehaVrRz2uvvdbg6NSPmTCuAJBOp3HjjTdi4cKFMAwDS5cuxT333NPAyEwAyCzGmWeeSe69916yY8cO8sc//pGcc845ZMGCBSQej4ttbrvtNhIOh8mDDz5Itm/fTjZu3Ej6+vpINBolhBCyfft2ct5555FHHnmE7Nq1i/z6178my5YtI+eff77lve68807y7LPPkrfffps8/fTTZN26dWTdunVlj29sbIz09PSQj3/842T79u3kwQcfJOFwmNxxxx1im927d5NrrrmG/OAHPyBHHXUUufbaa5s3QHViJozrtddeS26//Xbyhz/8gezcuZPccMMNxOPxkJdeeqmJI2XFTBi3l156idx///1kx44dZPfu3eQ//uM/SCAQIN/5zneaOFK1YSaMK8fo6ChZsmQJOeOMM8iRRx7Z+ODUgZkwnr/5zW8IAPL666+T/fv3i59cLtfEkaoNM2FcCSHkox/9KDnuuOPI448/Tnbv3k2ee+458vTTTzdplJqDWU187BgYGCAAyNatWwkhhBQKBdLb20tuu+02sU0qlSItLS3k7rvvLrmfn/70p8Tr9ZJsNltym5/97GdE0zSSyWRKbnPXXXeRlpYWkkqlxGO33normTNnDikUCkXbb9iwYUoQHzum+7hyrFq1inzlK18p+XyzMVPG7WMf+xj55Cc/WfL5ycZ0HteNGzeSL33pS+Smm246ZMTHjuk4npz4jIyMVPsxJx3TcVx/8YtfkJaWFjI0NFT15zwUmNWhLju4HNje3g4A2L17N/r7+3HGGWeIbQzDwIYNG/DMM8+U3U8kEoHb7dwKbXh4GPfddx9OOOEEeDyekvt59tlnsWHDBkuRqTPPPBP79u3D22+/XctHO6SYCeNaKBQQi8XEZ5gMzIRx27ZtG5555hls2LCh5H4nG9N1XO+99168+eabuOmmm6r6nJOF6TqeAHD00Uejr68Pp556Kn7zm99U/KyTiek4ro888giOOeYYfPWrX8XcuXNx+OGH4/rrr0cymaz6c08GFPFhIIRg06ZNOPHEE7F69WoAQH9/PwCgp6fHsm1PT494zo6hoSH80z/9E/72b/+26Ll/+Id/QDAYREdHB/bs2YOf/exnZY+pv7/f8b3lY5vqmCnj+rWvfQ2JRAIXXnhh2X03C9N93ObNmwfDMHDMMcfgqquuwuWXX15235OF6Tqub7zxBr74xS/ivvvuKzmBHQpM1/Hs6+vD//2//xcPPvggHnroISxfvhynnnoqfvvb31bxqSce03Vc33rrLfzud7/Djh078PDDD2Pz5s34r//6L1x11VVVfOrJgyI+DFdffTVefvll/PjHPy56TtM0y/+EkKLHACAajeKcc87BqlWrHFdlf//3f49t27bhscceg8vlwqWXXiqMYUcccQRCoRBCoRDOOuussu/t9PhUxUwY1x//+Me4+eab8cADD6C7u7uKT904pvu4PfXUU3jhhRdw9913Y/PmzY6f41BgOo5rPp/HxRdfjK985Ss4/PDDa//QE4jpOJ4AsHz5cnzmM5/B+9//fqxbtw533XUXzjnnHNxxxx01jsDEYLqOa6FQgKZpuO+++/CBD3wAZ599Nu688058//vfn1Kqz9RZOhxCfO5zn8MjjzyC3/72t5g3b554vLe3FwBls319feLxgYGBIuYbi8Xw4Q9/GKFQCA8//LCjZNjZ2YnOzk4cfvjhWLlyJebPn4/f//73WLduHbZs2YJsNgsA8Pv94v3tTH5gYABAMeufipgJ4/rAAw/gsssuw3/+53/itNNOq3coasJMGLfFixcDANasWYMDBw7g5ptvxkUXXVTXeDQL03VcY7EYXnjhBWzbtg1XX301ADrBEELgdrvx2GOP4ZRTTml0eGrGdB3PUjj++OPxox/9qJYhmBBM53Ht6+vD3Llz0dLSIrZZuXIlCCF49913sWzZsrrHpamYLDPRVEShUCBXXXUVmTNnDtm5c6fj8729veT2228Xj6XT6SIz2djYGDn++OPJhg0bSCKRqOq99+zZQwCQ3/zmNyW3ueuuu0hraytJp9Pisdtuu23Km5tnyrjef//9xOfzkYcffriq924UM2Xc7LjlllvIwoULqzqOicB0H9d8Pk+2b99u+fnsZz9Lli9fTrZv327J+pkMTPfxLIXzzz+fnHzyyVUdx0RgJozrd77zHeL3+0ksFhPb/Pd//zfRdZ2Mj49XdSyTgVlNfD772c+SlpYW8uSTT1pSGuUv6LbbbiMtLS3koYceItu3bycXXXSRJX0wGo2S4447jqxZs4bs2rXLMTXyueeeI//2b/9Gtm3bRt5++23yxBNPkBNPPJEsXbrU4pC3Y3R0lPT09JCLLrqIbN++nTz00EMkEokUpQ9u27aNbNu2jaxdu5ZcfPHFZNu2beSVV16ZgBGrDjNhXO+//37idrvJt771Lct7j46OTtCozYxx++Y3v0keeeQRsnPnTrJz505yzz33kEgkQm688cYJGrXKmAnjasehzOqaCeP59a9/nTz88MNk586dZMeOHeSLX/wiAUAefPDBCRq1ypgJ4xqLxci8efPIBRdcQF555RWydetWsmzZMnL55ZdP0KjVh1lNfAA4/tx7771im0KhQG666SbS29tLDMMgH/rQh8j27dvF8zwt0uln9+7dhBBCXn75ZXLyySeT9vZ2YhgGWbRoEbniiivIu+++W/EYX375ZbJ+/XpiGAbp7e0lN998c9Gqxem9D+UKeyaM64YNGxzf+1Of+lSzhqkIM2HcvvGNb5AjjjiCBAIBEolEyNFHH03uuusuks/nmzZOtWImjKsdh5L4zITxvP3228nSpUuJz+cjbW1t5MQTTyQ///nPmzZG9WAmjCshhLz66qvktNNOI36/n8ybN49s2rRpSqk9hBCiEVKiPKiCgoKCgoKCwgyDyupSUFBQUFBQmDVQxEdBQUFBQUFh1kARHwUFBQUFBYVZA0V8FBQUFBQUFGYNFPFRUFBQUFBQmDVQxEdBQUFBQUFh1kARHwUFBQUFBYVZA0V8FBQUFBQUFGYNFPFRUFBQUFBQmDVQxEdBQUFBQUFh1kARHwUFBQUFBYVZA0V8FBQUFBQUFGYN/n+rtiHk2GmERQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_sm_pred_df = pd.DataFrame(y_sm_pred, columns=y_train.columns).set_index(y_test.index).sort_index()\n", "y_sm_test_df = pd.DataFrame(y_sm_test, columns=y_train.columns).set_index(y_test.index).sort_index()\n", "\n", "plt.plot(y_sm_test_df, label='actual')\n", "plt.plot(y_sm_pred_df, label='forecast')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "id": "da676de4-813a-4926-b784-07fa573cc39f", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "linear-learner-2023-06-14-20-50-14-157\n" ] } ], "source": [ "endpoint_name = llearner_predictor.endpoint_name\n", "print(endpoint_name)" ] }, { "cell_type": "markdown", "id": "fe139412-7797-416a-8e28-a9be4c3fb7bf", "metadata": {}, "source": [ "# Clean up" ] }, { "cell_type": "code", "execution_count": null, "id": "12a14236-90a6-45ac-a36f-9b354b509940", "metadata": {}, "outputs": [], "source": [ "## Delete the endpoint \n", "\n", "## llearner_predictor.delete_endpoint()" ] } ], "metadata": { "availableInstances": [ { "_defaultOrder": 0, "_isFastLaunch": true, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 4, "name": "ml.t3.medium", "vcpuNum": 2 }, { "_defaultOrder": 1, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 8, "name": "ml.t3.large", "vcpuNum": 2 }, { "_defaultOrder": 2, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 16, "name": "ml.t3.xlarge", "vcpuNum": 4 }, { "_defaultOrder": 3, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 32, "name": "ml.t3.2xlarge", "vcpuNum": 8 }, { "_defaultOrder": 4, "_isFastLaunch": true, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 8, "name": "ml.m5.large", "vcpuNum": 2 }, { "_defaultOrder": 5, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 16, "name": "ml.m5.xlarge", "vcpuNum": 4 }, { "_defaultOrder": 6, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 32, "name": "ml.m5.2xlarge", "vcpuNum": 8 }, { "_defaultOrder": 7, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 64, "name": "ml.m5.4xlarge", "vcpuNum": 16 }, { "_defaultOrder": 8, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 128, "name": "ml.m5.8xlarge", "vcpuNum": 32 }, { "_defaultOrder": 9, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 192, "name": "ml.m5.12xlarge", "vcpuNum": 48 }, { "_defaultOrder": 10, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 256, "name": "ml.m5.16xlarge", "vcpuNum": 64 }, { "_defaultOrder": 11, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 384, "name": "ml.m5.24xlarge", "vcpuNum": 96 }, { "_defaultOrder": 12, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 8, "name": "ml.m5d.large", "vcpuNum": 2 }, { "_defaultOrder": 13, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 16, "name": "ml.m5d.xlarge", "vcpuNum": 4 }, { "_defaultOrder": 14, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 32, "name": "ml.m5d.2xlarge", "vcpuNum": 8 }, { "_defaultOrder": 15, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 64, "name": "ml.m5d.4xlarge", "vcpuNum": 16 }, { "_defaultOrder": 16, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 128, "name": "ml.m5d.8xlarge", "vcpuNum": 32 }, { "_defaultOrder": 17, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 192, "name": "ml.m5d.12xlarge", "vcpuNum": 48 }, { "_defaultOrder": 18, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 256, "name": "ml.m5d.16xlarge", "vcpuNum": 64 }, { "_defaultOrder": 19, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 384, "name": "ml.m5d.24xlarge", "vcpuNum": 96 }, { "_defaultOrder": 20, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": true, "memoryGiB": 0, "name": "ml.geospatial.interactive", "supportedImageNames": [ "sagemaker-geospatial-v1-0" ], "vcpuNum": 0 }, { "_defaultOrder": 21, "_isFastLaunch": true, "category": "Compute optimized", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 4, "name": "ml.c5.large", "vcpuNum": 2 }, { "_defaultOrder": 22, "_isFastLaunch": false, "category": "Compute optimized", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 8, "name": "ml.c5.xlarge", "vcpuNum": 4 }, { "_defaultOrder": 23, "_isFastLaunch": false, "category": "Compute optimized", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 16, "name": "ml.c5.2xlarge", "vcpuNum": 8 }, { "_defaultOrder": 24, "_isFastLaunch": false, "category": "Compute optimized", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 32, "name": "ml.c5.4xlarge", "vcpuNum": 16 }, { "_defaultOrder": 25, "_isFastLaunch": false, "category": "Compute optimized", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 72, "name": "ml.c5.9xlarge", "vcpuNum": 36 }, { "_defaultOrder": 26, "_isFastLaunch": false, "category": "Compute optimized", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 96, "name": "ml.c5.12xlarge", "vcpuNum": 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computing", "gpuNum": 1, "hideHardwareSpecs": false, "memoryGiB": 128, "name": "ml.g4dn.8xlarge", "vcpuNum": 32 }, { "_defaultOrder": 33, "_isFastLaunch": false, "category": "Accelerated computing", "gpuNum": 4, "hideHardwareSpecs": false, "memoryGiB": 192, "name": "ml.g4dn.12xlarge", "vcpuNum": 48 }, { "_defaultOrder": 34, "_isFastLaunch": false, "category": "Accelerated computing", "gpuNum": 1, "hideHardwareSpecs": false, "memoryGiB": 256, "name": "ml.g4dn.16xlarge", "vcpuNum": 64 }, { "_defaultOrder": 35, "_isFastLaunch": false, "category": "Accelerated computing", "gpuNum": 1, "hideHardwareSpecs": false, "memoryGiB": 61, "name": "ml.p3.2xlarge", "vcpuNum": 8 }, { "_defaultOrder": 36, "_isFastLaunch": false, "category": "Accelerated computing", "gpuNum": 4, "hideHardwareSpecs": false, "memoryGiB": 244, "name": "ml.p3.8xlarge", "vcpuNum": 32 }, { "_defaultOrder": 37, "_isFastLaunch": false, "category": "Accelerated computing", "gpuNum": 8, "hideHardwareSpecs": false, "memoryGiB": 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