mirror of
https://github.com/vale981/ray
synced 2025-03-10 13:26:39 -04:00

This PR updates the Ray AIR/Tune ipynb examples to use the Tuner() API instead of tune.run(). Signed-off-by: Kai Fricke <kai@anyscale.com> Signed-off-by: Richard Liaw <rliaw@berkeley.edu> Signed-off-by: Xiaowei Jiang <xwjiang2010@gmail.com> Signed-off-by: Kai Fricke <coding@kaifricke.com> Co-authored-by: Richard Liaw <rliaw@berkeley.edu> Co-authored-by: Xiaowei Jiang <xwjiang2010@gmail.com>
1398 lines
63 KiB
Text
1398 lines
63 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3b05af3b",
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"metadata": {},
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"source": [
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"(tune-mnist-keras)=\n",
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"\n",
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"# Using Keras & TensorFlow with Tune\n",
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"\n",
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"```{image} /images/tf_keras_logo.jpeg\n",
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":align: center\n",
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":alt: Keras & TensorFlow Logo\n",
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":height: 120px\n",
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":target: https://keras.io\n",
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"```\n",
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"\n",
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"```{contents}\n",
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":backlinks: none\n",
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":local: true\n",
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"```\n",
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"\n",
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"## Example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "19e3c389",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2022-07-22 16:16:58,114\tINFO services.py:1483 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8269\u001b[39m\u001b[22m\n",
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"2022-07-22 16:17:00,822\tWARNING function_trainable.py:619 -- Function checkpointing is disabled. This may result in unexpected behavior when using checkpointing features or certain schedulers. To enable, set the train function arguments to be `func(config, checkpoint_dir=None)`.\n"
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]
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},
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{
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"data": {
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"text/html": [
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"== Status ==<br>Current time: 2022-07-22 16:18:36 (running for 00:01:35.04)<br>Memory usage on this node: 9.0/16.0 GiB<br>Using AsyncHyperBand: num_stopped=0\n",
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"Bracket: Iter 320.000: None | Iter 80.000: None | Iter 20.000: None<br>Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/5.47 GiB heap, 0.0/2.0 GiB objects<br>Current best trial: 55a9b_00002 with mean_accuracy=0.9904166460037231 and parameters={'threads': 2, 'lr': 0.09518133271957563, 'momentum': 0.8254987643140009, 'hidden': 258}<br>Result logdir: /Users/kai/ray_results/exp<br>Number of trials: 10/10 (10 TERMINATED)<br><table>\n",
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"<thead>\n",
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"<tr><th>Trial name </th><th>status </th><th>loc </th><th style=\"text-align: right;\"> hidden</th><th style=\"text-align: right;\"> lr</th><th style=\"text-align: right;\"> momentum</th><th style=\"text-align: right;\"> acc</th><th style=\"text-align: right;\"> iter</th><th style=\"text-align: right;\"> total time (s)</th></tr>\n",
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"</thead>\n",
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"<tbody>\n",
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"<tr><td>train_mnist_55a9b_00000</td><td>TERMINATED</td><td>127.0.0.1:51968</td><td style=\"text-align: right;\"> 276</td><td style=\"text-align: right;\">0.0406397 </td><td style=\"text-align: right;\"> 0.817788</td><td style=\"text-align: right;\">0.98455 </td><td style=\"text-align: right;\"> 12</td><td style=\"text-align: right;\"> 78.3252</td></tr>\n",
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"<tr><td>train_mnist_55a9b_00001</td><td>TERMINATED</td><td>127.0.0.1:51977</td><td style=\"text-align: right;\"> 380</td><td style=\"text-align: right;\">0.0873557 </td><td style=\"text-align: right;\"> 0.524634</td><td style=\"text-align: right;\">0.983717</td><td style=\"text-align: right;\"> 12</td><td style=\"text-align: right;\"> 74.9888</td></tr>\n",
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"<tr><td>train_mnist_55a9b_00002</td><td>TERMINATED</td><td>127.0.0.1:51984</td><td style=\"text-align: right;\"> 258</td><td style=\"text-align: right;\">0.0951813 </td><td style=\"text-align: right;\"> 0.825499</td><td style=\"text-align: right;\">0.990417</td><td style=\"text-align: right;\"> 11</td><td style=\"text-align: right;\"> 64.1272</td></tr>\n",
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"<tr><td>train_mnist_55a9b_00003</td><td>TERMINATED</td><td>127.0.0.1:51991</td><td style=\"text-align: right;\"> 255</td><td style=\"text-align: right;\">0.0971683 </td><td style=\"text-align: right;\"> 0.23161 </td><td style=\"text-align: right;\">0.977633</td><td style=\"text-align: right;\"> 12</td><td style=\"text-align: right;\"> 60.8475</td></tr>\n",
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"<tr><td>train_mnist_55a9b_00004</td><td>TERMINATED</td><td>127.0.0.1:52000</td><td style=\"text-align: right;\"> 303</td><td style=\"text-align: right;\">0.00440117</td><td style=\"text-align: right;\"> 0.325439</td><td style=\"text-align: right;\">0.90775 </td><td style=\"text-align: right;\"> 12</td><td style=\"text-align: right;\"> 55.5722</td></tr>\n",
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"<tr><td>train_mnist_55a9b_00005</td><td>TERMINATED</td><td>127.0.0.1:52007</td><td style=\"text-align: right;\"> 92</td><td style=\"text-align: right;\">0.0651919 </td><td style=\"text-align: right;\"> 0.710183</td><td style=\"text-align: right;\">0.974867</td><td style=\"text-align: right;\"> 12</td><td style=\"text-align: right;\"> 44.8092</td></tr>\n",
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"<tr><td>train_mnist_55a9b_00006</td><td>TERMINATED</td><td>127.0.0.1:52016</td><td style=\"text-align: right;\"> 211</td><td style=\"text-align: right;\">0.0731116 </td><td style=\"text-align: right;\"> 0.127751</td><td style=\"text-align: right;\">0.97025 </td><td style=\"text-align: right;\"> 12</td><td style=\"text-align: right;\"> 42.1217</td></tr>\n",
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"<tr><td>train_mnist_55a9b_00007</td><td>TERMINATED</td><td>127.0.0.1:52021</td><td style=\"text-align: right;\"> 181</td><td style=\"text-align: right;\">0.0362389 </td><td style=\"text-align: right;\"> 0.790345</td><td style=\"text-align: right;\">0.979967</td><td style=\"text-align: right;\"> 12</td><td style=\"text-align: right;\"> 41.7632</td></tr>\n",
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"<tr><td>train_mnist_55a9b_00008</td><td>TERMINATED</td><td>127.0.0.1:52007</td><td style=\"text-align: right;\"> 142</td><td style=\"text-align: right;\">0.0323741 </td><td style=\"text-align: right;\"> 0.660418</td><td style=\"text-align: right;\">0.969367</td><td style=\"text-align: right;\"> 12</td><td style=\"text-align: right;\"> 14.1527</td></tr>\n",
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"<tr><td>train_mnist_55a9b_00009</td><td>TERMINATED</td><td>127.0.0.1:51984</td><td style=\"text-align: right;\"> 97</td><td style=\"text-align: right;\">0.0244971 </td><td style=\"text-align: right;\"> 0.175045</td><td style=\"text-align: right;\">0.9407 </td><td style=\"text-align: right;\"> 12</td><td style=\"text-align: right;\"> 12.6405</td></tr>\n",
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"</tbody>\n",
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"</table><br><br>"
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],
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"text/plain": [
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2022-07-22 16:17:01,834\tINFO plugin_schema_manager.py:52 -- Loading the default runtime env schemas: ['/Users/kai/coding/ray/python/ray/_private/runtime_env/../../runtime_env/schemas/working_dir_schema.json', '/Users/kai/coding/ray/python/ray/_private/runtime_env/../../runtime_env/schemas/pip_schema.json'].\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51968)\u001b[0m 2022-07-22 16:17:08.627419: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51968)\u001b[0m To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51968)\u001b[0m /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51968)\u001b[0m \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51968)\u001b[0m 2022-07-22 16:17:08.947939: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51977)\u001b[0m 2022-07-22 16:17:14.473677: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51977)\u001b[0m To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51977)\u001b[0m /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51977)\u001b[0m \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51977)\u001b[0m 2022-07-22 16:17:14.635104: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51984)\u001b[0m 2022-07-22 16:17:20.406624: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51984)\u001b[0m To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51984)\u001b[0m /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51984)\u001b[0m \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51984)\u001b[0m 2022-07-22 16:17:20.681960: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51991)\u001b[0m 2022-07-22 16:17:26.109460: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51991)\u001b[0m To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51991)\u001b[0m /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51991)\u001b[0m \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n",
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"\u001b[2m\u001b[36m(train_mnist pid=51991)\u001b[0m 2022-07-22 16:17:26.303375: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52000)\u001b[0m 2022-07-22 16:17:31.899252: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52000)\u001b[0m To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52000)\u001b[0m /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52000)\u001b[0m \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52000)\u001b[0m 2022-07-22 16:17:32.300424: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52007)\u001b[0m 2022-07-22 16:17:37.937471: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52007)\u001b[0m To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52007)\u001b[0m /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52007)\u001b[0m \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52007)\u001b[0m 2022-07-22 16:17:38.263888: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52016)\u001b[0m 2022-07-22 16:17:43.657379: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52016)\u001b[0m To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52016)\u001b[0m /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52016)\u001b[0m \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52016)\u001b[0m 2022-07-22 16:17:43.828809: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Result for train_mnist_55a9b_00000:\n",
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" date: 2022-07-22_16-17-10\n",
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" done: false\n",
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" experiment_id: 3659349c38c746cfb71b4db5eb9302a0\n",
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" hostname: Kais-MacBook-Pro.local\n",
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" iterations_since_restore: 1\n",
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" mean_accuracy: 0.8903833627700806\n",
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" node_ip: 127.0.0.1\n",
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" pid: 51968\n",
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" time_since_restore: 2.439258098602295\n",
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" time_this_iter_s: 2.439258098602295\n",
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" time_total_s: 2.439258098602295\n",
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" timestamp: 1658503030\n",
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" timesteps_since_restore: 0\n",
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" training_iteration: 1\n",
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" trial_id: 55a9b_00000\n",
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" warmup_time: 0.003445863723754883\n",
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" \n",
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"Result for train_mnist_55a9b_00004:\n",
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" date: 2022-07-22_16-17-33\n",
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" done: false\n",
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" experiment_id: 6eb62b7cb38f442a867a9094f0664701\n",
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" hostname: Kais-MacBook-Pro.local\n",
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" iterations_since_restore: 1\n",
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" mean_accuracy: 0.6376166939735413\n",
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" node_ip: 127.0.0.1\n",
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" pid: 52000\n",
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" time_since_restore: 2.4364511966705322\n",
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" time_this_iter_s: 2.4364511966705322\n",
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" time_total_s: 2.4364511966705322\n",
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" timestamp: 1658503053\n",
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" timesteps_since_restore: 0\n",
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" training_iteration: 1\n",
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" trial_id: 55a9b_00004\n",
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" warmup_time: 0.0030939579010009766\n",
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" \n",
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"Result for train_mnist_55a9b_00006:\n",
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" date: 2022-07-22_16-17-45\n",
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" done: false\n",
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" experiment_id: 9594405e38084311a891b48addd13f75\n",
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" hostname: Kais-MacBook-Pro.local\n",
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" iterations_since_restore: 1\n",
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" mean_accuracy: 0.8557000160217285\n",
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" node_ip: 127.0.0.1\n",
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" pid: 52016\n",
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" time_since_restore: 1.8570480346679688\n",
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" time_this_iter_s: 1.8570480346679688\n",
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" time_total_s: 1.8570480346679688\n",
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" timestamp: 1658503065\n",
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" timesteps_since_restore: 0\n",
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" training_iteration: 1\n",
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" trial_id: 55a9b_00006\n",
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" warmup_time: 0.003566741943359375\n",
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" \n",
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"Result for train_mnist_55a9b_00001:\n",
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" date: 2022-07-22_16-17-15\n",
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" done: false\n",
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" experiment_id: fcdeb049f9614755a9b7c9420ca2ae5e\n",
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" hostname: Kais-MacBook-Pro.local\n",
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" iterations_since_restore: 1\n",
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" mean_accuracy: 0.8887666463851929\n",
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" node_ip: 127.0.0.1\n",
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" pid: 51977\n",
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" time_since_restore: 1.9353628158569336\n",
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" time_this_iter_s: 1.9353628158569336\n",
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" time_total_s: 1.9353628158569336\n",
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" timestamp: 1658503035\n",
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" timesteps_since_restore: 0\n",
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" training_iteration: 1\n",
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" trial_id: 55a9b_00001\n",
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" warmup_time: 0.0029449462890625\n",
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" \n",
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"Result for train_mnist_55a9b_00005:\n",
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" date: 2022-07-22_16-17-39\n",
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" done: false\n",
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" experiment_id: 8dbd22e6caed4fe39351dffa3ef14eac\n",
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" hostname: Kais-MacBook-Pro.local\n",
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" iterations_since_restore: 1\n",
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" mean_accuracy: 0.8789666891098022\n",
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" node_ip: 127.0.0.1\n",
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" pid: 52007\n",
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" time_since_restore: 2.3337321281433105\n",
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" time_this_iter_s: 2.3337321281433105\n",
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" time_total_s: 2.3337321281433105\n",
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" timestamp: 1658503059\n",
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" timesteps_since_restore: 0\n",
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" training_iteration: 1\n",
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" trial_id: 55a9b_00005\n",
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" warmup_time: 0.005449056625366211\n",
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" \n",
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"Result for train_mnist_55a9b_00002:\n",
|
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" date: 2022-07-22_16-17-21\n",
|
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" done: false\n",
|
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" experiment_id: c4f803baf65f4d4e9fd6abc85b2fd00c\n",
|
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" hostname: Kais-MacBook-Pro.local\n",
|
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" iterations_since_restore: 1\n",
|
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" mean_accuracy: 0.9112833142280579\n",
|
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" node_ip: 127.0.0.1\n",
|
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" pid: 51984\n",
|
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" time_since_restore: 2.3220012187957764\n",
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" time_this_iter_s: 2.3220012187957764\n",
|
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" time_total_s: 2.3220012187957764\n",
|
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" timestamp: 1658503041\n",
|
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" timesteps_since_restore: 0\n",
|
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" training_iteration: 1\n",
|
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" trial_id: 55a9b_00002\n",
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" warmup_time: 0.0028328895568847656\n",
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" \n",
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"Result for train_mnist_55a9b_00003:\n",
|
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" date: 2022-07-22_16-17-27\n",
|
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" done: false\n",
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" experiment_id: 469478f02b4a43f5b44c40e59989ad39\n",
|
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" hostname: Kais-MacBook-Pro.local\n",
|
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" iterations_since_restore: 1\n",
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" mean_accuracy: 0.8743166923522949\n",
|
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" node_ip: 127.0.0.1\n",
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" pid: 51991\n",
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" time_since_restore: 2.0278611183166504\n",
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" time_this_iter_s: 2.0278611183166504\n",
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" time_total_s: 2.0278611183166504\n",
|
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" timestamp: 1658503047\n",
|
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" timesteps_since_restore: 0\n",
|
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" training_iteration: 1\n",
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" trial_id: 55a9b_00003\n",
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" warmup_time: 0.0033779144287109375\n",
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" \n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[2m\u001b[36m(train_mnist pid=52021)\u001b[0m 2022-07-22 16:17:51.567914: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
|
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"\u001b[2m\u001b[36m(train_mnist pid=52021)\u001b[0m To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
|
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"\u001b[2m\u001b[36m(train_mnist pid=52021)\u001b[0m /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
|
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"\u001b[2m\u001b[36m(train_mnist pid=52021)\u001b[0m \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n",
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"\u001b[2m\u001b[36m(train_mnist pid=52021)\u001b[0m 2022-07-22 16:17:52.977183: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
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]
|
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Result for train_mnist_55a9b_00005:\n",
|
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" date: 2022-07-22_16-17-54\n",
|
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" done: false\n",
|
|
" experiment_id: 8dbd22e6caed4fe39351dffa3ef14eac\n",
|
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" hostname: Kais-MacBook-Pro.local\n",
|
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" iterations_since_restore: 3\n",
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" mean_accuracy: 0.9490833282470703\n",
|
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" node_ip: 127.0.0.1\n",
|
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" pid: 52007\n",
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" time_since_restore: 17.22033405303955\n",
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" time_this_iter_s: 2.672102928161621\n",
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" time_total_s: 17.22033405303955\n",
|
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" timestamp: 1658503074\n",
|
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" timesteps_since_restore: 0\n",
|
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" training_iteration: 3\n",
|
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" trial_id: 55a9b_00005\n",
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" warmup_time: 0.005449056625366211\n",
|
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" \n",
|
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"Result for train_mnist_55a9b_00006:\n",
|
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" date: 2022-07-22_16-17-54\n",
|
|
" done: false\n",
|
|
" experiment_id: 9594405e38084311a891b48addd13f75\n",
|
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" hostname: Kais-MacBook-Pro.local\n",
|
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" iterations_since_restore: 3\n",
|
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" mean_accuracy: 0.9327999949455261\n",
|
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" node_ip: 127.0.0.1\n",
|
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" pid: 52016\n",
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" time_since_restore: 11.758372068405151\n",
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" time_this_iter_s: 3.0426323413848877\n",
|
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" time_total_s: 11.758372068405151\n",
|
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" timestamp: 1658503074\n",
|
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" timesteps_since_restore: 0\n",
|
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" training_iteration: 3\n",
|
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" trial_id: 55a9b_00006\n",
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" warmup_time: 0.003566741943359375\n",
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" \n",
|
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"Result for train_mnist_55a9b_00003:\n",
|
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" date: 2022-07-22_16-17-55\n",
|
|
" done: false\n",
|
|
" experiment_id: 469478f02b4a43f5b44c40e59989ad39\n",
|
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" hostname: Kais-MacBook-Pro.local\n",
|
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" iterations_since_restore: 3\n",
|
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" mean_accuracy: 0.9454166889190674\n",
|
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" node_ip: 127.0.0.1\n",
|
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" pid: 51991\n",
|
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" time_since_restore: 29.733185052871704\n",
|
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" time_this_iter_s: 3.0363340377807617\n",
|
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" time_total_s: 29.733185052871704\n",
|
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" timestamp: 1658503075\n",
|
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" timesteps_since_restore: 0\n",
|
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" training_iteration: 3\n",
|
|
" trial_id: 55a9b_00003\n",
|
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" warmup_time: 0.0033779144287109375\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00000:\n",
|
|
" date: 2022-07-22_16-17-55\n",
|
|
" done: false\n",
|
|
" experiment_id: 3659349c38c746cfb71b4db5eb9302a0\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 3\n",
|
|
" mean_accuracy: 0.958216667175293\n",
|
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" node_ip: 127.0.0.1\n",
|
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" pid: 51968\n",
|
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" time_since_restore: 47.272178173065186\n",
|
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" time_this_iter_s: 3.2986061573028564\n",
|
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" time_total_s: 47.272178173065186\n",
|
|
" timestamp: 1658503075\n",
|
|
" timesteps_since_restore: 0\n",
|
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" training_iteration: 3\n",
|
|
" trial_id: 55a9b_00000\n",
|
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" warmup_time: 0.003445863723754883\n",
|
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" \n",
|
|
"Result for train_mnist_55a9b_00004:\n",
|
|
" date: 2022-07-22_16-17-55\n",
|
|
" done: false\n",
|
|
" experiment_id: 6eb62b7cb38f442a867a9094f0664701\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 3\n",
|
|
" mean_accuracy: 0.8524500131607056\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52000\n",
|
|
" time_since_restore: 24.11396098136902\n",
|
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" time_this_iter_s: 3.2331089973449707\n",
|
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" time_total_s: 24.11396098136902\n",
|
|
" timestamp: 1658503075\n",
|
|
" timesteps_since_restore: 0\n",
|
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" training_iteration: 3\n",
|
|
" trial_id: 55a9b_00004\n",
|
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" warmup_time: 0.0030939579010009766\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00002:\n",
|
|
" date: 2022-07-22_16-17-55\n",
|
|
" done: false\n",
|
|
" experiment_id: c4f803baf65f4d4e9fd6abc85b2fd00c\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 3\n",
|
|
" mean_accuracy: 0.9695500135421753\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51984\n",
|
|
" time_since_restore: 35.78592824935913\n",
|
|
" time_this_iter_s: 3.021165132522583\n",
|
|
" time_total_s: 35.78592824935913\n",
|
|
" timestamp: 1658503075\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 3\n",
|
|
" trial_id: 55a9b_00002\n",
|
|
" warmup_time: 0.0028328895568847656\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00001:\n",
|
|
" date: 2022-07-22_16-17-56\n",
|
|
" done: false\n",
|
|
" experiment_id: fcdeb049f9614755a9b7c9420ca2ae5e\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 3\n",
|
|
" mean_accuracy: 0.9560333490371704\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51977\n",
|
|
" time_since_restore: 42.38909387588501\n",
|
|
" time_this_iter_s: 3.753290891647339\n",
|
|
" time_total_s: 42.38909387588501\n",
|
|
" timestamp: 1658503076\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 3\n",
|
|
" trial_id: 55a9b_00001\n",
|
|
" warmup_time: 0.0029449462890625\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00005:\n",
|
|
" date: 2022-07-22_16-18-00\n",
|
|
" done: false\n",
|
|
" experiment_id: 8dbd22e6caed4fe39351dffa3ef14eac\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 5\n",
|
|
" mean_accuracy: 0.9611166715621948\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52007\n",
|
|
" time_since_restore: 23.303561210632324\n",
|
|
" time_this_iter_s: 2.933852195739746\n",
|
|
" time_total_s: 23.303561210632324\n",
|
|
" timestamp: 1658503080\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 5\n",
|
|
" trial_id: 55a9b_00005\n",
|
|
" warmup_time: 0.005449056625366211\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00007:\n",
|
|
" date: 2022-07-22_16-18-01\n",
|
|
" done: false\n",
|
|
" experiment_id: d9469b1fc58b41db88da5446dc2a3b23\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 1\n",
|
|
" mean_accuracy: 0.8797500133514404\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52021\n",
|
|
" time_since_restore: 12.469872951507568\n",
|
|
" time_this_iter_s: 12.469872951507568\n",
|
|
" time_total_s: 12.469872951507568\n",
|
|
" timestamp: 1658503081\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 1\n",
|
|
" trial_id: 55a9b_00007\n",
|
|
" warmup_time: 0.0028028488159179688\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00006:\n",
|
|
" date: 2022-07-22_16-18-01\n",
|
|
" done: false\n",
|
|
" experiment_id: 9594405e38084311a891b48addd13f75\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 5\n",
|
|
" mean_accuracy: 0.9499499797821045\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52016\n",
|
|
" time_since_restore: 18.780059814453125\n",
|
|
" time_this_iter_s: 3.3080599308013916\n",
|
|
" time_total_s: 18.780059814453125\n",
|
|
" timestamp: 1658503081\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 5\n",
|
|
" trial_id: 55a9b_00006\n",
|
|
" warmup_time: 0.003566741943359375\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00003:\n",
|
|
" date: 2022-07-22_16-18-02\n",
|
|
" done: false\n",
|
|
" experiment_id: 469478f02b4a43f5b44c40e59989ad39\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 5\n",
|
|
" mean_accuracy: 0.9601166844367981\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51991\n",
|
|
" time_since_restore: 36.93912100791931\n",
|
|
" time_this_iter_s: 3.4057939052581787\n",
|
|
" time_total_s: 36.93912100791931\n",
|
|
" timestamp: 1658503082\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 5\n",
|
|
" trial_id: 55a9b_00003\n",
|
|
" warmup_time: 0.0033779144287109375\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00000:\n",
|
|
" date: 2022-07-22_16-18-02\n",
|
|
" done: false\n",
|
|
" experiment_id: 3659349c38c746cfb71b4db5eb9302a0\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 5\n",
|
|
" mean_accuracy: 0.970466673374176\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51968\n",
|
|
" time_since_restore: 54.49850010871887\n",
|
|
" time_this_iter_s: 3.4417831897735596\n",
|
|
" time_total_s: 54.49850010871887\n",
|
|
" timestamp: 1658503082\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 5\n",
|
|
" trial_id: 55a9b_00000\n",
|
|
" warmup_time: 0.003445863723754883\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00004:\n",
|
|
" date: 2022-07-22_16-18-02\n",
|
|
" done: false\n",
|
|
" experiment_id: 6eb62b7cb38f442a867a9094f0664701\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 5\n",
|
|
" mean_accuracy: 0.8777499794960022\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52000\n",
|
|
" time_since_restore: 31.513713121414185\n",
|
|
" time_this_iter_s: 3.506195068359375\n",
|
|
" time_total_s: 31.513713121414185\n",
|
|
" timestamp: 1658503082\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 5\n",
|
|
" trial_id: 55a9b_00004\n",
|
|
" warmup_time: 0.0030939579010009766\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00002:\n",
|
|
" date: 2022-07-22_16-18-02\n",
|
|
" done: false\n",
|
|
" experiment_id: c4f803baf65f4d4e9fd6abc85b2fd00c\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 5\n",
|
|
" mean_accuracy: 0.979283332824707\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51984\n",
|
|
" time_since_restore: 43.266417026519775\n",
|
|
" time_this_iter_s: 3.3383469581604004\n",
|
|
" time_total_s: 43.266417026519775\n",
|
|
" timestamp: 1658503082\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 5\n",
|
|
" trial_id: 55a9b_00002\n",
|
|
" warmup_time: 0.0028328895568847656\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00001:\n",
|
|
" date: 2022-07-22_16-18-04\n",
|
|
" done: false\n",
|
|
" experiment_id: fcdeb049f9614755a9b7c9420ca2ae5e\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 5\n",
|
|
" mean_accuracy: 0.9692999720573425\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51977\n",
|
|
" time_since_restore: 50.620792865753174\n",
|
|
" time_this_iter_s: 4.001068115234375\n",
|
|
" time_total_s: 50.620792865753174\n",
|
|
" timestamp: 1658503084\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 5\n",
|
|
" trial_id: 55a9b_00001\n",
|
|
" warmup_time: 0.0029449462890625\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00005:\n",
|
|
" date: 2022-07-22_16-18-06\n",
|
|
" done: false\n",
|
|
" experiment_id: 8dbd22e6caed4fe39351dffa3ef14eac\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 7\n",
|
|
" mean_accuracy: 0.96711665391922\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52007\n",
|
|
" time_since_restore: 29.40476107597351\n",
|
|
" time_this_iter_s: 2.976076126098633\n",
|
|
" time_total_s: 29.40476107597351\n",
|
|
" timestamp: 1658503086\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 7\n",
|
|
" trial_id: 55a9b_00005\n",
|
|
" warmup_time: 0.005449056625366211\n",
|
|
" \n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Result for train_mnist_55a9b_00007:\n",
|
|
" date: 2022-07-22_16-18-07\n",
|
|
" done: false\n",
|
|
" experiment_id: d9469b1fc58b41db88da5446dc2a3b23\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 3\n",
|
|
" mean_accuracy: 0.951033353805542\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52021\n",
|
|
" time_since_restore: 18.96213722229004\n",
|
|
" time_this_iter_s: 3.252371311187744\n",
|
|
" time_total_s: 18.96213722229004\n",
|
|
" timestamp: 1658503087\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 3\n",
|
|
" trial_id: 55a9b_00007\n",
|
|
" warmup_time: 0.0028028488159179688\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00006:\n",
|
|
" date: 2022-07-22_16-18-08\n",
|
|
" done: false\n",
|
|
" experiment_id: 9594405e38084311a891b48addd13f75\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 7\n",
|
|
" mean_accuracy: 0.9584500193595886\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52016\n",
|
|
" time_since_restore: 25.336583852767944\n",
|
|
" time_this_iter_s: 3.311979055404663\n",
|
|
" time_total_s: 25.336583852767944\n",
|
|
" timestamp: 1658503088\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 7\n",
|
|
" trial_id: 55a9b_00006\n",
|
|
" warmup_time: 0.003566741943359375\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00003:\n",
|
|
" date: 2022-07-22_16-18-09\n",
|
|
" done: false\n",
|
|
" experiment_id: 469478f02b4a43f5b44c40e59989ad39\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 7\n",
|
|
" mean_accuracy: 0.9675499796867371\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51991\n",
|
|
" time_since_restore: 43.7107310295105\n",
|
|
" time_this_iter_s: 3.3927559852600098\n",
|
|
" time_total_s: 43.7107310295105\n",
|
|
" timestamp: 1658503089\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 7\n",
|
|
" trial_id: 55a9b_00003\n",
|
|
" warmup_time: 0.0033779144287109375\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00000:\n",
|
|
" date: 2022-07-22_16-18-09\n",
|
|
" done: false\n",
|
|
" experiment_id: 3659349c38c746cfb71b4db5eb9302a0\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 7\n",
|
|
" mean_accuracy: 0.9763000011444092\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51968\n",
|
|
" time_since_restore: 61.30248522758484\n",
|
|
" time_this_iter_s: 3.4063682556152344\n",
|
|
" time_total_s: 61.30248522758484\n",
|
|
" timestamp: 1658503089\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 7\n",
|
|
" trial_id: 55a9b_00000\n",
|
|
" warmup_time: 0.003445863723754883\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00002:\n",
|
|
" date: 2022-07-22_16-18-09\n",
|
|
" done: false\n",
|
|
" experiment_id: c4f803baf65f4d4e9fd6abc85b2fd00c\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 7\n",
|
|
" mean_accuracy: 0.9840666651725769\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51984\n",
|
|
" time_since_restore: 50.212465047836304\n",
|
|
" time_this_iter_s: 3.43766188621521\n",
|
|
" time_total_s: 50.212465047836304\n",
|
|
" timestamp: 1658503089\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 7\n",
|
|
" trial_id: 55a9b_00002\n",
|
|
" warmup_time: 0.0028328895568847656\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00004:\n",
|
|
" date: 2022-07-22_16-18-09\n",
|
|
" done: false\n",
|
|
" experiment_id: 6eb62b7cb38f442a867a9094f0664701\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 7\n",
|
|
" mean_accuracy: 0.8899999856948853\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52000\n",
|
|
" time_since_restore: 38.63890194892883\n",
|
|
" time_this_iter_s: 3.5783908367156982\n",
|
|
" time_total_s: 38.63890194892883\n",
|
|
" timestamp: 1658503089\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 7\n",
|
|
" trial_id: 55a9b_00004\n",
|
|
" warmup_time: 0.0030939579010009766\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00005:\n",
|
|
" date: 2022-07-22_16-18-12\n",
|
|
" done: false\n",
|
|
" experiment_id: 8dbd22e6caed4fe39351dffa3ef14eac\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 9\n",
|
|
" mean_accuracy: 0.9712333083152771\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52007\n",
|
|
" time_since_restore: 35.3185760974884\n",
|
|
" time_this_iter_s: 3.0241990089416504\n",
|
|
" time_total_s: 35.3185760974884\n",
|
|
" timestamp: 1658503092\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 9\n",
|
|
" trial_id: 55a9b_00005\n",
|
|
" warmup_time: 0.005449056625366211\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00001:\n",
|
|
" date: 2022-07-22_16-18-12\n",
|
|
" done: false\n",
|
|
" experiment_id: fcdeb049f9614755a9b7c9420ca2ae5e\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 7\n",
|
|
" mean_accuracy: 0.9755333065986633\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51977\n",
|
|
" time_since_restore: 58.57745599746704\n",
|
|
" time_this_iter_s: 3.936232089996338\n",
|
|
" time_total_s: 58.57745599746704\n",
|
|
" timestamp: 1658503092\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 7\n",
|
|
" trial_id: 55a9b_00001\n",
|
|
" warmup_time: 0.0029449462890625\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00007:\n",
|
|
" date: 2022-07-22_16-18-14\n",
|
|
" done: false\n",
|
|
" experiment_id: d9469b1fc58b41db88da5446dc2a3b23\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 5\n",
|
|
" mean_accuracy: 0.9648333191871643\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52021\n",
|
|
" time_since_restore: 25.25843620300293\n",
|
|
" time_this_iter_s: 3.094501256942749\n",
|
|
" time_total_s: 25.25843620300293\n",
|
|
" timestamp: 1658503094\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 5\n",
|
|
" trial_id: 55a9b_00007\n",
|
|
" warmup_time: 0.0028028488159179688\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00006:\n",
|
|
" date: 2022-07-22_16-18-15\n",
|
|
" done: false\n",
|
|
" experiment_id: 9594405e38084311a891b48addd13f75\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 9\n",
|
|
" mean_accuracy: 0.9646666646003723\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52016\n",
|
|
" time_since_restore: 32.048911809921265\n",
|
|
" time_this_iter_s: 3.315690755844116\n",
|
|
" time_total_s: 32.048911809921265\n",
|
|
" timestamp: 1658503095\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 9\n",
|
|
" trial_id: 55a9b_00006\n",
|
|
" warmup_time: 0.003566741943359375\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00003:\n",
|
|
" date: 2022-07-22_16-18-15\n",
|
|
" done: false\n",
|
|
" experiment_id: 469478f02b4a43f5b44c40e59989ad39\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 9\n",
|
|
" mean_accuracy: 0.9729499816894531\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51991\n",
|
|
" time_since_restore: 50.50909209251404\n",
|
|
" time_this_iter_s: 3.4110782146453857\n",
|
|
" time_total_s: 50.50909209251404\n",
|
|
" timestamp: 1658503095\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 9\n",
|
|
" trial_id: 55a9b_00003\n",
|
|
" warmup_time: 0.0033779144287109375\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00000:\n",
|
|
" date: 2022-07-22_16-18-16\n",
|
|
" done: false\n",
|
|
" experiment_id: 3659349c38c746cfb71b4db5eb9302a0\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 9\n",
|
|
" mean_accuracy: 0.9807666540145874\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51968\n",
|
|
" time_since_restore: 68.26757216453552\n",
|
|
" time_this_iter_s: 3.4475879669189453\n",
|
|
" time_total_s: 68.26757216453552\n",
|
|
" timestamp: 1658503096\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 9\n",
|
|
" trial_id: 55a9b_00000\n",
|
|
" warmup_time: 0.003445863723754883\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00002:\n",
|
|
" date: 2022-07-22_16-18-16\n",
|
|
" done: false\n",
|
|
" experiment_id: c4f803baf65f4d4e9fd6abc85b2fd00c\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 9\n",
|
|
" mean_accuracy: 0.9872999787330627\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51984\n",
|
|
" time_since_restore: 57.01431703567505\n",
|
|
" time_this_iter_s: 3.3804008960723877\n",
|
|
" time_total_s: 57.01431703567505\n",
|
|
" timestamp: 1658503096\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 9\n",
|
|
" trial_id: 55a9b_00002\n",
|
|
" warmup_time: 0.0028328895568847656\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00004:\n",
|
|
" date: 2022-07-22_16-18-16\n",
|
|
" done: false\n",
|
|
" experiment_id: 6eb62b7cb38f442a867a9094f0664701\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 9\n",
|
|
" mean_accuracy: 0.8989166617393494\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52000\n",
|
|
" time_since_restore: 45.67929005622864\n",
|
|
" time_this_iter_s: 3.4561610221862793\n",
|
|
" time_total_s: 45.67929005622864\n",
|
|
" timestamp: 1658503096\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 9\n",
|
|
" trial_id: 55a9b_00004\n",
|
|
" warmup_time: 0.0030939579010009766\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00005:\n",
|
|
" date: 2022-07-22_16-18-18\n",
|
|
" done: false\n",
|
|
" experiment_id: 8dbd22e6caed4fe39351dffa3ef14eac\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 11\n",
|
|
" mean_accuracy: 0.9744333624839783\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52007\n",
|
|
" time_since_restore: 41.49077916145325\n",
|
|
" time_this_iter_s: 3.172250270843506\n",
|
|
" time_total_s: 41.49077916145325\n",
|
|
" timestamp: 1658503098\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 11\n",
|
|
" trial_id: 55a9b_00005\n",
|
|
" warmup_time: 0.005449056625366211\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00001:\n",
|
|
" date: 2022-07-22_16-18-20\n",
|
|
" done: false\n",
|
|
" experiment_id: fcdeb049f9614755a9b7c9420ca2ae5e\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 9\n",
|
|
" mean_accuracy: 0.9806166887283325\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51977\n",
|
|
" time_since_restore: 66.64132380485535\n",
|
|
" time_this_iter_s: 4.0674309730529785\n",
|
|
" time_total_s: 66.64132380485535\n",
|
|
" timestamp: 1658503100\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 9\n",
|
|
" trial_id: 55a9b_00001\n",
|
|
" warmup_time: 0.0029449462890625\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00007:\n",
|
|
" date: 2022-07-22_16-18-20\n",
|
|
" done: false\n",
|
|
" experiment_id: d9469b1fc58b41db88da5446dc2a3b23\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 7\n",
|
|
" mean_accuracy: 0.970716655254364\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52021\n",
|
|
" time_since_restore: 31.897236108779907\n",
|
|
" time_this_iter_s: 3.3691420555114746\n",
|
|
" time_total_s: 31.897236108779907\n",
|
|
" timestamp: 1658503100\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 7\n",
|
|
" trial_id: 55a9b_00007\n",
|
|
" warmup_time: 0.0028028488159179688\n",
|
|
" \n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Result for train_mnist_55a9b_00005:\n",
|
|
" date: 2022-07-22_16-18-21\n",
|
|
" done: true\n",
|
|
" experiment_id: 8dbd22e6caed4fe39351dffa3ef14eac\n",
|
|
" experiment_tag: 5_hidden=92,lr=0.0652,momentum=0.7102\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 12\n",
|
|
" mean_accuracy: 0.9748666882514954\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52007\n",
|
|
" time_since_restore: 44.80922222137451\n",
|
|
" time_this_iter_s: 3.3184430599212646\n",
|
|
" time_total_s: 44.80922222137451\n",
|
|
" timestamp: 1658503101\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 12\n",
|
|
" trial_id: 55a9b_00005\n",
|
|
" warmup_time: 0.005449056625366211\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00006:\n",
|
|
" date: 2022-07-22_16-18-22\n",
|
|
" done: false\n",
|
|
" experiment_id: 9594405e38084311a891b48addd13f75\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 11\n",
|
|
" mean_accuracy: 0.9679166674613953\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52016\n",
|
|
" time_since_restore: 39.08963179588318\n",
|
|
" time_this_iter_s: 3.4860758781433105\n",
|
|
" time_total_s: 39.08963179588318\n",
|
|
" timestamp: 1658503102\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 11\n",
|
|
" trial_id: 55a9b_00006\n",
|
|
" warmup_time: 0.003566741943359375\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00003:\n",
|
|
" date: 2022-07-22_16-18-23\n",
|
|
" done: false\n",
|
|
" experiment_id: 469478f02b4a43f5b44c40e59989ad39\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 11\n",
|
|
" mean_accuracy: 0.9771833419799805\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51991\n",
|
|
" time_since_restore: 57.6213219165802\n",
|
|
" time_this_iter_s: 3.4615819454193115\n",
|
|
" time_total_s: 57.6213219165802\n",
|
|
" timestamp: 1658503103\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 11\n",
|
|
" trial_id: 55a9b_00003\n",
|
|
" warmup_time: 0.0033779144287109375\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00000:\n",
|
|
" date: 2022-07-22_16-18-23\n",
|
|
" done: false\n",
|
|
" experiment_id: 3659349c38c746cfb71b4db5eb9302a0\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 11\n",
|
|
" mean_accuracy: 0.98416668176651\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51968\n",
|
|
" time_since_restore: 75.32713007926941\n",
|
|
" time_this_iter_s: 3.443808078765869\n",
|
|
" time_total_s: 75.32713007926941\n",
|
|
" timestamp: 1658503103\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 11\n",
|
|
" trial_id: 55a9b_00000\n",
|
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" warmup_time: 0.003445863723754883\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00002:\n",
|
|
" date: 2022-07-22_16-18-23\n",
|
|
" done: true\n",
|
|
" experiment_id: c4f803baf65f4d4e9fd6abc85b2fd00c\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 11\n",
|
|
" mean_accuracy: 0.9904166460037231\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51984\n",
|
|
" time_since_restore: 64.12720203399658\n",
|
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" time_this_iter_s: 3.508151054382324\n",
|
|
" time_total_s: 64.12720203399658\n",
|
|
" timestamp: 1658503103\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 11\n",
|
|
" trial_id: 55a9b_00002\n",
|
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" warmup_time: 0.0028328895568847656\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00004:\n",
|
|
" date: 2022-07-22_16-18-23\n",
|
|
" done: false\n",
|
|
" experiment_id: 6eb62b7cb38f442a867a9094f0664701\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 11\n",
|
|
" mean_accuracy: 0.9052166938781738\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52000\n",
|
|
" time_since_restore: 52.687995195388794\n",
|
|
" time_this_iter_s: 3.420351982116699\n",
|
|
" time_total_s: 52.687995195388794\n",
|
|
" timestamp: 1658503103\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 11\n",
|
|
" trial_id: 55a9b_00004\n",
|
|
" warmup_time: 0.0030939579010009766\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00006:\n",
|
|
" date: 2022-07-22_16-18-25\n",
|
|
" done: true\n",
|
|
" experiment_id: 9594405e38084311a891b48addd13f75\n",
|
|
" experiment_tag: 6_hidden=211,lr=0.0731,momentum=0.1278\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 12\n",
|
|
" mean_accuracy: 0.9702500104904175\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52016\n",
|
|
" time_since_restore: 42.1216938495636\n",
|
|
" time_this_iter_s: 3.03206205368042\n",
|
|
" time_total_s: 42.1216938495636\n",
|
|
" timestamp: 1658503105\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 12\n",
|
|
" trial_id: 55a9b_00006\n",
|
|
" warmup_time: 0.003566741943359375\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00003:\n",
|
|
" date: 2022-07-22_16-18-26\n",
|
|
" done: true\n",
|
|
" experiment_id: 469478f02b4a43f5b44c40e59989ad39\n",
|
|
" experiment_tag: 3_hidden=255,lr=0.0972,momentum=0.2316\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 12\n",
|
|
" mean_accuracy: 0.9776333570480347\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51991\n",
|
|
" time_since_restore: 60.8474760055542\n",
|
|
" time_this_iter_s: 3.226154088973999\n",
|
|
" time_total_s: 60.8474760055542\n",
|
|
" timestamp: 1658503106\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 12\n",
|
|
" trial_id: 55a9b_00003\n",
|
|
" warmup_time: 0.0033779144287109375\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00000:\n",
|
|
" date: 2022-07-22_16-18-26\n",
|
|
" done: true\n",
|
|
" experiment_id: 3659349c38c746cfb71b4db5eb9302a0\n",
|
|
" experiment_tag: 0_hidden=276,lr=0.0406,momentum=0.8178\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 12\n",
|
|
" mean_accuracy: 0.9845499992370605\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51968\n",
|
|
" time_since_restore: 78.32520508766174\n",
|
|
" time_this_iter_s: 2.998075008392334\n",
|
|
" time_total_s: 78.32520508766174\n",
|
|
" timestamp: 1658503106\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 12\n",
|
|
" trial_id: 55a9b_00000\n",
|
|
" warmup_time: 0.003445863723754883\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00007:\n",
|
|
" date: 2022-07-22_16-18-26\n",
|
|
" done: false\n",
|
|
" experiment_id: d9469b1fc58b41db88da5446dc2a3b23\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 9\n",
|
|
" mean_accuracy: 0.9751333594322205\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52021\n",
|
|
" time_since_restore: 37.76195311546326\n",
|
|
" time_this_iter_s: 2.7159180641174316\n",
|
|
" time_total_s: 37.76195311546326\n",
|
|
" timestamp: 1658503106\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 9\n",
|
|
" trial_id: 55a9b_00007\n",
|
|
" warmup_time: 0.0028028488159179688\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00004:\n",
|
|
" date: 2022-07-22_16-18-26\n",
|
|
" done: true\n",
|
|
" experiment_id: 6eb62b7cb38f442a867a9094f0664701\n",
|
|
" experiment_tag: 4_hidden=303,lr=0.0044,momentum=0.3254\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 12\n",
|
|
" mean_accuracy: 0.9077500104904175\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52000\n",
|
|
" time_since_restore: 55.57219409942627\n",
|
|
" time_this_iter_s: 2.8841989040374756\n",
|
|
" time_total_s: 55.57219409942627\n",
|
|
" timestamp: 1658503106\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 12\n",
|
|
" trial_id: 55a9b_00004\n",
|
|
" warmup_time: 0.0030939579010009766\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00001:\n",
|
|
" date: 2022-07-22_16-18-27\n",
|
|
" done: false\n",
|
|
" experiment_id: fcdeb049f9614755a9b7c9420ca2ae5e\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 11\n",
|
|
" mean_accuracy: 0.9830166697502136\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51977\n",
|
|
" time_since_restore: 73.19760584831238\n",
|
|
" time_this_iter_s: 2.7281620502471924\n",
|
|
" time_total_s: 73.19760584831238\n",
|
|
" timestamp: 1658503107\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 11\n",
|
|
" trial_id: 55a9b_00001\n",
|
|
" warmup_time: 0.0029449462890625\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00008:\n",
|
|
" date: 2022-07-22_16-18-28\n",
|
|
" done: false\n",
|
|
" experiment_id: 8dbd22e6caed4fe39351dffa3ef14eac\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 1\n",
|
|
" mean_accuracy: 0.8477166891098022\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52007\n",
|
|
" time_since_restore: 6.2436230182647705\n",
|
|
" time_this_iter_s: 6.2436230182647705\n",
|
|
" time_total_s: 6.2436230182647705\n",
|
|
" timestamp: 1658503108\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 1\n",
|
|
" trial_id: 55a9b_00008\n",
|
|
" warmup_time: 0.005449056625366211\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00001:\n",
|
|
" date: 2022-07-22_16-18-28\n",
|
|
" done: true\n",
|
|
" experiment_id: fcdeb049f9614755a9b7c9420ca2ae5e\n",
|
|
" experiment_tag: 1_hidden=380,lr=0.0874,momentum=0.5246\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 12\n",
|
|
" mean_accuracy: 0.9837166666984558\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51977\n",
|
|
" time_since_restore: 74.98881888389587\n",
|
|
" time_this_iter_s: 1.791213035583496\n",
|
|
" time_total_s: 74.98881888389587\n",
|
|
" timestamp: 1658503108\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 12\n",
|
|
" trial_id: 55a9b_00001\n",
|
|
" warmup_time: 0.0029449462890625\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00009:\n",
|
|
" date: 2022-07-22_16-18-29\n",
|
|
" done: false\n",
|
|
" experiment_id: c4f803baf65f4d4e9fd6abc85b2fd00c\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 1\n",
|
|
" mean_accuracy: 0.7675999999046326\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51984\n",
|
|
" time_since_restore: 5.303471088409424\n",
|
|
" time_this_iter_s: 5.303471088409424\n",
|
|
" time_total_s: 5.303471088409424\n",
|
|
" timestamp: 1658503109\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 1\n",
|
|
" trial_id: 55a9b_00009\n",
|
|
" warmup_time: 0.0028328895568847656\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00007:\n",
|
|
" date: 2022-07-22_16-18-30\n",
|
|
" done: true\n",
|
|
" experiment_id: d9469b1fc58b41db88da5446dc2a3b23\n",
|
|
" experiment_tag: 7_hidden=181,lr=0.0362,momentum=0.7903\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 12\n",
|
|
" mean_accuracy: 0.9799666404724121\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52021\n",
|
|
" time_since_restore: 41.763158082962036\n",
|
|
" time_this_iter_s: 1.0622038841247559\n",
|
|
" time_total_s: 41.763158082962036\n",
|
|
" timestamp: 1658503110\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 12\n",
|
|
" trial_id: 55a9b_00007\n",
|
|
" warmup_time: 0.0028028488159179688\n",
|
|
" \n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Result for train_mnist_55a9b_00008:\n",
|
|
" date: 2022-07-22_16-18-33\n",
|
|
" done: false\n",
|
|
" experiment_id: 8dbd22e6caed4fe39351dffa3ef14eac\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 8\n",
|
|
" mean_accuracy: 0.9599000215530396\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52007\n",
|
|
" time_since_restore: 11.612935304641724\n",
|
|
" time_this_iter_s: 0.6818761825561523\n",
|
|
" time_total_s: 11.612935304641724\n",
|
|
" timestamp: 1658503113\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 8\n",
|
|
" trial_id: 55a9b_00008\n",
|
|
" warmup_time: 0.005449056625366211\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00009:\n",
|
|
" date: 2022-07-22_16-18-34\n",
|
|
" done: false\n",
|
|
" experiment_id: c4f803baf65f4d4e9fd6abc85b2fd00c\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 9\n",
|
|
" mean_accuracy: 0.9319833517074585\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51984\n",
|
|
" time_since_restore: 10.803268194198608\n",
|
|
" time_this_iter_s: 0.606992244720459\n",
|
|
" time_total_s: 10.803268194198608\n",
|
|
" timestamp: 1658503114\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 9\n",
|
|
" trial_id: 55a9b_00009\n",
|
|
" warmup_time: 0.0028328895568847656\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00008:\n",
|
|
" date: 2022-07-22_16-18-36\n",
|
|
" done: true\n",
|
|
" experiment_id: 8dbd22e6caed4fe39351dffa3ef14eac\n",
|
|
" experiment_tag: 8_hidden=142,lr=0.0324,momentum=0.6604\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 12\n",
|
|
" mean_accuracy: 0.9693666696548462\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 52007\n",
|
|
" time_since_restore: 14.152745008468628\n",
|
|
" time_this_iter_s: 0.5980076789855957\n",
|
|
" time_total_s: 14.152745008468628\n",
|
|
" timestamp: 1658503116\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 12\n",
|
|
" trial_id: 55a9b_00008\n",
|
|
" warmup_time: 0.005449056625366211\n",
|
|
" \n",
|
|
"Result for train_mnist_55a9b_00009:\n",
|
|
" date: 2022-07-22_16-18-36\n",
|
|
" done: true\n",
|
|
" experiment_id: c4f803baf65f4d4e9fd6abc85b2fd00c\n",
|
|
" experiment_tag: 9_hidden=97,lr=0.0245,momentum=0.1750\n",
|
|
" hostname: Kais-MacBook-Pro.local\n",
|
|
" iterations_since_restore: 12\n",
|
|
" mean_accuracy: 0.9406999945640564\n",
|
|
" node_ip: 127.0.0.1\n",
|
|
" pid: 51984\n",
|
|
" time_since_restore: 12.640528202056885\n",
|
|
" time_this_iter_s: 0.5808131694793701\n",
|
|
" time_total_s: 12.640528202056885\n",
|
|
" timestamp: 1658503116\n",
|
|
" timesteps_since_restore: 0\n",
|
|
" training_iteration: 12\n",
|
|
" trial_id: 55a9b_00009\n",
|
|
" warmup_time: 0.0028328895568847656\n",
|
|
" \n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2022-07-22 16:18:36,803\tINFO tune.py:738 -- Total run time: 95.98 seconds (95.03 seconds for the tuning loop).\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Best hyperparameters found were: {'threads': 2, 'lr': 0.09518133271957563, 'momentum': 0.8254987643140009, 'hidden': 258}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import argparse\n",
|
|
"import os\n",
|
|
"\n",
|
|
"from filelock import FileLock\n",
|
|
"from tensorflow.keras.datasets import mnist\n",
|
|
"\n",
|
|
"import ray\n",
|
|
"from ray import air, tune\n",
|
|
"from ray.tune.schedulers import AsyncHyperBandScheduler\n",
|
|
"from ray.tune.integration.keras import TuneReportCallback\n",
|
|
"\n",
|
|
"\n",
|
|
"def train_mnist(config):\n",
|
|
" # https://github.com/tensorflow/tensorflow/issues/32159\n",
|
|
" import tensorflow as tf\n",
|
|
"\n",
|
|
" batch_size = 128\n",
|
|
" num_classes = 10\n",
|
|
" epochs = 12\n",
|
|
"\n",
|
|
" with FileLock(os.path.expanduser(\"~/.data.lock\")):\n",
|
|
" (x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
|
|
" x_train, x_test = x_train / 255.0, x_test / 255.0\n",
|
|
" model = tf.keras.models.Sequential(\n",
|
|
" [\n",
|
|
" tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
|
|
" tf.keras.layers.Dense(config[\"hidden\"], activation=\"relu\"),\n",
|
|
" tf.keras.layers.Dropout(0.2),\n",
|
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" tf.keras.layers.Dense(num_classes, activation=\"softmax\"),\n",
|
|
" ]\n",
|
|
" )\n",
|
|
"\n",
|
|
" model.compile(\n",
|
|
" loss=\"sparse_categorical_crossentropy\",\n",
|
|
" optimizer=tf.keras.optimizers.SGD(lr=config[\"lr\"], momentum=config[\"momentum\"]),\n",
|
|
" metrics=[\"accuracy\"],\n",
|
|
" )\n",
|
|
"\n",
|
|
" model.fit(\n",
|
|
" x_train,\n",
|
|
" y_train,\n",
|
|
" batch_size=batch_size,\n",
|
|
" epochs=epochs,\n",
|
|
" verbose=0,\n",
|
|
" validation_data=(x_test, y_test),\n",
|
|
" callbacks=[TuneReportCallback({\"mean_accuracy\": \"accuracy\"})],\n",
|
|
" )\n",
|
|
"\n",
|
|
"\n",
|
|
"def tune_mnist(num_training_iterations):\n",
|
|
" sched = AsyncHyperBandScheduler(\n",
|
|
" time_attr=\"training_iteration\", max_t=400, grace_period=20\n",
|
|
" )\n",
|
|
" \n",
|
|
" tuner = tune.Tuner(\n",
|
|
" tune.with_resources(\n",
|
|
" train_mnist,\n",
|
|
" resources={\"cpu\": 2, \"gpu\": 0}\n",
|
|
" ),\n",
|
|
" tune_config=tune.TuneConfig(\n",
|
|
" metric=\"mean_accuracy\",\n",
|
|
" mode=\"max\",\n",
|
|
" scheduler=sched,\n",
|
|
" num_samples=10,\n",
|
|
" ),\n",
|
|
" run_config=air.RunConfig(\n",
|
|
" name=\"exp\",\n",
|
|
" stop={\"mean_accuracy\": 0.99, \"training_iteration\": num_training_iterations},\n",
|
|
" ),\n",
|
|
" param_space={\n",
|
|
" \"threads\": 2,\n",
|
|
" \"lr\": tune.uniform(0.001, 0.1),\n",
|
|
" \"momentum\": tune.uniform(0.1, 0.9),\n",
|
|
" \"hidden\": tune.randint(32, 512),\n",
|
|
" },\n",
|
|
" )\n",
|
|
" results = tuner.fit()\n",
|
|
"\n",
|
|
" print(\"Best hyperparameters found were: \", results.get_best_result().config)\n",
|
|
"\n",
|
|
"\n",
|
|
"if __name__ == \"__main__\":\n",
|
|
" parser = argparse.ArgumentParser()\n",
|
|
" parser.add_argument(\n",
|
|
" \"--smoke-test\", action=\"store_true\", help=\"Finish quickly for testing\"\n",
|
|
" )\n",
|
|
" parser.add_argument(\n",
|
|
" \"--server-address\",\n",
|
|
" type=str,\n",
|
|
" default=None,\n",
|
|
" required=False,\n",
|
|
" help=\"The address of server to connect to if using \" \"Ray Client.\",\n",
|
|
" )\n",
|
|
" args, _ = parser.parse_known_args()\n",
|
|
" if args.smoke_test:\n",
|
|
" ray.init(num_cpus=4)\n",
|
|
" elif args.server_address:\n",
|
|
" ray.init(f\"ray://{args.server_address}\")\n",
|
|
"\n",
|
|
" tune_mnist(num_training_iterations=5 if args.smoke_test else 300)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d7e46189",
|
|
"metadata": {},
|
|
"source": [
|
|
"## More Keras and TensorFlow Examples\n",
|
|
"\n",
|
|
"- {doc}`/tune/examples/includes/pbt_memnn_example`: Example of training a Memory NN on bAbI with Keras using PBT.\n",
|
|
"- {doc}`/tune/examples/includes/tf_mnist_example`: Converts the Advanced TF2.0 MNIST example to use Tune\n",
|
|
" with the Trainable. This uses `tf.function`.\n",
|
|
" Original code from tensorflow: https://www.tensorflow.org/tutorials/quickstart/advanced\n",
|
|
"- {doc}`/tune/examples/includes/pbt_tune_cifar10_with_keras`:\n",
|
|
" A contributed example of tuning a Keras model on CIFAR10 with the PopulationBasedTraining scheduler.\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.7"
|
|
},
|
|
"orphan": true
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|