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ray/doc/source/tune/examples/bohb_example.ipynb
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2022-07-27 18:43:27 +00:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "986bcaab",
"metadata": {},
"source": [
"# Running Tune experiments with BOHB\n",
"\n",
"In this tutorial we introduce BOHB, while running a simple Ray Tune experiment.\n",
"Tune’s Search Algorithms integrate with BOHB and, as a result,\n",
"allow you to seamlessly scale up a BOHB optimization\n",
"process - without sacrificing performance.\n",
"\n",
"Bayesian Optimization HyperBand (BOHB) combines the benefits of Bayesian optimization\n",
"together with Bandit-based methods (e.g. HyperBand). BOHB does not rely on\n",
"the gradient of the objective function,\n",
"but instead, learns from samples of the search space.\n",
"It is suitable for optimizing functions that are non-differentiable,\n",
"with many local minima, or even unknown but only testable.\n",
"Therefore, this approach belongs to the domain of\n",
"\"derivative-free optimization\" and \"black-box optimization\".\n",
"\n",
"In this example we minimize a simple objective to briefly demonstrate the usage of\n",
"BOHB with Ray Tune via `BOHBSearch`. It's useful to keep in mind that despite\n",
"the emphasis on machine learning experiments, Ray Tune optimizes any implicit\n",
"or explicit objective. Here we assume `ConfigSpace==0.4.18` and `hpbandster==0.7.4`\n",
"libraries are installed. To learn more, please refer to the\n",
"[BOHB website](https://github.com/automl/HpBandSter)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d12bd979",
"metadata": {
"tags": [
"remove-cell"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting ConfigSpace==0.4.18\n",
" Using cached ConfigSpace-0.4.18-cp37-cp37m-macosx_10_15_x86_64.whl\n",
"Requirement already satisfied: pyparsing in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from ConfigSpace==0.4.18) (2.4.7)\n",
"Requirement already satisfied: numpy in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from ConfigSpace==0.4.18) (1.21.6)\n",
"Requirement already satisfied: cython in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from ConfigSpace==0.4.18) (0.29.30)\n",
"Installing collected packages: ConfigSpace\n",
" Attempting uninstall: ConfigSpace\n",
" Found existing installation: ConfigSpace 0.4.21\n",
" Uninstalling ConfigSpace-0.4.21:\n",
" Successfully uninstalled ConfigSpace-0.4.21\n",
"Successfully installed ConfigSpace-0.4.18\n",
"\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n",
"\u001b[0mRequirement already satisfied: hpbandster==0.7.4 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (0.7.4)\n",
"Requirement already satisfied: netifaces in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from hpbandster==0.7.4) (0.11.0)\n",
"Requirement already satisfied: scipy in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from hpbandster==0.7.4) (1.4.1)\n",
"Requirement already satisfied: Pyro4 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from hpbandster==0.7.4) (4.82)\n",
"Requirement already satisfied: serpent in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from hpbandster==0.7.4) (1.40)\n",
"Requirement already satisfied: statsmodels in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from hpbandster==0.7.4) (0.13.2)\n",
"Requirement already satisfied: ConfigSpace in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from hpbandster==0.7.4) (0.4.18)\n",
"Requirement already satisfied: numpy in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from hpbandster==0.7.4) (1.21.6)\n",
"Requirement already satisfied: cython in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from ConfigSpace->hpbandster==0.7.4) (0.29.30)\n",
"Requirement already satisfied: pyparsing in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from ConfigSpace->hpbandster==0.7.4) (2.4.7)\n",
"Requirement already satisfied: pandas>=0.25 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from statsmodels->hpbandster==0.7.4) (1.3.5)\n",
"Requirement already satisfied: packaging>=21.3 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from statsmodels->hpbandster==0.7.4) (21.3)\n",
"Requirement already satisfied: patsy>=0.5.2 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from statsmodels->hpbandster==0.7.4) (0.5.2)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from pandas>=0.25->statsmodels->hpbandster==0.7.4) (2.8.2)\n",
"Requirement already satisfied: pytz>=2017.3 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from pandas>=0.25->statsmodels->hpbandster==0.7.4) (2022.1)\n",
"Requirement already satisfied: six in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from patsy>=0.5.2->statsmodels->hpbandster==0.7.4) (1.16.0)\n",
"\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n",
"\u001b[0m"
]
}
],
"source": [
"# !pip install ray[tune]\n",
"!pip install ConfigSpace==0.4.18\n",
"!pip install hpbandster==0.7.4"
]
},
{
"cell_type": "markdown",
"id": "96641e94",
"metadata": {},
"source": [
"Click below to see all the imports we need for this example.\n",
"You can also launch directly into a Binder instance to run this notebook yourself.\n",
"Just click on the rocket symbol at the top of the navigation."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0e65ccdb",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"import time\n",
"\n",
"import ray\n",
"from ray import air, tune\n",
"from ray.air import session\n",
"from ray.tune.schedulers.hb_bohb import HyperBandForBOHB\n",
"from ray.tune.search.bohb import TuneBOHB\n",
"import ConfigSpace as CS"
]
},
{
"cell_type": "markdown",
"id": "edba942a",
"metadata": {},
"source": [
"Let's start by defining a simple evaluation function.\n",
"We artificially sleep for a bit (`0.1` seconds) to simulate a long-running ML experiment.\n",
"This setup assumes that we're running multiple `step`s of an experiment and try to tune\n",
"two hyperparameters, namely `width` and `height`, and `activation`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "af512205",
"metadata": {},
"outputs": [],
"source": [
"def evaluate(step, width, height, activation):\n",
" time.sleep(0.1)\n",
" activation_boost = 10 if activation==\"relu\" else 1\n",
" return (0.1 + width * step / 100) ** (-1) + height * 0.1 + activation_boost"
]
},
{
"cell_type": "markdown",
"id": "c073ea21",
"metadata": {},
"source": [
"Next, our `objective` function takes a Tune `config`, evaluates the `score` of your\n",
"experiment in a training loop, and uses `session.report` to report the `score` back to Tune."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8a086e87",
"metadata": {},
"outputs": [],
"source": [
"def objective(config):\n",
" for step in range(config[\"steps\"]):\n",
" score = evaluate(step, config[\"width\"], config[\"height\"], config[\"activation\"])\n",
" session.report({\"iterations\": step, \"mean_loss\": score})"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "05d07329",
"metadata": {
"tags": [
"remove-cell"
]
},
"outputs": [
{
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" <div style=\"margin-left: 50px;display: flex;flex-direction: row;align-items: center\">\n",
" <h3 style=\"color: var(--jp-ui-font-color0)\">Ray</h3>\n",
" <svg version=\"1.1\" id=\"ray\" width=\"3em\" viewBox=\"0 0 144.5 144.6\" style=\"margin-left: 3em;margin-right: 3em\">\n",
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" </g>\n",
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" <table>\n",
" <tr>\n",
" <td style=\"text-align: left\"><b>Python version:</b></td>\n",
" <td style=\"text-align: left\"><b>3.7.7</b></td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left\"><b>Ray version:</b></td>\n",
" <td style=\"text-align: left\"><b> 2.0.0rc0</b></td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left\"><b>Dashboard:</b></td>\n",
" <td style=\"text-align: left\"><b><a href=\"http://127.0.0.1:8265\" target=\"_blank\">http://127.0.0.1:8265</a></b></td>\n",
"</tr>\n",
"\n",
" </table>\n",
" </div>\n",
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"text/plain": [
"RayContext(dashboard_url='127.0.0.1:8265', python_version='3.7.7', ray_version='2.0.0rc0', ray_commit='{{RAY_COMMIT_SHA}}', address_info={'node_ip_address': '127.0.0.1', 'raylet_ip_address': '127.0.0.1', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-07-22_15-07-22_184925_45026/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-07-22_15-07-22_184925_45026/sockets/raylet', 'webui_url': '127.0.0.1:8265', 'session_dir': '/tmp/ray/session_2022-07-22_15-07-22_184925_45026', 'metrics_export_port': 57745, 'gcs_address': '127.0.0.1:58825', 'address': '127.0.0.1:58825', 'dashboard_agent_listen_port': 52365, 'node_id': 'c1cef9dffdd1fc9628495d1329d74226b0fa64cb8709e45af80fc254'})"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ray.init(configure_logging=False)"
]
},
{
"cell_type": "markdown",
"id": "32ee1ba7",
"metadata": {},
"source": [
"Next we define a search space. The critical assumption is that the optimal\n",
"hyperparameters live within this space. Yet, if the space is very large,\n",
"then those hyperparameters may be difficult to find in a short amount of time."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "21598e54",
"metadata": {},
"outputs": [],
"source": [
"search_space = {\n",
" \"steps\": 100,\n",
" \"width\": tune.uniform(0, 20),\n",
" \"height\": tune.uniform(-100, 100),\n",
" \"activation\": tune.choice([\"relu\", \"tanh\"]),\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "def82932",
"metadata": {},
"source": [
"Next we define the search algorithm built from `TuneBOHB`, constrained\n",
"to a maximum of `4` concurrent trials with a `ConcurrencyLimiter`.\n",
"Below `algo` will take care of the BO (Bayesian optimization) part of BOHB,\n",
"while scheduler will take care the HB (HyperBand) part."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e847b5b6",
"metadata": {},
"outputs": [],
"source": [
"algo = TuneBOHB()\n",
"algo = tune.search.ConcurrencyLimiter(algo, max_concurrent=4)\n",
"scheduler = HyperBandForBOHB(\n",
" time_attr=\"training_iteration\",\n",
" max_t=100,\n",
" reduction_factor=4,\n",
" stop_last_trials=False,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "1787a842",
"metadata": {},
"source": [
"The number of samples is the number of hyperparameter combinations\n",
"that will be tried out. This Tune run is set to `1000` samples.\n",
"(you can decrease this if it takes too long on your machine)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "702eb3d4",
"metadata": {},
"outputs": [],
"source": [
"num_samples = 1000"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "dfb3ecad",
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"tags": [
"remove-cell"
]
},
"outputs": [],
"source": [
"num_samples = 10"
]
},
{
"cell_type": "markdown",
"id": "aa5936df",
"metadata": {},
"source": [
"Finally, we run the experiment to `min`imize the \"mean_loss\" of the `objective`\n",
"by searching within `\"steps\": 100` via `algo`, `num_samples` times. This previous\n",
"sentence is fully characterizes the search problem we aim to solve.\n",
"With this in mind, notice how efficient it is to execute `tuner.fit()`."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4bdfb12d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"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"
]
},
{
"data": {
"text/html": [
"== Status ==<br>Current time: 2022-07-22 15:07:54 (running for 00:00:26.41)<br>Memory usage on this node: 9.9/16.0 GiB<br>Using HyperBand: num_stopped=9 total_brackets=1\n",
"Round #0:\n",
" Bracket(Max Size (n)=1, Milestone (r)=64, completed=66.8%): {TERMINATED: 10} <br>Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/4.95 GiB heap, 0.0/2.0 GiB objects<br>Current best trial: a0c11456 with mean_loss=-4.53376204004117 and parameters={'steps': 100, 'width': 3.7250202606878258, 'height': -57.97769618290691, 'activation': 'tanh'}<br>Result logdir: /Users/kai/ray_results/bohb_exp<br>Number of trials: 10/10 (10 TERMINATED)<br><table>\n",
"<thead>\n",
"<tr><th>Trial name </th><th>status </th><th>loc </th><th>activation </th><th style=\"text-align: right;\"> height</th><th style=\"text-align: right;\"> width</th><th style=\"text-align: right;\"> loss</th><th style=\"text-align: right;\"> iter</th><th style=\"text-align: right;\"> total time (s)</th><th style=\"text-align: right;\"> ts</th><th style=\"text-align: right;\"> iterations</th><th style=\"text-align: right;\"> neg_mean_loss</th></tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td>objective_9e8d8b06</td><td>TERMINATED</td><td>127.0.0.1:45117</td><td>tanh </td><td style=\"text-align: right;\"> 37.6516 </td><td style=\"text-align: right;\">12.2188 </td><td style=\"text-align: right;\"> 5.28254</td><td style=\"text-align: right;\"> 16</td><td style=\"text-align: right;\"> 2.23943 </td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 15</td><td style=\"text-align: right;\"> -5.28254</td></tr>\n",
"<tr><td>objective_a052a214</td><td>TERMINATED</td><td>127.0.0.1:45150</td><td>relu </td><td style=\"text-align: right;\"> -4.10627</td><td style=\"text-align: right;\">17.9931 </td><td style=\"text-align: right;\">11.1524 </td><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\"> 0.531915</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> -11.1524 </td></tr>\n",
"<tr><td>objective_a06180d6</td><td>TERMINATED</td><td>127.0.0.1:45151</td><td>tanh </td><td style=\"text-align: right;\"> 89.5711 </td><td style=\"text-align: right;\"> 8.05512</td><td style=\"text-align: right;\">12.884 </td><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\"> 0.534212</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> -12.884 </td></tr>\n",
"<tr><td>objective_a077899e</td><td>TERMINATED</td><td>127.0.0.1:45152</td><td>relu </td><td style=\"text-align: right;\"> 67.3538 </td><td style=\"text-align: right;\">13.6388 </td><td style=\"text-align: right;\">18.6994 </td><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\"> 0.538702</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> -18.6994 </td></tr>\n",
"<tr><td>objective_a0865c76</td><td>TERMINATED</td><td>127.0.0.1:45153</td><td>relu </td><td style=\"text-align: right;\"> 25.9876 </td><td style=\"text-align: right;\"> 9.57103</td><td style=\"text-align: right;\">15.1819 </td><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\"> 0.559531</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> -15.1819 </td></tr>\n",
"<tr><td>objective_a0a42d1e</td><td>TERMINATED</td><td>127.0.0.1:45154</td><td>relu </td><td style=\"text-align: right;\"> 80.9133 </td><td style=\"text-align: right;\">13.0972 </td><td style=\"text-align: right;\">20.1201 </td><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\"> 0.538588</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> -20.1201 </td></tr>\n",
"<tr><td>objective_a0c11456</td><td>TERMINATED</td><td>127.0.0.1:45117</td><td>tanh </td><td style=\"text-align: right;\">-57.9777 </td><td style=\"text-align: right;\"> 3.72502</td><td style=\"text-align: right;\">-4.53376</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 13.0563 </td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 99</td><td style=\"text-align: right;\"> 4.53376</td></tr>\n",
"<tr><td>objective_a0dce442</td><td>TERMINATED</td><td>127.0.0.1:45200</td><td>tanh </td><td style=\"text-align: right;\"> -1.68715</td><td style=\"text-align: right;\"> 1.87185</td><td style=\"text-align: right;\"> 3.4575 </td><td style=\"text-align: right;\"> 16</td><td style=\"text-align: right;\"> 2.27704 </td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 15</td><td style=\"text-align: right;\"> -3.4575 </td></tr>\n",
"<tr><td>objective_a0f84156</td><td>TERMINATED</td><td>127.0.0.1:45157</td><td>tanh </td><td style=\"text-align: right;\"> 65.9879 </td><td style=\"text-align: right;\"> 5.41575</td><td style=\"text-align: right;\">11.4087 </td><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\"> 0.548028</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> -11.4087 </td></tr>\n",
"<tr><td>objective_a1142416</td><td>TERMINATED</td><td>127.0.0.1:45201</td><td>tanh </td><td style=\"text-align: right;\"> 5.35569</td><td style=\"text-align: right;\"> 4.85644</td><td style=\"text-align: right;\"> 2.74262</td><td style=\"text-align: right;\"> 16</td><td style=\"text-align: right;\"> 2.27749 </td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 15</td><td style=\"text-align: right;\"> -2.74262</td></tr>\n",
"</tbody>\n",
"</table><br><br>"
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"text": [
"Result for objective_9e8d8b06:\n",
" date: 2022-07-22_15-07-31\n",
" done: false\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 14.765164520733162\n",
" neg_mean_loss: -14.765164520733162\n",
" node_ip: 127.0.0.1\n",
" pid: 45117\n",
" time_since_restore: 0.10084700584411621\n",
" time_this_iter_s: 0.10084700584411621\n",
" time_total_s: 0.10084700584411621\n",
" timestamp: 1658498851\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: 9e8d8b06\n",
" warmup_time: 0.005752086639404297\n",
" \n",
"Result for objective_a06180d6:\n",
" date: 2022-07-22_15-07-31\n",
" done: false\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 19.957107852020386\n",
" neg_mean_loss: -19.957107852020386\n",
" node_ip: 127.0.0.1\n",
" pid: 45117\n",
" time_since_restore: 0.10333395004272461\n",
" time_this_iter_s: 0.10333395004272461\n",
" time_total_s: 0.10333395004272461\n",
" timestamp: 1658498851\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: a06180d6\n",
" warmup_time: 0.005752086639404297\n",
" \n",
"Result for objective_a0a42d1e:\n",
" date: 2022-07-22_15-07-31\n",
" done: false\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 28.091327935768636\n",
" neg_mean_loss: -28.091327935768636\n",
" node_ip: 127.0.0.1\n",
" pid: 45117\n",
" time_since_restore: 0.10419368743896484\n",
" time_this_iter_s: 0.10419368743896484\n",
" time_total_s: 0.10419368743896484\n",
" timestamp: 1658498851\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: a0a42d1e\n",
" warmup_time: 0.005752086639404297\n",
" \n",
"Result for objective_a0c11456:\n",
" date: 2022-07-22_15-07-31\n",
" done: false\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 5.202230381709309\n",
" neg_mean_loss: -5.202230381709309\n",
" node_ip: 127.0.0.1\n",
" pid: 45117\n",
" time_since_restore: 0.10060286521911621\n",
" time_this_iter_s: 0.10060286521911621\n",
" time_total_s: 0.10060286521911621\n",
" timestamp: 1658498851\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: a0c11456\n",
" warmup_time: 0.005752086639404297\n",
" \n",
"Result for objective_a0dce442:\n",
" date: 2022-07-22_15-07-32\n",
" done: false\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 10.831285273000042\n",
" neg_mean_loss: -10.831285273000042\n",
" node_ip: 127.0.0.1\n",
" pid: 45117\n",
" time_since_restore: 0.10183191299438477\n",
" time_this_iter_s: 0.10183191299438477\n",
" time_total_s: 0.10183191299438477\n",
" timestamp: 1658498852\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: a0dce442\n",
" warmup_time: 0.005752086639404297\n",
" \n",
"Result for objective_a0f84156:\n",
" date: 2022-07-22_15-07-32\n",
" done: false\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 17.598785421495204\n",
" neg_mean_loss: -17.598785421495204\n",
" node_ip: 127.0.0.1\n",
" pid: 45117\n",
" time_since_restore: 0.10328507423400879\n",
" time_this_iter_s: 0.10328507423400879\n",
" time_total_s: 0.10328507423400879\n",
" timestamp: 1658498852\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: a0f84156\n",
" warmup_time: 0.005752086639404297\n",
" \n",
"Result for objective_a1142416:\n",
" date: 2022-07-22_15-07-32\n",
" done: false\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 11.53556906389516\n",
" neg_mean_loss: -11.53556906389516\n",
" node_ip: 127.0.0.1\n",
" pid: 45117\n",
" time_since_restore: 0.10475611686706543\n",
" time_this_iter_s: 0.10475611686706543\n",
" time_total_s: 0.10475611686706543\n",
" timestamp: 1658498852\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: a1142416\n",
" warmup_time: 0.005752086639404297\n",
" \n",
"Result for objective_a052a214:\n",
" date: 2022-07-22_15-07-34\n",
" done: false\n",
" experiment_id: 0fb96edfd1b34561b337e7146a3c64aa\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 19.5893732640325\n",
" neg_mean_loss: -19.5893732640325\n",
" node_ip: 127.0.0.1\n",
" pid: 45126\n",
" time_since_restore: 0.10437989234924316\n",
" time_this_iter_s: 0.10437989234924316\n",
" time_total_s: 0.10437989234924316\n",
" timestamp: 1658498854\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: a052a214\n",
" warmup_time: 0.003122091293334961\n",
" \n",
"Result for objective_a077899e:\n",
" date: 2022-07-22_15-07-34\n",
" done: false\n",
" experiment_id: 008f8a44fb0c4475a3e9dad32f1bd2c9\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 26.735381559909975\n",
" neg_mean_loss: -26.735381559909975\n",
" node_ip: 127.0.0.1\n",
" pid: 45129\n",
" time_since_restore: 0.10449695587158203\n",
" time_this_iter_s: 0.10449695587158203\n",
" time_total_s: 0.10449695587158203\n",
" timestamp: 1658498854\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: a077899e\n",
" warmup_time: 0.0031490325927734375\n",
" \n",
"Result for objective_a0865c76:\n",
" date: 2022-07-22_15-07-34\n",
" done: false\n",
" experiment_id: 06a194a4b7784ca5932a66e44e3cd815\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 22.598763488408313\n",
" neg_mean_loss: -22.598763488408313\n",
" node_ip: 127.0.0.1\n",
" pid: 45132\n",
" time_since_restore: 0.10512685775756836\n",
" time_this_iter_s: 0.10512685775756836\n",
" time_total_s: 0.10512685775756836\n",
" timestamp: 1658498854\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: a0865c76\n",
" warmup_time: 0.00397801399230957\n",
" \n"
]
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"\u001b[2m\u001b[36m(objective pid=45117)\u001b[0m 2022-07-22 15:07:34,468\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp/objective_9e8d8b06_1_activation=tanh,height=37.6516,steps=100,width=12.2188_2022-07-22_15-07-28/checkpoint_tmpf674c6\n",
"\u001b[2m\u001b[36m(objective pid=45117)\u001b[0m 2022-07-22 15:07:34,469\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10084700584411621, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45154)\u001b[0m 2022-07-22 15:07:37,958\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp/objective_a0a42d1e_6_activation=relu,height=80.9133,steps=100,width=13.0972_2022-07-22_15-07-31/checkpoint_tmp30fafa\n",
"\u001b[2m\u001b[36m(objective pid=45154)\u001b[0m 2022-07-22 15:07:37,958\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10419368743896484, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45155)\u001b[0m 2022-07-22 15:07:37,952\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp/objective_a0c11456_7_activation=tanh,height=-57.9777,steps=100,width=3.7250_2022-07-22_15-07-31/checkpoint_tmp7c225a\n",
"\u001b[2m\u001b[36m(objective pid=45155)\u001b[0m 2022-07-22 15:07:37,952\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10060286521911621, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45151)\u001b[0m 2022-07-22 15:07:37,933\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp/objective_a06180d6_3_activation=tanh,height=89.5711,steps=100,width=8.0551_2022-07-22_15-07-31/checkpoint_tmpcde58c\n",
"\u001b[2m\u001b[36m(objective pid=45151)\u001b[0m 2022-07-22 15:07:37,933\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10333395004272461, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45150)\u001b[0m 2022-07-22 15:07:37,929\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp/objective_a052a214_2_activation=relu,height=-4.1063,steps=100,width=17.9931_2022-07-22_15-07-31/checkpoint_tmp24072a\n",
"\u001b[2m\u001b[36m(objective pid=45150)\u001b[0m 2022-07-22 15:07:37,929\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10437989234924316, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45153)\u001b[0m 2022-07-22 15:07:38,010\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp/objective_a0865c76_5_activation=relu,height=25.9876,steps=100,width=9.5710_2022-07-22_15-07-31/checkpoint_tmpad3367\n",
"\u001b[2m\u001b[36m(objective pid=45153)\u001b[0m 2022-07-22 15:07:38,010\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10512685775756836, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45158)\u001b[0m 2022-07-22 15:07:38,010\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp/objective_a1142416_10_activation=tanh,height=5.3557,steps=100,width=4.8564_2022-07-22_15-07-32/checkpoint_tmpe18b15\n",
"\u001b[2m\u001b[36m(objective pid=45158)\u001b[0m 2022-07-22 15:07:38,010\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10475611686706543, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45156)\u001b[0m 2022-07-22 15:07:38,008\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp/objective_a0dce442_8_activation=tanh,height=-1.6871,steps=100,width=1.8718_2022-07-22_15-07-32/checkpoint_tmp01ad4f\n",
"\u001b[2m\u001b[36m(objective pid=45156)\u001b[0m 2022-07-22 15:07:38,008\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10183191299438477, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45157)\u001b[0m 2022-07-22 15:07:38,025\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp/objective_a0f84156_9_activation=tanh,height=65.9879,steps=100,width=5.4158_2022-07-22_15-07-32/checkpoint_tmp57fedd\n",
"\u001b[2m\u001b[36m(objective pid=45157)\u001b[0m 2022-07-22 15:07:38,025\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10328507423400879, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45152)\u001b[0m 2022-07-22 15:07:38,022\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp/objective_a077899e_4_activation=relu,height=67.3538,steps=100,width=13.6388_2022-07-22_15-07-31/checkpoint_tmpfdb6b4\n",
"\u001b[2m\u001b[36m(objective pid=45152)\u001b[0m 2022-07-22 15:07:38,022\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10449695587158203, '_episodes_total': 0}\n"
]
},
{
"name": "stdout",
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"text": [
"Result for objective_a06180d6:\n",
" date: 2022-07-22_15-07-38\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 19.957107852020386\n",
" neg_mean_loss: -19.957107852020386\n",
" node_ip: 127.0.0.1\n",
" pid: 45151\n",
" time_since_restore: 0.10456109046936035\n",
" time_this_iter_s: 0.10456109046936035\n",
" time_total_s: 0.20789504051208496\n",
" timestamp: 1658498858\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: a06180d6\n",
" warmup_time: 0.010712862014770508\n",
" \n",
"Result for objective_a0c11456:\n",
" date: 2022-07-22_15-07-38\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 5.202230381709309\n",
" neg_mean_loss: -5.202230381709309\n",
" node_ip: 127.0.0.1\n",
" pid: 45155\n",
" time_since_restore: 0.10466408729553223\n",
" time_this_iter_s: 0.10466408729553223\n",
" time_total_s: 0.20526695251464844\n",
" timestamp: 1658498858\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: a0c11456\n",
" warmup_time: 0.007361888885498047\n",
" \n",
"Result for objective_a0a42d1e:\n",
" date: 2022-07-22_15-07-38\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 28.091327935768636\n",
" neg_mean_loss: -28.091327935768636\n",
" node_ip: 127.0.0.1\n",
" pid: 45154\n",
" time_since_restore: 0.10032892227172852\n",
" time_this_iter_s: 0.10032892227172852\n",
" time_total_s: 0.20452260971069336\n",
" timestamp: 1658498858\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: a0a42d1e\n",
" warmup_time: 0.008682966232299805\n",
" \n",
"Result for objective_a0dce442:\n",
" date: 2022-07-22_15-07-38\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 10.831285273000042\n",
" neg_mean_loss: -10.831285273000042\n",
" node_ip: 127.0.0.1\n",
" pid: 45156\n",
" time_since_restore: 0.10120797157287598\n",
" time_this_iter_s: 0.10120797157287598\n",
" time_total_s: 0.20303988456726074\n",
" timestamp: 1658498858\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: a0dce442\n",
" warmup_time: 0.0077381134033203125\n",
" \n",
"Result for objective_a1142416:\n",
" date: 2022-07-22_15-07-38\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 11.53556906389516\n",
" neg_mean_loss: -11.53556906389516\n",
" node_ip: 127.0.0.1\n",
" pid: 45158\n",
" time_since_restore: 0.10349392890930176\n",
" time_this_iter_s: 0.10349392890930176\n",
" time_total_s: 0.2082500457763672\n",
" timestamp: 1658498858\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: a1142416\n",
" warmup_time: 0.007157087326049805\n",
" \n",
"Result for objective_a0f84156:\n",
" date: 2022-07-22_15-07-38\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 17.598785421495204\n",
" neg_mean_loss: -17.598785421495204\n",
" node_ip: 127.0.0.1\n",
" pid: 45157\n",
" time_since_restore: 0.10368680953979492\n",
" time_this_iter_s: 0.10368680953979492\n",
" time_total_s: 0.2069718837738037\n",
" timestamp: 1658498858\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: a0f84156\n",
" warmup_time: 0.006359100341796875\n",
" \n",
"Result for objective_a052a214:\n",
" date: 2022-07-22_15-07-38\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: 0fb96edfd1b34561b337e7146a3c64aa\n",
" experiment_tag: 2_activation=relu,height=-4.1063,steps=100,width=17.9931\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 3\n",
" iterations_since_restore: 4\n",
" mean_loss: 11.152378612292932\n",
" neg_mean_loss: -11.152378612292932\n",
" node_ip: 127.0.0.1\n",
" pid: 45150\n",
" time_since_restore: 0.4275352954864502\n",
" time_this_iter_s: 0.1124732494354248\n",
" time_total_s: 0.5319151878356934\n",
" timestamp: 1658498858\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 4\n",
" trial_id: a052a214\n",
" warmup_time: 0.010692834854125977\n",
" \n",
"Result for objective_9e8d8b06:\n",
" date: 2022-07-22_15-07-39\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 14.765164520733162\n",
" neg_mean_loss: -14.765164520733162\n",
" node_ip: 127.0.0.1\n",
" pid: 45117\n",
" time_since_restore: 0.10145044326782227\n",
" time_this_iter_s: 0.10145044326782227\n",
" time_total_s: 0.6308252811431885\n",
" timestamp: 1658498859\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: 9e8d8b06\n",
" warmup_time: 0.005752086639404297\n",
" \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(objective pid=45117)\u001b[0m 2022-07-22 15:07:39,222\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp/objective_9e8d8b06_1_activation=tanh,height=37.6516,steps=100,width=12.2188_2022-07-22_15-07-28/checkpoint_tmpacb5ff\n",
"\u001b[2m\u001b[36m(objective pid=45117)\u001b[0m 2022-07-22 15:07:39,223\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.5293748378753662, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45199)\u001b[0m 2022-07-22 15:07:41,833\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp/objective_a0c11456_7_activation=tanh,height=-57.9777,steps=100,width=3.7250_2022-07-22_15-07-31/checkpoint_tmpdf2071\n",
"\u001b[2m\u001b[36m(objective pid=45199)\u001b[0m 2022-07-22 15:07:41,833\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.53450608253479, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45200)\u001b[0m 2022-07-22 15:07:41,833\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp/objective_a0dce442_8_activation=tanh,height=-1.6871,steps=100,width=1.8718_2022-07-22_15-07-32/checkpoint_tmp9cd0fc\n",
"\u001b[2m\u001b[36m(objective pid=45200)\u001b[0m 2022-07-22 15:07:41,833\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.5349018573760986, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45201)\u001b[0m 2022-07-22 15:07:41,882\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp/objective_a1142416_10_activation=tanh,height=5.3557,steps=100,width=4.8564_2022-07-22_15-07-32/checkpoint_tmp7bd297\n",
"\u001b[2m\u001b[36m(objective pid=45201)\u001b[0m 2022-07-22 15:07:41,882\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.5445291996002197, '_episodes_total': 0}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Result for objective_a0c11456:\n",
" date: 2022-07-22_15-07-43\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 11\n",
" iterations_since_restore: 12\n",
" mean_loss: -2.8360322412948014\n",
" neg_mean_loss: 2.8360322412948014\n",
" node_ip: 127.0.0.1\n",
" pid: 45199\n",
" time_since_restore: 1.3028512001037598\n",
" time_this_iter_s: 0.10884499549865723\n",
" time_total_s: 1.8373572826385498\n",
" timestamp: 1658498863\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 12\n",
" trial_id: a0c11456\n",
" warmup_time: 0.0074689388275146484\n",
" \n",
"Result for objective_a0dce442:\n",
" date: 2022-07-22_15-07-43\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 11\n",
" iterations_since_restore: 12\n",
" mean_loss: 4.100295137851358\n",
" neg_mean_loss: -4.100295137851358\n",
" node_ip: 127.0.0.1\n",
" pid: 45200\n",
" time_since_restore: 1.3082969188690186\n",
" time_this_iter_s: 0.10707712173461914\n",
" time_total_s: 1.8431987762451172\n",
" timestamp: 1658498863\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 12\n",
" trial_id: a0dce442\n",
" warmup_time: 0.007519960403442383\n",
" \n",
"Result for objective_a1142416:\n",
" date: 2022-07-22_15-07-43\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 11\n",
" iterations_since_restore: 12\n",
" mean_loss: 3.112339173810433\n",
" neg_mean_loss: -3.112339173810433\n",
" node_ip: 127.0.0.1\n",
" pid: 45201\n",
" time_since_restore: 1.2954580783843994\n",
" time_this_iter_s: 0.1081690788269043\n",
" time_total_s: 1.8399872779846191\n",
" timestamp: 1658498863\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 12\n",
" trial_id: a1142416\n",
" warmup_time: 0.006086826324462891\n",
" \n",
"Result for objective_a1142416:\n",
" date: 2022-07-22_15-07-43\n",
" done: true\n",
" episodes_total: 0\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 15\n",
" iterations_since_restore: 16\n",
" mean_loss: 2.7426202715773025\n",
" neg_mean_loss: -2.7426202715773025\n",
" node_ip: 127.0.0.1\n",
" pid: 45201\n",
" time_since_restore: 1.7329609394073486\n",
" time_this_iter_s: 0.1072549819946289\n",
" time_total_s: 2.2774901390075684\n",
" timestamp: 1658498863\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 16\n",
" trial_id: a1142416\n",
" warmup_time: 0.006086826324462891\n",
" \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(objective pid=45117)\u001b[0m 2022-07-22 15:07:43,765\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp/objective_a0c11456_7_activation=tanh,height=-57.9777,steps=100,width=3.7250_2022-07-22_15-07-31/checkpoint_tmp3f09eb\n",
"\u001b[2m\u001b[36m(objective pid=45117)\u001b[0m 2022-07-22 15:07:43,765\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 2.273958206176758, '_episodes_total': 0}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Result for objective_a0c11456:\n",
" date: 2022-07-22_15-07-48\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 40\n",
" iterations_since_restore: 41\n",
" mean_loss: -4.168842006342518\n",
" neg_mean_loss: 4.168842006342518\n",
" node_ip: 127.0.0.1\n",
" pid: 45117\n",
" time_since_restore: 4.4259912967681885\n",
" time_this_iter_s: 0.10874629020690918\n",
" time_total_s: 6.699949502944946\n",
" timestamp: 1658498868\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 41\n",
" trial_id: a0c11456\n",
" warmup_time: 0.005752086639404297\n",
" \n",
"Result for objective_a0c11456:\n",
" date: 2022-07-22_15-07-53\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 87\n",
" iterations_since_restore: 88\n",
" mean_loss: -4.498437215767879\n",
" neg_mean_loss: 4.498437215767879\n",
" node_ip: 127.0.0.1\n",
" pid: 45117\n",
" time_since_restore: 9.488422155380249\n",
" time_this_iter_s: 0.10667800903320312\n",
" time_total_s: 11.762380361557007\n",
" timestamp: 1658498873\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 88\n",
" trial_id: a0c11456\n",
" warmup_time: 0.005752086639404297\n",
" \n",
"Result for objective_a0c11456:\n",
" date: 2022-07-22_15-07-54\n",
" done: true\n",
" episodes_total: 0\n",
" experiment_id: c1cb9895c3f04e73b7cce9435cd92c68\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 99\n",
" iterations_since_restore: 100\n",
" mean_loss: -4.53376204004117\n",
" neg_mean_loss: 4.53376204004117\n",
" node_ip: 127.0.0.1\n",
" pid: 45117\n",
" time_since_restore: 10.782363176345825\n",
" time_this_iter_s: 0.1065070629119873\n",
" time_total_s: 13.056321382522583\n",
" timestamp: 1658498874\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 100\n",
" trial_id: a0c11456\n",
" warmup_time: 0.005752086639404297\n",
" \n"
]
}
],
"source": [
"tuner = tune.Tuner(\n",
" objective,\n",
" tune_config=tune.TuneConfig(\n",
" metric=\"mean_loss\",\n",
" mode=\"min\",\n",
" search_alg=algo,\n",
" scheduler=scheduler,\n",
" num_samples=num_samples,\n",
" ),\n",
" run_config=air.RunConfig(\n",
" name=\"bohb_exp\",\n",
" stop={\"training_iteration\": 100},\n",
" ),\n",
" param_space=search_space,\n",
")\n",
"results = tuner.fit()"
]
},
{
"cell_type": "markdown",
"id": "3e89853c",
"metadata": {},
"source": [
"Here are the hyperparameters found to minimize the mean loss of the defined objective."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "4be691d5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best hyperparameters found were: {'steps': 100, 'width': 3.7250202606878258, 'height': -57.97769618290691, 'activation': 'tanh'}\n"
]
}
],
"source": [
"print(\"Best hyperparameters found were: \", results.get_best_result().config)"
]
},
{
"cell_type": "markdown",
"id": "800a19d9",
"metadata": {},
"source": [
"## Optional: Passing the search space via the TuneBOHB algorithm\n",
"\n",
"We can define the hyperparameter search space using `ConfigSpace`,\n",
"which is the format accepted by BOHB."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "b96cb496",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/ipykernel_launcher.py:3: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" This is separate from the ipykernel package so we can avoid doing imports until\n",
"/Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/ipykernel_launcher.py:6: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" \n",
"/Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/ipykernel_launcher.py:9: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" if __name__ == \"__main__\":\n",
"/Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/ipykernel_launcher.py:13: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" del sys.path[0]\n"
]
},
{
"data": {
"text/plain": [
"activation, Type: Categorical, Choices: {relu, tanh}, Default: relu"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"config_space = CS.ConfigurationSpace()\n",
"config_space.add_hyperparameter(\n",
" CS.Constant(\"steps\", 100)\n",
")\n",
"config_space.add_hyperparameter(\n",
" CS.UniformFloatHyperparameter(\"width\", lower=0, upper=20)\n",
")\n",
"config_space.add_hyperparameter(\n",
" CS.UniformFloatHyperparameter(\"height\", lower=-100, upper=100)\n",
")\n",
"config_space.add_hyperparameter(\n",
" CS.CategoricalHyperparameter(\n",
" \"activation\", choices=[\"relu\", \"tanh\"]\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "9cb77270",
"metadata": {},
"outputs": [],
"source": [
"# As we are passing config space directly to the searcher,\n",
"# we need to define metric and mode in it as well, in addition\n",
"# to Tuner()\n",
"algo = TuneBOHB(\n",
" space=config_space,\n",
" metric=\"mean_loss\",\n",
" mode=\"max\",\n",
")\n",
"algo = tune.search.ConcurrencyLimiter(algo, max_concurrent=4)\n",
"scheduler = HyperBandForBOHB(\n",
" time_attr=\"training_iteration\",\n",
" max_t=100,\n",
" reduction_factor=4,\n",
" stop_last_trials=False,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "8305c975",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/html": [
"== Status ==<br>Current time: 2022-07-22 15:11:40 (running for 00:00:29.52)<br>Memory usage on this node: 10.3/16.0 GiB<br>Using HyperBand: num_stopped=9 total_brackets=1\n",
"Round #0:\n",
" Bracket(Max Size (n)=1, Milestone (r)=64, completed=66.8%): {TERMINATED: 10} <br>Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/4.95 GiB heap, 0.0/2.0 GiB objects<br>Current best trial: 25b64488 with mean_loss=-3.74634537130406 and parameters={'activation': 'tanh', 'height': -48.451797714080236, 'steps': 100, 'width': 10.119125894538891}<br>Result logdir: /Users/kai/ray_results/bohb_exp_2<br>Number of trials: 10/10 (10 TERMINATED)<br><table>\n",
"<thead>\n",
"<tr><th>Trial name </th><th>status </th><th>loc </th><th>activation </th><th style=\"text-align: right;\"> height</th><th style=\"text-align: right;\"> steps</th><th style=\"text-align: right;\"> width</th><th style=\"text-align: right;\"> loss</th><th style=\"text-align: right;\"> iter</th><th style=\"text-align: right;\"> total time (s)</th><th style=\"text-align: right;\"> ts</th><th style=\"text-align: right;\"> iterations</th><th style=\"text-align: right;\"> neg_mean_loss</th></tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td>objective_2397442c</td><td>TERMINATED</td><td>127.0.0.1:45401</td><td>tanh </td><td style=\"text-align: right;\"> 32.8422</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\">12.1847 </td><td style=\"text-align: right;\"> 4.80297</td><td style=\"text-align: right;\"> 16</td><td style=\"text-align: right;\"> 2.25951 </td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 15</td><td style=\"text-align: right;\"> -4.80297</td></tr>\n",
"<tr><td>objective_25b4a998</td><td>TERMINATED</td><td>127.0.0.1:45401</td><td>relu </td><td style=\"text-align: right;\"> 20.2852</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 2.08202 </td><td style=\"text-align: right;\">18.1839 </td><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\"> 0.535426</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> -18.1839 </td></tr>\n",
"<tr><td>objective_25b64488</td><td>TERMINATED</td><td>127.0.0.1:45453</td><td>tanh </td><td style=\"text-align: right;\">-48.4518</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\">10.1191 </td><td style=\"text-align: right;\">-3.74635</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 11.5319 </td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 99</td><td style=\"text-align: right;\"> 3.74635</td></tr>\n",
"<tr><td>objective_25b7dfe6</td><td>TERMINATED</td><td>127.0.0.1:45403</td><td>relu </td><td style=\"text-align: right;\">-18.8439</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\">19.1277 </td><td style=\"text-align: right;\"> 9.59966</td><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\"> 0.581903</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> -9.59966</td></tr>\n",
"<tr><td>objective_25cfab4e</td><td>TERMINATED</td><td>127.0.0.1:45404</td><td>relu </td><td style=\"text-align: right;\"> 17.2057</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 0.317083</td><td style=\"text-align: right;\">20.8519 </td><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\"> 0.59468 </td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> -20.8519 </td></tr>\n",
"<tr><td>objective_278eba4c</td><td>TERMINATED</td><td>127.0.0.1:45454</td><td>relu </td><td style=\"text-align: right;\">-27.0179</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\">13.577 </td><td style=\"text-align: right;\"> 7.76626</td><td style=\"text-align: right;\"> 16</td><td style=\"text-align: right;\"> 2.31198 </td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 15</td><td style=\"text-align: right;\"> -7.76626</td></tr>\n",
"<tr><td>objective_279d01a6</td><td>TERMINATED</td><td>127.0.0.1:45407</td><td>relu </td><td style=\"text-align: right;\"> 59.1103</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 2.4466 </td><td style=\"text-align: right;\">21.6781 </td><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\"> 0.575556</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> -21.6781 </td></tr>\n",
"<tr><td>objective_27aa31e6</td><td>TERMINATED</td><td>127.0.0.1:45409</td><td>relu </td><td style=\"text-align: right;\"> 50.058 </td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\">17.3776 </td><td style=\"text-align: right;\">16.6153 </td><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\"> 0.537561</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> -16.6153 </td></tr>\n",
"<tr><td>objective_27b7e2be</td><td>TERMINATED</td><td>127.0.0.1:45455</td><td>relu </td><td style=\"text-align: right;\">-51.2093</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 8.94948 </td><td style=\"text-align: right;\"> 5.57235</td><td style=\"text-align: right;\"> 16</td><td style=\"text-align: right;\"> 2.64238 </td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 15</td><td style=\"text-align: right;\"> -5.57235</td></tr>\n",
"<tr><td>objective_27c59a80</td><td>TERMINATED</td><td>127.0.0.1:45446</td><td>relu </td><td style=\"text-align: right;\"> 29.165 </td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 4.26995 </td><td style=\"text-align: right;\">17.3006 </td><td style=\"text-align: right;\"> 4</td><td style=\"text-align: right;\"> 0.539177</td><td style=\"text-align: right;\"> 0</td><td style=\"text-align: right;\"> 3</td><td style=\"text-align: right;\"> -17.3006 </td></tr>\n",
"</tbody>\n",
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"text": [
"Result for objective_2397442c:\n",
" date: 2022-07-22_15-11-15\n",
" done: false\n",
" experiment_id: 1a4ebf62df50443492dc6df792fcb67a\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 14.284216630043918\n",
" neg_mean_loss: -14.284216630043918\n",
" node_ip: 127.0.0.1\n",
" pid: 45353\n",
" time_since_restore: 0.10108494758605957\n",
" time_this_iter_s: 0.10108494758605957\n",
" time_total_s: 0.10108494758605957\n",
" timestamp: 1658499075\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: 2397442c\n",
" warmup_time: 0.002635955810546875\n",
" \n",
"Result for objective_25b7dfe6:\n",
" date: 2022-07-22_15-11-17\n",
" done: false\n",
" experiment_id: 0fd6607aa9674cb5a05b6ce63e474fd3\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 18.115606120430186\n",
" neg_mean_loss: -18.115606120430186\n",
" node_ip: 127.0.0.1\n",
" pid: 45371\n",
" time_since_restore: 0.10365676879882812\n",
" time_this_iter_s: 0.10365676879882812\n",
" time_total_s: 0.10365676879882812\n",
" timestamp: 1658499077\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: 25b7dfe6\n",
" warmup_time: 0.005063056945800781\n",
" \n",
"Result for objective_25b4a998:\n",
" date: 2022-07-22_15-11-17\n",
" done: false\n",
" experiment_id: 20a5d76dc18749e4b1c9f15c5d8b43cf\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 22.028519616352035\n",
" neg_mean_loss: -22.028519616352035\n",
" node_ip: 127.0.0.1\n",
" pid: 45369\n",
" time_since_restore: 0.10431909561157227\n",
" time_this_iter_s: 0.10431909561157227\n",
" time_total_s: 0.10431909561157227\n",
" timestamp: 1658499077\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: 25b4a998\n",
" warmup_time: 0.004379987716674805\n",
" \n",
"Result for objective_25b64488:\n",
" date: 2022-07-22_15-11-17\n",
" done: false\n",
" experiment_id: 75e7c1ad20a2495cac29630df6c3c782\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 6.154820228591976\n",
" neg_mean_loss: -6.154820228591976\n",
" node_ip: 127.0.0.1\n",
" pid: 45370\n",
" time_since_restore: 0.10407018661499023\n",
" time_this_iter_s: 0.10407018661499023\n",
" time_total_s: 0.10407018661499023\n",
" timestamp: 1658499077\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: 25b64488\n",
" warmup_time: 0.003113985061645508\n",
" \n",
"Result for objective_25cfab4e:\n",
" date: 2022-07-22_15-11-18\n",
" done: false\n",
" experiment_id: fdc43ca37ed44cde857ca150a8f1e84f\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 21.72056884033249\n",
" neg_mean_loss: -21.72056884033249\n",
" node_ip: 127.0.0.1\n",
" pid: 45378\n",
" time_since_restore: 0.10431408882141113\n",
" time_this_iter_s: 0.10431408882141113\n",
" time_total_s: 0.10431408882141113\n",
" timestamp: 1658499078\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: 25cfab4e\n",
" warmup_time: 0.0029649734497070312\n",
" \n",
"Result for objective_279d01a6:\n",
" date: 2022-07-22_15-11-18\n",
" done: false\n",
" experiment_id: 20a5d76dc18749e4b1c9f15c5d8b43cf\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 25.911032294938884\n",
" neg_mean_loss: -25.911032294938884\n",
" node_ip: 127.0.0.1\n",
" pid: 45369\n",
" time_since_restore: 0.10361599922180176\n",
" time_this_iter_s: 0.10361599922180176\n",
" time_total_s: 0.10361599922180176\n",
" timestamp: 1658499078\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: 279d01a6\n",
" warmup_time: 0.004379987716674805\n",
" \n",
"Result for objective_27aa31e6:\n",
" date: 2022-07-22_15-11-18\n",
" done: false\n",
" experiment_id: 75e7c1ad20a2495cac29630df6c3c782\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 25.005798678308594\n",
" neg_mean_loss: -25.005798678308594\n",
" node_ip: 127.0.0.1\n",
" pid: 45370\n",
" time_since_restore: 0.10430693626403809\n",
" time_this_iter_s: 0.10430693626403809\n",
" time_total_s: 0.10430693626403809\n",
" timestamp: 1658499078\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: 27aa31e6\n",
" warmup_time: 0.003113985061645508\n",
" \n",
"Result for objective_27b7e2be:\n",
" date: 2022-07-22_15-11-18\n",
" done: false\n",
" experiment_id: fdc43ca37ed44cde857ca150a8f1e84f\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 14.879072389639937\n",
" neg_mean_loss: -14.879072389639937\n",
" node_ip: 127.0.0.1\n",
" pid: 45378\n",
" time_since_restore: 0.10371994972229004\n",
" time_this_iter_s: 0.10371994972229004\n",
" time_total_s: 0.10371994972229004\n",
" timestamp: 1658499078\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: 27b7e2be\n",
" warmup_time: 0.0029649734497070312\n",
" \n",
"Result for objective_27c59a80:\n",
" date: 2022-07-22_15-11-18\n",
" done: false\n",
" experiment_id: 20a5d76dc18749e4b1c9f15c5d8b43cf\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 22.916501000037474\n",
" neg_mean_loss: -22.916501000037474\n",
" node_ip: 127.0.0.1\n",
" pid: 45369\n",
" time_since_restore: 0.10353732109069824\n",
" time_this_iter_s: 0.10353732109069824\n",
" time_total_s: 0.10353732109069824\n",
" timestamp: 1658499078\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: 27c59a80\n",
" warmup_time: 0.004379987716674805\n",
" \n",
"Result for objective_278eba4c:\n",
" date: 2022-07-22_15-11-20\n",
" done: false\n",
" experiment_id: 90186993d7ff42698c4615640d47d896\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 17.29821256734647\n",
" neg_mean_loss: -17.29821256734647\n",
" node_ip: 127.0.0.1\n",
" pid: 45393\n",
" time_since_restore: 0.10304784774780273\n",
" time_this_iter_s: 0.10304784774780273\n",
" time_total_s: 0.10304784774780273\n",
" timestamp: 1658499080\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: 278eba4c\n",
" warmup_time: 0.0027189254760742188\n",
" \n",
"Result for objective_2397442c:\n",
" date: 2022-07-22_15-11-20\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: 1a4ebf62df50443492dc6df792fcb67a\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 14.284216630043918\n",
" neg_mean_loss: -14.284216630043918\n",
" node_ip: 127.0.0.1\n",
" pid: 45370\n",
" time_since_restore: 0.10411787033081055\n",
" time_this_iter_s: 0.10411787033081055\n",
" time_total_s: 0.20520281791687012\n",
" timestamp: 1658499080\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: 2397442c\n",
" warmup_time: 0.003113985061645508\n",
" \n"
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"\u001b[2m\u001b[36m(objective pid=45370)\u001b[0m 2022-07-22 15:11:20,826\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp_2/objective_2397442c_1_activation=tanh,height=32.8422,steps=100,width=12.1847_2022-07-22_15-11-11/checkpoint_tmpf4b290\n",
"\u001b[2m\u001b[36m(objective pid=45370)\u001b[0m 2022-07-22 15:11:20,826\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10108494758605957, '_episodes_total': 0}\n"
]
},
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"name": "stdout",
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"text": [
"Result for objective_25b4a998:\n",
" date: 2022-07-22_15-11-24\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: 20a5d76dc18749e4b1c9f15c5d8b43cf\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 22.028519616352035\n",
" neg_mean_loss: -22.028519616352035\n",
" node_ip: 127.0.0.1\n",
" pid: 45401\n",
" time_since_restore: 0.10445284843444824\n",
" time_this_iter_s: 0.10445284843444824\n",
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" timestamp: 1658499084\n",
" timesteps_since_restore: 0\n",
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" training_iteration: 1\n",
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" warmup_time: 0.010488033294677734\n",
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"\u001b[2m\u001b[36m(objective pid=45405)\u001b[0m 2022-07-22 15:11:23,963\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp_2/objective_278eba4c_6_activation=relu,height=-27.0179,steps=100,width=13.5770_2022-07-22_15-11-18/checkpoint_tmpd366c5\n",
"\u001b[2m\u001b[36m(objective pid=45405)\u001b[0m 2022-07-22 15:11:23,964\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10304784774780273, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45401)\u001b[0m 2022-07-22 15:11:23,959\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp_2/objective_25b4a998_2_activation=relu,height=20.2852,steps=100,width=2.0820_2022-07-22_15-11-14/checkpoint_tmpc96cdb\n",
"\u001b[2m\u001b[36m(objective pid=45401)\u001b[0m 2022-07-22 15:11:23,959\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10431909561157227, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45402)\u001b[0m 2022-07-22 15:11:23,966\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp_2/objective_25b64488_3_activation=tanh,height=-48.4518,steps=100,width=10.1191_2022-07-22_15-11-14/checkpoint_tmp5ce175\n",
"\u001b[2m\u001b[36m(objective pid=45402)\u001b[0m 2022-07-22 15:11:23,966\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10407018661499023, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45407)\u001b[0m 2022-07-22 15:11:23,966\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp_2/objective_279d01a6_7_activation=relu,height=59.1103,steps=100,width=2.4466_2022-07-22_15-11-18/checkpoint_tmp942eac\n",
"\u001b[2m\u001b[36m(objective pid=45407)\u001b[0m 2022-07-22 15:11:23,966\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10361599922180176, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45404)\u001b[0m 2022-07-22 15:11:23,960\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp_2/objective_25cfab4e_5_activation=relu,height=17.2057,steps=100,width=0.3171_2022-07-22_15-11-15/checkpoint_tmpc8ceee\n",
"\u001b[2m\u001b[36m(objective pid=45404)\u001b[0m 2022-07-22 15:11:23,960\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10431408882141113, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45403)\u001b[0m 2022-07-22 15:11:23,966\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp_2/objective_25b7dfe6_4_activation=relu,height=-18.8439,steps=100,width=19.1277_2022-07-22_15-11-14/checkpoint_tmp24108b\n",
"\u001b[2m\u001b[36m(objective pid=45403)\u001b[0m 2022-07-22 15:11:23,966\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10365676879882812, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45409)\u001b[0m 2022-07-22 15:11:23,997\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp_2/objective_27aa31e6_8_activation=relu,height=50.0580,steps=100,width=17.3776_2022-07-22_15-11-18/checkpoint_tmp4218e5\n",
"\u001b[2m\u001b[36m(objective pid=45409)\u001b[0m 2022-07-22 15:11:23,997\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10430693626403809, '_episodes_total': 0}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Result for objective_27aa31e6:\n",
" date: 2022-07-22_15-11-24\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: 75e7c1ad20a2495cac29630df6c3c782\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 25.005798678308594\n",
" neg_mean_loss: -25.005798678308594\n",
" node_ip: 127.0.0.1\n",
" pid: 45409\n",
" time_since_restore: 0.10203695297241211\n",
" time_this_iter_s: 0.10203695297241211\n",
" time_total_s: 0.2063438892364502\n",
" timestamp: 1658499084\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: 27aa31e6\n",
" warmup_time: 0.0062160491943359375\n",
" \n",
"Result for objective_279d01a6:\n",
" date: 2022-07-22_15-11-24\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: 20a5d76dc18749e4b1c9f15c5d8b43cf\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 25.911032294938884\n",
" neg_mean_loss: -25.911032294938884\n",
" node_ip: 127.0.0.1\n",
" pid: 45407\n",
" time_since_restore: 0.10443115234375\n",
" time_this_iter_s: 0.10443115234375\n",
" time_total_s: 0.20804715156555176\n",
" timestamp: 1658499084\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: 279d01a6\n",
" warmup_time: 0.010073184967041016\n",
" \n",
"Result for objective_25b7dfe6:\n",
" date: 2022-07-22_15-11-24\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: 0fd6607aa9674cb5a05b6ce63e474fd3\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 18.115606120430186\n",
" neg_mean_loss: -18.115606120430186\n",
" node_ip: 127.0.0.1\n",
" pid: 45403\n",
" time_since_restore: 0.1044917106628418\n",
" time_this_iter_s: 0.1044917106628418\n",
" time_total_s: 0.20814847946166992\n",
" timestamp: 1658499084\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: 25b7dfe6\n",
" warmup_time: 0.011971235275268555\n",
" \n",
"Result for objective_25cfab4e:\n",
" date: 2022-07-22_15-11-24\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: fdc43ca37ed44cde857ca150a8f1e84f\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 21.72056884033249\n",
" neg_mean_loss: -21.72056884033249\n",
" node_ip: 127.0.0.1\n",
" pid: 45404\n",
" time_since_restore: 0.10379600524902344\n",
" time_this_iter_s: 0.10379600524902344\n",
" time_total_s: 0.20811009407043457\n",
" timestamp: 1658499084\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: 25cfab4e\n",
" warmup_time: 0.009023904800415039\n",
" \n",
"Result for objective_25b64488:\n",
" date: 2022-07-22_15-11-24\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: 75e7c1ad20a2495cac29630df6c3c782\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 6.154820228591976\n",
" neg_mean_loss: -6.154820228591976\n",
" node_ip: 127.0.0.1\n",
" pid: 45402\n",
" time_since_restore: 0.10440897941589355\n",
" time_this_iter_s: 0.10440897941589355\n",
" time_total_s: 0.2084791660308838\n",
" timestamp: 1658499084\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: 25b64488\n",
" warmup_time: 0.011686325073242188\n",
" \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(objective pid=45424)\u001b[0m 2022-07-22 15:11:24,383\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp_2/objective_27b7e2be_9_activation=relu,height=-51.2093,steps=100,width=8.9495_2022-07-22_15-11-18/checkpoint_tmp996dec\n",
"\u001b[2m\u001b[36m(objective pid=45424)\u001b[0m 2022-07-22 15:11:24,384\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10371994972229004, '_episodes_total': 0}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Result for objective_27b7e2be:\n",
" date: 2022-07-22_15-11-24\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: fdc43ca37ed44cde857ca150a8f1e84f\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 14.879072389639937\n",
" neg_mean_loss: -14.879072389639937\n",
" node_ip: 127.0.0.1\n",
" pid: 45424\n",
" time_since_restore: 0.1031639575958252\n",
" time_this_iter_s: 0.1031639575958252\n",
" time_total_s: 0.20688390731811523\n",
" timestamp: 1658499084\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: 27b7e2be\n",
" warmup_time: 0.0069200992584228516\n",
" \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(objective pid=45446)\u001b[0m 2022-07-22 15:11:26,749\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp_2/objective_27c59a80_10_activation=relu,height=29.1650,steps=100,width=4.2700_2022-07-22_15-11-18/checkpoint_tmp49d063\n",
"\u001b[2m\u001b[36m(objective pid=45446)\u001b[0m 2022-07-22 15:11:26,749\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.10353732109069824, '_episodes_total': 0}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Result for objective_27c59a80:\n",
" date: 2022-07-22_15-11-26\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: 20a5d76dc18749e4b1c9f15c5d8b43cf\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 22.916501000037474\n",
" neg_mean_loss: -22.916501000037474\n",
" node_ip: 127.0.0.1\n",
" pid: 45446\n",
" time_since_restore: 0.10502910614013672\n",
" time_this_iter_s: 0.10502910614013672\n",
" time_total_s: 0.20856642723083496\n",
" timestamp: 1658499086\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: 27c59a80\n",
" warmup_time: 0.007359027862548828\n",
" \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(objective pid=45401)\u001b[0m 2022-07-22 15:11:27,287\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp_2/objective_2397442c_1_activation=tanh,height=32.8422,steps=100,width=12.1847_2022-07-22_15-11-11/checkpoint_tmp2e0d09\n",
"\u001b[2m\u001b[36m(objective pid=45401)\u001b[0m 2022-07-22 15:11:27,287\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.535153865814209, '_episodes_total': 0}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Result for objective_2397442c:\n",
" date: 2022-07-22_15-11-27\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: 1a4ebf62df50443492dc6df792fcb67a\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 14.284216630043918\n",
" neg_mean_loss: -14.284216630043918\n",
" node_ip: 127.0.0.1\n",
" pid: 45401\n",
" time_since_restore: 0.1044008731842041\n",
" time_this_iter_s: 0.1044008731842041\n",
" time_total_s: 0.6395547389984131\n",
" timestamp: 1658499087\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: 2397442c\n",
" warmup_time: 0.010488033294677734\n",
" \n",
"Result for objective_278eba4c:\n",
" date: 2022-07-22_15-11-29\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: 90186993d7ff42698c4615640d47d896\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 17.29821256734647\n",
" neg_mean_loss: -17.29821256734647\n",
" node_ip: 127.0.0.1\n",
" pid: 45454\n",
" time_since_restore: 0.1037449836730957\n",
" time_this_iter_s: 0.1037449836730957\n",
" time_total_s: 0.6844677925109863\n",
" timestamp: 1658499089\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: 278eba4c\n",
" warmup_time: 0.006754875183105469\n",
" \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(objective pid=45454)\u001b[0m 2022-07-22 15:11:29,879\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp_2/objective_278eba4c_6_activation=relu,height=-27.0179,steps=100,width=13.5770_2022-07-22_15-11-18/checkpoint_tmp2835d4\n",
"\u001b[2m\u001b[36m(objective pid=45454)\u001b[0m 2022-07-22 15:11:29,879\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.5807228088378906, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45455)\u001b[0m 2022-07-22 15:11:29,909\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp_2/objective_27b7e2be_9_activation=relu,height=-51.2093,steps=100,width=8.9495_2022-07-22_15-11-18/checkpoint_tmpd7ea63\n",
"\u001b[2m\u001b[36m(objective pid=45455)\u001b[0m 2022-07-22 15:11:29,910\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.9150340557098389, '_episodes_total': 0}\n",
"\u001b[2m\u001b[36m(objective pid=45453)\u001b[0m 2022-07-22 15:11:29,930\tINFO trainable.py:655 -- Restored on 127.0.0.1 from checkpoint: /Users/kai/ray_results/bohb_exp_2/objective_25b64488_3_activation=tanh,height=-48.4518,steps=100,width=10.1191_2022-07-22_15-11-14/checkpoint_tmp11824e\n",
"\u001b[2m\u001b[36m(objective pid=45453)\u001b[0m 2022-07-22 15:11:29,930\tINFO trainable.py:663 -- Current state after restoring: {'_iteration': 0, '_timesteps_total': 0, '_time_total': 0.5960800647735596, '_episodes_total': 0}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Result for objective_27b7e2be:\n",
" date: 2022-07-22_15-11-30\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: fdc43ca37ed44cde857ca150a8f1e84f\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 14.879072389639937\n",
" neg_mean_loss: -14.879072389639937\n",
" node_ip: 127.0.0.1\n",
" pid: 45455\n",
" time_since_restore: 0.10332393646240234\n",
" time_this_iter_s: 0.10332393646240234\n",
" time_total_s: 1.0183579921722412\n",
" timestamp: 1658499090\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: 27b7e2be\n",
" warmup_time: 0.006285190582275391\n",
" \n",
"Result for objective_25b64488:\n",
" date: 2022-07-22_15-11-30\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: 75e7c1ad20a2495cac29630df6c3c782\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 0\n",
" iterations_since_restore: 1\n",
" mean_loss: 6.154820228591976\n",
" neg_mean_loss: -6.154820228591976\n",
" node_ip: 127.0.0.1\n",
" pid: 45453\n",
" time_since_restore: 0.1026160717010498\n",
" time_this_iter_s: 0.1026160717010498\n",
" time_total_s: 0.6986961364746094\n",
" timestamp: 1658499090\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 1\n",
" trial_id: 25b64488\n",
" warmup_time: 0.006460905075073242\n",
" \n",
"Result for objective_25b64488:\n",
" date: 2022-07-22_15-11-35\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: 75e7c1ad20a2495cac29630df6c3c782\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 46\n",
" iterations_since_restore: 47\n",
" mean_loss: -3.634865890857194\n",
" neg_mean_loss: 3.634865890857194\n",
" node_ip: 127.0.0.1\n",
" pid: 45453\n",
" time_since_restore: 5.1935131549835205\n",
" time_this_iter_s: 0.10786604881286621\n",
" time_total_s: 5.78959321975708\n",
" timestamp: 1658499095\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 47\n",
" trial_id: 25b64488\n",
" warmup_time: 0.006460905075073242\n",
" \n",
"Result for objective_25b64488:\n",
" date: 2022-07-22_15-11-40\n",
" done: false\n",
" episodes_total: 0\n",
" experiment_id: 75e7c1ad20a2495cac29630df6c3c782\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 93\n",
" iterations_since_restore: 94\n",
" mean_loss: -3.740036002402735\n",
" neg_mean_loss: 3.740036002402735\n",
" node_ip: 127.0.0.1\n",
" pid: 45453\n",
" time_since_restore: 10.256795167922974\n",
" time_this_iter_s: 0.10682511329650879\n",
" time_total_s: 10.852875232696533\n",
" timestamp: 1658499100\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 94\n",
" trial_id: 25b64488\n",
" warmup_time: 0.006460905075073242\n",
" \n",
"Result for objective_25b64488:\n",
" date: 2022-07-22_15-11-40\n",
" done: true\n",
" episodes_total: 0\n",
" experiment_id: 75e7c1ad20a2495cac29630df6c3c782\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations: 99\n",
" iterations_since_restore: 100\n",
" mean_loss: -3.74634537130406\n",
" neg_mean_loss: 3.74634537130406\n",
" node_ip: 127.0.0.1\n",
" pid: 45453\n",
" time_since_restore: 10.935801029205322\n",
" time_this_iter_s: 0.10489487648010254\n",
" time_total_s: 11.531881093978882\n",
" timestamp: 1658499100\n",
" timesteps_since_restore: 0\n",
" timesteps_total: 0\n",
" training_iteration: 100\n",
" trial_id: 25b64488\n",
" warmup_time: 0.006460905075073242\n",
" \n"
]
}
],
"source": [
"tuner = tune.Tuner(\n",
" objective,\n",
" tune_config=tune.TuneConfig(\n",
" metric=\"mean_loss\",\n",
" mode=\"min\",\n",
" search_alg=algo,\n",
" scheduler=scheduler,\n",
" num_samples=num_samples,\n",
" ),\n",
" run_config=air.RunConfig(\n",
" name=\"bohb_exp_2\",\n",
" stop={\"training_iteration\": 100},\n",
" ),\n",
")\n",
"results = tuner.fit()"
]
},
{
"cell_type": "markdown",
"id": "5bf0dd87",
"metadata": {},
"source": [
"Here again are the hyperparameters found to minimize the mean loss of the\n",
"defined objective."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "1ae613e4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best hyperparameters found were: {'activation': 'tanh', 'height': -48.451797714080236, 'steps': 100, 'width': 10.119125894538891}\n"
]
}
],
"source": [
"print(\"Best hyperparameters found were: \", results.get_best_result().config)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "6b83ef6d",
"metadata": {
"tags": [
"remove-cell"
]
},
"outputs": [],
"source": [
"ray.shutdown()"
]
}
],
"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,
"vscode": {
"interpreter": {
"hash": "3c0d54d489a08ae47a06eae2fd00ff032d6cddb527c382959b7b2575f6a8167f"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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