wehub-resource-sync
1159 lines
45 KiB
Plaintext
1159 lines
45 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "db54cdf9",
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"metadata": {},
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"source": [
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"# Running Tune experiments with BayesOpt\n",
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"\n",
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"<a id=\"try-anyscale-quickstart-ray-tune-bayesopt_example\" href=\"https://console.anyscale.com/register/ha?render_flow=ray&utm_source=ray_docs&utm_medium=docs&utm_campaign=ray-tune-bayesopt_example\">\n",
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" <img src=\"../../_static/img/run-on-anyscale.svg\" alt=\"try-anyscale-quickstart\">\n",
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"</a>\n",
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"<br></br>\n",
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"\n",
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"In this tutorial we introduce BayesOpt, while running a simple Ray Tune experiment. Tune’s Search Algorithms integrate with BayesOpt and, as a result, allow you to seamlessly scale up a BayesOpt optimization process - without sacrificing performance.\n",
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"\n",
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"BayesOpt is a constrained global optimization package utilizing Bayesian inference on gaussian processes, where the emphasis is on finding the maximum value of an unknown function in as few iterations as possible. BayesOpt's techniques are particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. Therefore BayesOpt falls in the domain of \"derivative-free\" and \"black-box\" optimization. In this example we minimize a simple objective to briefly demonstrate the usage of BayesOpt with Ray Tune via `BayesOptSearch`, including conditional search spaces. It's useful to keep in mind that despite the emphasis on machine learning experiments, Ray Tune optimizes any implicit or explicit objective. Here we assume `bayesian-optimization==1.2.0` library is installed. To learn more, please refer to [BayesOpt website](https://github.com/fmfn/BayesianOptimization).\n",
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"\n",
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"First, install the pre-requisites for this 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": "7ed16354",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install -q bayesian-optimization==1.2.0 \"ray[tune]\""
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "2236f834",
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"metadata": {},
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"source": [
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"Click below to see all the imports we need for this 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": 2,
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"id": "6d36c78b",
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"metadata": {
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"tags": [
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"hide-input"
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]
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},
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"outputs": [],
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"source": [
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"import time\n",
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"\n",
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"import ray\n",
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"from ray import tune\n",
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"from ray.tune.search import ConcurrencyLimiter\n",
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"from ray.tune.search.bayesopt import BayesOptSearch"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "6257a3a8",
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"metadata": {},
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"source": [
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"Let's start by defining a simple evaluation function.\n",
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"We artificially sleep for a bit (`0.1` seconds) to simulate a long-running ML experiment.\n",
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"This setup assumes that we're running multiple `step`s of an experiment and try to tune two hyperparameters,\n",
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"namely `width` and `height`."
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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": 3,
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"id": "646c75a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"def evaluate(step, width, height):\n",
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" time.sleep(0.1)\n",
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" return (0.1 + width * step / 100) ** (-1) + height * 0.1"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "d89b7fdc",
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"metadata": {},
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"source": [
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"Next, our ``objective`` function takes a Tune ``config``, evaluates the `score` of your experiment in a training loop,\n",
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"and uses `tune.report` to report the `score` back to Tune."
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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": 4,
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"id": "e9adf637",
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"metadata": {},
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"outputs": [],
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"source": [
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"def objective(config):\n",
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" for step in range(config[\"steps\"]):\n",
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" score = evaluate(step, config[\"width\"], config[\"height\"])\n",
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" tune.report({\"iterations\": step, \"mean_loss\": score})"
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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": null,
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"id": "bc634b1d",
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"metadata": {
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"lines_to_next_cell": 0,
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"tags": [
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"remove-cell"
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]
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},
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"outputs": [],
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"source": [
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"ray.init(configure_logging=False)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "0b9a2c4d",
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"metadata": {},
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"source": [
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"Now we define the search algorithm built from `BayesOptSearch`, constrained to a maximum of `4` concurrent trials with a `ConcurrencyLimiter`."
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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": 6,
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"id": "6f1d2fe7",
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"metadata": {},
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"outputs": [],
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"source": [
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"algo = BayesOptSearch(utility_kwargs={\"kind\": \"ucb\", \"kappa\": 2.5, \"xi\": 0.0})\n",
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"algo = ConcurrencyLimiter(algo, max_concurrent=4)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "27963e39",
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"metadata": {},
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"source": [
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"The number of samples is the number of hyperparameter combinations that will be tried out. This Tune run is set to `1000` samples.\n",
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"(you can decrease this if it takes too long on your machine)."
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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": 7,
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"id": "d777201c",
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"metadata": {},
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"outputs": [],
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"source": [
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"num_samples = 1000"
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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": 8,
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"id": "bb5f39a6",
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"metadata": {
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"tags": [
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"remove-cell"
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]
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},
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"outputs": [],
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"source": [
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"# We reduce the num samples in this hidden cell for our smoke tests.\n",
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"num_samples = 10"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "752523c8",
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"metadata": {},
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"source": [
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"Next we define a search space. The critical assumption is that the optimal hyperparameters live within this space. Yet, if the space is very large, then those hyperparameters may be difficult to find in a short amount of time."
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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": 9,
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"id": "116f8757",
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"metadata": {},
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"outputs": [],
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"source": [
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"search_space = {\n",
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" \"steps\": 100,\n",
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" \"width\": tune.uniform(0, 20),\n",
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" \"height\": tune.uniform(-100, 100),\n",
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"}"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "1754bf85",
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"metadata": {},
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"source": [
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"Finally, we run the experiment to `\"min\"`imize the \"mean_loss\" of the `objective` by searching `search_config` via `algo`, `num_samples` times. This previous sentence is fully characterizes the search problem we aim to solve. With this in mind, notice how efficient it is to execute `tuner.fit()`."
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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": 10,
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"id": "5c44a0c5",
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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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"\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 15:30:53 (running for 00:00:43.91)<br>Memory usage on this node: 10.4/16.0 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/4.47 GiB heap, 0.0/2.0 GiB objects<br>Current best trial: d42ac71c with mean_loss=-9.536507956046009 and parameters={'steps': 100, 'width': 19.398197043239886, 'height': -95.88310114083951}<br>Result logdir: ~/ray_results/objective_2022-07-22_15-30-08<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;\"> 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;\"> iterations</th><th style=\"text-align: right;\"> neg_mean_loss</th></tr>\n",
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"</thead>\n",
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"<tbody>\n",
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"<tr><td>objective_c9daa5d4</td><td>TERMINATED</td><td>127.0.0.1:46960</td><td style=\"text-align: right;\">-25.092 </td><td style=\"text-align: right;\">19.0143 </td><td style=\"text-align: right;\">-2.45636</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 10.9865</td><td style=\"text-align: right;\"> 99</td><td style=\"text-align: right;\"> 2.45636</td></tr>\n",
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"<tr><td>objective_cb9bc830</td><td>TERMINATED</td><td>127.0.0.1:46968</td><td style=\"text-align: right;\"> 46.3988</td><td style=\"text-align: right;\">11.9732 </td><td style=\"text-align: right;\"> 4.72354</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 11.5661</td><td style=\"text-align: right;\"> 99</td><td style=\"text-align: right;\"> -4.72354</td></tr>\n",
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||
"<tr><td>objective_cb9d338c</td><td>TERMINATED</td><td>127.0.0.1:46969</td><td style=\"text-align: right;\">-68.7963</td><td style=\"text-align: right;\"> 3.11989</td><td style=\"text-align: right;\">-6.56602</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 11.648 </td><td style=\"text-align: right;\"> 99</td><td style=\"text-align: right;\"> 6.56602</td></tr>\n",
|
||
"<tr><td>objective_cb9e97e0</td><td>TERMINATED</td><td>127.0.0.1:46970</td><td style=\"text-align: right;\">-88.3833</td><td style=\"text-align: right;\">17.3235 </td><td style=\"text-align: right;\">-8.78036</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 11.6948</td><td style=\"text-align: right;\"> 99</td><td style=\"text-align: right;\"> 8.78036</td></tr>\n",
|
||
"<tr><td>objective_d229961e</td><td>TERMINATED</td><td>127.0.0.1:47009</td><td style=\"text-align: right;\"> 20.223 </td><td style=\"text-align: right;\">14.1615 </td><td style=\"text-align: right;\"> 2.09312</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 10.8549</td><td style=\"text-align: right;\"> 99</td><td style=\"text-align: right;\"> -2.09312</td></tr>\n",
|
||
"<tr><td>objective_d42ac71c</td><td>TERMINATED</td><td>127.0.0.1:47036</td><td style=\"text-align: right;\">-95.8831</td><td style=\"text-align: right;\">19.3982 </td><td style=\"text-align: right;\">-9.53651</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 10.7931</td><td style=\"text-align: right;\"> 99</td><td style=\"text-align: right;\"> 9.53651</td></tr>\n",
|
||
"<tr><td>objective_d43ca61c</td><td>TERMINATED</td><td>127.0.0.1:47039</td><td style=\"text-align: right;\"> 66.4885</td><td style=\"text-align: right;\"> 4.24678</td><td style=\"text-align: right;\"> 6.88118</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 10.7606</td><td style=\"text-align: right;\"> 99</td><td style=\"text-align: right;\"> -6.88118</td></tr>\n",
|
||
"<tr><td>objective_d43fb190</td><td>TERMINATED</td><td>127.0.0.1:47040</td><td style=\"text-align: right;\">-63.635 </td><td style=\"text-align: right;\"> 3.66809</td><td style=\"text-align: right;\">-6.09551</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 10.7997</td><td style=\"text-align: right;\"> 99</td><td style=\"text-align: right;\"> 6.09551</td></tr>\n",
|
||
"<tr><td>objective_da1ff46c</td><td>TERMINATED</td><td>127.0.0.1:47057</td><td style=\"text-align: right;\">-39.1516</td><td style=\"text-align: right;\">10.4951 </td><td style=\"text-align: right;\">-3.81983</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 10.7762</td><td style=\"text-align: right;\"> 99</td><td style=\"text-align: right;\"> 3.81983</td></tr>\n",
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||
"<tr><td>objective_dc25c796</td><td>TERMINATED</td><td>127.0.0.1:47062</td><td style=\"text-align: right;\">-13.611 </td><td style=\"text-align: right;\"> 5.82458</td><td style=\"text-align: right;\">-1.19064</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 10.7213</td><td style=\"text-align: right;\"> 99</td><td style=\"text-align: right;\"> 1.19064</td></tr>\n",
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"</tbody>\n",
|
||
"</table><br><br>"
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]
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"output_type": "display_data"
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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 objective_c9daa5d4:\n",
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" date: 2022-07-22_15-30-12\n",
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" done: false\n",
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" experiment_id: 422a6d2a512a470480e33913d7825a7a\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
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" iterations: 0\n",
|
||
" iterations_since_restore: 1\n",
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" mean_loss: 7.490802376947249\n",
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||
" neg_mean_loss: -7.490802376947249\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46960\n",
|
||
" time_since_restore: 0.1042318344116211\n",
|
||
" time_this_iter_s: 0.1042318344116211\n",
|
||
" time_total_s: 0.1042318344116211\n",
|
||
" timestamp: 1658500212\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 1\n",
|
||
" trial_id: c9daa5d4\n",
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" warmup_time: 0.0032601356506347656\n",
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" \n",
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"Result for objective_cb9bc830:\n",
|
||
" date: 2022-07-22_15-30-15\n",
|
||
" done: false\n",
|
||
" experiment_id: 3a9a6bef89ec4b57bd0fa24dd3b407e6\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 0\n",
|
||
" iterations_since_restore: 1\n",
|
||
" mean_loss: 14.639878836228101\n",
|
||
" neg_mean_loss: -14.639878836228101\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46968\n",
|
||
" time_since_restore: 0.10442280769348145\n",
|
||
" time_this_iter_s: 0.10442280769348145\n",
|
||
" time_total_s: 0.10442280769348145\n",
|
||
" timestamp: 1658500215\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 1\n",
|
||
" trial_id: cb9bc830\n",
|
||
" warmup_time: 0.0038840770721435547\n",
|
||
" \n",
|
||
"Result for objective_cb9e97e0:\n",
|
||
" date: 2022-07-22_15-30-15\n",
|
||
" done: false\n",
|
||
" experiment_id: b0266e323ced4991b155344b34c25c59\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 0\n",
|
||
" iterations_since_restore: 1\n",
|
||
" mean_loss: 1.1616722433639897\n",
|
||
" neg_mean_loss: -1.1616722433639897\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46970\n",
|
||
" time_since_restore: 0.10328483581542969\n",
|
||
" time_this_iter_s: 0.10328483581542969\n",
|
||
" time_total_s: 0.10328483581542969\n",
|
||
" timestamp: 1658500215\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 1\n",
|
||
" trial_id: cb9e97e0\n",
|
||
" warmup_time: 0.004090070724487305\n",
|
||
" \n",
|
||
"Result for objective_cb9d338c:\n",
|
||
" date: 2022-07-22_15-30-15\n",
|
||
" done: false\n",
|
||
" experiment_id: 2731a83e40eb468fb79e19f872b8f597\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 0\n",
|
||
" iterations_since_restore: 1\n",
|
||
" mean_loss: 3.120372808848731\n",
|
||
" neg_mean_loss: -3.120372808848731\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46969\n",
|
||
" time_since_restore: 0.1042470932006836\n",
|
||
" time_this_iter_s: 0.1042470932006836\n",
|
||
" time_total_s: 0.1042470932006836\n",
|
||
" timestamp: 1658500215\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 1\n",
|
||
" trial_id: cb9d338c\n",
|
||
" warmup_time: 0.003387928009033203\n",
|
||
" \n",
|
||
"Result for objective_c9daa5d4:\n",
|
||
" date: 2022-07-22_15-30-17\n",
|
||
" done: false\n",
|
||
" experiment_id: 422a6d2a512a470480e33913d7825a7a\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 45\n",
|
||
" iterations_since_restore: 46\n",
|
||
" mean_loss: -2.393676542940848\n",
|
||
" neg_mean_loss: 2.393676542940848\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46960\n",
|
||
" time_since_restore: 5.1730430126190186\n",
|
||
" time_this_iter_s: 0.10674905776977539\n",
|
||
" time_total_s: 5.1730430126190186\n",
|
||
" timestamp: 1658500217\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 46\n",
|
||
" trial_id: c9daa5d4\n",
|
||
" warmup_time: 0.0032601356506347656\n",
|
||
" \n",
|
||
"Result for objective_cb9bc830:\n",
|
||
" date: 2022-07-22_15-30-20\n",
|
||
" done: false\n",
|
||
" experiment_id: 3a9a6bef89ec4b57bd0fa24dd3b407e6\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 47\n",
|
||
" iterations_since_restore: 48\n",
|
||
" mean_loss: 4.8144784432736065\n",
|
||
" neg_mean_loss: -4.8144784432736065\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46968\n",
|
||
" time_since_restore: 5.1083409786224365\n",
|
||
" time_this_iter_s: 0.10834097862243652\n",
|
||
" time_total_s: 5.1083409786224365\n",
|
||
" timestamp: 1658500220\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 48\n",
|
||
" trial_id: cb9bc830\n",
|
||
" warmup_time: 0.0038840770721435547\n",
|
||
" \n",
|
||
"Result for objective_cb9e97e0:\n",
|
||
" date: 2022-07-22_15-30-20\n",
|
||
" done: false\n",
|
||
" experiment_id: b0266e323ced4991b155344b34c25c59\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 47\n",
|
||
" iterations_since_restore: 48\n",
|
||
" mean_loss: -8.716998803293404\n",
|
||
" neg_mean_loss: 8.716998803293404\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46970\n",
|
||
" time_since_restore: 5.117117881774902\n",
|
||
" time_this_iter_s: 0.10473918914794922\n",
|
||
" time_total_s: 5.117117881774902\n",
|
||
" timestamp: 1658500220\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 48\n",
|
||
" trial_id: cb9e97e0\n",
|
||
" warmup_time: 0.004090070724487305\n",
|
||
" \n",
|
||
"Result for objective_cb9d338c:\n",
|
||
" date: 2022-07-22_15-30-20\n",
|
||
" done: false\n",
|
||
" experiment_id: 2731a83e40eb468fb79e19f872b8f597\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 47\n",
|
||
" iterations_since_restore: 48\n",
|
||
" mean_loss: -6.241199660085543\n",
|
||
" neg_mean_loss: 6.241199660085543\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46969\n",
|
||
" time_since_restore: 5.1075780391693115\n",
|
||
" time_this_iter_s: 0.1051321029663086\n",
|
||
" time_total_s: 5.1075780391693115\n",
|
||
" timestamp: 1658500220\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 48\n",
|
||
" trial_id: cb9d338c\n",
|
||
" warmup_time: 0.003387928009033203\n",
|
||
" \n",
|
||
"Result for objective_c9daa5d4:\n",
|
||
" date: 2022-07-22_15-30-22\n",
|
||
" done: false\n",
|
||
" experiment_id: 422a6d2a512a470480e33913d7825a7a\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 92\n",
|
||
" iterations_since_restore: 93\n",
|
||
" mean_loss: -2.452357296882761\n",
|
||
" neg_mean_loss: 2.452357296882761\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46960\n",
|
||
" time_since_restore: 10.23116397857666\n",
|
||
" time_this_iter_s: 0.10653018951416016\n",
|
||
" time_total_s: 10.23116397857666\n",
|
||
" timestamp: 1658500222\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 93\n",
|
||
" trial_id: c9daa5d4\n",
|
||
" warmup_time: 0.0032601356506347656\n",
|
||
" \n",
|
||
"Result for objective_c9daa5d4:\n",
|
||
" date: 2022-07-22_15-30-23\n",
|
||
" done: true\n",
|
||
" experiment_id: 422a6d2a512a470480e33913d7825a7a\n",
|
||
" experiment_tag: 1_height=-25.0920,steps=100,width=19.0143\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 99\n",
|
||
" iterations_since_restore: 100\n",
|
||
" mean_loss: -2.456355072354658\n",
|
||
" neg_mean_loss: 2.456355072354658\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46960\n",
|
||
" time_since_restore: 10.986503839492798\n",
|
||
" time_this_iter_s: 0.10757803916931152\n",
|
||
" time_total_s: 10.986503839492798\n",
|
||
" timestamp: 1658500223\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 100\n",
|
||
" trial_id: c9daa5d4\n",
|
||
" warmup_time: 0.0032601356506347656\n",
|
||
" \n",
|
||
"Result for objective_cb9bc830:\n",
|
||
" date: 2022-07-22_15-30-24\n",
|
||
" done: false\n",
|
||
" experiment_id: 3a9a6bef89ec4b57bd0fa24dd3b407e6\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 91\n",
|
||
" iterations_since_restore: 92\n",
|
||
" mean_loss: 4.73082443425139\n",
|
||
" neg_mean_loss: -4.73082443425139\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46968\n",
|
||
" time_since_restore: 9.829612970352173\n",
|
||
" time_this_iter_s: 0.10725593566894531\n",
|
||
" time_total_s: 9.829612970352173\n",
|
||
" timestamp: 1658500224\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 92\n",
|
||
" trial_id: cb9bc830\n",
|
||
" warmup_time: 0.0038840770721435547\n",
|
||
" \n",
|
||
"Result for objective_cb9e97e0:\n",
|
||
" date: 2022-07-22_15-30-24\n",
|
||
" done: false\n",
|
||
" experiment_id: b0266e323ced4991b155344b34c25c59\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 90\n",
|
||
" iterations_since_restore: 91\n",
|
||
" mean_loss: -8.774597648541096\n",
|
||
" neg_mean_loss: 8.774597648541096\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46970\n",
|
||
" time_since_restore: 9.72621202468872\n",
|
||
" time_this_iter_s: 0.10692906379699707\n",
|
||
" time_total_s: 9.72621202468872\n",
|
||
" timestamp: 1658500224\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 91\n",
|
||
" trial_id: cb9e97e0\n",
|
||
" warmup_time: 0.004090070724487305\n",
|
||
" \n",
|
||
"Result for objective_cb9d338c:\n",
|
||
" date: 2022-07-22_15-30-24\n",
|
||
" done: false\n",
|
||
" experiment_id: 2731a83e40eb468fb79e19f872b8f597\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 90\n",
|
||
" iterations_since_restore: 91\n",
|
||
" mean_loss: -6.535736572413468\n",
|
||
" neg_mean_loss: 6.535736572413468\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46969\n",
|
||
" time_since_restore: 9.71235203742981\n",
|
||
" time_this_iter_s: 0.10665416717529297\n",
|
||
" time_total_s: 9.71235203742981\n",
|
||
" timestamp: 1658500224\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 91\n",
|
||
" trial_id: cb9d338c\n",
|
||
" warmup_time: 0.003387928009033203\n",
|
||
" \n",
|
||
"Result for objective_d229961e:\n",
|
||
" date: 2022-07-22_15-30-25\n",
|
||
" done: false\n",
|
||
" experiment_id: d8bb04569c644d6fabad5064c1828ba3\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 0\n",
|
||
" iterations_since_restore: 1\n",
|
||
" mean_loss: 12.022300234864176\n",
|
||
" neg_mean_loss: -12.022300234864176\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47009\n",
|
||
" time_since_restore: 0.1041719913482666\n",
|
||
" time_this_iter_s: 0.1041719913482666\n",
|
||
" time_total_s: 0.1041719913482666\n",
|
||
" timestamp: 1658500225\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 1\n",
|
||
" trial_id: d229961e\n",
|
||
" warmup_time: 0.003198862075805664\n",
|
||
" \n",
|
||
"Result for objective_cb9bc830:\n",
|
||
" date: 2022-07-22_15-30-26\n",
|
||
" done: true\n",
|
||
" experiment_id: 3a9a6bef89ec4b57bd0fa24dd3b407e6\n",
|
||
" experiment_tag: 2_height=46.3988,steps=100,width=11.9732\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 99\n",
|
||
" iterations_since_restore: 100\n",
|
||
" mean_loss: 4.723536776402224\n",
|
||
" neg_mean_loss: -4.723536776402224\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46968\n",
|
||
" time_since_restore: 11.566141843795776\n",
|
||
" time_this_iter_s: 0.10738396644592285\n",
|
||
" time_total_s: 11.566141843795776\n",
|
||
" timestamp: 1658500226\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 100\n",
|
||
" trial_id: cb9bc830\n",
|
||
" warmup_time: 0.0038840770721435547\n",
|
||
" \n",
|
||
"Result for objective_cb9d338c:\n",
|
||
" date: 2022-07-22_15-30-26\n",
|
||
" done: true\n",
|
||
" experiment_id: 2731a83e40eb468fb79e19f872b8f597\n",
|
||
" experiment_tag: 3_height=-68.7963,steps=100,width=3.1199\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 99\n",
|
||
" iterations_since_restore: 100\n",
|
||
" mean_loss: -6.566018929214734\n",
|
||
" neg_mean_loss: 6.566018929214734\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46969\n",
|
||
" time_since_restore: 11.647998809814453\n",
|
||
" time_this_iter_s: 0.1123647689819336\n",
|
||
" time_total_s: 11.647998809814453\n",
|
||
" timestamp: 1658500226\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 100\n",
|
||
" trial_id: cb9d338c\n",
|
||
" warmup_time: 0.003387928009033203\n",
|
||
" \n",
|
||
"Result for objective_cb9e97e0:\n",
|
||
" date: 2022-07-22_15-30-26\n",
|
||
" done: true\n",
|
||
" experiment_id: b0266e323ced4991b155344b34c25c59\n",
|
||
" experiment_tag: 4_height=-88.3833,steps=100,width=17.3235\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 99\n",
|
||
" iterations_since_restore: 100\n",
|
||
" mean_loss: -8.780357708936942\n",
|
||
" neg_mean_loss: 8.780357708936942\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 46970\n",
|
||
" time_since_restore: 11.694752931594849\n",
|
||
" time_this_iter_s: 0.12678027153015137\n",
|
||
" time_total_s: 11.694752931594849\n",
|
||
" timestamp: 1658500226\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 100\n",
|
||
" trial_id: cb9e97e0\n",
|
||
" warmup_time: 0.004090070724487305\n",
|
||
" \n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Result for objective_d42ac71c:\n",
|
||
" date: 2022-07-22_15-30-29\n",
|
||
" done: false\n",
|
||
" experiment_id: 3fdfaecb7adc4c5cb54c0aa76849d532\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 0\n",
|
||
" iterations_since_restore: 1\n",
|
||
" mean_loss: 0.41168988591604894\n",
|
||
" neg_mean_loss: -0.41168988591604894\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47036\n",
|
||
" time_since_restore: 0.10324597358703613\n",
|
||
" time_this_iter_s: 0.10324597358703613\n",
|
||
" time_total_s: 0.10324597358703613\n",
|
||
" timestamp: 1658500229\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 1\n",
|
||
" trial_id: d42ac71c\n",
|
||
" warmup_time: 0.0028409957885742188\n",
|
||
" \n",
|
||
"Result for objective_d43ca61c:\n",
|
||
" date: 2022-07-22_15-30-29\n",
|
||
" done: false\n",
|
||
" experiment_id: 8f92f519ea5443be9efd6f4a8937b8ee\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 0\n",
|
||
" iterations_since_restore: 1\n",
|
||
" mean_loss: 16.648852816008436\n",
|
||
" neg_mean_loss: -16.648852816008436\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47039\n",
|
||
" time_since_restore: 0.10412001609802246\n",
|
||
" time_this_iter_s: 0.10412001609802246\n",
|
||
" time_total_s: 0.10412001609802246\n",
|
||
" timestamp: 1658500229\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 1\n",
|
||
" trial_id: d43ca61c\n",
|
||
" warmup_time: 0.002924203872680664\n",
|
||
" \n",
|
||
"Result for objective_d43fb190:\n",
|
||
" date: 2022-07-22_15-30-29\n",
|
||
" done: false\n",
|
||
" experiment_id: 18283da742c74042ad3db1846fa7b460\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 0\n",
|
||
" iterations_since_restore: 1\n",
|
||
" mean_loss: 3.6364993441420124\n",
|
||
" neg_mean_loss: -3.6364993441420124\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47040\n",
|
||
" time_since_restore: 0.10391902923583984\n",
|
||
" time_this_iter_s: 0.10391902923583984\n",
|
||
" time_total_s: 0.10391902923583984\n",
|
||
" timestamp: 1658500229\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 1\n",
|
||
" trial_id: d43fb190\n",
|
||
" warmup_time: 0.0027680397033691406\n",
|
||
" \n",
|
||
"Result for objective_d229961e:\n",
|
||
" date: 2022-07-22_15-30-30\n",
|
||
" done: false\n",
|
||
" experiment_id: d8bb04569c644d6fabad5064c1828ba3\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 46\n",
|
||
" iterations_since_restore: 47\n",
|
||
" mean_loss: 2.1734885512401174\n",
|
||
" neg_mean_loss: -2.1734885512401174\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47009\n",
|
||
" time_since_restore: 5.153247117996216\n",
|
||
" time_this_iter_s: 0.10638809204101562\n",
|
||
" time_total_s: 5.153247117996216\n",
|
||
" timestamp: 1658500230\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 47\n",
|
||
" trial_id: d229961e\n",
|
||
" warmup_time: 0.003198862075805664\n",
|
||
" \n",
|
||
"Result for objective_d42ac71c:\n",
|
||
" date: 2022-07-22_15-30-34\n",
|
||
" done: false\n",
|
||
" experiment_id: 3fdfaecb7adc4c5cb54c0aa76849d532\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 46\n",
|
||
" iterations_since_restore: 47\n",
|
||
" mean_loss: -9.477484325687673\n",
|
||
" neg_mean_loss: 9.477484325687673\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47036\n",
|
||
" time_since_restore: 5.123893976211548\n",
|
||
" time_this_iter_s: 0.10898423194885254\n",
|
||
" time_total_s: 5.123893976211548\n",
|
||
" timestamp: 1658500234\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 47\n",
|
||
" trial_id: d42ac71c\n",
|
||
" warmup_time: 0.0028409957885742188\n",
|
||
" \n",
|
||
"Result for objective_d43ca61c:\n",
|
||
" date: 2022-07-22_15-30-34\n",
|
||
" done: false\n",
|
||
" experiment_id: 8f92f519ea5443be9efd6f4a8937b8ee\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 47\n",
|
||
" iterations_since_restore: 48\n",
|
||
" mean_loss: 7.12595486600941\n",
|
||
" neg_mean_loss: -7.12595486600941\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47039\n",
|
||
" time_since_restore: 5.194939136505127\n",
|
||
" time_this_iter_s: 0.10889291763305664\n",
|
||
" time_total_s: 5.194939136505127\n",
|
||
" timestamp: 1658500234\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 48\n",
|
||
" trial_id: d43ca61c\n",
|
||
" warmup_time: 0.002924203872680664\n",
|
||
" \n",
|
||
"Result for objective_d43fb190:\n",
|
||
" date: 2022-07-22_15-30-34\n",
|
||
" done: false\n",
|
||
" experiment_id: 18283da742c74042ad3db1846fa7b460\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 47\n",
|
||
" iterations_since_restore: 48\n",
|
||
" mean_loss: -5.815255760980219\n",
|
||
" neg_mean_loss: 5.815255760980219\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47040\n",
|
||
" time_since_restore: 5.2366979122161865\n",
|
||
" time_this_iter_s: 0.10901784896850586\n",
|
||
" time_total_s: 5.2366979122161865\n",
|
||
" timestamp: 1658500234\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 48\n",
|
||
" trial_id: d43fb190\n",
|
||
" warmup_time: 0.0027680397033691406\n",
|
||
" \n",
|
||
"Result for objective_d229961e:\n",
|
||
" date: 2022-07-22_15-30-35\n",
|
||
" done: false\n",
|
||
" experiment_id: d8bb04569c644d6fabad5064c1828ba3\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 93\n",
|
||
" iterations_since_restore: 94\n",
|
||
" mean_loss: 2.097657333615391\n",
|
||
" neg_mean_loss: -2.097657333615391\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47009\n",
|
||
" time_since_restore: 10.209784984588623\n",
|
||
" time_this_iter_s: 0.10757803916931152\n",
|
||
" time_total_s: 10.209784984588623\n",
|
||
" timestamp: 1658500235\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 94\n",
|
||
" trial_id: d229961e\n",
|
||
" warmup_time: 0.003198862075805664\n",
|
||
" \n",
|
||
"Result for objective_d229961e:\n",
|
||
" date: 2022-07-22_15-30-36\n",
|
||
" done: true\n",
|
||
" experiment_id: d8bb04569c644d6fabad5064c1828ba3\n",
|
||
" experiment_tag: 5_height=20.2230,steps=100,width=14.1615\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 99\n",
|
||
" iterations_since_restore: 100\n",
|
||
" mean_loss: 2.093122581973529\n",
|
||
" neg_mean_loss: -2.093122581973529\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47009\n",
|
||
" time_since_restore: 10.854872226715088\n",
|
||
" time_this_iter_s: 0.10703516006469727\n",
|
||
" time_total_s: 10.854872226715088\n",
|
||
" timestamp: 1658500236\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 100\n",
|
||
" trial_id: d229961e\n",
|
||
" warmup_time: 0.003198862075805664\n",
|
||
" \n",
|
||
"Result for objective_da1ff46c:\n",
|
||
" date: 2022-07-22_15-30-39\n",
|
||
" done: false\n",
|
||
" experiment_id: 9163132451a14ace8ddf394aeaae9018\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 0\n",
|
||
" iterations_since_restore: 1\n",
|
||
" mean_loss: 6.0848448591907545\n",
|
||
" neg_mean_loss: -6.0848448591907545\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47057\n",
|
||
" time_since_restore: 0.10405993461608887\n",
|
||
" time_this_iter_s: 0.10405993461608887\n",
|
||
" time_total_s: 0.10405993461608887\n",
|
||
" timestamp: 1658500239\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 1\n",
|
||
" trial_id: da1ff46c\n",
|
||
" warmup_time: 0.0030031204223632812\n",
|
||
" \n",
|
||
"Result for objective_d42ac71c:\n",
|
||
" date: 2022-07-22_15-30-39\n",
|
||
" done: false\n",
|
||
" experiment_id: 3fdfaecb7adc4c5cb54c0aa76849d532\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 93\n",
|
||
" iterations_since_restore: 94\n",
|
||
" mean_loss: -9.533184304791206\n",
|
||
" neg_mean_loss: 9.533184304791206\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47036\n",
|
||
" time_since_restore: 10.145818948745728\n",
|
||
" time_this_iter_s: 0.10763311386108398\n",
|
||
" time_total_s: 10.145818948745728\n",
|
||
" timestamp: 1658500239\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 94\n",
|
||
" trial_id: d42ac71c\n",
|
||
" warmup_time: 0.0028409957885742188\n",
|
||
" \n",
|
||
"Result for objective_d43ca61c:\n",
|
||
" date: 2022-07-22_15-30-39\n",
|
||
" done: false\n",
|
||
" experiment_id: 8f92f519ea5443be9efd6f4a8937b8ee\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 94\n",
|
||
" iterations_since_restore: 95\n",
|
||
" mean_loss: 6.893233568918634\n",
|
||
" neg_mean_loss: -6.893233568918634\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47039\n",
|
||
" time_since_restore: 10.217039108276367\n",
|
||
" time_this_iter_s: 0.10719418525695801\n",
|
||
" time_total_s: 10.217039108276367\n",
|
||
" timestamp: 1658500239\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 95\n",
|
||
" trial_id: d43ca61c\n",
|
||
" warmup_time: 0.002924203872680664\n",
|
||
" \n",
|
||
"Result for objective_d43fb190:\n",
|
||
" date: 2022-07-22_15-30-39\n",
|
||
" done: false\n",
|
||
" experiment_id: 18283da742c74042ad3db1846fa7b460\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 94\n",
|
||
" iterations_since_restore: 95\n",
|
||
" mean_loss: -6.08165210701758\n",
|
||
" neg_mean_loss: 6.08165210701758\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47040\n",
|
||
" time_since_restore: 10.262099027633667\n",
|
||
" time_this_iter_s: 0.10874485969543457\n",
|
||
" time_total_s: 10.262099027633667\n",
|
||
" timestamp: 1658500239\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 95\n",
|
||
" trial_id: d43fb190\n",
|
||
" warmup_time: 0.0027680397033691406\n",
|
||
" \n",
|
||
"Result for objective_d42ac71c:\n",
|
||
" date: 2022-07-22_15-30-39\n",
|
||
" done: true\n",
|
||
" experiment_id: 3fdfaecb7adc4c5cb54c0aa76849d532\n",
|
||
" experiment_tag: 6_height=-95.8831,steps=100,width=19.3982\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 99\n",
|
||
" iterations_since_restore: 100\n",
|
||
" mean_loss: -9.536507956046009\n",
|
||
" neg_mean_loss: 9.536507956046009\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47036\n",
|
||
" time_since_restore: 10.793061017990112\n",
|
||
" time_this_iter_s: 0.10741710662841797\n",
|
||
" time_total_s: 10.793061017990112\n",
|
||
" timestamp: 1658500239\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 100\n",
|
||
" trial_id: d42ac71c\n",
|
||
" warmup_time: 0.0028409957885742188\n",
|
||
" \n",
|
||
"Result for objective_d43ca61c:\n",
|
||
" date: 2022-07-22_15-30-40\n",
|
||
" done: true\n",
|
||
" experiment_id: 8f92f519ea5443be9efd6f4a8937b8ee\n",
|
||
" experiment_tag: 7_height=66.4885,steps=100,width=4.2468\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 99\n",
|
||
" iterations_since_restore: 100\n",
|
||
" mean_loss: 6.881177852950684\n",
|
||
" neg_mean_loss: -6.881177852950684\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47039\n",
|
||
" time_since_restore: 10.760617017745972\n",
|
||
" time_this_iter_s: 0.10911297798156738\n",
|
||
" time_total_s: 10.760617017745972\n",
|
||
" timestamp: 1658500240\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 100\n",
|
||
" trial_id: d43ca61c\n",
|
||
" warmup_time: 0.002924203872680664\n",
|
||
" \n",
|
||
"Result for objective_d43fb190:\n",
|
||
" date: 2022-07-22_15-30-40\n",
|
||
" done: true\n",
|
||
" experiment_id: 18283da742c74042ad3db1846fa7b460\n",
|
||
" experiment_tag: 8_height=-63.6350,steps=100,width=3.6681\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 99\n",
|
||
" iterations_since_restore: 100\n",
|
||
" mean_loss: -6.09550539698523\n",
|
||
" neg_mean_loss: 6.09550539698523\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47040\n",
|
||
" time_since_restore: 10.799743175506592\n",
|
||
" time_this_iter_s: 0.1067342758178711\n",
|
||
" time_total_s: 10.799743175506592\n",
|
||
" timestamp: 1658500240\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 100\n",
|
||
" trial_id: d43fb190\n",
|
||
" warmup_time: 0.0027680397033691406\n",
|
||
" \n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Result for objective_dc25c796:\n",
|
||
" date: 2022-07-22_15-30-42\n",
|
||
" done: false\n",
|
||
" experiment_id: c0f302c32b284f8e99dbdfa90657ee7d\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 0\n",
|
||
" iterations_since_restore: 1\n",
|
||
" mean_loss: 8.638900372842315\n",
|
||
" neg_mean_loss: -8.638900372842315\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47062\n",
|
||
" time_since_restore: 0.10459494590759277\n",
|
||
" time_this_iter_s: 0.10459494590759277\n",
|
||
" time_total_s: 0.10459494590759277\n",
|
||
" timestamp: 1658500242\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 1\n",
|
||
" trial_id: dc25c796\n",
|
||
" warmup_time: 0.002794981002807617\n",
|
||
" \n",
|
||
"Result for objective_da1ff46c:\n",
|
||
" date: 2022-07-22_15-30-44\n",
|
||
" done: false\n",
|
||
" experiment_id: 9163132451a14ace8ddf394aeaae9018\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 47\n",
|
||
" iterations_since_restore: 48\n",
|
||
" mean_loss: -3.7164550549457847\n",
|
||
" neg_mean_loss: 3.7164550549457847\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47057\n",
|
||
" time_since_restore: 5.180424928665161\n",
|
||
" time_this_iter_s: 0.10843396186828613\n",
|
||
" time_total_s: 5.180424928665161\n",
|
||
" timestamp: 1658500244\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 48\n",
|
||
" trial_id: da1ff46c\n",
|
||
" warmup_time: 0.0030031204223632812\n",
|
||
" \n",
|
||
"Result for objective_dc25c796:\n",
|
||
" date: 2022-07-22_15-30-47\n",
|
||
" done: false\n",
|
||
" experiment_id: c0f302c32b284f8e99dbdfa90657ee7d\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 47\n",
|
||
" iterations_since_restore: 48\n",
|
||
" mean_loss: -1.0086834162426133\n",
|
||
" neg_mean_loss: 1.0086834162426133\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47062\n",
|
||
" time_since_restore: 5.151978015899658\n",
|
||
" time_this_iter_s: 0.10736894607543945\n",
|
||
" time_total_s: 5.151978015899658\n",
|
||
" timestamp: 1658500247\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 48\n",
|
||
" trial_id: dc25c796\n",
|
||
" warmup_time: 0.002794981002807617\n",
|
||
" \n",
|
||
"Result for objective_da1ff46c:\n",
|
||
" date: 2022-07-22_15-30-49\n",
|
||
" done: false\n",
|
||
" experiment_id: 9163132451a14ace8ddf394aeaae9018\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 94\n",
|
||
" iterations_since_restore: 95\n",
|
||
" mean_loss: -3.814808150093952\n",
|
||
" neg_mean_loss: 3.814808150093952\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47057\n",
|
||
" time_since_restore: 10.23661208152771\n",
|
||
" time_this_iter_s: 0.1076211929321289\n",
|
||
" time_total_s: 10.23661208152771\n",
|
||
" timestamp: 1658500249\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 95\n",
|
||
" trial_id: da1ff46c\n",
|
||
" warmup_time: 0.0030031204223632812\n",
|
||
" \n",
|
||
"Result for objective_da1ff46c:\n",
|
||
" date: 2022-07-22_15-30-49\n",
|
||
" done: true\n",
|
||
" experiment_id: 9163132451a14ace8ddf394aeaae9018\n",
|
||
" experiment_tag: 9_height=-39.1516,steps=100,width=10.4951\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 99\n",
|
||
" iterations_since_restore: 100\n",
|
||
" mean_loss: -3.819827867781687\n",
|
||
" neg_mean_loss: 3.819827867781687\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47057\n",
|
||
" time_since_restore: 10.77621078491211\n",
|
||
" time_this_iter_s: 0.10817480087280273\n",
|
||
" time_total_s: 10.77621078491211\n",
|
||
" timestamp: 1658500249\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 100\n",
|
||
" trial_id: da1ff46c\n",
|
||
" warmup_time: 0.0030031204223632812\n",
|
||
" \n",
|
||
"Result for objective_dc25c796:\n",
|
||
" date: 2022-07-22_15-30-52\n",
|
||
" done: false\n",
|
||
" experiment_id: c0f302c32b284f8e99dbdfa90657ee7d\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 94\n",
|
||
" iterations_since_restore: 95\n",
|
||
" mean_loss: -1.1817308993292515\n",
|
||
" neg_mean_loss: 1.1817308993292515\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47062\n",
|
||
" time_since_restore: 10.179337978363037\n",
|
||
" time_this_iter_s: 0.1043100357055664\n",
|
||
" time_total_s: 10.179337978363037\n",
|
||
" timestamp: 1658500252\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 95\n",
|
||
" trial_id: dc25c796\n",
|
||
" warmup_time: 0.002794981002807617\n",
|
||
" \n",
|
||
"Result for objective_dc25c796:\n",
|
||
" date: 2022-07-22_15-30-53\n",
|
||
" done: true\n",
|
||
" experiment_id: c0f302c32b284f8e99dbdfa90657ee7d\n",
|
||
" experiment_tag: 10_height=-13.6110,steps=100,width=5.8246\n",
|
||
" hostname: Kais-MacBook-Pro.local\n",
|
||
" iterations: 99\n",
|
||
" iterations_since_restore: 100\n",
|
||
" mean_loss: -1.190635502081924\n",
|
||
" neg_mean_loss: 1.190635502081924\n",
|
||
" node_ip: 127.0.0.1\n",
|
||
" pid: 47062\n",
|
||
" time_since_restore: 10.721266031265259\n",
|
||
" time_this_iter_s: 0.10741806030273438\n",
|
||
" time_total_s: 10.721266031265259\n",
|
||
" timestamp: 1658500253\n",
|
||
" timesteps_since_restore: 0\n",
|
||
" training_iteration: 100\n",
|
||
" trial_id: dc25c796\n",
|
||
" warmup_time: 0.002794981002807617\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",
|
||
" num_samples=num_samples,\n",
|
||
" ),\n",
|
||
" param_space=search_space,\n",
|
||
")\n",
|
||
"results = tuner.fit()"
|
||
]
|
||
},
|
||
{
|
||
"attachments": {},
|
||
"cell_type": "markdown",
|
||
"id": "477f099b",
|
||
"metadata": {},
|
||
"source": [
|
||
"Here are the hyperparameters found to minimize the mean loss of the defined objective."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "3488aefa",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Best hyperparameters found were: {'steps': 100, 'width': 19.398197043239886, 'height': -95.88310114083951}\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"Best hyperparameters found were: \", results.get_best_result().config)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "2936353a",
|
||
"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
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|