diff --git a/flatland/evaluators/service.py b/flatland/evaluators/service.py
index d77f8cbbd6a9d40c172c20d386dda174cd6bc19a..66532cf1e680de6fb878f6b4709d7dfd98c9aab4 100644
--- a/flatland/evaluators/service.py
+++ b/flatland/evaluators/service.py
@@ -49,9 +49,8 @@ m.patch()
# CONSTANTS
########################################################
-# Don't proceed to next Test if the previous one
-# didn't reach this mean completion percentage
-TEST_MIN_PERCENTAGE_COMPLETE_MEAN = 0.25
+# Don't proceed to next Test if the previous one didn't reach this mean completion percentage
+TEST_MIN_PERCENTAGE_COMPLETE_MEAN = float(os.getenv("TEST_MIN_PERCENTAGE_COMPLETE_MEAN", 0.25))
# After this number of consecutive timeouts, kill the submission:
# this probably means the submission has crashed
@@ -159,6 +158,14 @@ class FlatlandRemoteEvaluationService:
print("Env shuffling is ENABLED! not suitable for infinite wave")
print("=" * 20)
+ print("=" * 20)
+ print("Max pre-planning time:", INTIAL_PLANNING_TIMEOUT)
+ print("Max step time:", PER_STEP_TIMEOUT)
+ print("Max overall time:", OVERALL_TIMEOUT)
+ print("Max submission startup time:", DEFAULT_COMMAND_TIMEOUT)
+ print("Max consecutive timeouts:", MAX_SUCCESSIVE_TIMEOUTS)
+ print("=" * 20)
+
# Test Env folder Paths
self.test_env_folder = test_env_folder
self.video_generation_envs = video_generation_envs
@@ -394,7 +401,6 @@ class FlatlandRemoteEvaluationService:
self.evaluation_metadata_df["nb_malfunctioning_trains"] = np.nan
self.evaluation_metadata_df["nb_deadlocked_trains"] = np.nan
-
# Add client specific columns
# TODO: This needs refactoring
self.evaluation_metadata_df["controller_inference_time_min"] = np.nan
@@ -423,55 +429,55 @@ class FlatlandRemoteEvaluationService:
# generate the lists of names for the stats (input names and output names)
sPrefixIn = "current_episode_controller_inference_time_"
sPrefixOut = "controller_inference_time_"
- lsStatIn = [ sPrefixIn + sStat for sStat in ["min", "mean", "max"] ]
- lsStatOut = [ sPrefixOut + sStat for sStat in ["min", "mean", "max"] ]
+ lsStatIn = [sPrefixIn + sStat for sStat in ["min", "mean", "max"]]
+ lsStatOut = [sPrefixOut + sStat for sStat in ["min", "mean", "max"]]
if lsStatIn[0] in self.stats:
- lrStats = [ self.stats[sStat] for sStat in lsStatIn ]
+ lrStats = [self.stats[sStat] for sStat in lsStatIn]
else:
- lrStats = [ 0.0 ] * len(lsStatIn)
-
- lsFields = ("reward, normalized_reward, percentage_complete, " +\
- "steps, simulation_time, nb_malfunctioning_trains, nb_deadlocked_trains").split(", ") +\
- lsStatOut
-
- loValues = [ self.simulation_rewards[-1],
- self.simulation_rewards_normalized[-1],
- self.simulation_percentage_complete[-1],
- self.simulation_steps[-1],
- self.simulation_times[-1],
- self.nb_malfunctioning_trains[-1],
- self.nb_deadlocked_trains[-1]
- ] + lrStats
+ lrStats = [0.0] * len(lsStatIn)
+
+ lsFields = ("reward, normalized_reward, percentage_complete, " + \
+ "steps, simulation_time, nb_malfunctioning_trains, nb_deadlocked_trains").split(", ") + \
+ lsStatOut
+
+ loValues = [self.simulation_rewards[-1],
+ self.simulation_rewards_normalized[-1],
+ self.simulation_percentage_complete[-1],
+ self.simulation_steps[-1],
+ self.simulation_times[-1],
+ self.nb_malfunctioning_trains[-1],
+ self.nb_deadlocked_trains[-1]
+ ] + lrStats
# update the dataframe without the updating-a-copy warning
df = self.evaluation_metadata_df
df.loc[last_simulation_env_file_path, lsFields] = loValues
- #_row.reward = self.simulation_rewards[-1]
- #_row.normalized_reward = self.simulation_rewards_normalized[-1]
- #_row.percentage_complete = self.simulation_percentage_complete[-1]
- #_row.steps = self.simulation_steps[-1]
- #_row.simulation_time = self.simulation_times[-1]
- #_row.nb_malfunctioning_trains = self.nb_malfunctioning_trains[-1]
-
- #_row.controller_inference_time_min = self.stats[
- # "current_episode_controller_inference_time_min"
- #]
- #_row.controller_inference_time_mean = self.stats[
- # "current_episode_controller_inference_time_mean"
- #]
- #_row.controller_inference_time_max = self.stats[
- # "current_episode_controller_inference_time_max"
- #]
- #else:
+ # _row.reward = self.simulation_rewards[-1]
+ # _row.normalized_reward = self.simulation_rewards_normalized[-1]
+ # _row.percentage_complete = self.simulation_percentage_complete[-1]
+ # _row.steps = self.simulation_steps[-1]
+ # _row.simulation_time = self.simulation_times[-1]
+ # _row.nb_malfunctioning_trains = self.nb_malfunctioning_trains[-1]
+
+ # _row.controller_inference_time_min = self.stats[
+ # "current_episode_controller_inference_time_min"
+ # ]
+ # _row.controller_inference_time_mean = self.stats[
+ # "current_episode_controller_inference_time_mean"
+ # ]
+ # _row.controller_inference_time_max = self.stats[
+ # "current_episode_controller_inference_time_max"
+ # ]
+ # else:
# _row.controller_inference_time_min = 0.0
# _row.controller_inference_time_mean = 0.0
# _row.controller_inference_time_max = 0.0
- #self.evaluation_metadata_df.loc[
+ # self.evaluation_metadata_df.loc[
# last_simulation_env_file_path
- #] = _row
+ # ] = _row
# Delete this key from the stats to ensure that it
# gets computed again from scratch in the next episode
@@ -676,7 +682,7 @@ class FlatlandRemoteEvaluationService:
if self.simulation_count >= len(self.env_file_paths):
self.evaluation_done = True
# Hack - just ensure these are set
- test_env_file_path = self.env_file_paths[self.simulation_count-1]
+ test_env_file_path = self.env_file_paths[self.simulation_count - 1]
env_test, env_level = self.get_env_test_and_level(test_env_file_path)
else:
test_env_file_path = self.env_file_paths[self.simulation_count]
@@ -692,7 +698,8 @@ class FlatlandRemoteEvaluationService:
if mean_test_complete_percentage < TEST_MIN_PERCENTAGE_COMPLETE_MEAN:
print("=" * 15)
- msg = "The mean percentage of done agents during the last 10 environments was too low: {:.3f} < {}".format(
+ msg = "The mean percentage of done agents during the last Test ({} environments) was too low: {:.3f} < {}".format(
+ len(self.simulation_percentage_complete_per_test[self.current_test]),
mean_test_complete_percentage,
TEST_MIN_PERCENTAGE_COMPLETE_MEAN
)
@@ -901,7 +908,7 @@ class FlatlandRemoteEvaluationService:
if len(self.env.cur_episode) > 0:
g3Ep = np.array(self.env.cur_episode)
- self.nb_deadlocked_trains.append(np.sum(g3Ep[-1,:,5]))
+ self.nb_deadlocked_trains.append(np.sum(g3Ep[-1, :, 5]))
else:
self.nb_deadlocked_trains.append(np.nan)
@@ -1223,11 +1230,11 @@ class FlatlandRemoteEvaluationService:
self.evaluation_done = True
# JW - change the command to a submit
print("Creating fake submit message after excessive timeouts.")
- command = {
- "type":messages.FLATLAND_RL.ENV_SUBMIT,
+ command = {
+ "type": messages.FLATLAND_RL.ENV_SUBMIT,
"payload": {},
- "response_channel": self.previous_command.get("response_channel") }
-
+ "response_channel": self.previous_command.get("response_channel")}
+
return self.handle_env_submit(command)
continue
diff --git a/notebooks/test-service-timeouts.ipynb b/notebooks/test-service-timeouts.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..67a849d8e8df06c6fa0b4dc253aa0ebe553aa4d9
--- /dev/null
+++ b/notebooks/test-service-timeouts.ipynb
@@ -0,0 +1,841 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Test Service\n",
+ "\n",
+ "Intended to test the service.py evaluator.\n",
+ "Runs the service.py and a simple client."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "baXcVq3ii0Cb"
+ },
+ "source": [
+ "Setup\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import PIL\n",
+ "from flatland.utils.rendertools import RenderTool\n",
+ "import imageio\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "PU5GkH271guD"
+ },
+ "outputs": [],
+ "source": [
+ "from flatland.envs.rail_env import RailEnv\n",
+ "from flatland.envs.rail_generators import sparse_rail_generator\n",
+ "from flatland.envs.schedule_generators import sparse_schedule_generator\n",
+ "from flatland.envs.malfunction_generators import malfunction_from_file, no_malfunction_generator\n",
+ "from flatland.envs.rail_generators import rail_from_file\n",
+ "from flatland.envs.schedule_generators import schedule_from_file\n",
+ "from flatland.core.env_observation_builder import DummyObservationBuilder\n",
+ "from flatland.envs.persistence import RailEnvPersister\n",
+ "from flatland.evaluators.client import FlatlandRemoteClient, TimeoutException\n",
+ "import flatland.evaluators.service as fes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "PU5GkH271guD"
+ },
+ "outputs": [],
+ "source": [
+ "import pickle\n",
+ "import redis\n",
+ "import subprocess as sp\n",
+ "import shlex\n",
+ "import time\n",
+ "import pkg_resources as pr\n",
+ "import importlib_resources as ir\n",
+ "import sys, os\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sDirRoot = \"/Users/flaurent/Sites/flatland/neurips2020-flatland-starter-kit/scratch/test-neurips2020-round2-v0\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!ps -ef | grep -i python | grep -i flatland.evaluators.service"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def kill_evaluator():\n",
+ " # kill previous evaluator\n",
+ " !ps -ef | grep -i python | grep -i flatland.evaluators.service | awk '{print $2}' | xargs kill"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cleanup():\n",
+ " oRedis = redis.Redis()\n",
+ " lKeys = oRedis.keys(\"flatland*\")\n",
+ " for sKey in lKeys:\n",
+ " print(\"Deleting:\", sKey)\n",
+ " oRedis.delete(sKey)\n",
+ " \n",
+ " !rm -f /tmp/output.csv\n",
+ " \n",
+ " kill_evaluator()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def configure_env(overall=8*60*60, planning=5*60, step=10, successive=10, mean_percentage=0.25):\n",
+ " osEnv2 = os.environ.copy()\n",
+ " osEnv2[\"FLATLAND_OVERALL_TIMEOUT\"]=str(overall)\n",
+ " osEnv2[\"FLATLAND_PER_STEP_TIMEOUT\"] = str(step)\n",
+ " osEnv2[\"FLATLAND_INITIAL_PLANNING_TIMEOUT\"] = str(planning)\n",
+ " osEnv2[\"FLATLAND_MAX_SUCCESSIVE_TIMEOUTS\"] = str(successive)\n",
+ " osEnv2[\"TEST_MIN_PERCENTAGE_COMPLETE_MEAN\"] = str(mean_percentage)\n",
+ " return osEnv2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def start_evaluator(env):\n",
+ " sCmd = f\"python -m flatland.evaluators.service --test_folder {sDirRoot} --pickle\" # --verbose\"\n",
+ " lsCmd = shlex.split(sCmd)\n",
+ " print(sCmd)\n",
+ " \n",
+ " oPipe = sp.Popen(lsCmd, env=env)\n",
+ " oPipe.poll()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def start_client():\n",
+ " oFRC = FlatlandRemoteClient(test_envs_root=sDirRoot, verbose=False, use_pickle=True)\n",
+ " env, env_dict = RailEnvPersister.load_new(f\"{sDirRoot}/Test_0/Level_0.pkl\")\n",
+ " return oFRC"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def random_controller(obs, _env):\n",
+ " np.random.seed(0)\n",
+ " \n",
+ " dAct = {}\n",
+ " for iAg in range(len(_env.agents)):\n",
+ " dAct[iAg] = np.random.randint(0, 5)\n",
+ " return dAct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def run_submission(oFRC, slow_ep=-1, debug=False):\n",
+ " def log(txt, end=\"\\n\"):\n",
+ " if debug: print(txt, end)\n",
+ " \n",
+ " dummy_obs = DummyObservationBuilder()\n",
+ " episode = 0\n",
+ " obs = True\n",
+ " \n",
+ " while obs:\n",
+ " obs, info = oFRC.env_create(obs_builder_object=dummy_obs)\n",
+ " log(oFRC.current_env_path)\n",
+ " log(f\"Episode : {episode}\")\n",
+ " \n",
+ " if not obs:\n",
+ " log(\"None observation - all envs completed!\")\n",
+ " break\n",
+ " \n",
+ " while True:\n",
+ " action = random_controller(obs, oFRC.env)\n",
+ " \n",
+ " if slow_ep != -1:\n",
+ " # make a specific episode artificially slow\n",
+ " if (episode == slow_ep) and (oFRC.env._elapsed_steps > 10):\n",
+ " time.sleep(2)\n",
+ " \n",
+ " try:\n",
+ " observation, all_rewards, done, info = oFRC.env_step(action)\n",
+ " #log(\".\", end=\"\")\n",
+ " if done['__all__']:\n",
+ " log(\"\\nCompleted Episode : \", episode)\n",
+ " log(\"Reward : \", sum(list(all_rewards.values())))\n",
+ " break\n",
+ " except TimeoutException as err:\n",
+ " log(\"Timeout: \", err)\n",
+ " break\n",
+ " \n",
+ " episode += 1\n",
+ " \n",
+ " #print(f\"Evaluation Complete - episodes={episode} - send submit message...\")\n",
+ " #print(oFRC.submit())\n",
+ " #print(\"All done.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Tests\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "\n",
+ "# Normal submission\n",
+ "cleanup()\n",
+ "config = configure_env(overall=8*60*60, planning=5*60, step=10, successive=10, mean_percentage=0.1)\n",
+ "start_evaluator(config)\n",
+ "client = start_client()\n",
+ "run_submission(client)\n",
+ "\n",
+ "#try:\n",
+ "# run_submission(client)\n",
+ "#except Exception as e:\n",
+ "# print(\"== EXCEPTION! ===\")\n",
+ "# print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "\n",
+ "# Overall timeout\n",
+ "cleanup()\n",
+ "config = configure_env(overall=3, planning=5*60, step=10, successive=10, mean_percentage=0.0)\n",
+ "start_evaluator(config)\n",
+ "client = start_client()\n",
+ "run_submission(client, debug=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "\n",
+ "# Step timeout\n",
+ "# FIXME fails!\n",
+ "\"\"\"\n",
+ "cleanup()\n",
+ "config = configure_env(overall=3, planning=5*60, step=10, successive=10, mean_percentage=0.0)\n",
+ "start_evaluator(config)\n",
+ "client = start_client()\n",
+ "run_submission(client, slow_ep=2, debug=True)\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#pd.read_csv(\"/tmp/output.csv\").T"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Cleanup\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "kill_evaluator()"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [],
+ "name": "Flatland Round 2 Replays",
+ "provenance": []
+ },
+ "hide_input": false,
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.6.8"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ },
+ "varInspector": {
+ "cols": {
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+ "lenVar": 40
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+ "kernels_config": {
+ "python": {
+ "delete_cmd_postfix": "",
+ "delete_cmd_prefix": "del ",
+ "library": "var_list.py",
+ "varRefreshCmd": "print(var_dic_list())"
+ },
+ "r": {
+ "delete_cmd_postfix": ") ",
+ "delete_cmd_prefix": "rm(",
+ "library": "var_list.r",
+ "varRefreshCmd": "cat(var_dic_list()) "
+ }
+ },
+ "types_to_exclude": [
+ "module",
+ "function",
+ "builtin_function_or_method",
+ "instance",
+ "_Feature"
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+ "window_display": false
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