#2628: commit

benchmark/benchmark.py

@@ -0,0 +1,21 @@
+from typing import Any, Dict, Optional, Tuple
+
+from gymnasium.core import ObsType
+
+from primaite.session.environment import PrimaiteGymEnv
+
+
+class BenchmarkPrimaiteGymEnv(PrimaiteGymEnv):
+    """
+    Class that extends the PrimaiteGymEnv.
+
+    The reset method is extended so that the average rewards per episode are recorded.
+    """
+
+    total_time_steps: int = 0
+
+    def reset(self, seed: Optional[int] = None) -> Tuple[ObsType, Dict[str, Any]]:
+        """Overrides the PrimAITEGymEnv reset so that the total timesteps is saved."""
+        self.total_time_steps += self.game.step_counter
+
+        return super().reset(seed=seed)

benchmark/primaite_benchmark.py

@@ -4,11 +4,11 @@ from datetime import datetime
 from pathlib import Path
 from typing import Any, Dict, Final, Tuple
 
+from report import build_benchmark_latex_report
 from stable_baselines3 import PPO
 
 import primaite
-from benchmark.utils.benchmark import BenchmarkPrimaiteGymEnv
+from benchmark import BenchmarkPrimaiteGymEnv
-from benchmark.utils.report import build_benchmark_latex_report
 from primaite.config.load import data_manipulation_config_path
 
 _LOGGER = primaite.getLogger(__name__)
@@ -65,15 +65,12 @@ class BenchmarkSession:
         """Run the training session."""
         # start timer for session
         self.start_time = datetime.now()
-
+        # TODO check these parameters are correct
-        model = PPO(
+        # EPISODE_LEN = 10
-            policy="MlpPolicy",
+        TOTAL_TIMESTEPS = 131072
-            env=self.gym_env,
+        LEARNING_RATE = 3e-4
-            learning_rate=self.learning_rate,
+        model = PPO("MlpPolicy", self.gym_env, learning_rate=LEARNING_RATE, verbose=0, tensorboard_log="./PPO_UC2/")
-            n_steps=self.num_steps * self.num_episodes,
+        model.learn(total_timesteps=TOTAL_TIMESTEPS)
-            batch_size=self.num_steps * self.num_episodes,
-        )
-        model.learn(total_timesteps=self.num_episodes * self.num_steps)
 
         # end timer for session
         self.end_time = datetime.now()
@@ -142,10 +139,10 @@ def _prepare_session_directory():
 
 
 def run(
-    number_of_sessions: int = 10,
+    number_of_sessions: int = 5,
-    num_episodes: int = 1000,
+    num_episodes: int = 512,
     num_timesteps: int = 128,
-    batch_size: int = 1280,
+    batch_size: int = 128,
     learning_rate: float = 3e-4,
 ) -> None:  # 10  # 1000  # 256
     """Run the PrimAITE benchmark."""

benchmark/report.py

@@ -14,9 +14,9 @@ from pylatex import Command, Document
 from pylatex import Figure as LatexFigure
 from pylatex import Section, Subsection, Tabular
 from pylatex.utils import bold
+from utils import _get_system_info
 
 import primaite
-from benchmark.utils.utils import _get_system_info
 
 PLOT_CONFIG = {
     "size": {"auto_size": False, "width": 1500, "height": 900},

benchmark/utils/benchmark.py

@@ -1,122 +0,0 @@
-from datetime import datetime
-from typing import Any, Dict, List, Optional, Tuple
-
-from gymnasium.core import ObsType
-
-from primaite.session.environment import PrimaiteGymEnv
-
-
-class BenchmarkPrimaiteGymEnv(PrimaiteGymEnv):
-    """
-    Class that extends the PrimaiteGymEnv.
-
-    The reset method is extended so that the average rewards per episode are recorded.
-    """
-
-    total_time_steps: int = 0
-
-    def reset(self, seed: Optional[int] = None) -> Tuple[ObsType, Dict[str, Any]]:
-        """Overrides the PrimAITEGymEnv reset so that the total timesteps is saved."""
-        self.total_time_steps += self.game.step_counter
-
-        return super().reset(seed=seed)
-
-
-#####################################
-# IGNORE BELOW FOR NOW
-#####################################
-
-
-class BenchMarkOSInfo:
-    """Operating System Information about the machine that run the benchmark."""
-
-    operating_system: str
-    """The operating system the benchmark was run on."""
-
-    operating_system_version: str
-    """The operating system version the benchmark was run on."""
-
-    machine: str
-    """The type of machine running the benchmark."""
-
-    processor: str
-    """The processor used to run the benchmark."""
-
-
-class BenchMarkCPUInfo:
-    """CPU Information of the machine that ran the benchmark."""
-
-    physical_cores: int
-    """The number of CPU cores the machine that ran the benchmark had."""
-
-    total_cores: int
-    """The number of total cores the machine that run the benchmark had."""
-
-    max_frequency: int
-    """The CPU's maximum clock speed."""
-
-
-class BenchMarkMemoryInfo:
-    """The Memory Information of the machine that ran the benchmark."""
-
-    total: str
-    """The total amount of memory."""
-
-    swap_total: str
-    """Virtual memory."""
-
-
-class BenchMarkGPUInfo:
-    """The GPU Information of the machine that ran the benchmark."""
-
-    name: str
-    """GPU name."""
-
-    total_memory: str
-    """GPU memory."""
-
-
-class BenchMarkSystemInfo:
-    """Overall system information of the machine that ran the benchmark."""
-
-    system: BenchMarkOSInfo
-    cpu: BenchMarkCPUInfo
-    memory: BenchMarkMemoryInfo
-    gpu: List[BenchMarkMemoryInfo]
-
-
-class BenchMarkResult:
-    """Class containing the relevant benchmark results."""
-
-    benchmark_start_time: datetime
-    """Start time of the benchmark run."""
-
-    benchmark_end_time: datetime
-    """End time of the benchmark run."""
-
-    primaite_version: str
-    """The version of PrimAITE being benchmarked."""
-
-    system_info: BenchMarkSystemInfo
-    """System information of the machine that ran the benchmark."""
-
-    total_sessions: int
-    """The number of sessions that the benchmark ran."""
-
-    total_episodes: int
-    """The number of episodes over all the sessions that the benchmark ran."""
-
-    total_timesteps: int
-    """The number of timesteps over all the sessions that the benchmark ran."""
-
-    average_seconds_per_session: float
-    """The average time per session."""
-
-    average_seconds_per_step: float
-    """The average time per step."""
-
-    average_seconds_per_100_steps_and_10_nodes: float
-    """The average time per 100 steps on a 10 node network."""
-
-    combined_average_reward_per_episode: Dict
-    """tbd."""