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#2648 - updated benchmark process to output markdown file instead of LaTeX. Added pipeline that runs benchmarking at 2am on a weekday and automatically upon creation of release branch

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This commit is contained in:
Chris McCarthy
2024-06-25 16:58:39 +01:00
parent bf5f443604
commit 824729276e
4 changed files with 139 additions and 82 deletions
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4
benchmark/primaite_benchmark.py
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@@ -117,14 +117,14 @@ class BenchmarkSession:
def generate_learn_metadata_dict(self) -> Dict[str, Any]:
"""Metadata specific to the learning session."""
total_s, s_per_step, s_per_100_steps_10_nodes = self._learn_benchmark_durations()
self.gym_env.average_reward_per_episode.pop(0) # remove episode 0
self.gym_env.total_reward_per_episode.pop(0) # remove episode 0
return {
"total_episodes": self.gym_env.episode_counter,
"total_time_steps": self.gym_env.total_time_steps,
"total_s": total_s,
"s_per_step": s_per_step,
"s_per_100_steps_10_nodes": s_per_100_steps_10_nodes,
"av_reward_per_episode": self.gym_env.average_reward_per_episode,
"av_reward_per_episode": self.gym_env.total_reward_per_episode,
}

133
benchmark/report.py
View File

@@ -9,10 +9,6 @@ import plotly.graph_objects as go
import polars as pl
import yaml
from plotly.graph_objs import Figure
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
@@ -140,7 +136,7 @@ def _plot_all_benchmarks_combined_session_av(results_directory: Path) -> Figure:
converted into a polars dataframe, and plotted as a scatter line in plotly.
"""
major_v = primaite.__version__.split(".")[0]
title = f"Learning Benchmarking of All Released Versions under Major v{major_v}.*.*"
title = f"Learning Benchmarking of All Released Versions under Major v{major_v}.#.#"
subtitle = "Rolling Av (Combined Session Av)"
if title:
if subtitle:
@@ -208,98 +204,77 @@ def build_benchmark_latex_report(
fig = _plot_all_benchmarks_combined_session_av(results_directory=results_root_path)
all_version_plot_path = results_root_path / "PrimAITE Versions Learning Benchmark.png"
all_version_plot_path = version_result_dir / "PrimAITE Versions Learning Benchmark.png"
fig.write_image(all_version_plot_path)
geometry_options = {"tmargin": "2.5cm", "rmargin": "2.5cm", "bmargin": "2.5cm", "lmargin": "2.5cm"}
data = benchmark_metadata_dict
primaite_version = data["primaite_version"]
# Create a new document
doc = Document("report", geometry_options=geometry_options)
# Title
doc.preamble.append(Command("title", f"PrimAITE {primaite_version} Learning Benchmark"))
doc.preamble.append(Command("author", "PrimAITE Dev Team"))
doc.preamble.append(Command("date", datetime.now().date()))
doc.append(Command("maketitle"))
with open(version_result_dir / f"PrimAITE v{primaite_version} Learning Benchmark.md", "w") as file:
# Title
file.write(f"# PrimAITE v{primaite_version} Learning Benchmark\n")
file.write("## PrimAITE Dev Team\n")
file.write(f"### {datetime.now().date()}\n")
file.write("\n---\n")
sessions = data["total_sessions"]
episodes = session_metadata[1]["total_episodes"] - 1
steps = data["config"]["game"]["max_episode_length"]
sessions = data["total_sessions"]
episodes = session_metadata[1]["total_episodes"] - 1
steps = data["config"]["game"]["max_episode_length"]
# Body
with doc.create(Section("Introduction")):
doc.append(
# Body
file.write("## 1 Introduction\n")
file.write(
f"PrimAITE v{primaite_version} was benchmarked automatically upon release. Learning rate metrics "
f"were captured to be referenced during system-level testing and user acceptance testing (UAT)."
f"were captured to be referenced during system-level testing and user acceptance testing (UAT).\n"
)
doc.append(
f"\nThe benchmarking process consists of running {sessions} training session using the same "
file.write(
f"The benchmarking process consists of running {sessions} training session using the same "
f"config file. Each session trains an agent for {episodes} episodes, "
f"with each episode consisting of {steps} steps."
f"with each episode consisting of {steps} steps.\n"
)
doc.append(
f"\nThe total reward per episode from each session is captured. This is then used to calculate an "
file.write(
f"The total reward per episode from each session is captured. This is then used to calculate an "
f"caverage total reward per episode from the {sessions} individual sessions for smoothing. "
f"Finally, a 25-widow rolling average of the average total reward per session is calculated for "
f"further smoothing."
f"further smoothing.\n"
)
with doc.create(Section("System Information")):
with doc.create(Subsection("Python")):
with doc.create(Tabular("|l|l|")) as table:
table.add_hline()
table.add_row((bold("Version"), sys.version))
table.add_hline()
file.write("## 2 System Information\n")
i = 1
file.write(f"### 2.{i} Python\n")
file.write(f"**Version:** {sys.version}\n")
for section, section_data in data["system_info"].items():
i += 1
if section_data:
with doc.create(Subsection(section)):
if isinstance(section_data, dict):
with doc.create(Tabular("|l|l|")) as table:
table.add_hline()
for key, value in section_data.items():
table.add_row((bold(key), value))
table.add_hline()
elif isinstance(section_data, list):
headers = section_data[0].keys()
tabs_str = "|".join(["l" for _ in range(len(headers))])
tabs_str = f"|{tabs_str}|"
with doc.create(Tabular(tabs_str)) as table:
table.add_hline()
table.add_row([bold(h) for h in headers])
table.add_hline()
for item in section_data:
table.add_row(item.values())
table.add_hline()
file.write(f"### 2.{i} {section}\n")
if isinstance(section_data, dict):
for key, value in section_data.items():
file.write(f"- **{key}:** {value}\n")
headers_map = {
"total_sessions": "Total Sessions",
"total_episodes": "Total Episodes",
"total_time_steps": "Total Steps",
"av_s_per_session": "Av Session Duration (s)",
"av_s_per_step": "Av Step Duration (s)",
"av_s_per_100_steps_10_nodes": "Av Duration per 100 Steps per 10 Nodes (s)",
}
with doc.create(Section("Stats")):
with doc.create(Subsection("Benchmark Results")):
with doc.create(Tabular("|l|l|")) as table:
table.add_hline()
for section, header in headers_map.items():
if section.startswith("av_"):
table.add_row((bold(header), f"{data[section]:.4f}"))
else:
table.add_row((bold(header), data[section]))
table.add_hline()
headers_map = {
"total_sessions": "Total Sessions",
"total_episodes": "Total Episodes",
"total_time_steps": "Total Steps",
"av_s_per_session": "Av Session Duration (s)",
"av_s_per_step": "Av Step Duration (s)",
"av_s_per_100_steps_10_nodes": "Av Duration per 100 Steps per 10 Nodes (s)",
}
with doc.create(Section("Graphs")):
with doc.create(Subsection(f"v{primaite_version} Learning Benchmark Plot")):
with doc.create(LatexFigure(position="h!")) as pic:
pic.add_image(str(this_version_plot_path))
pic.add_caption(f"PrimAITE {primaite_version} Learning Benchmark Plot")
file.write("## 3 Stats\n")
for section, header in headers_map.items():
if section.startswith("av_"):
file.write(f"- **{header}:** {data[section]:.4f}\n")
else:
file.write(f"- **{header}:** {data[section]}\n")
with doc.create(Subsection(f"Learning Benchmarking of All Released Versions under Major v{major_v}.*.*")):
with doc.create(LatexFigure(position="h!")) as pic:
pic.add_image(str(all_version_plot_path))
pic.add_caption(f"Learning Benchmarking of All Released Versions under Major v{major_v}.*.*")
file.write("## 4 Graphs\n")
doc.generate_pdf(str(this_version_plot_path).replace(".png", ""), clean_tex=True)
file.write(f"### 4.1 v{primaite_version} Learning Benchmark Plot\n")
file.write(f"![PrimAITE {primaite_version} Learning Benchmark Plot]({this_version_plot_path.name})\n")
file.write(f"### 4.2 Learning Benchmarking of All Released Versions under Major v{major_v}.#.#\n")
file.write(
f"![Learning Benchmarking of All Released Versions under "
f"Major v{major_v}.#.#]({all_version_plot_path.name})\n"
)