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Merge remote-tracking branch 'origin/dev' into feature/2641-prevent-databaseservice-interactions-from-occurring-in-fixing-state

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Czar Echavez
2024-06-28 16:46:52 +01:00
parent 3ac97f8c3f e7fc01781e
commit 7ac503794d
4 changed files with 149 additions and 91 deletions
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84
.azure/azure-benchmark-pipeline.yaml Normal file
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@@ -0,0 +1,84 @@
trigger:
branches:
exclude:
- '*'
include:
- 'refs/heads/release/*'
schedules:
- cron: "0 2 * * 1-5" # Run at 2 AM every weekday
displayName: "Weekday Schedule"
branches:
include:
- 'refs/heads/dev'
pool:
vmImage: ubuntu-latest
variables:
VERSION: ''
MAJOR_VERSION: ''
steps:
- checkout: self
persistCredentials: true
- script: |
VERSION=$(cat src/primaite/VERSION | tr -d '\n')
if [[ "$(Build.SourceBranch)" == "refs/heads/dev" ]]; then
DATE=$(date +%Y%m%d)
echo "${VERSION}+dev.${DATE}" > src/primaite/VERSION
fi
displayName: 'Update VERSION file for Dev Benchmark'
- script: |
VERSION=$(cat src/primaite/VERSION | tr -d '\n')
MAJOR_VERSION=$(echo $VERSION | cut -d. -f1)
echo "##vso[task.setvariable variable=VERSION]$VERSION"
echo "##vso[task.setvariable variable=MAJOR_VERSION]$MAJOR_VERSION"
displayName: 'Set Version Variables'
- task: UsePythonVersion@0
inputs:
versionSpec: '3.11'
addToPath: true
- script: |
python -m pip install --upgrade pip
pip install -e .[dev,rl]
primaite setup
displayName: 'Install Dependencies'
- script: |
cd benchmark
python3 primaite_benchmark.py
cd ..
displayName: 'Run Benchmarking Script'
- script: |
git config --global user.email "oss@dstl.gov.uk"
git config --global user.name "Defence Science and Technology Laboratory UK"
workingDirectory: $(System.DefaultWorkingDirectory)
displayName: 'Configure Git'
condition: and(succeeded(), eq(variables['Build.Reason'], 'Manual'), startsWith(variables['Build.SourceBranch'], 'refs/heads/release'))
- script: |
git add benchmark/results/v$(MAJOR_VERSION)/v$(VERSION)/*
git commit -m "Automated benchmark output commit for version $(VERSION)"
git push origin HEAD:refs/heads/$(Build.SourceBranchName)
displayName: 'Commit and Push Benchmark Results'
workingDirectory: $(System.DefaultWorkingDirectory)
env:
GIT_CREDENTIALS: $(System.AccessToken)
condition: and(succeeded(), startsWith(variables['Build.SourceBranch'], 'refs/heads/release'))
- script: |
tar czf primaite_v$(VERSION)_benchmark.tar.gz benchmark/results/v$(MAJOR_VERSION)/v$(VERSION)
displayName: 'Prepare Artifacts for Publishing'
- task: PublishPipelineArtifact@1
inputs:
targetPath: primaite_v$(VERSION)_benchmark.tar.gz
artifactName: 'benchmark-output'
publishLocation: 'pipeline'
displayName: 'Publish Benchmark Output as Artifact'

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,
"total_reward_per_episode": self.gym_env.total_reward_per_episode,
}

151
benchmark/report.py
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@@ -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
@@ -39,19 +35,19 @@ def _build_benchmark_results_dict(start_datetime: datetime, metadata_dict: Dict,
"av_s_per_step": sum(d["s_per_step"] for d in metadata_dict.values()) / num_sessions,
"av_s_per_100_steps_10_nodes": sum(d["s_per_100_steps_10_nodes"] for d in metadata_dict.values())
/ num_sessions,
"combined_av_reward_per_episode": {},
"session_av_reward_per_episode": {k: v["av_reward_per_episode"] for k, v in metadata_dict.items()},
"combined_total_reward_per_episode": {},
"session_total_reward_per_episode": {k: v["total_reward_per_episode"] for k, v in metadata_dict.items()},
"config": config,
}
# find the average of each episode across all sessions
episodes = metadata_dict[1]["av_reward_per_episode"].keys()
episodes = metadata_dict[1]["total_reward_per_episode"].keys()
for episode in episodes:
combined_av_reward = (
sum(metadata_dict[k]["av_reward_per_episode"][episode] for k in metadata_dict.keys()) / num_sessions
sum(metadata_dict[k]["total_reward_per_episode"][episode] for k in metadata_dict.keys()) / num_sessions
)
averaged_data["combined_av_reward_per_episode"][episode] = combined_av_reward
averaged_data["combined_total_reward_per_episode"][episode] = combined_av_reward
return averaged_data
@@ -87,7 +83,7 @@ def _plot_benchmark_metadata(
fig = go.Figure(layout=layout)
fig.update_layout(template=PLOT_CONFIG["template"])
for session, av_reward_dict in benchmark_metadata_dict["session_av_reward_per_episode"].items():
for session, av_reward_dict in benchmark_metadata_dict["session_total_reward_per_episode"].items():
df = _get_df_from_episode_av_reward_dict(av_reward_dict)
fig.add_trace(
go.Scatter(
@@ -100,7 +96,7 @@ def _plot_benchmark_metadata(
)
)
df = _get_df_from_episode_av_reward_dict(benchmark_metadata_dict["combined_av_reward_per_episode"])
df = _get_df_from_episode_av_reward_dict(benchmark_metadata_dict["combined_total_reward_per_episode"])
fig.add_trace(
go.Scatter(
x=df["episode"], y=df["av_reward"], mode="lines", name="Combined Session Av", line={"color": "#FF0000"}
@@ -136,11 +132,11 @@ def _plot_all_benchmarks_combined_session_av(results_directory: Path) -> Figure:
Does this by iterating over the ``benchmark/results`` directory and
extracting the benchmark metadata json for each version that has been
benchmarked. The combined_av_reward_per_episode is extracted from each,
benchmarked. The combined_total_reward_per_episode is extracted from each,
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:
@@ -162,7 +158,7 @@ def _plot_all_benchmarks_combined_session_av(results_directory: Path) -> Figure:
metadata_file = dir / f"{dir.name}_benchmark_metadata.json"
with open(metadata_file, "r") as file:
metadata_dict = json.load(file)
df = _get_df_from_episode_av_reward_dict(metadata_dict["combined_av_reward_per_episode"])
df = _get_df_from_episode_av_reward_dict(metadata_dict["combined_total_reward_per_episode"])
fig.add_trace(go.Scatter(x=df["episode"], y=df["rolling_av_reward"], mode="lines", name=dir.name))
@@ -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"
)

1
pyproject.toml
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@@ -64,7 +64,6 @@ dev = [
"gputil==1.4.0",
"pip-licenses==4.3.0",
"pre-commit==2.20.0",
"pylatex==1.4.1",
"pytest==7.2.0",
"pytest-xdist==3.3.1",
"pytest-cov==4.0.0",