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# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import glob
import json
import math
import os
import re
from datetime import datetime
import numpy as np
import yaml
from tensorboard.backend.event_processing import event_accumulator
import carb
def get_env_configs(configs_path):
"""Get environment configurations from yaml filepath."""
with open(configs_path) as env_configs_file:
env_configs = yaml.safe_load(env_configs_file)
return env_configs
def get_env_config(env_configs, mode, workflow, task):
"""Get the environment configuration."""
if mode not in env_configs:
raise ValueError(f"Mode {mode} is not supported in the config file.")
extended_task = f"{workflow}:{task}"
# return a direct match with extended task name
if extended_task in env_configs[mode]:
return env_configs[mode][extended_task]
# else, return a direct match with task name
if task in env_configs[mode]:
return env_configs[mode][task]
# else, return a regex match with extended task name
for env_config_key in env_configs[mode].keys():
if re.match(env_config_key, extended_task):
return env_configs[mode][env_config_key]
# else, return a regex match with task name
for env_config_key in env_configs[mode].keys():
if re.match(env_config_key, task):
return env_configs[mode][env_config_key]
# if no match is found, return None
return None
def evaluate_job(workflow, task, env_config, duration):
"""Evaluate the job."""
log_data = _retrieve_logs(workflow, task)
kpi_payload = {"success": True, "msg": ""}
# handle case where no log files are found
if not log_data:
kpi_payload["success"] = False
kpi_payload["msg"] = "error: training did not finish!"
return kpi_payload
thresholds = {**env_config.get("lower_thresholds", {}), **env_config.get("upper_thresholds", {})}
# evaluate all thresholds from the config
for threshold_name, threshold_val in thresholds.items():
uses_lower_threshold = threshold_name in env_config.get("lower_thresholds", {})
if threshold_name == "duration":
val = duration
else:
val = _extract_log_val(threshold_name, log_data, uses_lower_threshold, workflow)
# skip non-numeric values
if val is None or not isinstance(val, (int, float)) or (isinstance(val, float) and math.isnan(val)):
continue
val = round(val, 4)
if uses_lower_threshold:
# print(f"{threshold_name}: {val} > {round(threshold_val, 4)}")
if val < threshold_val:
kpi_payload["success"] = False
else:
# print(f"{threshold_name}: {val} < {round(threshold_val, 4)}")
if val > threshold_val:
kpi_payload["success"] = False
kpi_payload[threshold_name] = val
if threshold_name == "reward":
normalized_reward = val / threshold_val
kpi_payload[f"{threshold_name}_normalized"] = normalized_reward
kpi_payload[f"{threshold_name}_threshold"] = threshold_val
# add max iterations to the payload
max_iterations = env_config.get("max_iterations")
if max_iterations is not None:
kpi_payload["max_iterations"] = max_iterations
return kpi_payload
def process_kpi_data(kpi_payloads, tag=""):
"""Combine and augment the KPI payloads."""
# accumulate workflow outcomes
totals = {}
successes = {}
failures_did_not_finish = {}
failures_did_not_pass_thresholds = {}
for job_id, kpi_payload in kpi_payloads.items():
workflow = job_id.split(":")[0]
if workflow not in totals:
totals[workflow] = 0
successes[workflow] = 0
failures_did_not_finish[workflow] = 0
failures_did_not_pass_thresholds[workflow] = 0
totals[workflow] += 1
if kpi_payload["success"]:
successes[workflow] += 1
else:
if kpi_payload["msg"] == "error: training did not finish!":
failures_did_not_finish[workflow] += 1
else:
failures_did_not_pass_thresholds[workflow] += 1
kpi_payloads["overall"] = {
"totals": totals,
"successes": successes,
"failures_did_not_finish": failures_did_not_finish,
"failures_did_not_pass_thresholds": failures_did_not_pass_thresholds,
"timestamp": datetime.now().isoformat(),
"tag": tag,
}
return kpi_payloads
def output_payloads(payloads):
"""Output the KPI payloads to a json file."""
# first grab all log files
repo_path = os.path.join(carb.tokens.get_tokens_interface().resolve("${app}"), "..")
output_path = os.path.join(repo_path, "logs/kpi.json")
# create directory if it doesn't exist
if not os.path.exists(os.path.dirname(output_path)):
os.makedirs(os.path.dirname(output_path))
# save file
with open(output_path, "w") as payload_file:
json.dump(payloads, payload_file, indent=4)
def _retrieve_logs(workflow, task):
"""Retrieve training logs."""
# first grab all log files
repo_path = os.path.join(carb.tokens.get_tokens_interface().resolve("${app}"), "..")
from isaaclab.utils.version import get_isaac_sim_version
if get_isaac_sim_version().major < 5:
repo_path = os.path.join(repo_path, "..")
if workflow == "rl_games":
log_files_path = os.path.join(repo_path, f"logs/{workflow}/{task}/*/summaries/*")
else:
log_files_path = os.path.join(repo_path, f"logs/{workflow}/{task}/*/*.tfevents.*")
log_files = glob.glob(log_files_path)
# handle case where no log files are found
if not log_files:
return None
# find most recent
latest_log_file = max(log_files, key=os.path.getctime)
# parse tf file into a dictionary
log_data = _parse_tf_logs(latest_log_file)
return log_data
def _parse_tf_logs(log):
"""Parse the tensorflow filepath into a dictionary."""
log_data = {}
ea = event_accumulator.EventAccumulator(log)
ea.Reload()
tags = ea.Tags()["scalars"]
for tag in tags:
log_data[tag] = []
for event in ea.Scalars(tag):
log_data[tag].append((event.step, event.value))
return log_data
def _extract_log_val(name, log_data, uses_lower_threshold, workflow):
"""Extract the value from the log data."""
try:
if name == "reward":
reward_tags = {
"rl_games": "rewards/iter",
"rsl_rl": "Train/mean_reward",
"sb3": None, # TODO: complete when sb3 is fixed
"skrl": "Reward / Total reward (mean)",
}
tag = reward_tags.get(workflow)
if tag:
return _extract_reward(log_data, tag)
elif name == "episode_length":
episode_tags = {
"rl_games": "episode_lengths/iter",
"rsl_rl": "Train/mean_episode_length",
"sb3": None, # TODO: complete when sb3 is fixed
"skrl": "Episode / Total timesteps (mean)",
}
tag = episode_tags.get(workflow)
if tag:
return _extract_feature(log_data, tag, uses_lower_threshold)
elif name == "training_time":
return {"rl_games": log_data["rewards/time"][-1][0], "rsl_rl": None, "sb3": None, "skrl": None}.get(
workflow
)
except Exception:
return None
raise ValueError(f"Env Config name {name} is not supported.")
def _extract_feature(log_data, feature, uses_lower_threshold):
"""Extract the feature from the log data."""
log_data = np.array(log_data[feature])[:, 1]
if uses_lower_threshold:
return max(log_data)
else:
return min(log_data)
def _extract_reward(log_data, feature, k=8):
"""Extract the averaged max reward from the log data."""
log_data = np.array(log_data[feature])[:, 1]
# find avg of k max values
k = min(len(log_data), k)
averaged_reward = np.mean(np.partition(log_data, -k)[-k:])
return averaged_reward
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