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406662d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | # Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Launch Isaac Sim Simulator first."""
from isaaclab.app import AppLauncher
# Launch omniverse app
app_launcher = AppLauncher(headless=True, enable_cameras=True)
simulation_app = app_launcher.app
import os
import subprocess
import sys
import time
import env_benchmark_test_utils as utils
import gymnasium as gym
import pytest
import carb
from isaaclab_rl.utils.pretrained_checkpoint import WORKFLOW_EXPERIMENT_NAME_VARIABLE, WORKFLOW_TRAINER
def setup_environment():
"""Setup environment for testing."""
# Acquire all Isaac environments names
registered_task_specs = []
for task_spec in gym.registry.values():
if "Isaac" in task_spec.id and not task_spec.id.endswith("Play-v0"):
registered_task_specs.append(task_spec)
# Sort environments by name
registered_task_specs.sort(key=lambda x: x.id)
# This flag is necessary to prevent a bug where the simulation gets stuck randomly when running the
# test on many environments.
carb_settings_iface = carb.settings.get_settings()
carb_settings_iface.set_bool("/physics/cooking/ujitsoCollisionCooking", False)
return registered_task_specs
def train_job(workflow, task, env_config, num_gpus):
"""Train a single job for a given workflow, task, and configuration, and return the duration."""
cmd = [
sys.executable,
WORKFLOW_TRAINER[workflow],
"--task",
task,
"--enable_cameras",
"--headless",
]
# Add max iterations if specified
max_iterations = env_config.get("max_iterations")
if max_iterations is not None:
cmd.extend(["--max_iterations", str(max_iterations)])
if num_gpus > 1:
cmd.append(f"--nnprod_per_node={num_gpus}")
cmd.append("--distributed")
# Add experiment name variable
cmd.append(f"{WORKFLOW_EXPERIMENT_NAME_VARIABLE[workflow]}={task}")
print("Running : " + " ".join(cmd))
start_time = time.time()
subprocess.run(cmd)
duration = time.time() - start_time
return duration
@pytest.mark.parametrize("task_spec", setup_environment())
def test_train_environments(workflow, task_spec, config_path, mode, num_gpus, kpi_store):
"""Train environments provided in the config file, save KPIs, and evaluate against thresholds"""
# Skip if workflow not supported for this task
if workflow + "_cfg_entry_point" not in task_spec.kwargs:
pytest.skip(f"Workflow {workflow} not supported for task {task_spec.id}")
# Load environment config
task = task_spec.id
if config_path.startswith("/"):
full_config_path = config_path
else:
full_config_path = os.path.join(os.path.dirname(__file__), config_path)
env_configs = utils.get_env_configs(full_config_path)
env_config = utils.get_env_config(env_configs, mode, workflow, task)
# Skip if config not found
if env_config is None:
pytest.skip(f"No config found for task {task} in {mode} mode")
job_name = f"{workflow}:{task}"
print(f">>> Training: {job_name}")
# Train and capture duration
duration = train_job(workflow, task, env_config, num_gpus)
print(f">>> Evaluating trained: {job_name}")
# Check if training logs were output and all thresholds passed
kpi_payload = utils.evaluate_job(workflow, task, env_config, duration)
success_flag = kpi_payload["success"]
print(f">>> Trained {job_name} success flag: {success_flag}.")
print("-" * 80)
# Save KPI
kpi_store[job_name] = kpi_payload
# Verify job was successful
if not kpi_payload["success"]:
pytest.fail(f"Job {job_name} failed to meet success criteria")
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