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"""
Configuration helper for web deployment
Handles path resolution and model loading for deployment
"""
import os
from pathlib import Path
from typing import Optional
import logging
logger = logging.getLogger(__name__)
class WebConfig:
"""Configuration manager for web deployment"""
def __init__(self, base_path: Optional[Path] = None):
if base_path is None:
base_path = Path.cwd()
self.base_path = Path(base_path)
def get_config_path(self) -> Path:
"""Get configuration directory path"""
# Try multiple possible locations
possible_paths = [
self.base_path / "config",
self.base_path / "src" / ".." / "config",
Path(__file__).parent / "config"
]
for path in possible_paths:
if path.exists():
return path.resolve()
# Create default config directory
config_path = self.base_path / "config"
config_path.mkdir(exist_ok=True)
return config_path
def get_checkpoint_path(self) -> Path:
"""Find and return the best available checkpoint"""
# Try different possible locations and names
possible_checkpoints = [
self.base_path / "agent_epoch_00003.pt",
self.base_path / "agent_epoch_00003.pt",
self.base_path / "checkpoints" / "agent_epoch_00003.pt",
self.base_path / "checkpoints" / "agent_epoch_00003.pt",
self.base_path / "checkpoints" / "latest.pt",
]
for ckpt_path in possible_checkpoints:
if ckpt_path.exists():
logger.info(f"Found checkpoint: {ckpt_path}")
return ckpt_path
# If no checkpoint found, create a dummy message
logger.warning("No checkpoint found - you may need to download models")
return self.base_path / "checkpoints" / "model_not_found.pt"
def get_spawn_dir(self) -> Path:
"""Get spawn data directory"""
spawn_dir = self.base_path / "csgo" / "spawn"
spawn_dir.mkdir(parents=True, exist_ok=True)
# Create dummy spawn data if it doesn't exist
spawn_subdir = spawn_dir / "0"
spawn_subdir.mkdir(exist_ok=True)
# Create dummy files if they don't exist
dummy_files = ["act.npy", "full_res.npy", "info.json", "low_res.npy", "next_act.npy"]
for filename in dummy_files:
file_path = spawn_subdir / filename
if not file_path.exists():
if filename.endswith('.npy'):
import numpy as np
np.save(file_path, np.zeros((1, 10))) # Dummy array
elif filename.endswith('.json'):
import json
with open(file_path, 'w') as f:
json.dump({"dummy": True}, f)
return spawn_dir
def setup_environment_variables(self):
"""Set up environment variables for deployment"""
# Disable CUDA if not available (for CPU-only deployment)
if not self.has_cuda():
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# Set Python path
python_path = str(self.base_path / "src")
current_path = os.environ.get("PYTHONPATH", "")
if python_path not in current_path:
os.environ["PYTHONPATH"] = f"{python_path}:{current_path}" if current_path else python_path
def has_cuda(self) -> bool:
"""Check if CUDA is available"""
try:
import torch
return torch.cuda.is_available()
except ImportError:
return False
def create_default_configs(self):
"""Create default configuration files if they don't exist"""
config_dir = self.get_config_path()
# Create agent config
agent_dir = config_dir / "agent"
agent_dir.mkdir(exist_ok=True)
agent_config_path = agent_dir / "csgo.yaml"
if not agent_config_path.exists():
with open(agent_config_path, 'w') as f:
f.write("""_target_: agent.AgentConfig
denoiser:
_target_: models.diffusion.DenoiserConfig
sigma_data: 0.5
sigma_offset_noise: 0.1
noise_previous_obs: true
upsampling_factor: null
inner_model:
_target_: models.diffusion.InnerModelConfig
img_channels: 3
num_steps_conditioning: 4
cond_channels: 2048
depths: [2, 2, 2, 2]
channels: [128, 256, 512, 1024]
attn_depths: [0, 0, 1, 1]
upsampler:
_target_: models.diffusion.DenoiserConfig
sigma_data: 0.5
sigma_offset_noise: 0.1
noise_previous_obs: false
upsampling_factor: 5
inner_model:
_target_: models.diffusion.InnerModelConfig
img_channels: 3
num_steps_conditioning: 1
cond_channels: 2048
depths: [2, 2, 2, 2]
channels: [64, 64, 128, 256]
attn_depths: [0, 0, 0, 1]
rew_end_model: null
actor_critic: null
""")
# Create env config
env_dir = config_dir / "env"
env_dir.mkdir(exist_ok=True)
env_config_path = env_dir / "csgo.yaml"
if not env_config_path.exists():
with open(env_config_path, 'w') as f:
f.write("""train:
id: csgo
size: [150, 600]
num_actions: 51
path_data_low_res: /tmp/dummy_data_low_res
path_data_full_res: /tmp/dummy_data_full_res
keymap: csgo
""")
# Create world model env config
wm_env_dir = config_dir / "world_model_env"
wm_env_dir.mkdir(exist_ok=True)
wm_config_path = wm_env_dir / "fast.yaml"
if not wm_config_path.exists():
with open(wm_config_path, 'w') as f:
f.write("""_target_: envs.WorldModelEnvConfig
horizon: 1000
num_batches_to_preload: 1
diffusion_sampler_next_obs:
_target_: models.diffusion.DiffusionSamplerConfig
num_steps_denoising: 10
sigma_min: 0.002
sigma_max: 5.0
rho: 7
order: 1
diffusion_sampler_upsampling:
_target_: models.diffusion.DiffusionSamplerConfig
num_steps_denoising: 5
sigma_min: 0.002
sigma_max: 5.0
rho: 7
order: 1
""")
# Create trainer config
trainer_config_path = config_dir / "trainer.yaml"
if not trainer_config_path.exists():
with open(trainer_config_path, 'w') as f:
f.write("""defaults:
- _self_
- env: csgo
- agent: csgo
- world_model_env: fast
static_dataset:
path: /tmp/dummy_data_low_res
ignore_sample_weights: True
""")
# Global config instance
web_config = WebConfig()
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