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"""
Configuration for different compute environments
Switch between configs based on available hardware
"""
import torch
import os
class Config:
"""Base configuration"""
# Data paths
CURRICULUM_DATA = "neu_graph_analyzed_clean.pkl"
AGENT_DB = "curriculum_agent.db"
# Model settings (override in subclasses)
LLM_MODEL = None
EMBEDDING_MODEL = None
DEVICE = None
QUANTIZATION = None
# Agent settings
AGENT_CYCLE_MINUTES = 60
MAX_COURSES_PER_SEMESTER = 4
@classmethod
def get_device(cls):
if cls.DEVICE == "auto":
return torch.device("cuda" if torch.cuda.is_available() else "cpu")
return torch.device(cls.DEVICE)
class H200Config(Config):
"""Config for H200 GPU (80GB)"""
LLM_MODEL = "meta-llama/Llama-3.1-70B-Instruct"
EMBEDDING_MODEL = "BAAI/bge-large-en-v1.5"
DEVICE = "cuda"
QUANTIZATION = None # No need to quantize with 80GB
class ColabConfig(Config):
"""Config for Google Colab T4 (16GB)"""
LLM_MODEL = "meta-llama/Llama-3.1-8B-Instruct"
EMBEDDING_MODEL = "BAAI/bge-base-en-v1.5"
DEVICE = "cuda"
QUANTIZATION = "4bit"
class LocalGPUConfig(Config):
"""Config for local GPU (8-12GB)"""
LLM_MODEL = "mistralai/Mistral-7B-Instruct-v0.2"
EMBEDDING_MODEL = "sentence-transformers/all-mpnet-base-v2"
DEVICE = "cuda"
QUANTIZATION = "4bit"
class CPUConfig(Config):
"""Config for CPU only (no GPU)"""
LLM_MODEL = "microsoft/phi-2" # 2.7B params
EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2" # 22M params
DEVICE = "cpu"
QUANTIZATION = None
class MinimalConfig(Config):
"""Minimal config for testing/development"""
LLM_MODEL = None # No LLM, just embeddings
EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
DEVICE = "cpu"
QUANTIZATION = None
def get_config():
"""
Auto-detect best configuration
"""
# Check environment variable first
env_config = os.environ.get("CURRICULUM_CONFIG", "").lower()
if env_config == "h200":
return H200Config
elif env_config == "colab":
return ColabConfig
elif env_config == "cpu":
return CPUConfig
elif env_config == "minimal":
return MinimalConfig
# Auto-detect based on hardware
if torch.cuda.is_available():
gpu_mem = torch.cuda.get_device_properties(0).total_memory / 1e9 # GB
if gpu_mem > 70:
print(f"Detected high-end GPU ({gpu_mem:.1f}GB), using H200Config")
return H200Config
elif gpu_mem > 14:
print(f"Detected mid-range GPU ({gpu_mem:.1f}GB), using ColabConfig")
return ColabConfig
else:
print(f"Detected small GPU ({gpu_mem:.1f}GB), using LocalGPUConfig")
return LocalGPUConfig
else:
print("No GPU detected, using CPUConfig")
return CPUConfig
# Usage example
if __name__ == "__main__":
config = get_config()
print(f"Selected config: {config.__name__}")
print(f"LLM: {config.LLM_MODEL}")
print(f"Embedder: {config.EMBEDDING_MODEL}")
print(f"Device: {config.DEVICE}")
print(f"Quantization: {config.QUANTIZATION}") |