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import openai
import os.path as osp
import shutil
import json
import argparse
import multiprocessing
import torch
import os
import time
import sys
from datetime import datetime
from aider.coders import Coder
from aider.models import Model
from aider.io import InputOutput
from ai_scientist.generate_ideas import generate_next_idea, check_idea_novelty
from ai_scientist.perform_experiments import perform_experiments
from ai_scientist.perform_writeup import perform_writeup, generate_latex
from ai_scientist.perform_review import perform_review, load_paper, perform_improvement
NUM_REFLECTIONS = 3
def print_time():
print(datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
def parse_arguments():
parser = argparse.ArgumentParser(description="Run AI scientist experiments")
# add type of experiment (nanoGPT, Boston, etc.)
parser.add_argument(
"--experiment",
type=str,
default="nanoGPT",
help="Experiment to run AI Scientist on.",
)
parser.add_argument(
"--model",
type=str,
default="claude-3-5-sonnet-20240620",
choices=[
"claude-3-5-sonnet-20240620",
"gpt-4o-2024-05-13",
"deepseek-coder-v2-0724",
"llama3.1-405b",
# Anthropic Claude models via Amazon Bedrock
"bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
"bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
"bedrock/anthropic.claude-3-haiku-20240307-v1:0",
"bedrock/anthropic.claude-3-opus-20240229-v1:0"
],
help="Model to use for AI Scientist.",
)
parser.add_argument(
"--writeup",
type=str,
default="latex",
choices=["latex"],
help="What format to use for writeup",
)
parser.add_argument(
"--parallel",
type=int,
default=0,
help="Number of parallel processes to run. 0 for sequential execution.",
)
parser.add_argument(
"--improvement",
action="store_true",
help="Improve based on reviews.",
)
parser.add_argument(
"--gpus",
type=str,
default=None,
help="Comma-separated list of GPU IDs to use (e.g., '0,1,2'). If not specified, all available GPUs will be used.",
)
parser.add_argument(
"--num-ideas",
type=int,
default=50,
help="Number of ideas to generate",
)
return parser.parse_args()
def get_available_gpus(gpu_ids=None):
if gpu_ids is not None:
return [int(gpu_id) for gpu_id in gpu_ids.split(",")]
return list(range(torch.cuda.device_count()))
def worker(
queue,
base_dir,
results_dir,
model,
client,
client_model,
writeup,
improvement,
gpu_id,
idea_archive,
lock,
):
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
print(f"Worker {gpu_id} started.")
while True:
_ = queue.get()
with lock:
idea_archive = generate_next_idea(
base_dir,
client=client,
model=client_model,
prev_idea_archive=idea_archive,
num_reflections=NUM_REFLECTIONS,
)
idea_archive = check_idea_novelty(
idea_archive,
base_dir=base_dir,
client=client,
model=client_model,
)
idea = idea_archive[-1]
if _ is None:
break
success, score, _ = do_idea(
base_dir,
results_dir,
idea,
model,
client,
client_model,
writeup,
improvement,
log_file=True,
)
print(f"Completed idea: {idea['Name']}, Success: {success}, Score: {score}")
with lock:
for x in idea_archive:
if x["Name"] == idea["Name"] and x["Title"] == idea["Title"]:
x["Score"] = score
break
print(f"Worker {gpu_id} finished.")
def do_idea(
base_dir,
results_dir,
idea,
model,
client,
client_model,
writeup,
improvement,
log_file=False,
):
## CREATE PROJECT FOLDER
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
idea_name = f"{timestamp}_{idea['Name']}"
folder_name = osp.join(results_dir, idea_name)
assert not osp.exists(folder_name), f"Folder {folder_name} already exists."
destination_dir = folder_name
shutil.copytree(base_dir, destination_dir, dirs_exist_ok=True)
with open(osp.join(base_dir, "run_0", "final_info.json"), "r") as f:
baseline_results = json.load(f)
baseline_results = {k: v["means"] for k, v in baseline_results.items()}
exp_file = osp.join(folder_name, "experiment.py")
vis_file = osp.join(folder_name, "plot.py")
notes = osp.join(folder_name, "notes.txt")
with open(notes, "w") as f:
f.write(f"# Title: {idea['Title']}\n")
f.write(f"# Experiment description: {idea['Experiment']}\n")
f.write(f"## Run 0: Baseline\n")
f.write(f"Results: {baseline_results}\n")
f.write(f"Description: Baseline results.\n")
if log_file:
original_stdout = sys.stdout
original_stderr = sys.stderr
log_path = osp.join(folder_name, "log.txt")
log = open(log_path, "a")
sys.stdout = log
sys.stderr = log
try:
print_time()
print(f"*Starting idea: {idea_name}*")
## PERFORM EXPERIMENTS
fnames = [exp_file, vis_file, notes]
io = InputOutput(
yes=True, chat_history_file=f"{folder_name}/{idea_name}_aider.txt"
)
if model == "deepseek-coder-v2-0724":
main_model = Model("deepseek/deepseek-coder")
elif model == "llama3.1-405b":
main_model = Model("openrouter/meta-llama/llama-3.1-405b-instruct")
else:
main_model = Model(model)
coder = Coder.create(
main_model=main_model,
fnames=fnames,
io=io,
stream=False,
use_git=False,
edit_format="diff",
)
print_time()
print(f"*Starting Experiments*")
try:
success = perform_experiments(idea, folder_name, coder, baseline_results)
except Exception as e:
print(f"Error during experiments: {e}")
print(f"Experiments failed for idea {idea_name}")
return False, 0, idea
if not success:
print(f"Experiments failed for idea {idea_name}")
return False, 0, idea
print_time()
print(f"*Starting Writeup*")
## PERFORM WRITEUP
if writeup == "latex":
writeup_file = osp.join(folder_name, "latex", "template.tex")
fnames = [exp_file, writeup_file, notes]
if model == "deepseek-coder-v2-0724":
main_model = Model("deepseek/deepseek-coder")
elif model == "llama3.1-405b":
main_model = Model("openrouter/meta-llama/llama-3.1-405b-instruct")
else:
main_model = Model(model)
coder = Coder.create(
main_model=main_model,
fnames=fnames,
io=io,
stream=False,
use_git=False,
edit_format="diff",
)
try:
perform_writeup(idea, folder_name, coder, client, client_model)
except Exception as e:
print(f"Failed to perform writeup: {e}")
return False, 0, idea
print("Done writeup")
else:
raise ValueError(f"Writeup format {writeup} not supported.")
print_time()
print(f"*Starting Review*")
## REVIEW PAPER
if writeup == "latex":
try:
paper_text = load_paper(f"{folder_name}/{idea['Name']}.pdf")
review = perform_review(
paper_text,
model="gpt-4o-2024-05-13",
client=openai.OpenAI(),
num_reflections=5,
num_fs_examples=1,
num_reviews_ensemble=5,
temperature=0.1,
)
review_score = review["Overall"]
# Store the review in separate review.txt file
with open(osp.join(folder_name, "review.txt"), "w") as f:
f.write(json.dumps(review))
except Exception as e:
print(f"Failed to perform review: {e}")
return False, 0, idea
## IMPROVE WRITEUP
if writeup == "latex" and improvement:
print_time()
print(f"*Starting Improvement*")
try:
perform_improvement(review, coder)
generate_latex(
coder, folder_name, f"{folder_name}/{idea['Name']}_improved.pdf"
)
paper_text = load_paper(f"{folder_name}/{idea['Name']}_improved.pdf")
review = perform_review(
paper_text,
model="gpt-4o-2024-05-13",
client=openai.OpenAI(),
num_reflections=5,
num_fs_examples=1,
num_reviews_ensemble=5,
temperature=0.1,
)
review_score = review["Overall"]
# Store the review in separate review.txt file
with open(osp.join(folder_name, "review_improved.txt"), "w") as f:
f.write(json.dumps(review))
except Exception as e:
print(f"Failed to perform improvement: {e}")
return False, 0, idea
return True, review_score, idea
except Exception as e:
print(f"Failed to evaluate idea {idea_name}: {str(e)}")
return False, 0, idea
finally:
print("FINISHED IDEA")
if log_file:
sys.stdout = original_stdout
sys.stderr = original_stderr
log.close()
if __name__ == "__main__":
args = parse_arguments()
# Check available GPUs and adjust parallel processes if necessary
available_gpus = get_available_gpus(args.gpus)
if args.parallel > len(available_gpus):
print(
f"Warning: Requested {args.parallel} parallel processes, but only {len(available_gpus)} GPUs available. Adjusting to {len(available_gpus)}."
)
args.parallel = len(available_gpus)
print(f"Using GPUs: {available_gpus}")
# Create client
if args.model == "claude-3-5-sonnet-20240620":
import anthropic
print(f"Using Anthropic API with model {args.model}.")
client_model = "claude-3-5-sonnet-20240620"
client = anthropic.Anthropic()
elif args.model.startswith("bedrock") and "claude" in args.model:
import anthropic
# Expects: bedrock/<MODEL_ID>
client_model = args.model.split("/")[-1]
print(f"Using Amazon Bedrock with model {client_model}.")
client = anthropic.AnthropicBedrock()
elif args.model.startswith("vertex_ai") and "claude" in args.model:
import anthropic
# Expects: vertex_ai/<MODEL_ID>
client_model = args.model.split("/")[-1]
print(f"Using Vertex AI with model {client_model}.")
client = anthropic.AnthropicVertex()
elif args.model == "gpt-4o-2024-05-13":
import openai
print(f"Using OpenAI API with model {args.model}.")
client_model = "gpt-4o-2024-05-13"
client = openai.OpenAI()
elif args.model == "deepseek-coder-v2-0724":
import openai
print(f"Using OpenAI API with {args.model}.")
client_model = "deepseek-coder-v2-0724"
client = openai.OpenAI(
api_key=os.environ["DEEPSEEK_API_KEY"], base_url="https://api.deepseek.com"
)
elif args.model == "llama3.1-405b":
import openai
print(f"Using OpenAI API with {args.model}.")
client_model = "meta-llama/llama-3.1-405b-instruct"
client = openai.OpenAI(
api_key=os.environ["OPENROUTER_API_KEY"],
base_url="https://openrouter.ai/api/v1",
)
else:
raise ValueError(f"Model {args.model} not supported.")
base_dir = osp.join("templates", args.experiment)
results_dir = osp.join("results", args.experiment)
idea_archive = []
if args.parallel > 0:
print(f"Running {args.parallel} parallel processes")
queue = multiprocessing.Queue()
lock = multiprocessing.Lock()
for _ in range(args.num_ideas):
queue.put(_)
processes = []
for i in range(args.parallel):
gpu_id = available_gpus[i % len(available_gpus)]
p = multiprocessing.Process(
target=worker,
args=(
queue,
base_dir,
results_dir,
args.model,
client,
client_model,
args.writeup,
args.improvement,
gpu_id,
idea_archive,
lock,
),
)
p.start()
time.sleep(150)
processes.append(p)
# Signal workers to exit
for _ in range(args.parallel):
queue.put(None)
for p in processes:
p.join()
print("All parallel processes completed.")
else:
for _ in range(args.num_ideas):
idea_archive = generate_next_idea(
base_dir,
client=client,
model=client_model,
prev_idea_archive=idea_archive,
num_reflections=NUM_REFLECTIONS,
)
idea_archive = check_idea_novelty(
idea_archive,
base_dir=base_dir,
client=client,
model=client_model,
)
idea = idea_archive[-1]
print(f"Processing idea: {idea['Name']}")
try:
success, score, _ = do_idea(
base_dir,
results_dir,
idea,
args.model,
client,
client_model,
args.writeup,
args.improvement,
)
print(
f"Completed idea: {idea['Name']}, Success: {success}, Score: {score}"
)
idea["Score"] = score
except Exception as e:
print(f"Failed to evaluate idea {idea['Name']}: {str(e)}")
print("All ideas evaluated.")
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