In this tutorial, we implement a Colab-ready version of the AutoResearch framework originally proposed by Andrej Karpathy. We build an automated experimentation pipeline that clones the AutoResearch repository, prepares a lightweight training environment, and runs a baseline experiment to establish initial performance metrics. We then create an automated research loop that programmatically edits the hyperparameters in train.py, runs new training iterations, evaluates the resulting model using the validation bits-per-byte metric, and logs every experiment in a structured results table. By running this workflow in Google Colab, we demonstrate how we can reproduce the core idea of autonomous machine learning research: iteratively modifying training configurations, evaluating performance, and preserving the best configurations, without requiring specialized hardware or complex infrastructure.
import os, sys, subprocess, json, re, random, shutil, time
from pathlib import Path
def pip_install(pkg):
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", pkg])
for pkg in [
"numpy","pandas","pyarrow","requests",
"rustbpe","tiktoken","openai"
]:
try:
__import__(pkg)
except:
pip_install(pkg)
import pandas as pd
if not Path("autoresearch").exists():
subprocess.run(["git","clone","https://github.com/karpathy/autoresearch.git"])
os.chdir("autoresearch")
OPENAI_API_KEY=None
try:
from google.colab import userdata
OPENAI_API_KEY = userdata.get("OPENAI_API_KEY")
except:
OPENAI_API_KEY=os.environ.get("OPENAI_API_KEY")
if OPENAI_API_KEY:
os.environ["OPENAI_API_KEY"]=OPENAI_API_KEYWe begin by importing the core Python libraries required for the automated research workflow. We install all necessary dependencies and clone the autoresearch repository directly from GitHub, ensuring the environment includes the original training framework. We also configure access to the OpenAI API key, if available, allowing the system to optionally support LLM-assisted experimentation later in the pipeline.
prepare_path=Path("prepare.py")
train_path=Path("train.py")
program_path=Path("program.md")
prepare_text=prepare_path.read_text()
train_text=train_path.read_text()
prepare_text=re.sub(r"MAX_SEQ_LEN = \d+","MAX_SEQ_LEN = 512",prepare_text)
prepare_text=re.sub(r"TIME_BUDGET = \d+","TIME_BUDGET = 120",prepare_text)
prepare_text=re.sub(r"EVAL_TOKENS = .*","EVAL_TOKENS = 4 * 65536",prepare_text)
train_text=re.sub(r"DEPTH = \d+","DEPTH = 4",train_text)
train_text=re.sub(r"DEVICE_BATCH_SIZE = \d+","DEVICE_BATCH_SIZE = 16",train_text)
train_text=re.sub(r"TOTAL_BATCH_SIZE = .*","TOTAL_BATCH_SIZE = 2**17",train_text)
train_text=re.sub(r'WINDOW_PATTERN = "SSSL"','WINDOW_PATTERN = "L"',train_text)
prepare_path.write_text(prepare_text)
train_path.write_text(train_text)
program_path.write_text("""
Goal:
Run autonomous research loop on Google Colab.
Rules:
Only modify train.py hyperparameters.
Metric:
Lower val_bpb is better.
""")
subprocess.run(["python","prepare.py","--num-shards","4","--download-workers","2"])We modify key configuration parameters inside the repository to make the training workflow compatible with Google Colab hardware. We reduce the context length, training time budget, and evaluation token counts so the experiments run within limited GPU resources. After applying these patches, we prepare the dataset shards required for training so that the model can immediately begin experiments.
subprocess.run("python train.py > baseline.log 2>&1",shell=True)
def parse_run_log(log_path):
text=Path(log_path).read_text(errors="ignore")
def find(p):
m=re.search(p,text,re.MULTILINE)
return float(m.group(1)) if m else None
return {
"val_bpb":find(r"^val_bpb:\s*([0-9.]+)"),
"training_seconds":find(r"^training_seconds:\s*([0-9.]+)"),
"peak_vram_mb":find(r"^peak_vram_mb:\s*([0-9.]+)"),
"num_steps":find(r"^num_steps:\s*([0-9.]+)")
}
baseline=parse_run_log("baseline.log")
results_path=Path("results.tsv")
rows=[{
"commit":"baseline",
"val_bpb":baseline["val_bpb"] if baseline["val_bpb"] else 0,
"memory_gb":round((baseline["peak_vram_mb"] or 0)/1024,1),
"status":"keep",
"description":"baseline"
}]
pd.DataFrame(rows).to_csv(results_path,sep="\t",index=False)
print("Baseline:",baseline)We execute the baseline training run to establish an initial performance reference for the model. We implement a log-parsing function that extracts key training metrics, including validation bits-per-byte, training time, GPU memory usage, and optimization steps. We then store these baseline results in a structured experiment table so that all future experiments can be compared against this starting configuration.
TRAIN_FILE=Path("train.py")
BACKUP_FILE=Path("train.base.py")
if not BACKUP_FILE.exists():
shutil.copy2(TRAIN_FILE,BACKUP_FILE)
HP_KEYS=[
"WINDOW_PATTERN",
"TOTAL_BATCH_SIZE",
"EMBEDDING_LR",
"UNEMBEDDING_LR",
"MATRIX_LR",
"SCALAR_LR",
"WEIGHT_DECAY",
"ADAM_BETAS",
"WARMUP_RATIO",
"WARMDOWN_RATIO",
"FINAL_LR_FRAC",
"DEPTH",
"DEVICE_BATCH_SIZE"
]
def read_text(path):
return Path(path).read_text()
def write_text(path,text):
Path(path).write_text(text)
def extract_hparams(text):
vals={}
for k in HP_KEYS:
m=re.search(rf"^{k}\s*=\s*(.+?)$",text,re.MULTILINE)
if m:
vals[k]=m.group(1).strip()
return vals
def set_hparam(text,key,value):
return re.sub(rf"^{key}\s*=.*$",f"{key} = {value}",text,flags=re.MULTILINE)
base_text=read_text(BACKUP_FILE)
base_hparams=extract_hparams(base_text)
SEARCH_SPACE={
"WINDOW_PATTERN":['"L"','"SSSL"'],
"TOTAL_BATCH_SIZE":["2**16","2**17","2**18"],
"EMBEDDING_LR":["0.2","0.4","0.6"],
"MATRIX_LR":["0.01","0.02","0.04"],
"SCALAR_LR":["0.3","0.5","0.7"],
"WEIGHT_DECAY":["0.05","0.1","0.2"],
"ADAM_BETAS":["(0.8,0.95)","(0.9,0.95)"],
"WARMUP_RATIO":["0.0","0.05","0.1"],
"WARMDOWN_RATIO":["0.3","0.5","0.7"],
"FINAL_LR_FRAC":["0.0","0.05"],
"DEPTH":["3","4","5","6"],
"DEVICE_BATCH_SIZE":["8","12","16","24"]
}
def sample_candidate():
keys=random.sample(list(SEARCH_SPACE.keys()),random.choice([2,3,4]))
cand=dict(base_hparams)
changes={}
for k in keys:
cand[k]=random.choice(SEARCH_SPACE[k])
changes[k]=cand[k]
return cand,changes
def apply_hparams(candidate):
text=read_text(BACKUP_FILE)
for k,v in candidate.items():
text=set_hparam(text,k,v)
write_text(TRAIN_FILE,text)
def run_experiment(tag):
log=f"{tag}.log"
subprocess.run(f"python train.py > {log} 2>&1",shell=True)
metrics=parse_run_log(log)
metrics["log"]=log
return metricsWe build the core utilities that enable automated hyperparameter experimentation. We extract the hyperparameters from train.py, define the searchable parameter space, and implement functions that can programmatically edit these values. We also create mechanisms to generate candidate configurations, apply them to the training script, and run experiments while recording their outputs.
N_EXPERIMENTS=3
df=pd.read_csv(results_path,sep="\t")
best=df["val_bpb"].replace(0,999).min()
for i in range(N_EXPERIMENTS):
tag=f"exp_{i+1}"
candidate,changes=sample_candidate()
apply_hparams(candidate)
metrics=run_experiment(tag)
if metrics["val_bpb"] and metrics["val_bpb"]We run the automated research loop that repeatedly proposes new hyperparameter configurations and evaluates their performance. For each experiment, we modify the training script, run the training process, and compare the resulting validation score with the best configuration discovered so far. We log all experiment results, preserve improved configurations, and export the best training script along with the experiment history for further analysis.
In conclusion, we constructed a complete automated research workflow that demonstrates how machines can iteratively explore model configurations and improve training performance with minimal manual intervention. Throughout the tutorial, we prepared the dataset, established a baseline experiment, and implemented a search loop that proposes new hyperparameter configurations, runs experiments, and tracks results across multiple trials. By maintaining experiment logs and automatically preserving improved configurations, we created a reproducible and extensible research process that mirrors the workflow used in modern machine learning experimentation. This approach illustrates how we can combine automation, experimentation tracking, and lightweight infrastructure to accelerate model development and enable scalable research directly from a cloud notebook environment.
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