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submit-pre-training.py
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submit-pre-training.py
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"""
Example submission script for a GraphINVENT training job (distribution-
based training, not fine-tuning/optimization job). This can be used to
pre-train a model before a fine-tuning (via reinforcement learning) job.
To run, type:
(graphinvent) ~/GraphINVENT$ python submit-pre-training.py
"""
# load general packages and functions
import csv
import sys
import os
from pathlib import Path
import subprocess
import time
import torch
# define what you want to do for the specified job(s)
DATASET = "gdb13_1K-debug" # dataset name in "./data/pre-training/"
JOB_TYPE = "train" # "preprocess", "train", "generate", or "test"
JOBDIR_START_IDX = 0 # where to start indexing job dirs
N_JOBS = 1 # number of jobs to run per model
RESTART = False # whether or not this is a restart job
FORCE_OVERWRITE = True # overwrite job directories which already exist
JOBNAME = "example-job-name" # used to create a sub directory
# if running using SLURM sbatch, specify params below
USE_SLURM = False # use SLURM or not
RUN_TIME = "1-00:00:00" # hh:mm:ss
MEM_GB = 20 # required RAM in GB
# for SLURM jobs, set partition to run job on (preprocessing jobs run entirely on
# CPU, so no need to request GPU partition; all other job types benefit from running
# on a GPU)
if JOB_TYPE == "preprocess":
PARTITION = "core"
CPUS_PER_TASK = 1
else:
PARTITION = "gpu"
CPUS_PER_TASK = 4
# set paths here
HOME = str(Path.home())
PYTHON_PATH = f"{HOME}/miniconda3/envs/graphinvent/bin/python"
GRAPHINVENT_PATH = "./graphinvent/"
DATA_PATH = "./data/pre-training/"
if torch.cuda.is_available():
DEVICE = "cuda"
else:
DEVICE = "cpu"
# define dataset-specific parameters
params = {
"atom_types" : ["C", "N", "O", "S", "Cl"],
"formal_charge": [-1, 0, +1],
"max_n_nodes" : 13,
"job_type" : JOB_TYPE,
"dataset_dir" : f"{DATA_PATH}{DATASET}/",
"restart" : RESTART,
"model" : "GGNN",
"sample_every" : 2,
"init_lr" : 1e-4,
"epochs" : 100,
"batch_size" : 50,
"block_size" : 1000,
"device" : DEVICE,
"n_samples" : 100,
# additional paramaters can be defined here, if different from the "defaults"
# for instance, for "generate" jobs, don't forget to specify "generation_epoch"
# and "n_samples"
}
def submit() -> None:
"""
Creates and submits submission script. Uses global variables defined at top
of this file.
"""
check_paths()
# create an output directory
dataset_output_path = f"{HOME}/GraphINVENT/output_{DATASET}"
tensorboard_path = os.path.join(dataset_output_path, "tensorboard")
if JOBNAME != "":
dataset_output_path = os.path.join(dataset_output_path, JOBNAME)
tensorboard_path = os.path.join(tensorboard_path, JOBNAME)
os.makedirs(dataset_output_path, exist_ok=True)
os.makedirs(tensorboard_path, exist_ok=True)
print(f"* Creating dataset directory {dataset_output_path}/", flush=True)
# submit `N_JOBS` separate jobs
jobdir_end_idx = JOBDIR_START_IDX + N_JOBS
for job_idx in range(JOBDIR_START_IDX, jobdir_end_idx):
# specify and create the job subdirectory if it does not exist
params["job_dir"] = f"{dataset_output_path}/job_{job_idx}/"
params["tensorboard_dir"] = f"{tensorboard_path}/job_{job_idx}/"
# create the directory if it does not exist already, otherwise raises an
# error, which is good because *might* not want to override data our
# existing directories!
os.makedirs(params["tensorboard_dir"], exist_ok=True)
try:
job_dir_exists_already = bool(
JOB_TYPE in ["generate", "test"] or FORCE_OVERWRITE
)
os.makedirs(params["job_dir"], exist_ok=job_dir_exists_already)
print(
f"* Creating model subdirectory {dataset_output_path}/job_{job_idx}/",
flush=True,
)
except FileExistsError:
print(
f"-- Model subdirectory {dataset_output_path}/job_{job_idx}/ already exists.",
flush=True,
)
if not RESTART:
continue
# write the `input.csv` file
write_input_csv(params_dict=params, filename="input.csv")
# write `submit.sh` and submit
if USE_SLURM:
print("* Writing submission script.", flush=True)
write_submission_script(job_dir=params["job_dir"],
job_idx=job_idx,
job_type=params["job_type"],
max_n_nodes=params["max_n_nodes"],
runtime=RUN_TIME,
mem=MEM_GB,
ptn=PARTITION,
cpu_per_task=CPUS_PER_TASK,
python_bin_path=PYTHON_PATH)
print("* Submitting job to SLURM.", flush=True)
subprocess.run(["sbatch", params["job_dir"] + "submit.sh"],
check=True)
else:
print("* Running job as a normal process.", flush=True)
subprocess.run(["ls", f"{PYTHON_PATH}"], check=True)
subprocess.run([f"{PYTHON_PATH}",
f"{GRAPHINVENT_PATH}main.py",
"--job-dir",
params["job_dir"]],
check=True)
# sleep a few secs before submitting next job
print("-- Sleeping 2 seconds.")
time.sleep(2)
def write_input_csv(params_dict : dict, filename : str="params.csv") -> None:
"""
Writes job parameters/hyperparameters in `params_dict` to CSV using the specified
`filename`.
"""
dict_path = params_dict["job_dir"] + filename
with open(dict_path, "w") as csv_file:
writer = csv.writer(csv_file, delimiter=";")
for key, value in params_dict.items():
writer.writerow([key, value])
def write_submission_script(job_dir : str, job_idx : int, job_type : str, max_n_nodes : int,
runtime : str, mem : int, ptn : str, cpu_per_task : int,
python_bin_path : str) -> None:
"""
Writes a submission script (`submit.sh`).
Args:
----
job_dir (str) : Job running directory.
job_idx (int) : Job idx.
job_type (str) : Type of job to run.
max_n_nodes (int) : Maximum number of nodes in dataset.
runtime (str) : Job run-time limit in hh:mm:ss format.
mem (int) : Gigabytes to reserve.
ptn (str) : Partition to use, either "core" (CPU) or "gpu" (GPU).
cpu_per_task (int) : How many CPUs to use per task.
python_bin_path (str) : Path to Python binary to use.
"""
submit_filename = job_dir + "submit.sh"
with open(submit_filename, "w") as submit_file:
submit_file.write("#!/bin/bash\n")
submit_file.write(f"#SBATCH --job-name={job_type}{max_n_nodes}_{job_idx}\n")
submit_file.write(f"#SBATCH --output={job_type}{max_n_nodes}_{job_idx}o\n")
submit_file.write(f"#SBATCH --time={runtime}\n")
submit_file.write(f"#SBATCH --mem={mem}g\n")
submit_file.write(f"#SBATCH --partition={ptn}\n")
submit_file.write("#SBATCH --nodes=1\n")
submit_file.write(f"#SBATCH --cpus-per-task={cpu_per_task}\n")
if ptn == "gpu":
submit_file.write("#SBATCH --gres=gpu:1\n")
submit_file.write("hostname\n")
submit_file.write("export QT_QPA_PLATFORM='offscreen'\n")
submit_file.write(f"{python_bin_path} {GRAPHINVENT_PATH}main.py --job-dir {job_dir}")
submit_file.write(f" > {job_dir}output.o${{SLURM_JOB_ID}}\n")
def check_paths() -> None:
"""
Checks that paths to Python binary, data, and GraphINVENT are properly
defined before running a job, and tells the user to define them if not.
"""
for path in [PYTHON_PATH, GRAPHINVENT_PATH, DATA_PATH]:
if "path/to/" in path:
print("!!!")
print("* Update the following paths in `submit.py` before running:")
print("-- `PYTHON_PATH`\n-- `GRAPHINVENT_PATH`\n-- `DATA_PATH`")
sys.exit(0)
if __name__ == "__main__":
submit()