Checkpointing

Overview

Checkpointing is the process of saving the current state of a running job at regular intervals so that it can be resumed later from that state, rather than starting from scratch. This is especially useful in long-running or resource-intensive tasks on HPC systems like Wulver, where interruptions or failures may occur.

Checkpointing typically involves:

• Periodic saving of application state (memory, variables, file handles, etc.)
• Resuming computation from the last saved state
• Integration with SLURM job re-submission or recovery workflows

Why Use Checkpointing?

Benefit	Description
Failure Recovery	Resume jobs from the last checkpoint after a node crash or time expiration.
Efficient Resource Use	Prevents waste of computation time on long jobs that are interrupted.
Preemption Tolerance	Helps tolerate job preemption on shared clusters or spot instances.
Job Time Limit Bypass	Breaks large jobs into smaller chunks to fit within SLURM time limits.

Examples for checkpointing

PythonPytorchTensorflowC/C++GROMACSLAMMPSOpenFOAM (CFD)

Save intermediate state using Python’s built-in pickle module — ideal for lightweight scripts.

import pickle
import time

def save_checkpoint(data, filename="checkpoint.pkl"):
    with open(filename, "wb") as f:
        pickle.dump(data, f)

def load_checkpoint(filename="checkpoint.pkl"):
    try:
        with open(filename, "rb") as f:
            return pickle.load(f)
    except FileNotFoundError:
        return {"iteration": 0}

state = load_checkpoint()
for i in range(state["iteration"], 1000):
    # Do some work
    time.sleep(1)
    print(f"Running step {i}")

    # Save progress every 100 steps
    if i % 100 == 0:
        save_checkpoint({"iteration": i})

A common practice in PyTorch to checkpoint model weights, optimizer state, and epoch index — useful for training recovery.

import torch

# Save checkpoint
torch.save({
    'epoch': epoch,
    'model_state_dict': model.state_dict(),
    'optimizer_state_dict': optimizer.state_dict()
}, 'checkpoint.pth')

# Load checkpoint
checkpoint = torch.load('checkpoint.pth')
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch = checkpoint['epoch']

Using Keras callbacks, checkpoints are saved automatically during training. Only model weights are saved to keep storage efficient.

import tensorflow as tf

model = tf.keras.models.Sequential([...])
checkpoint_path = "checkpoints/model.ckpt"
checkpoint_cb = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
                                                save_weights_only=True,
                                                save_freq='epoch')

model.fit(data, labels, epochs=10, callbacks=[checkpoint_cb])

In C/C++, you can implement basic checkpointing by writing a loop index or state to a file and loading it at the next run. Ideal for simple simulations or compute-intensive loops.

int main() {
    int current_step = load_checkpoint("state.dat"); // Custom function
    for (int i = current_step; i < MAX; i++) {
        // Work
        if (i % 1000 == 0) {
            save_checkpoint(i, "state.dat");
        }
    }
}

GROMACS supports checkpointing with .cpt files during molecular simulations:

gmx mdrun -deffnm simulation -cpt 15  # Saves checkpoints every 15 minutes

# Restart from checkpoint
gmx mdrun -s topol.tpr -cpi simulation.cpt

LAMMPS checkpointing is usually done using write_restart and read_restart:

write_restart restart.equilibration

# In a new job
read_restart restart.equilibration

Checkpointing is done by writing time steps to disk and restarting from a previous time directory:

# Set in controlDict
writeInterval   100;

# Restart
startFrom       latestTime;