cuda-samples/python/2_CoreConcepts/processCheckpoint

processCheckpoint (Python)

Description

This sample demonstrates how to use the CUDA process checkpoint API via cuda.core.checkpoint.Process to suspend, capture, and restore the CUDA state of a running Linux process.

CUDA process checkpointing is the driver-level primitive that powers CRIU + cuda-checkpoint integration.

The sample:

1. Allocates a GPU buffer and fills it with a deterministic pattern via a small kernel.
2. Reads the buffer back to host and computes a SHA-256 hash.
3. Runs the full checkpoint lifecycle on its own process: lock → checkpoint → restore → unlock.
4. Reads the buffer back again and verifies that the hash is unchanged, proving that GPU memory contents survived the round trip.

The sample prints the CUDA process state after each step so the full state machine is visible:

         lock()          checkpoint()              restore()         unlock()
running ---------> locked ------------> checkpointed -----------> locked ---------> running

What You'll Learn

• Creating a cuda.core.checkpoint.Process for the current process by PID and observing its .state transitions.
• Running the full lock → checkpoint → restore → unlock cycle with a lock timeout.
• The fact that restore() leaves the process in the locked state; you must still call unlock() to return to running.
• Verifying that GPU memory is preserved across the checkpoint round-trip by comparing SHA-256 hashes of the buffer before and after.
• The rough cost of each step (checkpoint and restore dominate and scale with the device-memory footprint being captured).

Key Libraries

• cuda.core
  • device management, memory allocation, kernel compilation and launch, and the checkpoint.Process wrapper.
• cuda.bindings
  • used directly for a pageable cuMemcpyDtoH.

Key APIs

From cuda.core.checkpoint

• checkpoint.Process(pid) - create a handle to a CUDA process by PID. Accepts os.getpid() for the self-checkpoint case shown here.
• Process.state - one of "running", "locked", "checkpointed", or "failed".
• Process.lock(timeout_ms=…) - block further CUDA API calls on the process; completes already-submitted work. Always pass a non-zero timeout to avoid deadlocks.
• Process.checkpoint() - copy device memory to host-side driver allocations and release GPU resources. Process state becomes checkpointed.
• Process.restore(gpu_mapping=None) - re-acquire GPU resources and copy memory back to device. Leaves the process in the locked state.
• Process.unlock() - return the process to running.
• Process.restore_thread_id - thread ID that restore() must be called from in the target process (not used in the self-checkpoint case here).

From cuda.core

• Device.set_current() / Device.memory_resource.allocate(...) / Stream, LaunchConfig, Program, launch - standard device, compile, and launch primitives used to produce the buffer contents.

From cuda.bindings.driver

• cuMemcpyDtoH(host_ptr, device_handle, nbytes) - synchronous D2H copy into a pageable host buffer.

Requirements

Hardware

• Any NVIDIA GPU supported by CUDA process checkpointing. CUDA checkpointing is currently limited to x86-64 Linux.

Software

• Linux (the CUDA checkpoint API is Linux-only).
• NVIDIA driver with CUDA process checkpoint support.
• CUDA Toolkit 13.0 or newer.
• Python 3.10 or newer.
• cuda-core >= 1.0.0.

Installation

Install the required packages from requirements.txt:

cd /path/to/cuda-samples/python/2_CoreConcepts/processCheckpoint
pip install -r requirements.txt

How to Run

Basic usage

python processCheckpoint.py

Larger GPU footprint to see checkpoint time scale

python processCheckpoint.py --buffer-mib 512

Use a specific GPU

python processCheckpoint.py --device 1

All options

--device           CUDA device ID (default: 0)
--buffer-mib       GPU buffer size in MiB (default: 16)
--lock-timeout-ms  Timeout passed to Process.lock in ms (default: 5000)

Expected Output

On an RTX 4090 with a 16 MiB buffer:

[Process Checkpoint Sample using CUDA Core API]
PID:                748330
Device:             NVIDIA GeForce RTX 4090
Compute Capability: sm_89
Buffer size:        16 MiB
Lock timeout:       5000 ms

Compiling kernel ...
Writing deterministic pattern to GPU buffer ...
Buffer hash (before): b045f7975dc23352

Running checkpoint lifecycle on self ...

step               duration (ms)       state after
--------------------------------------------------
initial                        -           running
lock                       0.578            locked
checkpoint               268.369      checkpointed
restore                  235.024            locked
unlock                     1.648           running
--------------------------------------------------
total                    505.618

Buffer hash (before): b045f7975dc23352
Buffer hash (after):  b045f7975dc23352

PASS: GPU buffer contents survived checkpoint/restore.

Done

What to look for:

• The four state transitions are all observable: running → locked → checkpointed → locked → running. Note that restore() leaves the process in locked, not running.
• The checkpoint and restore steps dominate the wall-clock time (hundreds of ms even for a small buffer) - they copy GPU memory to and from driver-managed host allocations. Increasing --buffer-mib visibly increases the checkpoint time.
• The lock and unlock steps are essentially free (sub-ms) - they just flip the process state.
• The SHA-256 hashes before and after match, proving the GPU memory contents survived the round trip.

Exact timings vary with GPU model, driver version, system load, and the size of the device memory footprint being captured.

Files

• processCheckpoint.py - Python implementation using cuda.core.checkpoint
• README.md - This file
• requirements.txt - Sample dependencies

See Also

• CUDA Python Documentation
• NVIDIA/cuda-checkpoint
  • the CUDA checkpoint/restore utility, the CRIU plugin, and C reference programs (r570-features.c, r580-migration-api.c).
• Checkpointing CUDA Applications with CRIU
  • NVIDIA technical blog post on the broader CRIU workflow.