Metadata-Version: 2.4
Name: hegm
Version: 0.2.0
Summary: HeGM: Heterogeneous GPU Migration framework for deep learning (PyTorch today, TensorFlow-ready)
Author-email: Leehun <lehuannhatrang98@gmail.com>
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Project-URL: Homepage, https://github.com/lehuannhatrang/HeGM
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: POSIX :: Linux
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: System :: Clustering
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=1.10
Dynamic: license-file

# HeGM - Heterogeneous GPU Migration Framework

HeGM lets you live-migrate long-running deep learning jobs between GPUs
using CRIU (Checkpoint/Restore in Userspace), **without** requiring any code
changes in your training script. Your existing `for step, batch in
enumerate(dataloader)` loop works as-is -- HeGM transparently resumes from
the correct batch and step number after migration.

It targets PyTorch today and is structured to support additional backends
(e.g. TensorFlow) in the future.

![Introduce](docs/diagrams/hegm-introduce.png)

### Installation

#### Install the Python package

The HeGM Python package (launcher + runtime hooks) is published on PyPI as
[`hegm`](https://pypi.org/project/hegm/).

On any machine/container where you run training:

```bash
python -m pip install --upgrade pip
pip install hegm
```

This installs:

- the `hegm` package (transparent `enumerate()` patching, hooks, backends)
- a `sitecustomize.py` module in site-packages that automatically loads HeGM
- the `hegm-launcher` CLI

You can then start training under HeGM with:

```bash
hegm-launcher python -u train.py
```

In Kubernetes Pods, your command block typically becomes:

```bash
hegm-launcher python -u /workspace/train.py
```

#### Runtime prerequisites (cluster)

HeGM relies on a CRIU and CRI-O build that understand GPU checkpoint/restore
and the `/checkpoint/*/lock` convention. Install these first on your nodes:

- **CRIU (GPU migration fork)**  
  `leehun-criu` branch `2026-01-26/gpu-migration-support`  
  See the upstream README for build/install instructions:  
  - Repo: [`lehuannhatrang/leehun-criu`](https://github.com/lehuannhatrang/leehun-criu/tree/2026-01-26/gpu-migration-support)

- **CRI-O (restore-from-file fork)**  
  `leehun-cri-o` branch `2026-02-03/support-restore-from-file`  
  Install and configure it as your Kubernetes container runtime:  
  - Repo: [`lehuannhatrang/leehun-cri-o`](https://github.com/lehuannhatrang/leehun-cri-o/tree/2026-02-03/support-restore-from-file)

In addition you need:

- A Kubernetes cluster with GPU nodes and the NVIDIA drivers/runtime configured.
- `kubectl` access to the cluster.

#### Deploying HeGM example

From this repository:

1. Create the demo namespace and storage (if not already present):
   - `examples/dra/ns.yaml`
   - `examples/dra/storage.yaml`
2. Create the ConfigMaps:
   - `examples/dra/hegm-scripts.yaml` (Launcher + HeGM payload)
   - `examples/dra/training-script.yaml` (your `train.py`)
3. Create the resource claim(s):
   - `examples/dra/resource-claim.yaml` (or `resource-claim-restore.yaml`)
4. Launch a training pod:
   - Single worker: `examples/dra/training-pod.yaml`
   - Two workers in one Pod (for PID-isolation testing): `examples/dra/multiple-training-pod.yaml`
5. Trigger checkpoint from outside the pod:
   - Find the worker PID(s) with `ps -o pid,ppid,cmd`.
   - Send `SIGUSR1` to each `/workspace/train.py` PID with `kubectl exec ... -- kill -SIGUSR1 <pid>`.
6. Use your CRI-O / CRIU integration to checkpoint and restore the container,
   pointing CRI-O at the CRIU checkpoint tarball and re-using the same
   `hegm-scripts` and `train-script` ConfigMaps (see `examples/dra/restore-pod.yaml`).

### High-level architecture

HeGM is split into two main pieces:

- **Launcher (`launcher.py`)**: a small supervisor process that:
  - spawns your training script as a child process (the *Worker*)
  - injects `sitecustomize.py` via `PYTHONPATH`
  - watches the Worker's exit code:
    - `0`   → training finished, exit normally
    - `99`  → Worker saved a checkpoint and exited for migration
  - buffers the checkpoint file into RAM so CRIU can carry it across nodes
  - creates a per-process lock file so an external controller knows when it
    is safe to snapshot

- **Payload (`sitecustomize.py` + `hegm/` package)**: automatically loaded
  into the Worker by Python. It:
  - installs a PEP‑451 import hook for `torch`
  - monkey-patches `torch.nn.Module` and `torch.optim.Optimizer` to track
    models and optimizers
  - patches `builtins.enumerate` so `enumerate(DataLoader)` transparently
    resumes from the correct batch after a checkpoint restore
  - tracks global training steps and RNG state
  - handles `SIGUSR1` by saving a checkpoint and exiting with code `99`

All of this is delivered to your Pods via a ConfigMap (`examples/dra/hegm-scripts.yaml`).

### Key files

- `launcher.py`
  - Entry point you run instead of `python train.py`.
  - For each Launcher process, checkpoints and lock files are isolated by
    **PID**:
    - `/checkpoint/<PID>/latest.pt`
    - `/checkpoint/<PID>/lock`
  - The external controller can discover all ready instances via
    `/checkpoint/*/lock`.

- `sitecustomize.py`
  - Thin bootstrap that simply does `import hegm`.
  - Needs to stay at the top level of your `PYTHONPATH` so Python’s
    `sitecustomize` mechanism can find it.

- `hegm/`
  - `__init__.py`: patches `enumerate()` for transparent DataLoader resume,
    public API (`hegm.global_step()`), SIGUSR1 handler, import hooks.
  - `_config.py`: env‑driven configuration and lightweight logging.
  - `_hook.py`: generic PEP‑451 import hook logic.
  - `backends/__init__.py`: `AbstractBackend` interface and registry.
  - `backends/pytorch.py`: PyTorch backend (tracking, checkpointing, RNG,
    GPU teardown).

### Why `launcher.py` and `sitecustomize.py` live at the top level

The **package code** lives under `hegm/`, but we intentionally keep:

- `sitecustomize.py` at the top level so that Python’s automatic
  `sitecustomize` import works as soon as `/opt/hegm` is on `PYTHONPATH`.
- `launcher.py` at the top level so Kubernetes manifests can invoke it as
  `/opt/hegm/launcher.py` without needing to worry about Python module
  import paths.

Internally, both of these files are very thin:

- `sitecustomize.py` just imports the `hegm` package.
- `launcher.py` is a small script that wires the environment and hands off
  most logic to the shared configuration/checkpointing scheme.

In other words, the **reusable logic** already lives in `hegm/`; the two
top‑level scripts are just ergonomic entry points for Python and Kubernetes.
We can still add more entry points later (e.g. `hegm.launcher` console
script) without changing this layout.

### Examples

Working DRA examples live under `examples/dra/`:

- `training-pod.yaml` – single training job.
- `multiple-training-pod.yaml` – two `launcher.py` processes in one Pod,
  useful for testing PID‑isolated checkpoints and locks.
- `restore-pod.yaml` – example restore workflow using CRIU.

See `docs/HeGM_Architecture.md` for a more detailed architectural walk‑through.
