deep-variance 1.0.6

GPU Virtual Memory Stitching SDK: CUDA VMM allocator with chunk caching and DLPack tensors for PyTorch

deep-variance

GPU Virtual Memory Management SDK

CUDA virtual memory management (VMM) with physical chunk caching and DLPack-backed PyTorch tensors.

Ships as a pre-compiled wheel — no compiler or build tools required on install.

Requirements

Python	3.12 (cp312)
PyTorch	any CUDA build
CUDA	12.x driver and runtime
Platform	Linux x86_64 (glibc ≥ 2.34 — Ubuntu 22.04+, RHEL 9+)

Install

# 1. Install PyTorch for your CUDA version (https://pytorch.org)
pip install torch

# 2. On HPC, load CUDA if it is not in your path
module load cuda

# 3. Install deep-variance
pip install deep-variance

Usage

import torch
from deep_variance import (
    vmm_empty,
    vmm_empty_nd,
    set_cache_limit,
    cache_stats,
)

# 1-D allocation: 1 M float32 elements on CUDA device 0
t = vmm_empty(1_000_000, dtype=torch.float32, device="cuda:0")

# N-D allocation: (100, 1000) float32
t = vmm_empty_nd((100, 1000), dtype=torch.float32)

# Tune the physical chunk cache (2 GB per pool)
set_cache_limit(device_id=0, chunk_bytes=0, max_bytes=2 * 1024**3)

# Inspect cache utilisation
print(cache_stats())

Environment check

deep-variance-check                  # check C++, torch, CUDA
deep-variance-check --module-load    # also run `module load cuda` if CUDA not visible

Or from Python:

from deep_variance import check_environment, ensure_cuda_visible

ensure_cuda_visible(use_module=True)   # attempt `module load cuda` if needed
report = check_environment()
for name, (ok, msg) in report.items():
    print(f"{name}: {'ok' if ok else 'MISSING'} — {msg}")

Analytics (opt-out)

Usage telemetry is enabled by default. Events are sent from a background daemon thread and never block the caller. All network and I/O errors are silently ignored. No personally identifiable information is collected.

To opt out, set the environment variable before importing:

export DEEP_VARIANCE_NO_TELEMETRY=1

Or disable at runtime:

from deep_variance import disable_analytics, analytics_summary

disable_analytics()          # stop for this process
print(analytics_summary())  # inspect counts collected so far

To associate events with your account, provide an API key. This lets you view your usage history in the DeepVariance dashboard. No PII is attached.

# via environment variable (recommended — set once in your shell profile)
export DEEP_VARIANCE_API_KEY=your_api_key

# or at runtime before any allocations
from deep_variance import enable_analytics

enable_analytics(api_key="your_api_key")

Development

git clone <repo>
cd deepvariance-ms-sdk
pip install -e ".[dev]"
pytest                          # unit tests (no GPU required)
pytest --run-cuda-live          # + CUDA-live tests (requires CUDA GPU)

What's New

Version	Change
1.0.6	Code improvements and bug fixes.
1.0.5	Fixed startup hook placement in wheel for correct import-time initialisation.
1.0.4	Fixed __version__ reporting in compiled wheel (yanks 1.0.3).
1.0.3	Code improvements; added psutil as a required dependency.
1.0.2	Added a dedicated PyPI documentation page separate from the GitHub README.
1.0.1	Stub fixes and README cleanup.
1.0.0	Initial release: CUDA VMM allocator with physical chunk caching and DLPack-backed PyTorch tensors.

License

MIT