Skip to main content

efa

Low-level Python bindings for AWS Elastic Fabric Adapter (EFA), including Scalable Reliable Datagram (SRD), one-sided RDMA, and GPUDirect transfers to and from torch CUDA tensors.

The package wraps libibverbs and EFA's libefa direct-verbs API in Cython. Its data path calls the provider's inline verbs directly, releases the GIL around blocking and posting operations, and does not import torch or link against CUDA.

  • No Python runtime dependencies.
  • SRD and UD queue pairs with SEND, RDMA read, and RDMA write operations.
  • Host buffers, CUDA device pointers, and dma-buf memory registration.
  • Classic and extended CQs, sender GID and unsolicited-write metadata, and direct EFA CQ, SQ, RQ, MR, and AH queries.
  • One Linux abi3 wheel for CPython 3.9 and newer.

Requirements

  • Linux on an AWS instance with one or more EFA devices attached.
  • libibverbs.so.1 and libefa.so.1 at runtime. The AWS EFA installer and current rdma-core distributions provide both.
  • The EFA device nodes available inside the process or container, normally /dev/infiniband/uverbs*.
  • A C compiler, Cython, and the rdma-core development headers only when building from source.

For GPUDirect, the EFA device and instance type must support RDMA read/write, and the NVIDIA driver must support dma-buf export or nvidia_peermem.

Install

pip install efa

To build from this checkout:

pip install "Cython>=3.0" "setuptools>=77" wheel
pip install ./efa

SRD Quickstart

SRD is reliable and connectionless. Each process creates a ready-to-send QP, exchanges a 24-byte EndpointInfo out of band, and resolves the remote GID to an address handle:

import numpy as np
import efa

dev = efa.get_efa_device_list()[0]
ctx = dev.open()
pd = ctx.alloc_pd()
cq = ctx.create_cq(256)
qp = pd.create_qp(
    efa.QPInitAttr(send_cq=cq, recv_cq=cq)
).prepare(qkey=0x1234)

local_info = efa.local_endpoint_info(qp, qkey=0x1234)
# Exchange local_info.to_bytes() with the other process.
remote_info = efa.EndpointInfo.from_bytes(remote_bytes)
peer = remote_info.peer(pd)

buf = np.zeros(4096, dtype=np.uint8)
access = (
    efa.AccessFlags.LOCAL_WRITE
    | efa.AccessFlags.REMOTE_WRITE
    | efa.AccessFlags.REMOTE_READ
)
mr = efa.reg_tensor(pd, buf, access)

qp.post_send(efa.SendWR(
    wr_id=1,
    sg_list=[mr.sge()],
    opcode=efa.WROpcode.SEND,
    send_flags=efa.SendFlags.SIGNALED,
    dest=peer,
))
for wc in cq.poll(16):
    wc.raise_for_status()

Every handle is an idempotent context manager. A QP retains its PD and CQs, an MR retains its PD and backing tensor, and an SGE retains the MR it addresses.

One-sided RDMA

EFA requires the responder to have an address handle for the requester before it accepts RDMA reads or writes. Both processes should therefore resolve the other process's EndpointInfo, even if traffic is currently one-way. A missing reverse AH completes with REM_OP_ERR and EFA vendor status 0x0e (REMOTE_ERROR_UNKNOWN_PEER).

Large buffers can be split at the device's max_rdma_size:

wrs = efa.write_wrs(local_mr, peer, remote_addr, remote_rkey)
qp.post_send(wrs)

read_wrs provides the corresponding RDMA-read operation. Every generated WR is signaled, as required by EFA.

GPUDirect With Torch

The optional efa.cuda module is torch-free and duck-types objects exposing data_ptr(), numel(), and element_size(). For torch's dma-buf path, enable VMM-backed allocations before CUDA initializes:

import os
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"

import torch
import efa
import efa.cuda

src = torch.arange(1 << 20, dtype=torch.float32, device="cuda:0")
dst = torch.zeros_like(src)

src_mr = efa.cuda.register_tensor(pd, src, access)
dst_mr = efa.cuda.register_tensor(pd, dst, access)

torch.cuda.synchronize(src.device)
qp.post_send(efa.SendWR(
    wr_id=2,
    sg_list=[src_mr.sge()],
    opcode=efa.WROpcode.RDMA_WRITE,
    send_flags=efa.SendFlags.SIGNALED,
    remote_addr=remote_dst_addr,
    rkey=remote_dst_rkey,
    dest=peer,
))

After the receiver observes a completion or protocol-level write notification, it must order inbound NIC writes before CUDA consumes the destination:

with torch.cuda.device(dst.device):
    efa.cuda.flush_gpudirect_writes()

register_tensor first exports a dma-buf fd and calls ibv_reg_dmabuf_mr. If that path is unavailable, it falls back to ibv_reg_mr, which requires nvidia_peermem. The returned GpuMR retains the tensor allocation and keeps the actual CUDA virtual address because ibv_mr.addr is not meaningful for a dma-buf MR.

Direct EFA API

Area API
Device capabilities Context.query_efa_device
EFA CQ creation Context.create_cq_ex
Sender and unsolicited metadata CQEx.poll, WC.sgid, WC.unsolicited
CQ layout CQ.query_efa, CQEx.query_efa
SQ/RQ layout QP.query_wqs
MR interconnect IDs MR.query_efa
Address-handle number AH.ahn
SRD QP creation PD.create_qp with QPType.SRD

When using unsolicited RDMA write-with-immediate completions, create every communicating QP with QPInitAttr(..., unsolicited_write_recv=True) and use an extended CQ created with unsolicited=True. EFA requires peers to negotiate the same QP feature set.

The direct layout queries expose process-local addresses for advanced consumers. They do not transfer ownership of provider memory.

Testing

pip install -e "./efa[test,gpu]"
cd efa
pytest -rs

Tests marked integration exercise real EFA hardware. Tests marked gpu perform torch-verified GPU-to-GPU, GPU-to-host, and host-to-GPU transfers. Unavailable hardware capabilities are skipped explicitly. Set RDMA4PY_SKIP_HARDWARE_TESTS=1 to force all hardware-dependent tests to skip.

License

BSD-3-Clause. See LICENSE.

Metadata

Release files for efa 2026.10.16

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for efa 2026.10.16
File Size Uploaded
efa-2026.10.16.tar.gz 394.0 kB Details

Built distributions (wheels)

Table of built distributions (wheels) for efa 2026.10.16
File Interpreter ABI Platform
efa-2026.10.16-cp39-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl CPython 3.9 abi3 Linux glibc 2.17+ ARM64, Linux glibc 2.28+ ARM64 Details
efa-2026.10.16-cp39-abi3-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl CPython 3.9 abi3 Linux glibc 2.5+ x86-64, Linux glibc 2.28+ x86-64 Details

Total release size: 3.0 MB

Release files / efa-2026.10.16.tar.gz

Download URL efa-2026.10.16.tar.gz
Size 394.0 kB
Tags Source
SHA-256 checksum
How to use checksums
ad82a52f9a07dbaa1324994f7cdd8d86b9881c8c7d1eb5a76fc2d398151efb86
BLAKE2b-256 checksum
How to use checksums
763833f584b85e607104458acd14ce21901a64c3e958b893f757c2f9a88d0732
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 16, 2026.

Transparency log

Release files / efa-2026.10.16-cp39-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl

Download URL efa-2026.10.16-cp39-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl
Size 1.3 MB
Tags CPython 3.9 Linux glibc 2.17+ ARM64 Linux glibc 2.28+ ARM64 abi3
SHA-256 checksum
How to use checksums
eee39b6d2c324f76b8bf1c71489fac17644cc3f279c251fc6093dc24f98cd026
BLAKE2b-256 checksum
How to use checksums
9c06c93eb142d795d52f990758ad2d83134778aea27cdc10d236a81274adafee
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 16, 2026.

Transparency log

Release files / efa-2026.10.16-cp39-abi3-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl

Download URL efa-2026.10.16-cp39-abi3-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl
Size 1.3 MB
Tags CPython 3.9 Linux glibc 2.28+ x86-64 Linux glibc 2.5+ x86-64 abi3
SHA-256 checksum
How to use checksums
2080d74a41c6adf470baaf606fe9ad8efc78da4e4ac62e257e1dc2bb862326eb
BLAKE2b-256 checksum
How to use checksums
aa5355be53d53f1ee58219da773bf34099e8d651b0a9cb9e310e586e9aa114f3
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 16, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

2026.10.16 This release

3 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page