1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
|
# For the moment we only support the CPU and GPU backends of jaxlib. The TPU
# backend will require some additional work. Those wheels are located here:
# https://storage.googleapis.com/jax-releases/libtpu_releases.html.
# For future reference, the easiest way to test the GPU backend is to run
# NIX_PATH=.. nix-shell -p python3 python3Packages.jax "python3Packages.jaxlib.override { cudaSupport = true; }"
# export XLA_FLAGS=--xla_gpu_force_compilation_parallelism=1
# python -c "from jax.lib import xla_bridge; assert xla_bridge.get_backend().platform == 'gpu'"
# python -c "from jax import random; random.PRNGKey(0)"
# python -c "from jax import random; x = random.normal(random.PRNGKey(0), (100, 100)); x @ x"
# There's no convenient way to test the GPU backend in the derivation since the
# nix build environment blocks access to the GPU. See also:
# * https://github.com/google/jax/issues/971#issuecomment-508216439
# * https://github.com/google/jax/issues/5723#issuecomment-913038780
{ addOpenGLRunpath, autoPatchelfHook, buildPythonPackage, config
, fetchurl, isPy39, lib, stdenv
# propagatedBuildInputs
, absl-py, flatbuffers, scipy, cudatoolkit_11
# Options:
, cudaSupport ? config.cudaSupport or false
}:
assert cudaSupport -> lib.versionAtLeast cudatoolkit_11.version "11.1";
let
device = if cudaSupport then "gpu" else "cpu";
in
buildPythonPackage rec {
pname = "jaxlib";
version = "0.1.71";
format = "wheel";
# At the time of writing (8/19/21), there are releases for 3.7-3.9. Supporting
# all of them is a pain, so we focus on 3.9, the current nixpkgs python3
# version.
disabled = !isPy39;
src = {
cpu = fetchurl {
url = "https://storage.googleapis.com/jax-releases/nocuda/jaxlib-${version}-cp39-none-manylinux2010_x86_64.whl";
sha256 = "sha256:0rqhs6qabydizlv5d3rb20dbv6612rr7dqfniy9r6h4kazdinsn6";
};
gpu = fetchurl {
url = "https://storage.googleapis.com/jax-releases/cuda111/jaxlib-${version}+cuda111-cp39-none-manylinux2010_x86_64.whl";
sha256 = "sha256:065kyzjsk9m84d138p99iymdiiicm1qz8a3iwxz8rspl43rwrw89";
};
}.${device};
# Prebuilt wheels are dynamically linked against things that nix can't find.
# Run `autoPatchelfHook` to automagically fix them.
nativeBuildInputs = [ autoPatchelfHook ] ++ lib.optional cudaSupport addOpenGLRunpath;
# Dynamic link dependencies
buildInputs = [ stdenv.cc.cc ];
# jaxlib contains shared libraries that open other shared libraries via dlopen
# and these implicit dependencies are not recognized by ldd or
# autoPatchelfHook. That means we need to sneak them into rpath. This step
# must be done after autoPatchelfHook and the automatic stripping of
# artifacts. autoPatchelfHook runs in postFixup and auto-stripping runs in the
# patchPhase. Dependencies:
# * libcudart.so.11.0 -> cudatoolkit_11.lib
# * libcublas.so.11 -> cudatoolkit_11
# * libcuda.so.1 -> opengl driver in /run/opengl-driver/lib
preInstallCheck = lib.optional cudaSupport ''
shopt -s globstar
addOpenGLRunpath $out/**/*.so
for file in $out/**/*.so; do
rpath=$(patchelf --print-rpath $file)
# For some reason `makeLibraryPath` on `cudatoolkit_11` maps to
# <cudatoolkit_11.lib>/lib which is different from <cudatoolkit_11>/lib.
patchelf --set-rpath "$rpath:${cudatoolkit_11}/lib:${lib.makeLibraryPath [ cudatoolkit_11.lib ]}" $file
done
'';
# pip dependencies and optionally cudatoolkit.
propagatedBuildInputs = [ absl-py flatbuffers scipy ] ++ lib.optional cudaSupport cudatoolkit_11;
pythonImportsCheck = [ "jaxlib" ];
meta = with lib; {
description = "XLA library for JAX";
homepage = "https://github.com/google/jax";
license = licenses.asl20;
maintainers = with maintainers; [ samuela ];
platforms = [ "x86_64-linux" ];
};
}
|
