CODEWITHSUNDEEP
pythonubuntuinstallationpytorch
Chris Rigano
Chris Rigano

Reputation: 701

Installing PyTorch on Jetson Nano Ubuntu 18

I am trying to install PyTorch on Jetson Nano Ruining Ubuntu 1804. My reference is https://dev.to/evanilukhin/guide-to-install-pytorch-with-cuda-on-ubuntu-18-04-5217

When I try the following command this is what I get:

(my_env) crigano@crigano-desktop:~$ python3.8 -m pip install numpy ninja pyyaml mkl mkl-include setuptools cmake cffi typing
Collecting numpy
  Using cached numpy-1.20.2-cp38-cp38-manylinux2014_aarch64.whl (12.7 MB)
Collecting ninja
  Using cached ninja-1.10.0.post2.tar.gz (25 kB)
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
    Preparing wheel metadata ... done
Collecting pyyaml
  Using cached PyYAML-5.4.1-cp38-cp38-manylinux2014_aarch64.whl (818 kB)
ERROR: Could not find a version that satisfies the requirement mkl
ERROR: No matching distribution found for mkl

Upvotes: 1

Views: 1170

Answers (1)

Casey Jones
Casey Jones

Reputation: 153

If you just want to use PyTorch on the bare-metal Jetson Nano, simply install it with NVIDIA's pre-compiled binary wheel. Other packages can be found in the Jetson Zoo.

MKL is developed by Intel "to optimize code for current and future generations of Intel® CPUs and GPUs." [PyPI]. Apparently it does run on other x86-based chips like AMD's (although Intel has historically intentionally crippled the library for non-Intel chips [Wikipedia]), but unsurprisingly Intel is not interested in supporting ARM devices and has not ported MKL to ARM architectures.

If your goal is to use MKL for math optimization in numpy, openblas is a working alternative for ARM. libopenblas-base:arm64 and libopenblas-dev:arm64 come pre-installed on NVIDIA's "L4T PyTorch" Docker images. You can confirm that numpy detects them with numpy.__config__.show(). This is what I get using numpy 1.12 in python 3.69 on the l4t-pytorch:r32.5.0-pth1.6-py3 image:

blas_mkl_info:
  NOT AVAILABLE
blis_info:
  NOT AVAILABLE
openblas_info:
    libraries = ['openblas', 'openblas']
    library_dirs = ['/usr/lib/aarch64-linux-gnu']
    language = c
    define_macros = [('HAVE_CBLAS', None)]
blas_opt_info:
    libraries = ['openblas', 'openblas']
    library_dirs = ['/usr/lib/aarch64-linux-gnu']
    language = c
    define_macros = [('HAVE_CBLAS', None)]
lapack_mkl_info:
  NOT AVAILABLE
openblas_lapack_info:
    libraries = ['openblas', 'openblas']
    library_dirs = ['/usr/lib/aarch64-linux-gnu']
    language = c
    define_macros = [('HAVE_CBLAS', None)]
lapack_opt_info:
    libraries = ['openblas', 'openblas']
    library_dirs = ['/usr/lib/aarch64-linux-gnu']
    language = c
    define_macros = [('HAVE_CBLAS', None)]

So presumably it will use openblas in place of MKL for math optimization. If your use case is also for numpy optimization, you can likewise use openblas and shouldn't need MKL... which is fortunate, since it isn't available anyway. 😅

Upvotes: 1

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