Installation Update

[gerardgilliland]

As promised -- an update

Moved from mravanelli/pytorch-kaldi#88 to here at SyncNet
On pytorch-kaldi #88: I stated:
I am going to attempt to run the SyncNet speaker id experiment.
If it fails to run I will look into more hardware. So ...

Attempt to install on Raspberry Pi 3B+

Requirements:
Linux
Python 3.6/2.7
pytorch 1.0
pysoundfile
anaconda

Bottom line toward Requirements:
(I am skipping the installation output except for the last line)

Linux: Raspbian GNU/Linux 9 (stretch) *** YES ***

Python 3.6/2.7 *** NO *** I have Python 3.5.3 -- Anaconda installed 3.4

pi@raspberrypi:~ $ conda install anaconda-client
Anaconda *** Maybe *** The following NEW packages will be INSTALLED:
anaconda-client: 1.0.2-py34_0
clyent: 0.4.0-py34_0
freetype: 2.5.2-2
jpeg: 8d-0
libpng: 1.6.17-0
libtiff: 4.0.2-1
pillow: 2.9.0-py34_0
pip: 7.1.2-py34_0
python-dateutil: 2.4.2-py34_0
pytz: 2015.4-py34_0
setuptools: 18.1-py34_0
six: 1.9.0-py34_0
wheel: 0.24.0-py34_0

pi@raspberrypi:~ $ sudo apt-get install pytorch
pytorch E: Unable to locate package pytorch
pi@raspberrypi:~ $ pip3 install pytorch
Exception: You tried to install "pytorch". The package named for PyTorch is "torch"
pi@raspberrypi:~ $ sudo apt-get install torch
RuntimeError: PyTorch does not currently provide packages for PyPI (see status at pytorch/pytorch#566).
Please follow the instructions at http://pytorch.org/ to install with miniconda instead.
pi@raspberrypi:~ $ conda install pytorch=0.4.1 -c pytorch
pytorch *** NO *** Error: No packages found in current linux-armv7l channels matching: pytorch 0.4.1*

pysoundfile ( conda install -c conda-forge pysoundfile)
pi@raspberrypi:~ $ conda install -c conda-forge pysoundfile
Fetching package metadata: ......
Solving package specifications:
Error: Could not find some dependencies for pysoundfile: cffi
pi@raspberrypi:~ $ conda install --channel https://conda.anaconda.org/poppy-project cffi
The following packages conflict with each other:
cffi
python 3.4*
pysoundfile *** NO ***

Toward Requirements: 1.5 out of 5

************************* PLAN B *************************

Order a new system

https://developer.nvidia.com/embedded/buy/jetson-nano-devkit $99
NVIDIA® Jetson Nano™ Developer Kit is a small, powerful computer that lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing.
JetPack is compatible with NVIDIA’s world-leading AI platform for training and deploying AI software, and reduces complexity and effort for developers by supporting many popular AI frameworks, like TensorFlow, PyTorch, Caffe, and MXNet. It also includes a full desktop Linux environment and out-of-the-box support for a variety of popular peripherals, add-ons, and ready-to-use projects.

Technical Specifications
GPU 128-core Maxwell
CPU Quad-core ARM A57 @ 1.43 GHz
Memory 4 GB 64-bit LPDDR4 25.6 GB/s
Storage microSD (not included)
Video Encode 4K @ 30 | 4x 1080p @ 30 | 9x 720p @ 30 (H.264/H.265)
Video Decode 4K @ 60 | 2x 4K @ 30 | 8x 1080p @ 30 | 18x 720p @ 30 (H.264/H.265)
Camera 1x MIPI CSI-2 DPHY lanes
Connectivity Gigabit Ethernet, M.2 Key E
Display HDMI 2.0 and eDP 1.4
USB 4x USB 3.0, USB 2.0 Micro-B
Others GPIO, I2C, I2S, SPI, UART
Mechanical 100 mm x 80 mm x 29 mm

https://www.adafruit.com/product/1995 $8
5V 2.4A Switching Power Supply

https://www.sandisk.com/home/memory-cards
SanDisk microSDHC™ 64GB SD Card $13

Existing USB Keyboard, USB Mouse, HDMI screen

Toward Requirements:
Linux: Ubuntu 18.04 LTS
Python 3.6
PyTorch 1.1

Watched a tutorial on configuring, booting up, and running some examples.
And another on Introduction to Deep Learning

Unknown out of 5 (I will update you in a week / 10 days)

[TParcollet]

I have no idea on how Jetson will behave with Kaldi tough.

[gerardgilliland]

@TParcollet,
Thank you for your perspective. I suspect 'tough' (hard) is 'though' (although).
The Jetson Nano probably is under powered. It may not do Kaldi.
Realistically, I might not be able to do Kaldi.
But for $99, I see it as a good educational tool.
At least the documentation describes neural networks and uses PyTorch.
I will see how far I get with the SyncNet speaker id experiment.