A $1200 Deep Learning Rig

Brent Schneeman
Dec 30, 2017 · 5 min read

Hardware

PCPartPicker part list: https://pcpartpicker.com/list/tLjFQVCPU: Intel — Core i5–7600 3.5GHz Quad-Core Processor 
Motherboard: MSI — Z270I GAMING PRO CARBON AC Mini ITX LGA1151 Motherboard
Memory: PNY — Anarchy 16GB (2 x 8GB) DDR4–2133 Memory
Storage: Crucial — MX300 525GB M.2–2280 Solid State Drive
Video Card: EVGA — GeForce GTX 1070 Ti 8GB SC GAMING ACX 3.0 Black Edition Video Card
Case: Thermaltake — Core V1 Mini ITX Desktop Case
Power Supply: SeaSonic — 520W 80+ Bronze Certified Fully-Modular ATX Power Supply
Case Fan: ARCTIC — F8 PWM 31.0 CFM 80mm Fan
Case Fan: ARCTIC — F8 PWM 31.0 CFM 80mm Fan
The built system

Software

sudo apt-get update
sudo apt-get — assume-yes upgrade
sudo apt-get — assume-yes install tmux build-essentials gcc g++ make binutils
sudo apt-get — assume-yes install tmux build-essential gcc g++ make binutils
sudo apt-get — assume-yes install software-properties-common
sudo apt-get — assume-yes install git
The USB Stick in question
bash Miniconda3-latest-Linux-x86_64.sh -b
conda upgrade -y — all
source activate root
sudo apt install python3-pip
pip install tensorflow-gpu
mkdir projects
cd projects/
mkdir github.com
cd github.com/
git clone https://github.com/tensorflow/tensorflow.git
python tensorflow/tensorflow/examples/tutorials/mnist/fully_connected_feed.py
pip install keras
git clone https://github.com/fchollet/keras.git
cd keras
cd examples/
python imdb_fasttext.py

“Benchmarking”

python mnist_cnn.py 
python mnist_hierarchical_rnn.py
python imdb_bidirectional_lstm.py
python imdb_fasttext.py
python lstm_text_generation.py
2017–12–11 08:06:01.550986: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
2017–12–11 08:06:18.522490: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: Tesla P100-PCIE-12GB, pci bus id: 0000:04:00.0, compute capability: 6.0)

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