https://github.com/hokim98/n3

Neural Network Notation written in Rust
https://github.com/hokim98/n3

deep-learning framework productivity python

Last synced: about 7 hours ago
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Neural Network Notation written in Rust

• Host: GitHub
• URL: https://github.com/hokim98/n3
• Owner: HoKim98
• License: mit
• Created: 2020-09-03T14:42:03.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2021-09-09T06:07:12.000Z (about 3 years ago)
• Last Synced: 2024-11-09T18:11:52.229Z (7 days ago)
• Topics: deep-learning, framework, productivity, python
• Language: Rust
• Homepage:
• Size: 789 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        


  



# N3: Neural Network Notation

[![Rust](https://github.com/kerryeon/n3/workflows/Rust/badge.svg)](https://travis-ci.com/kerryeon/n3)

This project is WIP. Please be aware of using it.

```

node LeNet5:

    let K: kernel size = int 5

    let C: input channels = dim

    let W: width = dim

    let H: height = dim

    with Conv2D:

        set kernel size = K

        set padding = K / 2

        set stride = 2

    node MyConv:

        1. Conv2D

        2. Relu

    0. Input                    =  C, W  , H

    1. MyConv                   = 32, W/2, H/2

    2. MyConv                   = 64, W/4, H/4

    3. ToLinear

    4. Linear + Relu + Dropout  = 1024

    5. Linear                   = 10

```

# Quick Start

```bash

# install dependencies (apt)

sudo apt update

sudo apt install -y \

    gcc git

    sqlite3 libsqlite3-dev

# create an conda environment (can vary on your settings)

conda create -n n3 -c pytorch -c nvidia \

    python=3 pip \

    pytorch torchvision torchaudio cudatoolkit=11.1 \

    tqdm

conda install -n n3 -c conda-forge \

    inflection tensorboard tensorboardx

conda activate n3

# build

cargo b --all

# set environment variables

export N3_SOURCE_ROOT=$PWD/n3-torch/ffi/python/n3/

export PYTHONPATH=$PYTHONPATH:$N3_SOURCE_ROOT/../

export PATH=$PATH:$PWD/target/debug/

# set environment variables (by manual)

export PATH=$PATH:$(dirname $(which python)/../bin/)

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$(dirname $(which python)/../lib/)

# set environment variables (can vary on your settings)

export N3_MACHINES=cuda

# spawn a daemon (in the seperated tty)

n3-torchd

# train a model (in the seperated tty)

n3 train image_classification --model LeNet5 --data MNIST \

    --epoch 1 --batch_size 50

# monitor the progress (in the seperated tty)

n3 monitor

```

# Usage

## Server

```bash

$ sudo systemctl start n3-torchd

```

## Client

### Training

```bash

$ n3 train image_classification --model LeNet5 --data MNIST --machines cuda

```

### Evaluating

```bash

$ n3 eval image_classification --model LeNet5 --data MNIST --machines cuda

```

### Publishing

```bash

$ n3 publish image_classification --model LeNet5 --target android:java

```

* android: java, flutter

* ios: flutter

* universal: c++, python

### Monitoring using Tensorboard

```bash

$ n3 monitor # or, browse http://localhost::xxxx/

```

### Distributed Training

```bash

$ n3 train image_classification --model LeNet5 --data MNIST --machines w:180:cuda:0 w:192.168.0.181 cpu

```

* "w:180:cuda:0": the "cuda:0" machine in "xxx.xxx.xxx.180" (local)

* "w:192.168.0.181": automatically choose machines in "192.168.0.181"

* These can be defined as environment variables (N3_MACHINES)

## Docker

```bash

$ docker build --tag n3:1.0 .

$ docker run -it --rm n3:1.0 bash -c "n3-torchd & n3-net-api"

```