Quick Start

You can get a quick start by following these setps. After building and installing jdit package, you can make a new directory for a quick test. Assuming that you get a new directory example. run this code in ipython .(Create a main.py file is also acceptable.)

Fashion-mnist Classification

To start a simple classification task.

from jdit.trainer.instances.fashionClassification import start_fashionClassTrainer

Then you will see something like this as following.

===> Build dataset
use 8 thread!
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
===> Building model
SimpleModel Total number of parameters: 2776522
ResNet model use CPU!
apply kaiming weight init!
===> Building optimizer
===> Training
using `tensorboard --logdir=log` to see learning curves and net structure.
training and valid_epoch data, configures info and checkpoint were save in `log` directory.
  0%|            | 0/10 [00:00<?, ?epoch/s]
0step [00:00, ?step/s]
  • It will search a fashion mnist dataset.
  • Then build a simple network for classification.
  • For training process, you can find learning curves in tensorboard.
  • It will create a log directory in example/, which saves training processing data and configures.

Fashion-mnist Generation GAN

To start a simple generation gan task.

from jdit.trainer.instances import start_fashionGenerateGanTrainer

Then you will see something like this as following.

===> Build dataset
use 2 thread!
===> Building model
Discriminator Total number of parameters: 100865
Discriminator model use GPU(0)!
apply kaiming weight init!
Generator Total number of parameters: 951361
Generator model use GPU(0)!
apply kaiming weight init!
===> Building optimizer
===> Training
  0%|          | 0/200 [00:00<?, ?epoch/s]
0step [00:00, ?step/s]

You can get the training processes info from tensorboard and log directory. It contains:

  • Learning curves
  • Input and output visualization
  • The configures of Model , Trainer , Optimizer, Dataset and Performance in .csv .
  • Model checkpoint

Let’s build your own task

Although it is just an example, you still can build your own project easily by using jdit framework. Jdit framework can deal with

  • Data visualization. (learning curves, images in pilot process)
  • CPU, GPU or GPUs. (Training your model on specify devices)
  • Intermediate data storage. (Saving training data into a csv file)
  • Model checkpoint automatically.
  • Flexible templates can be used to integrate and custom overrides.

So, Let’s build your own task by using jdit.