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
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
Processing...
Done!
===> 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 inexample/
, 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
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
andPerformance
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.