@@ -31,30 +31,32 @@ The examples are not only for demonstration of the framework -- you can train th
## Features:
Describe your training task with three components:
It's Yet Another TF wrapper, but different in:
1. Not focus on models.
+ It includes only a few common models, and helpful tools such as `LinearWrap` to simplify large models.
But you can use any other TF wrappers here, such as slim/tflearn/tensorlayer.
1. __DataFlow__. process data in Python, with ease and speed.
2. Focus on large datasets.
+ __DataFlow__ allows you to process large datasets such as ImageNet in Python without blocking the training.
+ DataFlow has a unified interface, so you can compose and reuse them to perform complex preprocessing.
+ Allows you to process data in Python without blocking the training, by multiprocess prefetch & TF Queue prefetch.
+ All data producer has a unified interface, you can compose and reuse them to perform complex preprocessing.
3. Focus on training speed.
+ Tensorpack trainer is almost always faster than `feed_dict` based wrappers.
Even on a small CNN example, the training runs [2x faster](https://gist.github.com/ppwwyyxx/8d95da79f8d97036a7d67c2416c851b6) than the equivalent Keras code.
More improvements to come later.
2. __Callbacks__, like `tf.train.SessionRunHook`, plugins, or extensions. Write a callback to implement everything you want to do apart from the training iterations, such as:
+ Data-Parallel Multi-GPU training is off-the-shelf to use.
You can also define your own trainer for different style of training (e.g. GAN) without losing the efficiency.
4. Interface of extensible __Callbacks__.
Write a callback to implement everything you want to do apart from the training iterations, and
enable it with one line of code. Common examples include:
+ Change hyperparameters during training
+ Print some tensors of interest
+ Run inference on a test dataset
+ Run some operations once a while
+ Send loss to your phone
3. __Model__, or graph. `models/` has some scoped abstraction of common models, but you can just use
symbolic functions in tensorflow or slim/tflearn/tensorlayer/etc.
`LinearWrap` and `argscope` simplify large models (e.g. [vgg example](https://github.com/ppwwyyxx/tensorpack/blob/master/examples/load-vgg16.py)).
With the above components defined, tensorpack trainer runs the training iterations for you.
Even on a small CNN example, the training runs [2x faster](https://gist.github.com/ppwwyyxx/8d95da79f8d97036a7d67c2416c851b6) than the equivalent Keras code.
Multi-GPU training is off-the-shelf by simply switching the trainer.
You can also define your own trainer for different style of training (e.g. GAN).
