@@ -34,24 +34,26 @@ The examples are not only for demonstration of the framework -- you can train th
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@@ -34,24 +34,26 @@ The examples are not only for demonstration of the framework -- you can train th
Describe your training task with three components:
Describe your training task with three components:
1. __Model__, or graph. `models/` has some scoped abstraction of common models, but you can simply use
1. __DataFlow__. process data in Python, with ease and speed.
any symbolic functions available in tensorflow, or most functions in slim/tflearn/tensorlayer.
`LinearWrap` and `argscope` simplify large models ([vgg example](https://github.com/ppwwyyxx/tensorpack/blob/master/examples/load-vgg16.py)).
2. __DataFlow__. tensorpack allows and encourages complex data processing.
+ Allows you to process data in Python without blocking the training, thanks to multiprocess prefetch & TF Queue prefetch.
+ All data producer has a unified interface, so they can be composed and reused to perform complex preprocessing.
+ All data producer has an unified interface, so they can be composed and reused to perform complex preprocessing.
2. __Callbacks__, including everything you want to do apart from the training iterations, such as:
+ Allows you to process data from Python without blocking the training, thanks to multiprocess prefetch & TF Queue prefetch.
Even on a small CNN example, it runs [2x faster](https://gist.github.com/ppwwyyxx/8d95da79f8d97036a7d67c2416c851b6) than the equivalent Keras code.
3. __Callbacks__, including everything you want to do apart from the training iterations, such as:
+ Change hyperparameters during training
+ Change hyperparameters during training
+ Print some tensors of interest
+ Print some tensors of interest
+ Run inference on a test dataset
+ Run inference on a test dataset
+ Run some operations once a while
+ Run some operations once a while
+ Send loss to your phone
+ Send loss to your phone
3. __Model__, or graph. `models/` has some scoped abstraction of common models, but you can just use
symbolic functions available in tensorflow, or in slim/tflearn/tensorlayer.
`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 will run the training iterations for you.
With the above components defined, tensorpack trainer will run the training iterations for you.
Trainer was written with performance in mind.
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.
Multi-GPU training is off-the-shelf by simply switching the trainer.
You can also define your own trainer for non-standard training (e.g. GAN).
You can also define your own trainer for non-standard training (e.g. GAN).
