Training is running something again and again. Tensorpack base trainer implements the logic of running the iteration, and derived trainers implement what the iteration is.
Most neural network training tasks are single-cost optimization.
Tensorpack provides some trainer implementations for such tasks.
These trainers will by default minimizes
Therefore, you can use these trainers as long as you set
as most examples did.
Existing trainers were implemented with certain prefetch mechanism,
which will run significantly faster than a naive
There are also Multi-GPU trainers which include the logic of data-parallel Multi-GPU training. You can enable them by just changing one line, and all the necessary logic to achieve the best performance was baked into the trainers already. For example, SyncMultiGPUTrainer can train ResNet50 as fast as the official tensorflow benchmark.
To use trainers, pass a
TrainConfig to configure them:
config = TrainConfig( model=MyModel() dataflow=my_dataflow, callbacks=[...] ) # start training (with a slow trainer. See 'tutorials - Input Pipeline' for details): # SimpleTrainer(config).train() # start training with queue prefetch: QueueInputTrainer(config).train() # start multi-GPU training with a synchronous update: # SyncMultiGPUTrainer(config).train()
Trainers just run some iterations, so there is no limit in where the data come from or what to do in an iteration. For example, GAN trainer minimizes two cost functions alternatively.