Etiket: derin öğrenme
Makale: DIGITS: the Deep learning GPU Training System
DIGITS is a deep learning training system with a web interface. Tools are provided for designing custom network architectures and for rapidly evaluating their effectiveness through various visualizations of training outputs and learned network parameters allowing for rapid prototyping and collaboration.
NVIDIA DIGITS ve Derin Öğrenme ile İlgili Sorular ve Cevaplar
NVIDIA’nın DIGITS hakkında düzenlemiş olduğu online derste (12.08.2015) katılımcıların yazılı sorularına verilen cevaplar aşağıda yer almaktadır. Dersle ilgili daha fazla bilgi için tıklayınız.
Q: I own a Titan X. I read somewhere that its single-precision performance (FP32) is 7 TFLOPS and double-precision performance (FP64) is only 1.3 TFLOPS. Do the frameworks discussed here all use single-precision by default? If not, how can they be configured for best performance?
A: By default, all the frameworks use single precision floating point.
Q: How is the number of GPUS set in DIGITS?
A: The number of GPUs to use is set on the train model page
Q: Will the model we make on digits work on Nvidia’s fork of Caffe or will it work with vanilla caffe too?
A: It will work in the main branch of Caffe. Nvidia’s fork uses the same formats and layer types.
Q: Can digits work on a cluster? I have two GPUs on different machines. If I create a cluster out of them, can digits utilise the two GPU’s?
A: Yes, DIGITS can utilize two GPUs. Recall that DIGITS is built on top of 3rd party frameworks so provided those frameworks can use two GPUs, then DIGITS can also.
Q: I own a Titan X. I read somewhere that its single-precision performance (FP32) is 7 TFLOPS and double-precision performance (FP64) is only 1.3 TFLOPS. Do the frameworks discussed here all use single-precision by default? If not, how can they be configured for best performance?
A: It is single precision by default in Digits
Q: Is it possible to train voice datas with using NVIDIA DIGITS?
A: Currently DIGITS is designed for training on images, but we would like to add support for speech/voice in the future