Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 

MultiGPU_Study

Build a Deep Learning (DL) Server and Test

Farhad Navid

The purpose of this post is to serve as a guide on how to build a DL server with multiple GPUs to further enhance the understanding of the "model" and "data" parallelisms. A hardware parts list in the repository is merely a suggestion for building a server. Additionally, we have included the programs used to validate the build and framework versions. The powerpoint file does contain more details on the hardware selection and software revisions. The main point was to explore the Data parallelism as a starter.

Data Parallelism:

  • The model is copied on all GPUs or Distributed Systems,
  • Each GPU gets a different portion of the data. ( mini_batch= Batch / num_GPU).
  • The Results from each GPU are combined after each mini_batch.

Visualization of Data parallelism

parallelisem

  • Parameter averaging (In-Graph replication)
    1. Initialize the model parameters (Weights, biases)
    2. Distribute Copy of parameters to all GPU’s
    3. Train with mini_batch.
    4. Global Parameter to average each GPU’s parameters.
    5. Repeat Steps ii-iv while there is data to process.

data_par

Pitfalls of Synchronous Parameter averaging

Every iteration(minibatch averaging): This method has the potential to have a significant overhead (communication between CPU and GPU) especially with the larger number of GPU's. All models in GPUs have to complete the training and the parameter send to the CPU to get averaged, updated and fed back to the GPUs.

Infrequent averaging: local parameter may diverge resulting a poor model after averaging. Some preliminary research (Su et al., 2015) suggest averaging in about ~10 mini batches can have a reasonable performance at the cost of some reduced model accuracy.

Use of optimization (Adam, RMSProp, …) in each model necessitates averaging at the cost of increased network transfer, Resulting in a better convergence.

Optimization Base averaging (Between-Graph replication) Preferred approach.

  • this method is similar to the parameter averaging. The difference is to use the Gradient result, post-learning rate, instead of parameters from the model.
  • The Synchronous averaging holds for all optimizers such as SGD (Stochastic Gradient Decent). data_parl
  • lambda=1/n. where n number of GPUs

Potential downfall :

  • A large number of GPU’s delays the update of the model and lowers the speed especially if the GPUs are not similar can cause a delay in updating. A potential solution is Asynchronous Update
  • Updating the parameters as soon as they are available.

Benefit:

  • Gain higher throughput, Speed up the parameter updates.

Downfall Stall Gradient:

  • For n GPU averaging the gradient on average could be N steps out of date before adding to global parameter vector. (aka Stall Gradient) Staleness
  • High gradient stall Slow Down the network convergence.
  • Learning efficiency drops.
  • Zhang et al., 2016 Suggest soft Sync and limiting the staleness as the possible solution.

Quick Guide on when to use the data/ model parallelism

The following Graph is for high-level point of view and does suggest the following: what to use

  • Use single GPU if the dataset and Model fit on the GPU memory.
  • Use Multi GPU or system If the Model is moderate but the data set is large (potential under fitting)
  • Use Multi GPU or system If the data set is relatively small, but the model is large.(i.e. ResNet200 or deeper)
  • If the data set is extensive and the model is very deep then utilize Data and model parallelism.

Part List

  • Motherboard: Z10PE-08WS.
  • CPU: Xeon E5 V4 2620.
  • RAM: 128GB DDR4 2133 MHz speed.
  • SSD: M.2 1TB
  • GPU: NVIDIA GFORCE 1080 TI
  • HDD: 10TB HDD, 7200 RPM with 256 M buffer
  • PSU: 1300 W power supply
  • RAC: 4U Rack Mount Chassis.

Software Configuration

The following recommendation are good starting point:

  • OS: Ubuntu 16.04.5 LTS
  • Python 3.5.2
  • Keras 2.2.4
  • TensorFlow 1.12.0
  • Pytorch 0.4.1
  • Nvidia Driver 384.130 (Can be updated to 412.130)
  • CUDA V9.0.176
  • OpenCV 2.4.9.1

Files

  • FarhadNavid_final.pptx contains details of the study with a summary result.
  • Mnist_multi_gpu_keras.py This file is used to train the "mnist" data set on one or two GPU using Keras framework.
  • Multi_gpu.py This file is a utility file with two functions (get_available_gpus and make_parallel).
  • Cifar-fn.py This file is a modified version of the reference files which uses the CIFAR10 dataset and VGG 19 Model to run on one and two GPU using the PyTorch framework.
  • vgg.py file is the strip down version of the VGG file which has only two models on it VGG19 and VGG19_bn (batchNormalization).
  • Test Folder. This folder contains some of the files used to validate the functionality of the Keras version 2.2.4 and TensorFlow ver 1.12.0.

Conclusion

  • Small Data set and small network the improvement in training time utilizing two GPU is about 1.6 improvement (248.3/154.2).
  • Larger Network and larger size dataset. The improvement was 1.3 (49:12/38:16),
  • The majority of the over head in both cases could be attributed
    • Communication between the GPU and CPU for parameter averaging.
    • Some inefficiency due to the binary file of TensorFlow was not compiled for Xeon CPU. Identified a resource to compile the TensorFlow for Xeon processor.

Refrences

  • Chen, K., & Huo, Q. (2016). Scalable training of deep learning machines by incremental block training with intra-block parallel optimization and blockwise model-update filtering. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp.2016.7472805
  • Gupta, S., Zhang, W., & Wang, F. (2016, December 5). Retrieved from https://arxiv.org/pdf/1509.04210.pdf
  • Su, H., Chen, H., & Xu, H. (2015, July 1). [1507.01239] Experiments on Parallel Training of Deep Neural Network using Model Averaging. Retrieved from https://arxiv.org/abs/1507.01239
  • Zhang, W., Gupta, S., Lian, X., & Liu, J. (2016). Staleness-aware async-SGD for distributed deep learning. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI'16), 2350-2356. Retrieved from https://arxiv.org/pdf/1511.05950.pdf.

TensorFlow

PyTorch

Mnist-Multi

  • Perfect reference for Multi GPU in Keras. Used only the Keras portion of the code and modified the model section to run a personal model. Result outlined in conclusion

Cifar10-Multi

  • Excellent source for PyTorch classification utilizing multi GPU with both CIFAR10 data and imagenet. However, for this study only used the cifar10dataset and modified the directories to use only the vgg19_bn model (VGG 19 with Batch Normalization). The directory structure was modified locally on the server to run only the vgg19_bn model with One and two GPU. Result Outlined in conclusion.

About

Build a Deep Learning Server and Test

Resources

License

Releases

No releases published

Packages

No packages published