LBANN: Livermore Big Artificial Neural Network Toolkit

The Livermore Big Artificial Neural Network toolkit (LBANN) is an open-source, HPC-centric, deep learning training software stack that is optimized to compose multiple levels of parallelism. LBANN is built upon a distributed linear algebra library (https://github.com/LLNL/Elemental) and provides model parallel acceleration through domain decomposition to optimize for strong scaling of network training. LBANN is also able to compose model parallelism with both data parallelism and ensemble training methods for training large neural networks with massive amounts of data. LBANN is a C++ code that composes MPI+OpenMP with CUDA (plus cuDNN and cuBLAS), taking advantage of tightly coupled accelerators, low-latency high bandwidth networking, node-local NVRAM storage, and high bandwidth parallel file systems. We have also developed an open source asynchronous all-reduce library called Aluminum (https://github.com/LLNL/Aluminum), that provides efficient MPI implementations of communication algorithms that are optimized for deep learning training patterns, provide GPU-accelerated reduction kernels, and compose with OpenMP threaded code bases. Aluminum also includes support for using NVIDIA’s NCCL library and has been integrated into both LBANN and Hydrogen (our distributed dense linear algebra library).

LBANN supports state of the art training algorithms such as unsupervised, self-supervised, and generative (GAN) training methods in addition to traditional supervised learning. It also supports recurrent neural networks via back propagation through time (BPTT) training, transfer learning, and multi-model training methods such as the Livermore Tournament Fast Batch (LTFB) ensemble algorithm.

Building LBANN

The LBANN build system is documented here.

LBANN Container Builds

We provide basic container defintion files, and instructions for their use, in the containers subdirectory. We currently support Docker and Singularity.

Cmake (Non LC or OSX Systems/Script alternative)

1. Ensure the following dependencies are installed CMake MPI Elemental OpenCV CUDA (optional) cuDNN (optional) Protocol Buffers (optional) Doxygen (optional) Note: LBANN also requires a C++ compiler with OpenMP support. The GCC 5.0 and Intel 16.0 C++ compilers are recommended 2. Clone this repo using git clone https://github.com/LLNL/lbann.git 3. In the main LBANN directory create a build directory using mkdir build 4. cd into this directory and run the following commands cmake ../.. make make install Note: It may be necessary to manually set CMake variables to control the build configuration

Verifying LBANN on LC

1. Allocate compute resources using SLURM: salloc -N1 -t 60
2. Run a test experiment for the MNIST data set; from the main lbann directory run the following command:

 srun -n12 build/gnu.catalyst.llnl.gov/install/bin/lbann \
--model=model_zoo/models/lenet_mnist/model_lenet_mnist.prototext \
--reader=model_zoo/data_readers/data_reader_mnist.prototext \
--optimizer=model_zoo/optimizers/opt_adagrad.prototext \
--num_epochs=5

Note: srun -n12 build/gnu.catalyst.llnl.gov/install/bin/lbann assumes you are running on the LLNL catalyst platform; if running on some other platform, and/or have installed lbann in a different directory, you will need to adjust this command.

This should produce roughly the following final results on Catalyst:

--------------------------------------------------------------------------------
[4] Epoch : stats formated [tr/v/te] iter/epoch = [844/94/157]
            global MB = [  64/  64/  64] global last MB = [  48  /  48  /  16  ]
             local MB = [  64/  64/  64]  local last MB = [  48+0/  48+0/  16+0]
--------------------------------------------------------------------------------
Model 0 training epoch 4 objective function : 0.0471567
Model 0 training epoch 4 categorical accuracy : 99.6241%
Model 0 training epoch 4 run time : 7.64182s
Model 0 training epoch 4 mini-batch time statistics : 0.00901458s mean, 0.0212693s max, 0.0078979s min, 0.000458463s stdev
Model 0 validation objective function : 0.0670221
Model 0 validation categorical accuracy : 98.9%
Model 0 validation run time : 0.25341s
Model 0 validation mini-batch time statistics : 0.00269454s mean, 0.00285273s max, 0.0020936s min, 6.65695e-05s stdev
Model 0 test objective function : 0.0600125
Model 0 test categorical accuracy : 99.02%
Model 0 test run time : 0.421912s
Model 0 test mini-batch time statistics : 0.00268631s mean, 0.00278771s max, 0.00131827s min, 0.00011085s stdev

Note: LBANN performance will vary on a machine to machine basis. Results will also vary, but should not do so significantly.

Running other models

There are various prototext models under the lbann/model_zoo/models/ directory: alexnet, autoencoder_mnist, lenet_mnist, etc. Each of these directories should have a script called runme.py. Run this script with no command line parameters for complete usage. Basically, these scripts generate command lines similar to the one above (in the Verifying LBANN on LC section). The scripts take two required arguments: --nodes=<int> and --tasks=<int>. The "tasks" option is used to specify the number of tasks per node, hence, the total number of tasks (cores) is: nodes*tasks. The generated command lines are designed to be executed using srun on LC systems, so you may need to modify, e.g, substitute mpirun, depending on your specific system.

Note: some directories contain multiple models, e.g, as of this writing, the autoencoder_cifar10 directory contains both model_autoencoder_cifar10.prototext and model_conv_autoencoder_cifar10.prototext. In these cases there may be multiple python scripts, e.g, runme_conv.py.