TensorFlow at TACC

Last update: June 02, 2023

Scientists across domains are actively exploring and adopting deep learning as a cutting-edge methodology to make research breakthrough. At TACC, our mission is to enable discoveries that advance science and society through the application of advanced computing technologies. Thus, we are embracing this new type of application on our high end computing platforms.

TACC supports the TensorFlow+Horovod stack. This framework exposes high level interfaces for deep learning architecture specification, model training, tuning, and validation. Deep learning practitioners and domain scientists who are exploring the deep learning methodology should consider this framework for their research.

This document details how to install TensorFlow, then download and run benchmarks in both single- and multi-node modes. Due to variations in TensorFlow and Python versions, and their compatabilities with the Intel compilers and CUDA libraries, the installation instructions are quite specific. Pay careful attention to the installation instructions.

Installations

TensorFlow is installed on TACC's Lonestar6, Frontera, and Stampede2 resources.

  • Parallel Training with TensorFlow and Horovod is available on Stampede2.
  • TensorFlow v2.1 is available on Stampede2.

Caution

Running programs or performing computations on the login nodes may result in account suspension.
All of the following examples are run on compute, not login, nodes.
Use TACC's idev utility to grab compute node/s when conducting any TensorFlow activities.

TensorFlow on Lonestar6

These instructions detail installing and running TensorFlow benchmarks on Lonestar6. Lonestar6 runs TensorFlow 2.6.1 with CUDA/11.4, Python 3.9.7 and Intel 19.

To install Horovod: 

login1$ module load cuda/11.4 cudnn/8.2.4 nccl/2.11.4
login1$ pip3 install --user gast==0.4.0 keras==2.6.0 tensorflow-gpu==2.6.1 --no-cache-dir
login1$ HOROVOD_CUDA_HOME=$TACC_CUDA_DIR HOROVOD_NCCL_HOME=$TACC_NCCL_DIR \
            CC=icc HOROVOD_GPU_ALLREDUCE=NCCL HOROVOD_GPU_BROADCAST=NCCL HOROVOD_WITH_TENSORFLOW=1 \
            pip3 install horovod --no-cache-dir

Single-Node

To run a single-node job benchmark on one GPU, first create an idev session in LS6's gpu_a100 queue: 

login1$ idev -N 1 -n 2 -p gpu_a100

Once the idev session is created, run the benchmark on a single node, using one GPU:

c307-001.ls6$ cds; git clone https://github.com/tensorflow/benchmarks.git
c307-001.ls6$ cd benchmarks
c307-001.ls6$ module load cuda/11.4 cudnn/8.2.4 nccl/2.11.4
c307-001.ls6$ python3 scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py \
                --num_gpus=1 --model resnet50 --batch_size 32 --num_batches 200

Run the same benchmark using both GPUs:

c307-001.ls6$ python3 scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py \
                --num_gpus=2 --model resnet50 --batch_size 32 --num_batches 200

Multi-Node

To run a multi-node job benchmark, first create a multi-node idev session in LS6's gpu_a100 queue: 

login1$ idev -N 2 -n 4 -p gpu_a100

Once the idev session is created, run the benchmarks on two nodes, using four GPUs:

c305-000$ cds;git clone  https://github.com/tensorflow/benchmarks.git
c305-000$ cd benchmarks
c305-000$ module load cuda/11.4 cudnn/8.2.4 nccl/2.11.4
c305-000$ ibrun -np 4 python3 scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py \
            --num_gpus=1 --variable_update=horovod --model resnet50 --batch_size 32 --num_batches 200

TensorFlow on Frontera

These instructions detail installing and running TensorFlow benchmarks on Frontera RTX. Frontera RTX runs TensorFlow 2.1.0 with Python 3.7.0 and Intel 19. Frontera supports CUDA10.0 and CUDA/10.1. Use the appropriate CUDA version for your TensorFlow installation with Python 3.7.6.


c123-456$ module load python3 
c123-456$ module load cuda/10.1 cudnn/7.6.5 nccl/2.5.6
c123-456$ pip3 install --user grpcio==1.28.1 tensorflow-gpu==2.1.0 --no-cache-dir

We suggest installing Horovod version 0.19.2. If you wish to install other versions of Horovod, please submit a support ticket with the subject "Request for Horovod" and TACC staff will provide special instructions.

c123-456$ HOROVOD_CUDA_HOME=$TACC_CUDA_DIR HOROVOD_NCCL_HOME=$TACC_NCCL_DIR CC=gcc \
    HOROVOD_GPU_ALLREDUCE=NCCL HOROVOD_GPU_BROADCAST=NCCL HOROVOD_WITH_TENSORFLOW=1 pip3 install \
    --user horovod==0.19.2 --no-cache-dir

Single-Node

Download the tensorflow benchmark to your $WORK directory, then check out the branch that matches your tensorflow version.


c123-456$ cds; git clone https://github.com/tensorflow/benchmarks.git
c123-456$ cd benchmarks  
c123-456$ git checkout cnn_tf_v2.1_compatible

Benchmark the performance with synthetic dataset on 1 GPU


c123-456$ cd scripts/tf_cnn_benchmarks
c123-456$ module load python3/3.7.0 cuda/10.1 cudnn/7.6.5 nccl/2.5.6
c123-456$ python3 tf_cnn_benchmarks.py --num_gpus=1 --model resnet50 --batch_size 32 --num_batches 200

Benchmark the performance with synthetic dataset on 4 GPUs


c123-456$ cd scripts/tf_cnn_benchmarks
c123-456$ module load python3/3.7.0 cuda/10.1 cudnn/7.6.5 nccl/2.5.6
c123-456$ ibrun -np 4 python3 tf_cnn_benchmarks.py --variable_update=horovod --num_gpus=1 \
    --model resnet50 --batch_size 32 --num_batches 200 --allow_growth=True

Multi-Node

Download the TensorFlow benchmark to your $WORK directory. Check out the branch that matches your tensorflow version. This example runs on two nodes in the rtx queue (8 GPUs).


c123-456$ cds; git clone https://github.com/tensorflow/benchmarks.git
c123-456$ git checkout cnn_tf_v2.1_compatible

Benchmark the performance with synthetic dataset on these two 2 nodes using 8 GPUs


c123-456$ cd scripts/tf_cnn_benchmarks
c123-456$ module load python3/3.7.0 cuda/10.1 cudnn/7.6.5 nccl/2.5.6
c123-456$ ibrun -np 8 python3 tf_cnn_benchmarks.py --variable_update=horovod --num_gpus=1 \
    --model resnet50 --batch_size 32 --num_batches 200 --allow_growth=True

TensorFlow on Stampede2

These instructions detail installing and running TensorFlow benchmarks on Stampede2. Stampede2 runs TensorFlow 2.1.0 with Python 3.7 and Intel 18.

Use TACC's idev utility to grab a single compute node for 1 hour in Stampede2's skx-dev queue:

login1$ idev -p skx-dev -N 1 -n 1 -m 60

Install TensorFlow 2.1 using the default intel/18.0.2 compiler and Python 3.7:


c123-456$ module load intel/18.0.2 python3/3.7.0
c123-456$ pip3 install --user tensorflow==2.1.0 --no-cache-dir

To install horovod v0.19.2:


c123-456$ CC=gcc HOROVOD_WITH_TENSORFLOW=1 pip3 install --user horovod==0.19.2 --no-cache-dir --no-cache-dir

Single-Node

If you're not already on a compute node, then use TACC's idev utility to grab a single compute node for 1 hour: 


login1$ idev -p skx-dev -N 1 -n 1 -m 60

Download the TensorFlow benchmark to your $SCRATCH directory. Check out the corresponding branch for your TensorFlow version. In this example we used cnn_tf_v2.1_compatible.


c123-456$ cd $SCRATCH
c123-456$ git clone https://github.com/tensorflow/benchmarks.git
c123-456$ cd benchmarks
c123-456$ git checkout cnn_tf_v2.1_compatible

Benchmark the performance with a synthetic dataset:


c123-456$ cd scripts/tf_cnn_benchmarks
c123-456$ export KMP_BLOCKTIME=0
c123-456$ export KMP_AFFINITY="granularity=fine,verbose,compact,1,0"
c123-456$ export OMP_NUM_THREADS=46
c123-456$ python3 tf_cnn_benchmarks.py --model resnet50 --batch_size 128 --data_format NHWC \
    --num_intra_threads 46 --num_inter_threads 2 --distortions=False --num_batches 100

Multi-Node

If you're not already on a compute node, then use TACC's idev utility to grab two compute nodes for 1 hour:


login1$ idev -p skx-dev -N 2 -n 2 -m 60

Download the TensorFlow benchmark to your $SCRATCH directory. Check out the corresponding branch for your TensorFlow version. In this example, we used cnn_tf_v2.1_compatible.


c123-456$ cd $SCRATCH
c123-456$ git clone https://github.com/tensorflow/benchmarks.git
c123-456$ git checkout cnn_tf_v2.1_compatible

Benchmark the performance with a synthetic dataset on 4 nodes:


c123-456$ module load intel/18.0.2 python3/3.7.0 
c123-456$ cd benchmarks/scripts/tf_cnn_benchmarks
c123-456$ export KMP_BLOCKTIME=0
c123-456$ export KMP_AFFINITY="granularity=fine,verbose,compact,1,0"
c123-456$ export OMP_NUM_THREADS=46
c123-456$ ibrun -np 2 python3 tf_cnn_benchmarks.py --model resnet50 --batch_size 128 \
    --variable_update horovod --data_format NCHW --num_intra_threads 46 --num_inter_threads 2 \
    --num_batches 100

The parameters for this last command are defined as follows:

  • -model specifies the neural network model
  • -batch_size specifies the number of samples in each iteration
  • -variable_update specifies using horovod to synchronize gradients
  • -data_format informs TF the nested data format comes in the order of sample count, channel, height, and width
  • -num_intra_threads specifies the number of threads used for computation within a single operation
  • -num_inter_threads specifies the number of threads used for independent operations
  • -num_batches specifies the total number of iterations to run

FAQ

Q: I have missing Python packages when using TensorFlow. What shall I do?

A: Deep learning frameworks usually depend on many other packages. e.g., the Caffe package dependency list. On TACC resources, you can install these packages in user space by running:

$ pip install --user package-name

References