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