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TensorFlow

Description

TensorFlow is a deep learning framework developed by Google in 2015. It is maintained and continuously updated by implementing results of recent deep learning research. Therefore, TensorFlow supports a large variety of state-of-the-art neural network layers, activation functions, optimizers and tools for analyzing, profiling and debugging deep neural networks. In order to deliver good performance, the TensorFlow installation at NERSC utilizes the optimized MKL-DNN library from Intel, as well as CUDA and related libraries from NVIDIA. Explaining the full framework is beyond the scope of this website. For users who want to get started we recommend browsing the TensorFlow tutorials. The TensorFlow page also provides a complete API documentation.

TensorFlow at NERSC

Modules

In order to use TensorFlow at NERSC load the TensorFlow module via

module load tensorflow/<version>

where <version> should be replaced with the version string you are trying to load. To see which ones are available use module avail tensorflow. On Cori, modules built for Cori GPU have gpu in the version name, while those built for CPU nodes have intel in the version name.

Running TensorFlow on a single node is the same as on a local machine, just invoke the script with

python my_tensorflow_program.py

Customizing environments

Want to integrate your own packages with TensorFlow at NERSC? There are two suggested solutions:

  1. Install your packages on top of our TensorFlow + Python installations - You can use the $PYTHONUSERBASE environment variable (set automatically when you load one of our modules) and user installations with pip install --user ... to install your own packages on top of our PyTorch installations. For example, to add the netCDF package:

    module load tensorflow
    pip install netCDF --user
    
  2. Install TensorFlow into your custom conda environments - You can setup a conda environment as described in our Python documentation and install TensorFlow into it. TensorFlow documentation recommends installing via pip.

    For TensorFlow's GPU functionality you need to have the requisite CUDA libraries available. One option, as mentioned in the TensorFlow docs, is to install cudatoolkit and cudnn via conda, into your custom conda environment. Alternately, you can simply load the cudatoolkit and cudnn modules on Perlmutter. On Cori GPU, load the cuda module instead of the cudatoolkit module. Note for either machine, you should take care to match the CUDA version of the module against your TensorFlow version.

    Please contact us at consult@nersc.gov if you want to build Horovod for your private build.

Containers

It is also possible to use your own docker containers with TensorFlow using shifter. Refer to the NERSC shifter documentation for help deploying your own containers.

On Perlmutter and Cori-GPU, we provide NVIDIA GPU Cloud (NGC) containers. They are named like nersc/tensorflow:ngc-20.09-tf1-v0 and nersc/tensorflow:ngc-20.09-tf2-v0 for TF versions 1 and 2, respectively.

To use interactively run in a container:

shifter --image=nersc/tensorflow:ngc-22.04-tf2-v1

To run in a container in batch jobs we strongly recommend using Slurm image shifter options for best performance:

#SBATCH --image=nersc/tensorflow:ngc-22.04-tf2-v1

srun shifter python my_python_script.py

To add python packages that are not in the image you can install them under $PYTHONUSERBASE. You can also use Shifter --env option to set the path. For example, to add the netCDF package:

shifter --image=nersc/tensorflow:ngc-22.04-tf2-v1 --env PYTHONUSERBASE=$HOME/.local/perlmutter/my_tf_ngc-22.04-tf2-v1_env
pip install netCDF --user

You also need to set the $PYTHONUSERBASE in your Slurm batch scripts to use your custom libraries at runtime:

#SBATCH nersc/tensorflow:ngc-22.04-tf2-v1

srun shifter --env PYTHONUSERBASE=$HOME/cori/my_tf_ngc-20.09-tf2-v0_env python my_python_script.py

You can also customize the images further by building your own Docker/Shifter image based on NERSC or NGC images following the standard Shifter image building instructions. The recipes for NERSC NGC images, which are built on top of NVIDIA's NGC images, are a good starting point for building optimized GPU-enabled containers.

Please note that for recent NGC tensorflow containers (e.g. 21.XX), you will need to adjust your job step command as follows:

srun --mpi=pmi2 ... shifter --module=gpu ... bash -c "python my_python_script.py"

Where

  • --mpi=pmi2 and --module=gpu are needed if you're running Horovod distributed training
  • bash -c "..." is needed to load the CUDA driver compatibility libraries

Please refer to Perlmutter known issues for additional problems and suggested workarounds.

Distributed TensorFlow

We recommend using Uber Horovod for distributed data parallel training. The version of Horovod we provide is compiled against the optimized Cray MPI and thus integrates well with Slurm. Check out our example Slurm scripts for running Horovod on Cori CPU and GPU, using modules and containers. Also, Horovod provides TensorFlow 1 and 2 examples.

Splitting Data

It is important to note that splitting the data among the nodes is up to the user and needs to be done besides the modifications stated above. Here, utility functions can be used to determine the number of independent ranks via hvd.size() and the local rank id via hvd.rank(). If multiple ranks are employed per node, hvd.local_rank() and hvd.local_size() return the node-local rank-id's and number of ranks. If the dataset API is being used we recommend using the dataset.shard option to split the dataset. In other cases, the data sharding needs to be done manually and is application dependent.

Frequently Asked Questions

I/O Performance and Data Feeding Pipeline

For performance reasons, we recommend storing the data on the scratch filesystem, accessible via the SCRATCH environment variable. At high concurrency, i.e. when many nodes need to read the files we recommend staging them into burst buffer. For efficient data feeding we recommend using the TFRecord data format and using the dataset API to feed data to the model. Especially, please note that the TFRecordDataset constructor takes buffer_size and num_parallel_reads options which allow for prefetching and multi-threaded reads. Those should be tuned for good performance, but please note that a thread is dispatched for every independent read. Therefore, the number of inter-threads needs to be adjusted accordingly (see "Potential Issues" below). The buffer_size parameter is meant to be in bytes and should be an integer multiple of the node-local batch size for optimal performance.

On Cori GPU, there is 1TB of node-local temporary storage in a nvme SSD mounted at /tmp. This can be made use of to speed up data pipelines, either by staging data there once, at the start of a job, or by caching dataset elements there via the cache() option for tf.data.Datasets. Similarly, Perlmutter has 126GB of node-local DRAM temporary storage, also mounted at /tmp. This can be used for caching data as well.

Potential Issues

For best MKL-DNN performance, the module already sets a set of OpenMP environment variables and we encourage the user not changing those, especially not changing the OMP_NUM_THREADS variable. Setting this variable incorrectly can cause a resource starvation error which manifests in TensorFlow telling the user that too many threads are spawned. If that happens, we encourage to adjust the inter- and intra-task parallelism by changing the NUM_INTER_THREADS and NUM_INTRA_THREADS environment variables. Those parameters can also be changed in the TensorFlow python script as well via the tf.config.threading module.

Please note that num_inter_threads*num_intra_threads<=num_total_threads where num_total_threads is 64 on Haswell or 272 on KNL.