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Caffe is a Deep Learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.

About Caffe on ShARC


The use of Caffe is no longer recommended; the last release of Caffe was in 2017.

A GPU-enabled worker node must be requested in order to use the GPU version of this software. See Using GPUs on ShARC for more information.

Caffe is available on ShARC as both Apptainer/Singularity images and as a module.

Caffe Apptainer/Singularity Images

Apptainer (previously known as Singularity) images are self-contained virtual machines similar to Docker. For more information on Apptainer and how to use the images, see Apptainer/Singularity.

A symlinked file is provided that always point to the latest image:

#CPU Caffe

#GPU Caffe

To get a bash terminal in to an image for example, use the command:

apptainer exec --nv /usr/local/packages/singularity/images/caffe/gpu.img /bin/bash

The exec command can also be used to call any command/script inside the image e.g.

apptainer exec --nv /usr/local/packages/singularity/images/caffe/gpu.img caffe train -solver=your_solver.prototxt

The --nv flag enables the use of GPUs within the image and can be removed if the software you’re using does not use the GPU.

You may get a warning similar to groups: cannot find name for group ID ..., this can be ignored and will not have an affect on running the image.

The paths /fastdata, /data, /home, /scratch, /shared are automatically mounted to your ShARC filestore directories. For GPU-enabled images the /nvlib and /nvbin is mounted to the correct Nvidia driver version for the node that you’re using.

To submit jobs that use an Apptainer image, see Submitting Batch Jobs That Uses Apptainer Images for more detail.

Image Index

Paths to the actual images and definition files are provided below for downloading and building of custom images.

  • Shortcut to Latest Image
    • CPU
      • /usr/local/packages/singularity/images/caffe/cpu.img

    • GPU
      • /usr/local/packages/singularity/images/caffe/gpu.img

  • CPU Images
    • Latest: 1.0.0-CPU-Ubuntu16.04 (Python 2.7)
      • Path: /usr/local/packages/singularity/images/caffe/1.0.0-cpu-ubuntu16.04.img

    • rc3-CPU-Ubuntu16.04 (Python 2.7)
      • Path: /usr/local/packages/singularity/images/caffe/rc3-CPU-Ubuntu16.04.img

    • Def file: /sharc/software/apps/apptainer/caffe_cpu.def

  • GPU Images
    • Latest: 1.0.0-GPU-Ubuntu16.04-CUDA8-cudNN5.0 (Python 2.7)
      • Path: /usr/local/packages/singularity/images/caffe/1.0.0-gpu-ubuntu16.04-cuda8-cudnn6.0.img

    • rc3-GPU-Ubuntu16.04-CUDA8-cudNN5.0 (Python 2.7)
      • Path: /usr/local/packages/singularity/images/caffe/rc3-GPU-Ubuntu16.04-CUDA8-cudNN5.0.img

    • Def file: /sharc/software/apps/apptainer/caffe_gpu.def

Using the Caffe Module

First request a GPU interactive session (see Interactive use of the GPUs).

The Caffe module can be loaded with the following command:

module load apps/caffe/rc5/gcc-4.9.4-cuda-8.0-cudnn-5.1

Installing Additional Python Modules (Optional)

The Caffe module is pre-installed with Anaconda version 3.4.2. You can install additional python packages by creating a virtual python environment in your home directory using conda.

#Creates a conda environment named caffe
      conda create -n caffe python=3.5
#Activates the caffe python environment
source activate caffe

You will also need to install numpy which can be obtained from the conda repository.

conda install numpy

Every Session Afterwards and in Your Job Scripts

If you created a virtual python environment, you must activate it at every new session and within your job scripts:

      module load apps/caffe/rc5/gcc-4.9.4-cuda-8.0-cudnn-5.1

#Activation below is only needed if you've installed your on python modules
source activate caffe

Installation Notes

For the module:

module load apps/caffe/rc5/gcc-4.9.4-cuda-8.0-cudnn-5.1
The following modules are automatically loaded:
  • GCC 4.9.4

  • CUDA 8

  • cuDNN 5.1

And comes with the following libraries:
  • Anaconda 4.2.0 (Python 3)

  • boost

  • protobuf

  • hdf5

  • snappy

  • glog

  • gflags

  • openblas

  • leveldb

  • lmdb

  • yasm

  • libx264

  • libx265

  • libfdk_acc

  • libopus

  • libogg

  • libvorbis

  • freetype

  • ffmpeg

  • libjpeg

  • libpng

  • libtiff

  • opencv 3.2.0