Difference between revisions of "Caffe"

Caffe2 on Graham

Caffe2 is avaiable on Graham cluster. Please reference the page: [Caffe2 on Graham]

• If user get "ImportError: No module named google.protobuf.internal" problem, a temporary fix can be adding PYTHONPATH in the python command:

PYTHONPATH=/cvmfs/soft.computecanada.ca/easybuild/software/2017/avx2/Compiler/gcc5.4/python27-scipy-stack/2017a/lib/python2.7/site-packages/:$PYTHONPATH python code.py ...

Caffe on IBM Minsky deep learning server

Caffe is included in IBM PowerAI toolset. Please reference the page: [Caffe on Minsky]

Running Caffe (master branch Jun 28, 2016) on Copper and Mosaic

Sharcnet doesn't maintain Caffe as a module, but we provide all its dependencies and a precompiled Caffe as an example.

All the files that needed to build and run Caffe are under:

/opt/sharcnet/testing/caffe

Set up modules and environment variables

There is a script (caffe-set-env-cudnn4.sh) that can help setup all the modules and environment variables:

module unload intel gcc mkl openmpi hdf python cuda
module load intel/15.0.3
module load hdf/serial/5.1.8.11
module load cuda/7.5.18
module load python/intel/2.7.10
export MKL_CBWR=AUTO
export CAFFE_ROOT=/opt/sharcnet/testing/caffe/caffe-master-160628
export PATH=/opt/sharcnet/testing/caffe/caffe-libs/bin:/opt/sharcnet/testing/caffe/caffe-libs/include:/opt/sharcnet/testing/cudnn/cudnn4:$PATH
export LD_LIBRARY_PATH=/opt/sharcnet/testing/caffe/caffe-libs/lib:/opt/sharcnet/testing/cudnn/cudnn4:$LD_LIBRARY_PATH
export PYTHONPATH=/opt/sharcnet/testing/caffe/caffe-libs/lib/python2.7/site-packages:/opt/sharcnet/testing/caffe/caffe-master-160628/python:$PYTHONPATH

In this file, CAFFE_ROOT is set to a precompiled Caffe which was built on Jun 28th, 2016. If you want to use this version of caffe, you don't have to run the commands above, you can simply do:

source /opt/sharcnet/testing/caffe/caffe-set-env-cudnn4.sh

• You should run this "source" command every time you login to a sharcnet system, or put it into your .bashrc if you really know what these commands will do.

Prepare data set (e.g. Imagenet)

Although Caffe can take images directly as an input, for a better I/O performance, LMDB database is strongly recommended. Caffe provides a tool and a script to build lmdb database from images. You should copy the script to your place and modify it to meet your own needs. The script is

$CAFFE_ROOT/examples/imagenet/create_imagenet.sh

In this file, there are some variables you should modify:

• EXAMPLE, should be a folder that you want to storage the lmdb database file.

EXAMPLE=/work/yourname/imagentet/lmdb

• DATA, should be a folder where you keep the label files, train.txt and val.txt

DATA=/work/yourname/imagenet/labels

• TOOLS, should be tools folder in Caffe, here we use the precompiled Caffe as an example

TOOLS=$CAFFE_ROOT/build/tools

• TRAIN_DATA_ROOT and VAL_DATA_ROOT, should be folders that contains your images (e.g. .jpg files)

TRAIN_DATA_ROOT=/work/yourname/imagenet/train/
VAL_DATA_ROOT=/work/yourname/imagenet/val/

• RESIZE, should be set to true if you haven't resized the images to 256x256
• In the train.txt and val.txt, you should specify the image names with locations and lables. For example:

n01440764/n01440764_10254.JPEG 0
n01440764/n01440764_10281.JPEG 0

Each line contains one image location and label. "n01440764" is the subfolder's name under $TRAIN_DATA_ROOT. The full path to this image should be /work/yourname/imagenet/train/n01440764/n01440764_10254.JPEG

• You can use data/ilsvrc12/get_ilsvrc_aux.sh script in Caffe to download the labels for imagenet.

After building the LMDB database, we should also compute an image mean file (imagenet_mean.binaryproto) based on the data you have. The script is $CAFFE_ROOT/examples/imagenet/make_imagenet_mean.sh. You should modify the variables inside to the same as we modified in create_imagenet.sh. If you have run the get_ilsvrc_aux.sh, an imagenet_mean.binaryproto which was computed on imagenet dataset will be downloaded.

Define models (Alexnet as an example)

Caffe provides an Alexnet model in $CAFFE_ROOT/models/bvlc_alexnet. Here we copy this folder to /home or /work so that we can modify it. First we should modify "train_val.prototxt" file. We should change the "mean_file" to where we hold the image mean. And the "source" for training and validation.

name: "AlexNet"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    crop_size: 227
    mean_file: "/work/feimao/imagenet/imagenet_mean.binaryproto"
  }
  data_param {
    source: "/work/feimao/imagenet/ilsvrc12_train_lmdb"
    batch_size: 256
    backend: LMDB
  }
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mirror: false
    crop_size: 227
    mean_file: "/work/feimao/imagenet/imagenet_mean.binaryproto"
  }
  data_param {
    source: "/work/feimao/imagenet/ilsvrc12_val_lmdb"
    batch_size: 50
    backend: LMDB
  }
}

And then changing the solver.prototxt to locate the train_val.prototxt file and the output snapshot.

net: "/work/feimao/imagenet/bvlc_alexnet/train_val.prototxt"
test_iter: 1000
test_interval: 1000
base_lr: 0.01
lr_policy: "step"
gamma: 0.1
stepsize: 100000
display: 20
max_iter: 450000
momentum: 0.9
weight_decay: 0.0005
snapshot: 10000
snapshot_prefix: "/work/feimao/imagenet/bvlc_alexnet/caffe_alexnet_train"
solver_mode: GPU

Submit jobs

• On copper and Mosaic, please export MKL_CBWR=AUTO before submitting the jobs. This will be set if you do "source /opt/sharcnet/testing/caffe/caffe-set-env-cudnn4.sh"

Once we have solver.prototxt and train_val.prototxt files ready, we can submit jobs using sqsub. The command is

sqsub -q gpu -f threaded -n 2 --gpp=1 --mpp=32g -r 4h -o test-submit-caffe.out $CAFFE_ROOT/build/tools/caffe.bin train \
    --solver=/work/feimao/imagenet/bvlc_alexnet/solver.prototxt

Caffe can use 2 CPU cores, one for controlling GPU, one for loading data. The size of memory (mpp) depends on the model size. 32 GB is recommended as a smallest size to try. "gpp" should always be one because Caffe (master branch) supports only single GPU.

Building Caffe with cudnn v4(CUDA7.5) on Copper and Mosaic

How to build Caffe

If you'd like to build your own Caffe, you can simply get the newest Caffe from github (notice that you should stay in login node to have internet access):

[feimao@mos-login test-install-caffe]$ git clone https://github.com/BVLC/caffe.git

Then you go to mos1 (or cop1), load the modules:

module unload intel mkl openmpi cuda
module load intel/15.0.3
module load hdf/serial/5.1.8.11 
module load cuda/7.5.18 
module load python/intel/2.7.10

And export the environment paths to cudnn and caffe-libs folder:

export PATH=/opt/sharcnet/testing/caffe/caffe-libs/bin:/opt/sharcnet/testing/caffe/caffe-libs/include:/opt/sharcnet/testing/cudnn/cudnn4:$PATH
export LD_LIBRARY_PATH=/opt/sharcnet/testing/caffe/caffe-libs/lib:/opt/sharcnet/testing/cudnn/cudnn4:$LD_LIBRARY_PATH
export PYTHONPATH=/opt/sharcnet/testing/caffe/caffe-libs/lib/python2.7/site-packages:$PYTHONPATH

and go to the Caffe folder and copy the Makefile.config.example to Makefile.config:

[feimao@mos1 caffe]$ cp Makefile.config.example Makefile.config

Then modify the Makefile.config:

• 1, uncomment

USE_CUDNN := 1

• 2, uncomment

CUSTOM_CXX := g++

• 3, change CUDA_DIR to

CUDA_DIR := /opt/sharcnet/cuda/7.5.18

• 4, If on Copper, add a line "-gencode arch=compute_37,code=sm_37" to CUDA_ARCH
• 5, change BLAS to

BLAS := mkl

• 6, uncomment BLAS_INCLUDE, and change to

BLAS_INCLUDE := /opt/sharcnet/intel/15.0.3/mkl/include

• 7, uncomment BLAS_LIB, and change to

BLAS_LIB := /opt/sharcnet/intel/15.0.3/mkl/lib/intel64

• 8, change PYTHON_INCLUDE to

PYTHON_INCLUDE := /opt/sharcnet/python/2.7.10/intel/include \
                /opt/sharcnet/python/2.7.10/intel/include/python2.7 \
               /opt/sharcnet/python/2.7.10/intel/lib/python2.7/site-packages/numpy/core/

• 9, change PYTHON_LIB to

PYTHON_LIB := /opt/sharcnet/python/2.7.10/intel/lib

• 10, change INCLUDE_DIRS to

INCLUDE_DIRS := $(PYTHON_INCLUDE) /opt/sharcnet/hdf/5.1.8.11/serial/include /opt/sharcnet/testing/caffe/caffe-libs/include /opt/sharcnet/testing/cudnn/cudnn4 /usr/local/include

• 10, change LIBRARY_DIRS to

LIBRARY_DIRS := $(PYTHON_LIB) /opt/sharcnet/hdf/5.1.8.11/serial/lib /opt/sharcnet/testing/caffe/caffe-libs/lib /opt/sharcnet/testing/cudnn/cudnn4 /usr/local/lib /usr/lib

• 11, uncomment ALLOW_LMDB_NOLOCK if you need to run multiply jobs on the same date set at a same time.

ALLOW_LMDB_NOLOCK := 1

• 12, run command to build Caffe and pycaffe:

make all -j16 && make test -j16 && make pycaffe

How to test the Caffe build

When the "make all" and "make test" finish, you can run "make runtest" to test if you build Caffe properly. You may get errors related to "test_gradient_based_solver.cpp" or other numerical inconsistency between CPU and GPU. Those errors was caused by MKL's Conditional Numerical Reproducibility setting. To avoid these errors, you should set MKL_CBWR:

export MKL_CBWR=AUTO