Omnivore Optimizer and Distributed CcT
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README.md

Omnivore Optimizer and Distributed CcT

This is our Omnivore optimizer implemented for distributed CaffeConTroll

See our paper Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs

Compilation

Download the repository as well as the CaffeConTroll submodule

  • git clone https://github.com/HazyResearch/Omnivore.git
  • cd Omnivore
  • git submodule update --init

Install the following dependencies

  • zeromq
  • libconfig++
  • Python library: Protobuf 3.0.0, e.g. using the command pip install protobuf==3.0.0a3 (the C++ library still needs to be installed below)
  • Python library: LMDB, e.g. using the command pip install lmdb (the C++ library still needs to be installed below)

Also install the dependencies for the CaffeConTroll submodule (follow steps 1 and 3 here)

Compile dcct (the distributed CcT system)

make -j
cd tools/size_util/; make -j; cd -

This will produce a binary dcct.

Setup

Create a file containing each machine in your cluster, one per line

  • The machine name should be the same as the machine you would ssh into
  • For example if you would normally do: ssh root@node012, then your machine list would contain node012
  • See an example here

Create a solver and train prototxt file (same input as Caffe/CaffeConTroll)

Edit the top of the file run.py, located in the root directory, to update the script parameters:

  • Edit the line user = 'root' in the SSH parameters section to add the right username for ssh
    • For the example above (ssh root@node012), the username put on that line would be root
  • Edit the line extra_cmd = ... in the SSH parameters section to add any lines needed between when you first ssh into your machine and when you are in the Omnivore directory about to run ./omnivore.py (usually these are cd commands)

Edit the top of the file omnivore.py, located in the root directory, to update the script parameters:

  • Edit the line base_dir = to a directory name which will hold the output of your next optmizer run (e.g. base_dir = /home/Omnivore/imgnet1k_resnet)
  • Edit the line hw_type = to the type of hardware on each node, either CPU, GPU or 4GPU
  • The first time you run omnivore.py on a new dataset and cluster, edit the line MAKE_LMDB_FIRST = False to make it True. This will partition the LMDB before the first optimizer run. Once this happens the first time, set MAKE_LMDB_FIRST = False so that future runs of omnivore.py do not need to repeat this step. If the number of machines changes, partitions need to be recreated.

Usage

Run:

  • ./omnivore.py path/to/config.file

Example

  • ./omnivore.py example/imgnet_1000.config

Observing the output:

  • Sub-directories will be made for each optimizer run: first the cold start run, COLD, followed by each optimizer run, OPT1, OPT2, etc. Each of these runs consists of exploratory phases followed by the decision (final choice) for that run.
  • Within each sub-directory, a number of configuration files will be created in directories called something like server_input_files-2015-11-12-05-25-26
  • The output of the network that is training will be written to the file fc_server.cfg.out inside this directory

Advanced

  • omnivore.py runs the full optimizer. Individual dcct runs can be run using run.py