Multi-GPU mini-framework for Theano
Experimental multi-GPU mini-framework for Theano
It supports data-parallelism inside one compute node, not model-parallelism. For model-parallelism check Theano multiple GPUs tutorial.
In Platoon, there are two main components : workers, and controllers. Workers do the bulk of the work (training, monitoring, ...). Controllers interact with multiple workers to coordinate their work, collect the results and decide how to act on them. To use Platoon, you will need to write code which uses a worker. You can also extend the functionality of a worker or a controller by implementing your own. Platoon provides helper classes to facilitate this.
This framework is under development. Its interface is not polished and it is likely to undergo changes in the future.
The framework provides two separate worker interfaces that allow user to implement multiple data-parallel algorithms: param_sync and all_reduce. The default interface is param_sync. Installing optional dependencies listed in the features table below will make all_reduce interface available too.
|sync type||multi-node||Theano Ops||extra dependencies|
|allreduce||sync only||yes (if mpi4py is installed)||yes||NCCL, pygpu, Theano|
There are currently two algorithms for distributed gradient descent implemented with param_sync interface and three with all_reduce interface.
There are working examples in the examples directory.
The steps below describe what needs to be done to use Platoon for data-parallelism. The LSTM example in the folder 'example' was implemented following these steps and should be referred to for guidance.
You can simply install it using pip.
pip install git+https://github.com/mila-udem/platoon
If you would like to use the examples or help develop platoon first you have to clone the repo.
git clone https://github.com/mila-udem/platoon
Then install what you just cloned.
pip install -e
The simplest way to launch a multi-gpu experiment is to first implement a controller and a worker as described below and then launch it using the
platoon-launcher. It is not necessary that you have implemented a controller file if you want to use the existing controller functionality.
The launcher assume that you named both files as such:
Then to launch the experiment you just need to specify the experiment name and GPUs you want to use:
platoon-launcher -D gpu0 gpu1
You can also omit the
-Dargument and let launcher find all available CUDA GPUs to use in the single-node experiment:
For more configuration options, see
These steps describe how to implement the Python script that will launch your controller. In the included LSTM example, both of these steps are done in the file
1) Define which commands your controller can receive and how it responds to them. Commands starting by "platoon-" are reserved by platoon.
This is done by creating a new class that inherits from channel.Controller and having it override the method
handle_control()which will be called whenever your controller receives a request from a worker.
2) Instantiate and launch your custom controller.
Create a script that will instantiate your custom controller. Once this is done, define the port on which the controller should listen by calling the function
init_control. Finally, call your controller's
servemethod which will make him ready to receive requests from workers.
These steps describe how to start with a script that performs stand-alone training of a machine learning model and adapt it to serve as a worker in Platoon.
1) Add a new parameter to the script which will be used during execution to know whether the worker is the first one to be launched and should create the central parameters or not.
2) Before entering the main loop, the script must create an instance of the class channel.Worker, providing it with the same port number as used to initialize the controller. It is not necessary to sub-class Worker, you can instantiate it directly. This object will provide the necessary methods to handle communication with the controller.
3) After the model has been built and the parameters initialized, initialize the central parameters by calling the Worker's
init_shared_params()method. Every worker should call this method.
4) In the main loop, instead of deciding when to train and when to monitor performance, the worker should send control request to the controller to know what action it should take, according to the communication protocol established in the controller's
5) In the main loop, whenever the worker has performed
N(a hyper-parameter) iterations of training, it should synchronize it's parameters with the central parameters using it's Worker's
The optimal (as in more efficient for learning) hyper-parameters values are dependent on the number of workers. At least, consider tuning the learning rate and the alpha parameter of EASGD.
How to change the alpha hyper-parameter isn't clear. An alpha of 0.5 for the LSTM example with 2 workers seem to have good training efficiency for this model/dataset/hyper-parameter combination.
Using alpha = 1/N (with N being the number of workers) might be a reasonable guideline but the experiments performed with Platoon are insufficient to conclude anything.
In the EASGD paper it is shown that in some cases a larger number of workers can result in a better test error.
For param sync interface, see
For all reduce interface, see