Grid currently has three modes.
--tree -- experimental
Tree is the federated learning mode. The long term goal is to have node workers store data locally, and train public models to make them better.
--compute
Compute mode lets users offer compute to train models. Data scientists can easily publish models from a jupyter notebook and train them remotely. Grid also offers easy utilities to try n number of configurations concurrently.
--anchor
Helps clients find workers in tree or compute mode.
It is recommended you install Anaconda prior to starting the installation. The installation page for your operating system can be found here.
If you just want to install python3 and pip3 this can be done through brew on MacOS or apt on Ubuntu LTS 16.04.
MacOS:
brew install python3
Ubuntu LTS 16.04:
apt install python3
apt install python-pip3
If you want to use an Anaconda environment, make sure you've created and activated it before installing anything else, e.g.:
conda create -n openmined python=3.6 anaconda -y
source activate openmined #(or conda activate openmined, depending on your version of conda)
Grid requires PyTorch. Follow the instructions at https://pytorch.org to install the correct build for your platform/environment.
Finally, navigate to the local clone of the Grid repository and run python setup.py install. If you're going to be developing Grid, you can replace that with python setup.py develop.
Grid worker can now start the ipfs daemon for you, but if you want to start it manually it can be with:
start_ipfs
You can then run the worker daemon. If you want to run in compute mode run:
start_worker --compute
and if you want to run in tree mode, run:
start_worker --tree
If you have GPU and want to use them, you will need to have nvidia-docker2. Make sure you can see your gpus using
docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi
The main scrip to launch the grid is run_grid.sh. It has several flags
Usage: ./run_grid.sh [-g <gpu>] [-m <grid-mode>] [-a <host_ip>] [-n <name>] [-e <email>]
For instance, if you want to run a worker in tree mode with an aws instance with gpus on it whose ip is 10.34.23.45, then you can run
./run_grid.sh -g -m tree -a 10.34.23.45 -n jack -e jack@aws.com
If you have only a laptop without gpus in it, but you want to run an anchor because, why not
./run_grid.sh -m anchor -a 1.34.23.45 -n jack -e jack@laptop.com
If running those commands doesn't work make sure the location of the installed script is in your PATH. This can be done by adding a the following similar line to your '~/.bash_profile' file:
export PATH=$PATH:/anaconda3/bin
/anaconda3/ might need to be replace by the actual install location if not being used with anaconda or you installed anaconda locally for the current user.
And then running:
source ~/.bash_profile
If you have any troubles running an experiment such as the other peers not learning about your jobs make sure you're connected to the peer. You can check if you're connected to the peer by running:
ipfs pubsub peers | grep <ipfs address>
And then to connect to the peer if you're not connected:
ipfs swarm connect <ipfs_address>
The swarm connect IPFS address should look something like this /p2p-circuit/ipfs/QmXbV8HZwKYkkWAAZr1aYT3nRMCUnpp68KaxP3vccirUc8. And can be found in the output of the daemon when you start it.
TODO make this automatic
Before submitting a PR to this repo make sure the following packages are installed with pip:
pip install pycodestyle yapf pytest
First run yapf:
yapf --recursive --in-place .
Then run pycodestyle:
pycodestyle --max-line-length=130 --exclude=.ropeproject,ipfsapi,build,.eggs .
We still have some issues with long lines and bare excepts so those can be ignored for now. But everything else should be cleaned up.
To run the integration tests you MUST start up a local worker first (this is unfortunate see issue OpenMined#146)
Then run the tests:
pytest -s
If everything looks good to you, submit the PR.