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Environments

Below is the list of Deep Learning environments supported by FloydHub. Any of these can be specified in the floyd run command using the --env option.

If no --env is provided, it uses the keras image by default, which comes with Python 3, Keras 2.0.4 and Tensorflow 1.1.0 pre-installed.

Framework Env name (--env parameter) Description Docker Image
Keras keras Tensorflow 1.1.0 + keras 2.0.6 on Python3.5.
keras:py2 Tensorflow 1.1.0 + keras 2.0.6 on Python2.
Tensorflow 1.4 tensorflow-1.4 Tensorflow 1.4.0 + Keras 2.0.8 on Python3.6. floydhub/tensorflow
tensorflow-1.4:py2 Tensorflow 1.4.0 + Keras 2.0.8 on Python2. floydhub/tensorflow
Tensorflow 1.3 tensorflow-1.3 Tensorflow 1.3.0 + Keras 2.0.6 on Python3.6. floydhub/tensorflow
tensorflow-1.3:py2 Tensorflow 1.3.0 + Keras 2.0.6 on Python2. floydhub/tensorflow
Tensorflow 1.2 tensorflow-1.2 Tensorflow 1.2.0 + Keras 2.0.6 on Python3.5. floydhub/tensorflow
tensorflow-1.2:py2 Tensorflow 1.2.0 + Keras 2.0.6 on Python2. floydhub/tensorflow
Tensorflow 1.1 tensorflow Tensorflow 1.1.0 + Keras 2.0.6 on Python3.5. floydhub/tensorflow
tensorflow:py2 Tensorflow 1.1.0 + Keras 2.0.6 on Python2. floydhub/tensorflow
Tensorflow 1.0 tensorflow-1.0 Tensorflow 1.0.0 + Keras 2.0.6 on Python3.5. floydhub/tensorflow
tensorflow-1.0:py2 Tensorflow 1.0.0 + Keras 2.0.6 on Python2. floydhub/tensorflow
Tensorflow 0.12 tensorflow-0.12 Tensorflow 0.12.1 + Keras 1.2.2 on Python3.5. floydhub/tensorflow
tensorflow-0.12:py2 Tensorflow 0.12.1 + Keras 1.2.2 on Python2. floydhub/tensorflow
PyTorch 0.2 pytorch-0.2 PyTorch 0.2.0 on Python 3. floydhub/pytorch
pytorch-0.2:py2 PyTorch 0.2.0 on Python 2. floydhub/pytorch
PyTorch 0.1 pytorch-0.1 PyTorch 0.1.12 on Python 3. floydhub/pytorch
pytorch-0.1:py2 PyTorch 0.1.12 on Python 2. floydhub/pytorch
Theano 0.8 theano-0.8 Theano rel-0.8.2 + Keras 1.2.2 on Python3.5. floydhub/theano
theano-0.8:py2 Theano rel-0.8.2 + Keras 1.2.2 on Python2. floydhub/theano
Theano 0.9 theano-0.9 Theano rel-0.8.2 + Keras 2.0.3 on Python3.5. floydhub/theano
theano-0.9:py2 Theano rel-0.8.2 + Keras 2.0.3 on Python2. floydhub/theano
Caffe caffe Caffe rc4 on Python3.5. floydhub/caffe
caffe:py2 Caffe rc4 on Python2. floydhub/caffe
Torch torch Torch 7 with Python 3 env. floydhub/torch
torch:py2 Torch 7 with Python 2 env. floydhub/torch
Chainer 1.23 chainer-1.23 Chainer 1.23.0 on Python 3. floydhub/chainer
chainer-1.23:py2 Chainer 1.23.0 on Python 2. floydhub/chainer
Chainer 2.0 chainer-2.0 Chainer 1.23.0 on Python 3. floydhub/chainer
chainer-2.0:py2 Chainer 1.23.0 on Python 2. floydhub/chainer
MxNet (beta) mxnet:py2 MxNet 0.9.3a on Python 2. floydhub/mxnet
Kur kur Kur 0.3.0 on Python 3. floydhub/kur

All environments are available for both CPU and GPU execution. For example,

To run a Python2 Tensorflow job on CPU 

$ floyd run --env tensorflow:py2 "python mnist_cnn.py"

To run a Python2 Tensorflow job on GPU (CUDA, cuDNN, etc. installed) 

$ floyd run --env tensorflow:py2 --gpu "python mnist_cnn.py"

The following software packages (in addition to many other common libraries) are available in all the environments: 

h5py, iPython, Jupyter, matplotlib, numpy, OpenCV, Pandas, Pillow, scikit-learn, scipy, sklearn

Help make this document better

This guide, as well as the rest of our docs, are open-source and available on GitHub. We welcome your contributions.