Choosing an Image

Specific Deep Learning VM images are available to suit your choice of framework and processor.

Available images

Framework Processor Image Name(s)
TensorFlow GPU tf-latest-gpu
tf-VERSION-CUDA_VERSION (e.g. tf-1-11-cu100)
CPU tf-latest-cpu
tf-VERSION-cpu (e.g. tf-1-10-cpu)
PyTorch GPU pytorch-latest-cu92
pytorch-VERSION-cu92 (e.g. pytorch-0-4-cu92)
CPU pytorch-latest-cpu
pytorch-VERSION-cpu (e.g. pytorch-0-4-cpu)
PyTorch 1.0RC GPU pytorch-1-0-cu92-experimental
CPU pytorch-1-0-cpu-experimental
Base GPU common-cu100
common-cu92
common-cu91
common-cu90
CPU common-cpu
Chainer GPU chainer-latest-cu92-experimental
chainer-VERSION-cu92-experimental (e.g. chainer-4-4-cu92-experimental)
CPU chainer-latest-cpu-experimental
chainer-VERSION-cpu-experimental (e.g. chainer-4-4-cpu-experimental)
XGBoost GPU xgboost-latest-cu92-experimental
xgboost-VERSION-cu92-experimental (e.g. xgboost-0-80-cu92-experimental)
CPU xgboost-latest-cpu-experimental
xgboost-VERSION-cpu-experimental (e.g. xgboost-0-80-cpu-experimental)

Pre-installed packages

All images are based on Debian 9 "Stretch".

They include:

  • TensorFlow or PyTorch or Chainer or XGBoost
  • CUDA 9.0 or 9.1 or 9.2 or 10.0 (GPU only; version depends on framework)
  • CuDNN 7.3 (GPU only)
  • NCCL 2.3.*
  • Python (2.7 and 3.5) with the following packages:
    • numpy
    • scipy
    • matplotlib
    • pandas
    • jupyter notebook/lab
    • nltk
    • Pillow
    • scikit-image
    • Opencv-python
    • sklearn
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Deep Learning VM