TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Development tools and environments
tensorflow-1.4.1-gpu tensorflow-1.3.0-cpu tensorflow-1.3.0-cpu-python3 tensorflow-1.3.0-gpu tensorflow-1.3.0-gpu-python3 tensorflow-1.0.1-gpu tensorflow-1.0.1-gpu-python3 tensorflow-1.0.1-cpu tensorflow-1.0.1-cpu-python3 tensorflow-0.8.0-gpu tensorflow-0.8.0-cpu tensorflow-0.10.0-gpu tensorflow-0.10.0-gpu-python3 tensorflow-0.10.0-cpu tensorflow-0.10.0-cpu-python3
We have both CPU versions and GPU versions.
- CPU versions of TensorFlow can be run on any node.
- GPU versions of TensorFlow are based on the Cuda (Nvidia) library and CuDNN library and because of this can be only run on doom and zubat GPU clusters (GPU clusters konos and gram do not support CuDNN-based applications). Please add
gpu_cap=cuda35to your qsub command. You also have to accept the CuDNN license by clicking here, otherwise the GPU versions of TensorFlow will NOT work for you.
module add tensorflow-1.0.1-cpu (for CPU version of TensorFlow 1.0.1 in Python 2.7.6) module add tensorflow-1.0.1-cpu-python3 (for CPU version of TensorFlow 1.0.1 in Python 3.4.1) module add tensorflow-1.0.1-gpu (for GPU version of TensorFlow 1.0.1 in Python 2.7.6) module add tensorflow-1.0.1-gpu-python3 (for GPU version of TensorFlow 1.0.1 in Python 3.4.1)
Once you add the module that best suits your needs, the use is as simple as running python and importing the tensorflow module...
python >>> import tensorflow as tf >>> (etc.)