Lab Computing Servers:

• image: 6-core CPU (i7-6800K) with 128GB memory + 4 GPUs (4 x GTX 1080, 8GB memory each)

• text: 6-core CPU (i7-5820K) with 64GB memory + 4 GPUs (4 x TITAN X, 12GB memory each)

• video: 6-core CPU (Xeon E5-1650) with 64GB memory + 3 GPUs (1 x Tesla K40c* + 2 x TITAN X, 12GB memory each)

• language: 6-core CPU (Xeon E5-2620) with 64GB memory + 2 GPUs (2 x TITAN X, 12GB memory each)

• music: 6-core CPU (Xeon E5-1660) with 64GB memory + 2 GPUs (2 x TITAN X, 12GB each)

• voice: 6-core CPU (Xeon E5-2620) with 64GB memory + 2 GPUs (GTX 1080 + GTX 1070, 8GB memory each)

• audio: 4-core CPU (i7-950) with 16GB memory + 2 GPUs (2 x GTX1070, 8GB memory each)

• speech1: 4-core CPU (i7-6700) with 16G memory

• speech2: 4-core CPU (i7-6700) with 16G memory + 1 GPU (GTX 1070 8GB)

• speech3: 4-core CPU (i7-6700) with 16G memory + 2 GPU (GTX 1070 8GB + GTX 780 Ti 3GB memory each)

• speech4: 4-core CPU (i7-6700) with 16G memory + 2 GPUs (GTX 1070 8GB memory each)

• speech5: 4-core CPU (i7-6700) with 16G memory + 2 GPUs (GTX 1070 8GB memory each)

• speech6: 4-core CPU (i7-6700) with 16G memory + 2 GPUs (GTX 1070 8GB memory each)

• speech7: 4-core CPU (i7-6700) with 16G memory + 2 GPUs (2 x GTX 1070 8GB memory each)

Check the GPU assignments before you submit your jobs to GPUs. Check here for how to check the workloads. Never overload any machines. How to check the workload? .

Storage Space:

• The shared network disk (with backup) is located at /cs/research/asr.

• Each of the above machines has a local disk space (several TB's each) at /local (without any backup). In addition, more local disk space can be accessed from Linux directory /tmp.
  1. text: /local/scratch (6 TB)
  2. video: /local/scratch (6 TB); /local/scratch1 (6 TB)
  3. music: /local/scratch (2 TB); /local/scratch1 (6 TB)
  4. language: /local/scratch (1 TB)
  5. voice: /local/scratch (1 TB); /local/scratch1 (2 TB)
  6. audio: /local/scratch (1 TB)
  7. speech1 to speech7: /local/scratch (500 GB each).

Software:

• TensorFlow: see here (provided by Mingbin) for how to configure TensureFlow. Alternative, you may configure TensorFlow as here (provided by Chao).
• TensorFlow Tutorials:
  1. Tensorflow and deep learning - without a PhD by Martin Görner
  2. TensorFlow Tutorial (Sherry Moore, Google Brain)
  3. RNNs in Tensorflow, a Practical Guide and Undocumented Features – WildML
  4. Tfrecords Guide

* Acknowledgement: the Tesla graphical card was donated by NVIDIA Inc.