Add Tensorflow and Digits

.gitignore

 BLAST/dbs
 BLAST/rat-ests
+digits/digits.img
 FreeSurfer/buckner_data
 FreeSurfer/buckner_data-tutorial_subjs.tar.gz
 FSL/intro

TensorFlow/benchmarks/README.md

+## TensorFlow Benchmark Example
+
+This example runs the TensorFlow benchmarks (for V1.8) on the Spartan GPGPU partition. By default, it uses ResNet, a batch size of 64, and a whole node (4 GPUS and 24 CPUs), but this can be varied as needed.
+
+As of 18 July 2018, this particular configuration was achieving about 730 images/second across 4 GPUs.
+
+Benchmark: https://www.tensorflow.org/performance/benchmarks
+
+Source: https://github.com/tensorflow/benchmarks/

benchmarks @ 3b90c14f

+Subproject commit 3b90c14fb2bf02ca5d27c188aee878663229a0a7

TensorFlow/benchmarks/tf_benchmarks.slurm

+#!/bin/bash
+#SBATCH --nodes 1
+#SBATCH --partition gpgpu
+#SBATCH --gres=gpu:p100:4
+#SBATCH --time 01:00:00
+#SBATCH --cpus-per-task=24
+
+module load Tensorflow/1.8.0-intel-2017.u2-GCC-6.2.0-CUDA9-Python-3.5.2-GPU
+
+cd benchmarks/scripts/tf_cnn_benchmarks
+
+python tf_cnn_benchmarks.py --num_gpus=4 --batch_size=64 --model=resnet50 --variable_update=parameter_server
+

TensorFlow/simple/README.md

+## TensorFlow Example
+
+This is a very simple example which shows how to use TensorFlow with the Spartan GPGPU partition. It requests a single CPU and NVidia P100 GPU,  multiplies together two small matrices on the GPU, and prints the result. It will also print a little debug info showing that the calculation is being performed on the GPU (rather than CPU).
+
+It can be submitted with the command `sbatch tensor_flow.slurm`.
+
+You'll need access to the GPGPU partition before this example will work, see https://dashboard.hpc.unimelb.edu.au/gpu/ for details.
+
+N.B. If you belong to multiple projects, and the default one doesn't have access to the gpgpu partition, you might have to explictly specify the project with `sbatch -A <project name> tensor_flow.slurm`.
+
+This example is based on: https://www.tensorflow.org/guide/using_gpu

TensorFlow/simple/tensor_flow.py

+# Based on https://www.tensorflow.org/guide/using_gpu
+
+import tensorflow as tf
+
+# Creates a graph -- force it to run on the GPU
+with tf.device('/gpu:0'):
+  a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
+  b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
+c = tf.matmul(a, b)
+
+# Creates a session with log_device_placement set to True.
+sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
+
+# Runs the op.
+print(sess.run(c))
+

TensorFlow/simple/tensor_flow.slurm

+#!/bin/bash
+#SBATCH --nodes 1
+#SBATCH --partition gpgpu
+#SBATCH --gres=gpu:p100:1
+#SBATCH --time 00:05:00
+#SBATCH --cpus-per-task=1
+
+module load Tensorflow/1.8.0-intel-2017.u2-GCC-6.2.0-CUDA9-Python-3.5.2-GPU
+
+python tensor_flow.py

digits/README

+## DIGITS Spartan Example
+
+DIGITS is a deep-learning package from Nvidia with a web-based GUI. This example shows you how to run it on Spartan.
+
+1. As DIGITS makes use of GPUs, you'll first need access to our GPGPU partition. See: https://dashboard.hpc.unimelb.edu.au/gpu/
+
+2. Submit the job using `sbatch digits.slurm`. The example uses a whole node with 4 GPUs, with a wall time of 2 hours, but you can adjust to suit your needs.
+
+3. Check the job status using `squeue -u your_username`. Once it starts (which might take some time if the queue is busy), take note of the node your job is running on, e.g. `spartan-gpgpu025`
+
+4. As this is an interactive web application, we need to open a tunnel to the compute node so we can interact with it. You can do this with: `ssh -vNL 5000:spartan-gpgpu025:5000 your_username@spartan.hpc.unimelb.edu.au`
+
+5. Navigate to `localhost:5000` in your browser, and start playing with DIGITS. 
+
+N.B. DIGITS is running in a container, which means that the filesystem available to DIGITS will vary from that of the host (i.e. Spartan). Your home directory (i.e. `/home/your_username`) is mapped across however, so you can access your training data and models from there.
+
+

digits/digits.slurm

+#!/bin/bash
+#SBATCH --nodes 1
+#SBATCH --cpus-per-task=12
+#SBATCH --partition gpgpu
+#SBATCH --gres=gpu:4
+#SBATCH --time  02:00:00
+
+module load Singularity
+
+singularity exec --nv -B /tmp:/jobs -B /tmp:/scratch digits.img bash -c "export DIGITS_JOBS_DIR=/jobs && python -m digits"
+