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+# Tensorflow
+
+TensorFlow is a free end-to-end open-source software library for dataflow and differentiable programming across many tasks. It is a symbolic math library, used primarily for machine learning applications. It has a comprehensive, flexible ecosystem of tools, libraries and community resources.
+
+On taurus, Tensorflow 2 is the default module version. Please check \<a href="SoftwareModulesList" target="\_blank">
+the software modules list\</a> for information about available modules or use
+
+    module spider TensorFlow
+
+For compatibility hints between TF2 and TF1, see here.
+
+We recommend using **Alpha** and/or **ML** partitions when working with machine learning workflows and the Tensorflow library. For more details see here. You can find detailed hardware specification here.
+
+## Tensorflow Console
+
+On the **ML** partition load the module environment:
+    
+    module load modenv/ml
+
+On the **Alpha** partition load the module environment:
+    
+    module load modenv/scs5
+
+This example shows how to install and start working with TensorFlow (with using modules system)
+
+    srun -p ml --gres=gpu:1 -n 1 -c 7 --pty --mem-per-cpu=8000 bash   #Job submission in ml nodes with 1 gpu on 1 node with 8000 mb.
+    module load modenv/ml                    #example output: The following have been reloaded with a version change:  1) modenv/scs5 => modenv/ml
+    module load TensorFlow                   #load TensorFlow module. Example output: Module TensorFlow/1.10.0-PythonAnaconda-3.6 and 1 dependency loaded.
+
+Now we check that we can access Tensorflow. One example is tensorflow-test:
+    
+    tauruslogin6 :~> srun -p ml --gres=gpu:1 -n 1 --pty --mem-per-cpu=10000 bash
+    srun: job 4374195 queued and waiting for resources
+    srun: job 4374195 has been allocated resources
+    taurusml22 :~> module load TensorFlow/1.10.0-PythonAnaconda-3.6
+    taurusml22 :~> tensorflow-test
+    Basic test of tensorflow - A Hello World!!!...
+
+As another example we use a python virtual environment and import Tensorflow.
+
+    mkdir python-environments                #create folder 
+    which python                #check which python are you using
+    virtualenvv --system-site-packages python-environments/env   #create virtual environment "env" which inheriting with global site packages
+    source python-environments/env/bin/activate                  #Activate virtual environment "env". Example output: (env) bash-4.2$
+    python                                                       #start python
+    import tensorflow as tf
+    print(tf.VERSION)                                            #example output: 1.10.0
+
+## Tensorflow in JupyterHub
+In addition to using interactive and batch jobs, it is possible to work with Tensorflow using JupyterHub. The production and test environments of JupyterHub contain Python and R kernels, that both come with a Tensorflow support.
+
+![Tensorflow module in JupyterHub](misc/tensorflow_jupyter_module.png)
+{: align="center"}
+
+## Tensorflow in Containers
+Another option to use Tensorflow are containers. In the HPC domain, the [Singularity](https://singularity.hpcng.org/) container system is a widely used tool. In the following example, we use the tesnroflow-test in a Singularity container:
+
+    rotscher@tauruslogin6:~&gt; srun -p ml --gres=gpu:1 -n 1 -c 7 --pty --mem-per-cpu=8000 bash
+    [rotscher@taurusml22 ~]$ singularity shell --nv /scratch/singularity/powerai-1.5.3-all-ubuntu16.04-py3.img
+    Singularity powerai-1.5.3-all-ubuntu16.04-py3.img:~&gt; export PATH=/opt/anaconda3/bin:$PATH
+    Singularity powerai-1.5.3-all-ubuntu16.04-py3.img:~&gt; . /opt/DL/tensorflow/bin/tensorflow-activate
+    Singularity powerai-1.5.3-all-ubuntu16.04-py3.img:~&gt; tensorflow-test
+
+
+## Tensorflow with Python or R
+For further information on Tensorflow in combination with Python see [here](data_analytics_with_python.md), for R see [here](data_analytics_with_r.md).
+
+## Compatibility TF2 and TF1
+TensorFlow 2.0 includes many API changes, such as reordering arguments, renaming symbols, and changing default values for parameters. Thus in some cases, it makes code written for the TensorFlow 1 not compatible with TensorFlow 2. However, If you are using the high-level APIs (tf.keras) there may be little or no action you need to take to make your code fully [TensorFlow 2.0](https://www.tensorflow.org/guide/migrate) compatible. It is still possible to run 1.X code, unmodified (except for contrib), in TensorFlow 2.0:
+    
+    import tensorflow.compat.v1 as tf
+    tf.disable_v2_behavior()                                  #instead of "import tensorflow as tf"
+
+To make the transition to TF 2.0 as seamless as possible, the TensorFlow team has created the tf_upgrade_v2 utility to help transition legacy code to the new API.
+
+
+## Additional libraires
+
+The following NVIDIA libraries are available on all nodes:
+
+|       |                                       |
+|-------|---------------------------------------|
+| NCCL  | /usr/local/cuda/targets/ppc64le-linux |
+| cuDNN | /usr/local/cuda/targets/ppc64le-linux |
+
+Note: For optimal NCCL performance it is recommended to set the
+**NCCL_MIN_NRINGS** environment variable during execution. You can try
+different values but 4 should be a pretty good starting point.
+
+    export NCCL_MIN_NRINGS=4
+