All of our provided environments have Jupyter installed, so you can choose any of them, besides the base environment. You can find the available environments by using the conda info –envs command. We suggest using the latest TensorFlow environment (currently, tf2_4).

You do not need to install Jupyter for yourself in order to do the secure setup step, or use it afterwards. Loading any provided environment will work in the secure setup step, and the completed procedure will allow you to use any of the other environments with Juypter installed.

For a quick glance at the software that is currently provided in our machine learning environments, see Machine Learning: Overview.

You can also complete these steps to see the installed environments:

1. Load the miniconda3 module:

   % module use -a /swbuild/analytix/tools/modulefiles
   % module load miniconda3/v4 

2. Run the following command to see the available environments:

   % conda info --envs

You cannot directly run a Jupyter Notebook without using the web-based user interface. However, you can convert your notebook into a Python script and run the script though the regular Python shell command.

To convert a notebook into a Python script:

% jupyter nbconvert --to script notebook.ipynb

This will create a .py file of the specified notebook, with each cell run in sequential order.

Jupyter Notebook, an open-source application based on Interactive Python, is a useful tool for interactively exploring science data. You cannot run Jupyter on a front-end system (such as a PFE); instead, you must run it on a compute note dedicated to you alone.

To learn more about using Jupyter Notebook on NAS systems, see Using Jupyter Notebook for Machine Learning Development.

Note: A one-time security setup is required before you can begin using Jupyter Notebook. 

To learn how to run multiple instances of a Python code on the compute nodes, with each instance running your code, see Using GNU Parallel to Package Multiple Jobs (Instances) in a Single PBS Job.

To use multiple nodes, you can use TensorFlow's "MultiWorkerMirroredStrategy" variable, which is described in their article Distributed Training with TensorFlow.

To call the strategy in your code, use:

if dist_nodes :
     strategy=tf.distribute.experimental.MultiWorkerMirroredStrategy ( 
             tf.distribute.experimental.CollectiveCommunication.AUTO )
   else :
     strategy = tf.distribute.MirroredStrategy()

The collective operations include the following options:

CollectiveCommunication.RING

Implements ring-based collectives using gRPC as the communication layer.

CollectiveCommunication.NCCL

Uses NVIDIA's NCCL to implement collectives.

CollectiveCommunication.AUTO

Defers the choice to the runtime.

You also need to set up the TF_CONFIG environment variable, which is required by TensorFlow:

if dist_nodes :
    index = int(sys.argv[1])  # $@ has n indices indicating n nodes
    verbose = index < 1       # verbose is True only when index = 0
    nodes=[]
    # node names for each node, append a port # to the names
    for i in range(2,len(sys.argv)):
      nodes.append(sys.argv[i] + ':2001')
    # set TF_CONFIG variable that MultiWorkerMirroredStrategy needs
    os.environ['TF_CONFIG'] = json.dumps({
        'cluster': {
            'worker': nodes
        },
        'task': {'type': 'worker', 'index': index}
    })
    print(os.environ['TF_CONFIG'])
  ##

For more details about TensorFlow programming and an example of how to use the above methodology, please see Multiple CPU Nodes and Training in TensorFlow.