TJ CSL
  • TJ CSL
  • Services
    • Ion
      • Development
        • Overview
        • Setup
          • Docker Setup
          • Vagrant Setup
        • Environment
        • Fixtures
        • PR Workflow
        • Style Guide
        • Maintainer Workflow
        • Repository Maintenance
        • Data Generation
      • Production
      • User Experience
        • User Interface
    • Director
      • Development
        • Vagrant Setup
        • PR Workflow
        • Style Guide
        • Maintainer Workflow
      • Production
    • Workstations
    • Signage
      • Setup
      • Administration
      • Monitoring
      • Troubleshooting
      • Experimental
        • IonTap
        • SignageAdmin
    • Remote Access
      • Setup
      • Administration
    • Cluster
      • FAQ
      • Setup
        • SSH Setup
      • Administration
      • Slurm
      • Slurm Administration
      • Borg
    • Printing
      • Setup
      • Troubleshooting
    • WWW
      • Administration
      • Sites
        • Web Proxy
      • Setup
      • Troubleshooting
    • Academic Services
      • Tin
      • Othello
        • Administration
        • Setup
  • Technologies
    • Web
      • Nginx
      • Django
      • PHP-FPM
      • Node.js
      • Supervisord
    • DBs
      • PostgreSQL
      • MySQL
    • Authentication
      • Passcard
        • GPG Usage
      • SSHD
        • SSH Passwordless Login
      • FreeIPA
    • Storage
      • NFS
      • Ceph
        • Setup
        • Backups
        • CephFS
    • Operating Systems
      • Ubuntu Server
      • AlmaLinux
      • Debian
    • Tools
      • Ansible
      • Slack
      • GitBook
      • GitLab
        • Setup
        • Updating
    • Virtualization
      • QEMU/KVM
      • Libvirt
    • Advanced Computing
      • MPI
      • Tensorflow
    • Networking
      • Netbox
      • Cisco
      • Netboot
      • DNS
      • DHCP
      • NTP
      • BGP
    • Mail
      • Postfix
      • Dovecot
    • Monitoring
      • Prometheus
      • Grafana
      • Sentry
      • Uptime Robot
  • Machines
    • VM Servers
      • Utonium
      • Blossom
      • Bubbles
      • Buttercup
      • Antipodes
      • Chatham
      • Cocos
      • Galapagos
      • Gandalf
      • Gorgona
      • Overlord
      • Waverider
      • Torch
    • Ceph
      • Karel
      • Stobar
      • Wumpus
      • Waitaha
      • Barrel
      • Valdes
    • HPC Cluster
      • Zoidberg
    • Borg Cluster
    • Compute Sticks
    • Other
      • ASM
      • Duke
      • Snowy
      • Sauron
      • Sun Servers
        • Altair
        • Centauri
        • Deneb
        • Sirius
        • Vega
        • Betelgeuse
        • Ohare
    • Switches
      • Core0
      • Xnor
      • Xor
      • Imply
    • UPS
    • History
      • 2008 Sun AEG
      • 2011 Sun Upgrades
      • 2017 VM Disaster
      • 2018 Purchases
      • 2018 Cephpocalypse
    • VLANs
    • Remote Management
      • iLO
      • LOMs
    • Understudy
      • Switch Configuration
      • Server Configuration
        • Setting Up the Operating System
        • Network Configuration
        • Saruman
        • Fiordland
  • General
    • Sysadmins List
    • Organization
    • Documentation
      • Security
      • Runbooks
    • Communication
      • Terminology
    • Understudies
    • Account Structure
    • Machine Room
    • Branding
    • History
      • Fridge
      • The Brick
  • Procedures
    • Data Recovery
    • Account Provisioning
    • tjSTAR
      • Tech Support
    • Onboarding
      • New Sysadmin Onboarding
  • Guides
    • VM Creation
    • sshuttle Usage
    • Linux Wifi Setup
    • VNC Usage
    • Password Changes
    • Sun Server RAID Configuration
  • Policies
    • Data Release Policy
    • Upgrade Policy
    • Account Policy
    • Election Policy
  • Obsolete
    • Arcturus
    • Chuku
    • Cray SV1 Supercomputer
    • Ekhi
    • Mihr
    • Moloch
    • Sol
    • Rockhopper
    • Kerberos
    • LDAP
    • Agni
    • Moon
    • Apocalypse
    • AFS
      • OpenAFS
      • Setup
      • Client Setup
      • Administration
      • Troubleshooting
      • Directory Structure
      • Backups
      • Cross-Cell Authentication
    • Observium
    • OpenVPN
Powered by GitBook
On this page
  • Which Machines
  • Example Program
  1. Technologies
  2. Advanced Computing

Tensorflow

PreviousMPINextNetworking

Last updated 6 years ago

Tensorflow is a powerful compute-graph based software allowing high-performance neural networks to be constructed in Python. It's specific strength is in seamless GPU execution.

Which Machines

  • GPU-enabled nodes in the

Only Duke and Zoidberg are publicly accessible. For the rest of the machines, you must either use (for the Borg Cluster) or get a custom login from the Sysadmin in charge of the machine.

Example Program

'''
A linear regression learning algorithm example using TensorFlow library.
Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''

from __future__ import print_function

import tensorflow as tf
import numpy
import matplotlib.pyplot as plt
rng = numpy.random

# Parameters
learning_rate = 0.01
training_epochs = 1000
display_step = 50

# Training Data
train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples = train_X.shape[0]

# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")

# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")

# Construct a linear model
pred = tf.add(tf.multiply(X, W), b)

# Mean squared error
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
# Gradient descent
#  Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()

# Start training
with tf.Session() as sess:

    # Run the initializer
    sess.run(init)

    # Fit all training data
    for epoch in range(training_epochs):
        for (x, y) in zip(train_X, train_Y):
            sess.run(optimizer, feed_dict={X: x, Y: y})

        # Display logs per epoch step
        if (epoch+1) % display_step == 0:
            c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
                "W=", sess.run(W), "b=", sess.run(b))

    print("Optimization Finished!")
    training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
    print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')

    # Graphic display
    plt.plot(train_X, train_Y, 'ro', label='Original data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    plt.show()

    # Testing example, as requested (Issue #2)
    test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
    test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])

    print("Testing... (Mean square loss Comparison)")
    testing_cost = sess.run(
        tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]),
        feed_dict={X: test_X, Y: test_Y})  # same function as cost above
    print("Testing cost=", testing_cost)
    print("Absolute mean square loss difference:", abs(
        training_cost - testing_cost))

    plt.plot(test_X, test_Y, 'bo', label='Testing data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    plt.show()
ASM
Duke
Snowy
Zoidberg
Borg Cluster
Slurm