Machine learning

A pre-configured and fully integrated software stack with TensorFlow, an open source software library for machine learning, Python 2.7, and Jupiter Notebook, a browser-based interactive notebook for programming, mathematics, and data science. The stack is designed for research and development tasks and optimized for running on NVidia GPU.

Constructor:

Usage

You can install the appliance on any new or existing Linux server, download and run it as a virtual machine, use it as a base image for Docker or Vagrant, or launch it with a new cloud platform instance, VPS or dedicated server for supported hosting providers.

Launch in clouds
Aws ec2 Google cloud platform Digital ocean Microsoft azure Ibm bluemix Oracle cloud platform
Install
Install on Linux

You can install the appliance directly on any Linux with 64-bit kernel (>=2.6.32). Run from the command line:

curl -L http://jetware.io/appliances/aise/tensorflow14_python2_cuda9_lab-180105/file/installer:nub_tgz/setup | sh

You’ll be asked to execute some operations as root via sudo during the installation.

How to use
Download archive

You can download the archive, unpack it into the /jet directory, finish installation by executing the command /jet/enter /jet/own/bin/fasten and start the services by running /jet/enter start.

machine_learning-1regzxyth11li.tar.gz
1.83 GB
Run a container
Alpine 3.8
Ubuntu 18.04
Debian 9
CentOS 7
Docker

Copy Dockerfile into your project’s directory:

FROM alpine:3.8
RUN apk add sudo libcap tar
RUN adduser -S -h /jet -s /jet/login jet

RUN { wget -O - "http://jetware.io/appliances/aise/tensorflow14_python2_cuda9_lab-180105/file/container:docker/setup" || echo exit 1; } | sh

WORKDIR /jet
USER jet
VOLUME ["/jet/prs"]
ENTRYPOINT ["/jet/login"]
CMD ["main"]

or build an image directly from the URL by executing the command:

docker build 'http://jetware.io/appliances/aise/tensorflow14_python2_cuda9_lab-180105/file/container:docker/Dockerfile?os=alpine_3.8'

Copy Dockerfile into your project’s directory:

FROM ubuntu:18.04
RUN \
  echo "tzdata tzdata/Areas select Etc" | debconf-set-selections; \
  echo "tzdata tzdata/Zones/Etc select Universal" | debconf-set-selections; \
  apt-get update && \
    DEBIAN_FRONTEND=noninteractive DEBCONF_NONINTERACTIVE_SEEN=true \
      apt-get install -y tzdata netbase sudo wget libcap2-bin
RUN useradd --system -m --home /jet --shell /jet/login jet

RUN { wget -O - "http://jetware.io/appliances/aise/tensorflow14_python2_cuda9_lab-180105/file/container:docker/setup" || echo exit 1; } | sh

WORKDIR /jet
USER jet
VOLUME ["/jet/prs"]
ENTRYPOINT ["/jet/login"]
CMD ["main"]

or build an image directly from the URL by executing the command:

docker build 'http://jetware.io/appliances/aise/tensorflow14_python2_cuda9_lab-180105/file/container:docker/Dockerfile?os=ubuntu_18.04'

Copy Dockerfile into your project’s directory:

FROM debian:9-slim
RUN apt-get update && apt-get install -y netbase sudo wget libcap2-bin
RUN useradd --system -m --home /jet --shell /jet/login jet

RUN { wget -O - "http://jetware.io/appliances/aise/tensorflow14_python2_cuda9_lab-180105/file/container:docker/setup" || echo exit 1; } | sh

WORKDIR /jet
USER jet
VOLUME ["/jet/prs"]
ENTRYPOINT ["/jet/login"]
CMD ["main"]

or build an image directly from the URL by executing the command:

docker build 'http://jetware.io/appliances/aise/tensorflow14_python2_cuda9_lab-180105/file/container:docker/Dockerfile?os=debian_9'

Copy Dockerfile into your project’s directory:

FROM centos:7
RUN yum install -y sudo wget libcap
RUN useradd --system -m --home /jet --shell /jet/login jet

RUN { wget -O - "http://jetware.io/appliances/aise/tensorflow14_python2_cuda9_lab-180105/file/container:docker/setup" || echo exit 1; } | sh

WORKDIR /jet
USER jet
VOLUME ["/jet/prs"]
ENTRYPOINT ["/jet/login"]
CMD ["main"]

or build an image directly from the URL by executing the command:

docker build 'http://jetware.io/appliances/aise/tensorflow14_python2_cuda9_lab-180105/file/container:docker/Dockerfile?os=centos_7'
Download a virtual machine
Ubuntu 14.04
VMWare
machine_learning-1regzxyth11li-vmware-ubuntu_14.04.zip
VirtualBox
machine_learning-1regzxyth11li-virtualbox-ubuntu_14.04.ova
Qemu/KVM
machine_learning-1regzxyth11li-qemu-ubuntu_14.04.qcow2.gz

You can access the virtual machine via console or SSH:

Login: jet
Password: jet

Configuration

Main settings

jupyter_notebook

Configuration

Config: | /jet/etc/jupyter_notebook/jupyter_notebook_config.py Address: | http://server_address:8888 Password: | empty