Jetware - aise / course_mlcourse_open_2018_cuda8

aise / Open Machine Learning Course 2018 for NVidia GPU

The pre-configured and ready-to-use runtime environment for the Open Machine Learning Course, 2018. It includes Python 3.6, TensorFlow 1.4, Keras 2, XGBoost, LightGBM and Vowpal Wabbit. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.

Costruttore:

Machine Learning Education

cuda_only-nvidia_drivers

384.111

Install Containers Scaricare Configuration

Utilizzazione

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.

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://it.jetware.io/appliances/aise/course_mlcourse_open_2018_cuda8-180209/file/installer:nub_tgz/setup | sh

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

How to use

To enter the runtime environment or to execute a command inside the runtime environment you can use the utility /jet/enter. If no arguments are present, the standard shell will be executed inside the runtime environment. You can specify a command as an argument, it will be executed inside the runtime environment.

For example, to start all services in the runtime environment you can do /jet/enter start. To execute a mysql client you can do /jet/enter mysql; or run first /jet/enter, and than run from the new command line mysql.

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_education-1xheubtshlhzn.tar.gz

2.37 GB

Scaricare

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://it.jetware.io/appliances/aise/course_mlcourse_open_2018_cuda8-180209/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://it.jetware.io/appliances/aise/course_mlcourse_open_2018_cuda8-180209/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://it.jetware.io/appliances/aise/course_mlcourse_open_2018_cuda8-180209/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://it.jetware.io/appliances/aise/course_mlcourse_open_2018_cuda8-180209/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://it.jetware.io/appliances/aise/course_mlcourse_open_2018_cuda8-180209/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://it.jetware.io/appliances/aise/course_mlcourse_open_2018_cuda8-180209/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://it.jetware.io/appliances/aise/course_mlcourse_open_2018_cuda8-180209/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://it.jetware.io/appliances/aise/course_mlcourse_open_2018_cuda8-180209/file/container:docker/Dockerfile?os=centos_7'

Download a virtual machine

Ubuntu 14.04

VMWare

machine_learning_education-1xheubtshlhzn-vmware-ubuntu_14.04.zip

2.47 GB

Scaricare

VirtualBox

machine_learning_education-1xheubtshlhzn-virtualbox-ubuntu_14.04.ova

2.6 GB

Scaricare

Qemu/KVM

machine_learning_education-1xheubtshlhzn-qemu-ubuntu_14.04.qcow2.gz

Scaricare

You can access the virtual machine via console or SSH:

Login:	jet
Password:	jet

Configuration

Main settings

Full configuration

Full system configuration and quick start guide

Condividi il tuo appliance: