NGC on Alibaba Virtual Machines

This NGC on Alibaba Virtual Machines Guide explains how to set up an NVIDIA GPU Cloud Virtual Machine Image on Alibaba Cloud and includes release notes for each version of the NVIDIA virtual machine image.

To view the Chinese setup guide, go to the NGC with Alibaba Cloud - Chinese Portal.

1. NGC on Alibaba Cloud Virtual Machines

NVIDIA makes available on Alibaba Cloud three different virtual machine images (VMIs). These are GPU-optimized VMIs for Alibaba Cloud VM instances with NVIDIA V100 or NVIDIA T4 GPUs.

  • NVIDIA GPU-Optimized Image for Deep Learning, Machine Learning & HPC

    The base GPU-Optimized image Includes Ubuntu Server, the NVIDIA driver, Docker CE, and the NVIDIA Container Runtime for Docker

  • NVIDIA GPU-Optimized Image for TensorFlow

    The base image with NVIDIA’s GPU-Accelerated TensorFlow container pre-installed

  • NVIDIA GPU-Optimized Image for PyTorch

    The base image with NVIDIA’s GPU-Accelerated PyTorch container pre-installed

For those familiar with the Alibaba Cloud platform, the process of launching the instance is as simple as logging in, selecting the NVIDIA GPU-optimized Image of choice, selecting and configuring a cloud instance with at least one supported NVIDIA GPU, and then launching the VM. After launching the VM, you can SSH into it and start using the wide range of GPU-accelerated containers, pre-trained models, and other resources available from the NGC Catalog.

This document provides step-by-step instructions for accomplishing this, including how to use the Alibaba Cloud CLI.

Prerequisites

These instructions assume the following:

  • You have an Alibaba account - https://home-intl.console.aliyun.com/ with permissions to create resources.

  • Browse the NGC website and identified an available NGC container and tag to run on the VMI.
  • Windows Users: The CLI code snippets are for bash on Linux or Mac OS X. If you are using Windows and want to use the snippets as-is, you can use the Windows Subsystem for Linux and use the bash shell (you will be in Ubuntu Linux).

1.1. Before You Get Started

1.1.1. Set Up Your SSH Key Pair

If you do not already have SSH keys set up specifically for Alibaba, you will need to set one up and have it on the machine you will use to SSH to the VM. In the examples, the key is named "alibaba-key".

  1. From a browser, log in to the ECS console - https://ecs.console.aliyun.com/.
  2. Open the left navigation menu tab and then click SSH Key Pairs from the Network & Security group.
  3. From the upper right of the screen, click Create SSH Key Pair.
  4. Give it a name, such as "alibaba-key" and click OK. A .pem file will immediately download. This is the ONLY time you can download it.
  5. After downloading the .pem file, move it to the .ssh directory.
    mv alibaba-key.pem ~/.ssh/ 
    chmod 400 ~/.ssh/alibaba-key.pem 
    On Windows, the location will depend on the SSH client you use, so modify the path above and in the snippets or your SSH client configuration. See the Alibaba documentation for Creating an SSH key pair.

1.1.2. Set Up Security Groups for the Virtual Machine

In order to create instances, you need to put them in a Security Group.
  1. Log in to the ECS console - https://ecs.console.aliyun.com/.
  2. Open the left navigation menu tab and then click Security Groups from the Network & Security group.
  3. From the upper right of the screen, click Create Security Group.

  4. Give it a name and description, and create a Virutal Private Cloud (VPC) if one doesn't exist yet.
  5. Under the inbound tab, configure the following options.
    1. Add SSH and HTTPS.
    2. At Custom Port Range, select TCP and then enter 5000/5000.
    3. Set Authorization Object = 0.0.0.0/0 or the IP address from which you will access.
    4. Click OK.

1.2. Creating an NGC Certified Virtual Machine using the Alibaba Cloud Console

1.2.1. Log in and Locate the Image

  1. Log in to the Alibaba Console (Alibaba Cloud Marketplace (Find and Quickly Use Software as Images).
  2. Search for nvidia and select the NVIDIA GPU-Optimized image of your choice.

  3. Click Choose your plan.

1.2.2. Configure the VM and Launch

  1. Configure the following instance settings.
    • Billing Method: Pay-As-You-Go
    • Region: Select a region that has GPU instances (Note: Not all regions have GPUs)
    • Instance Type: Select Heterogeneous Computing and select an instance type with NVIDIA V100 or T4 GPUs
    • Image: Ensure the NVIDIAGPU-Optimized Image you chose previously is selected
    • Storage: Add a disk for dataset storage by clicking Add Disk under Data Disk, and then entering the storage size. Recommended minimum dataset storage size is 1 TB (1024 GB)
  2. Click Next: Networking and select the security group you previously created in the Before You Get Started section.
  3. Click Next: System Configuration and select the SSH Key Pair you previously created in the Before You Get Started section.
  4. Click Preview, review the configuration and accept the terms of service, and then click Create Instance.

1.2.3. Connect to Your VM Instance

  1. Click Console on the Create page.
  2. Wait until the status of your VM displays “Running” and then connect via SSH using the actions section of the VM details.
  3. Once started, you can SSH into your instance using the SSH key for the root user. If you followed the setup in this tutorial, your key is in ~/.ssh/.

    Command Syntax

    $ ssh -i <KEYPATH> root@<IP>

    Example

    $ ssh -i ~/.ssh/alibaba-key.pem root@47.89.248.188

    Refer to Connect to a Linux Instance for more instructions on connecting to your instance.

1.2.4. Start/Stop/Delete Your VM Instance

  1. Navigate to Instances under the Instances & Images section in the navigation pane on the left.
  2. Select the virtual machine instance you wish to manage and use the options bar at the bottom to start/stop, and release to terminate the instance and delete any associated resources.

1.3. Creating an NGC Certified Virtual Machine using the Alibaba Cloud CLI

This flow and the code snippets in this section are for Linux or Mac OS X. If you are using Windows, you can use the Windows Subsystem for Linux and use the bash shell (where you will be in Ubuntu Linux).

Many of these CLI command can have significant delays.

For complete CLI documentation and sample scripts visit the Alibaba Documentation Center.

1.3.1. Install the Alibaba CLI

To use the Alibaba CLI, follow the Alibaba CLI Install Instructions and also install the ECS SDK.
  1. Install the ECS SDK.
    sudo pip install aliyun-python-sdk-ecs 
  2. Configure the CLI with your keys.
    aliyuncli configure 

1.3.6. Get the NVIDIA Image ID

Once started, you can SSH into your instance using the SSH key for the root user. If you followed the setup in this tutorial, your key is in ~/.ssh/.

You need to specify a source ImageID when creating an instance. Use this command to find the latest ImageID of the NVIDIA-GPU-Cloud-Machine-Image:

aliyuncli ecs DescribeImages --RegionId us-west-1 \

  --ImageName "NVIDIA-GPU-Cloud-Virtual-Machine" \

  --output json --filter Images.Image[0].ImageId
It will output the Image ID such as "m-rj9iy0xjiod3ghkyhz4p"

1.3.3. Create a VM Instance

Creating an instance with the CLI is done using the `aliyuncli ecs CreateInstance` command.

Full syntax documentation - https://www.alibabacloud.com/help/doc-detail/25499.htm

Recommended Instance Options

  • "--InternetMaxBandwidthOut 10" sets the peak outbound network bandwidth to 10 Mbps. The valid range is [1, 200].
  • "--InstanceChargeType PostPaid" sets the billing method to pay-as-you-go. Change this to "PrePaid" to set it to a subscription billing.

Other Notable Create Instance Options

  • The inbound network bandwidth defaults to 200 Mbps. Use "--InternetMaxBandwidthIn" to change this. The valid range is [1, 200].
  • To change the size of the system disk (default is 40 GB), use the "--SystemDiskSize" option. Valid values are [40, 500].
  • To add a data disk (up to 16), use the "--DataDiskNSize" and "--DataDiskNCategory" options where "N" is [1, 16]. Valid values are:
    DataDiskNCategory DataDiskNSize Description
    cloud [5, 2000] (default) Basic cloud disk
    cloud_efficiency [20, 32768] Ultra cloud disk
    cloud_ssd [20, 32768] Cloud SSD
    ephemeral_ssd [5, 800] Ephemeral SSD

Launch Example

Launch the instance and capture the resulting JSON:
aliyuncli ecs CreateInstance \
  --RegionId us-west-1 \
  --ImageId "m-rj9iy0xjiod3ghkyhz4p" \
  --SecurityGroupId "sg-rj94krsusal2k5l6gnnz" \
  --InstanceType ecs.gn5-c4g1.xlarge \
  --InstanceName "my-instance" \
  --InternetMaxBandwidthOut 10 \
  --InstanceChargeType PostPaid \
  --KeyPairName alibaba-key

The output shows the instance ID.

{
  "InstanceId": "i-rj9a0iw25hryafj0fm4v",
  "RequestId": "440ECC70-09F9-492C-AB9E-21AA9C4E0531"
}

1.3.4. Assign a Public IP Address

Instances created via CLI are not automatically given a public IP address.

To assign a public IP address to the instance you just created, run:


aliyuncli ecs AllocatePublicIpAddress --RegionId us-west-1 \
  --InstanceId "i-rj9a0iw25hryafj0fm4v"

Successful completion of the command will return the IP address:

{
  "IpAddress": "47.89.248.188",
  "RequestId": "65EB59AE-FA75-446F-B5C7-2BA0F9A77CDC"
}

1.3.5. Start the Instance

Instances created via CLI are not started automatically.

To start the instance you just created, run:


aliyuncli ecs StartInstance --InstanceId "i-rj9a0iw25hryafj0fm4v"

Connect to Your VM Instance

Once started, you can SSH into your instance using the SSH key for the root user. If you followed the setup in this tutorial, your key is in ~/.ssh/.

Command syntax:

ssh -i <KEYPATH> root@<IP>

Example:

ssh -i ~/.ssh/alibaba-key.pem root@47.89.248.188
Refer to Connect to a Linux Instance for more instructions on connecting to your instance.

1.3.7. Start/Stop/Delete Your VM Instance

Once an instance is running, you can stop, (re)start, or delete your instance.

Stop:

aliyuncli ecs StopInstance --InstanceId INSTANCE_ID

Start or Restart:

aliyuncli ecs StartInstance --InstanceId INSTANCE_ID

Delete:

aliyuncli ecs DeleteInstance --InstanceId INSTANCE_ID

2. Release Notes for NVIDIA Virtual Machine Images on Alibaba Cloud

NVIDIA makes available on the Alibaba Cloud platform a customized NGC virtual machine image optimized for the NVIDIA® Volta™ GPU. Running NVIDIA GPU Cloud containers on this instance provides optimum performance for deep learning jobs.

See the NGC with Alibaba Cloud Setup Guide for instructions on setting up and using the VMI.

2.1. Version 20.03.0

Image Name

  • NGC Image: NVIDIA-GPU-Cloud-Image-20.03.0-2020.03.02
  • NGC with TensorFlow Image: nvidia-gpu-cloud-image-tensorflow-20.03.0-2020.03.02
  • NGC with PyTorch Image: nvidia-gpu-cloud-image-pytorch-20.03.0-2020.03.02

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 18.04 LTS
  • NVIDIA Driver:  440.64.01
  • Docker Engine:   19.03.6
  • NVIDIA Container Toolkit v1.0.5-1

    Includes new command to run containers: docker run --gpus all <container>

  • TensorFlow container (NGC with TensorFlow image): nvcr.io/nvidia/tensorflow:20.02-py3, nvcr.io/nvidia/tensorflow:20.02-py3
  • PyTorch container (NGC with PyTorch image): nvcr.io/nvidia/pytorch:19.10-py3

Key Changes

  • Updated Docker-CE to 19.03.6
  • Updated NVIDIA Driver to 440.64.01

Known Issues

Installing GPU drivers on the VM via a CUDA Install Succeeds Erroneously

Issue

Attempting to install CUDA on the VM will succeed, resulting in a potential conflict with the NVIDIA GPU driver included in the VM image.

Explanation

The configuration file to prevent driver installs is not working. This will be resolved in a later release of the VM image.

2.2. Version 19.11.3

Image Name

  • NGC Image: NVIDIA-GPU-Cloud-Image-19.11.3-2019.11.14
  • NGC with TensorFlow Image: nvidia-gpu-cloud-image-tensorflow-19.11.3-2019.11.14
  • NGC with PyTorch Image: nvidia-gpu-cloud-image-pytorch-19.11.3-2019.11.14

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 18.04 LTS
  • NVIDIA Driver:  440.33.01
  • Docker CE:   19.03.4-ce
  • NVIDIA Container Toolkit v1.0.5-1

    Includes new command to run containers: docker run --gpus all <container>

  • TensorFlow container (NGC with TensorFlow image): nvcr.io/nvidia/tensorflow:19.10-py3
  • PyTorch container (NGC with PyTorch image): nvcr.io/nvidia/pytorch:19.10-py3

Key Changes

  • Updated Docker-CE to 19.03.4
  • Updated NVIDIA Driver to 440.33.01

Known Issues

Installing GPU drivers on the VM via a CUDA Install Succeeds Erroneously

Issue

Attempting to install CUDA on the VM will succeed, resulting in a potential conflict with the NVIDIA GPU driver included in the VM image.

Explanation

The configuration file to prevent driver installs is not working. This will be resolved in a later release of the VM image.

2.3. Version 19.10.2

Image Name

  • NGC Image: NVIDIA-GPU-Cloud-Image-19.10.2-2019.11.01
  • NGC with TensorFlow Image: nvidia-gpu-cloud-image-tensorflow-19.10.2-2019.11.01
  • NGC with PyTorch Image: nvidia-gpu-cloud-image-pytorch-19.10.2-2019.11.01

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 18.04 LTS
  • NVIDIA Driver:  418.87.01
  • Docker CE:   19.03.2-ce
  • NVIDIA Container Toolkit v1.0.5-1
  • TensorFlow container (NGC with TensorFlow image): nvcr.io/nvidia/tensorflow:19.09-py3
  • PyTorch container (NGC with PyTorch image): nvcr.io/nvidia/pytorch:19.09-py3

Key Changes

  • Updated NVIDIA Driver to version 418.87.01
  • Updated Docker-CE to 19.03.2
  • Replaced the NVIDIA Container Runtime for Docker with the NVIDIA Container Toolkit.

    Includes new command to run containers: docker run --gpus all <container>

Known Issues

Installing GPU drivers on the VM via a CUDA Install Succeeds Erroneously

Issue

Attempting to install CUDA on the VM will succeed, resulting in a potential conflict with the NVIDIA GPU driver included in the VM image.

Explanation

The configuration file to prevent driver installs is not working. This will be resolved in a later release of the VM image.

2.4. Version 19.08.1

Image Name

NVIDIA-GPU-Cloud-Image-19.08.1-2019.07.22

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 18.04 LTS
  • NVIDIA Driver:  418.87
  • Docker CE:   18.09.8-ce
  • NVIDIA Container Runtime for Docker: (nvidia-docker2) v2.1.0-1

Key Changes

  • Updated NVIDIA Driver to version 418.87.
  • Updated Docker-CE to 18.09.8
  • Updated NVIDIA Container Runtime for Docker to v2.1.0-1

Known Issues

Installing GPU drivers on the VM via a CUDA Install Succeeds Erroneously

Issue

Attempting to install CUDA on the VM will succeed, resulting in a potential conflict with the NVIDIA GPU driver included in the VM image.

Explanation

The configuration file to prevent driver installs is not working. This will be resolved in a later release of the VM image.

2.5. Version 19.07.0

Image Name

NVIDIA-GPU-Cloud-Image-19.07.0-2019.06.28

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 18.04 LTS
  • NVIDIA Driver:  418.67
  • Docker CE:   18.09.7-ce
  • NVIDIA Container Runtime for Docker: (nvidia-docker2) v2.0.3

Key Changes

Known Issues

Installing GPU drivers on the VM via a CUDA Install Succeeds Erroneously

Issue

Attempting to install CUDA on the VM will succeed, resulting in a potential conflict with the NVIDIA GPU driver included in the VM image.

Explanation

The configuration file to prevent driver installs is not working. This will be resolved in a later release of the VM image.

Version 19.05.1

Image Name

NVIDIA-GPU-Cloud-Image-19.05.1-2019.05.22

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 18.04 LTS
  • NVIDIA Driver:  418.67
  • Docker CE:   18.09.4-ce
  • NVIDIA Container Runtime for Docker: (nvidia-docker2) v2.0.3

Key Changes

19.05.1

19.05.0

  • Updated the NVIDIA Driver to 418.67
  • Updated Docker to 18.09.4-ce

Known Issues

Installing GPU drivers on the VM via a CUDA Install Succeeds Erroneously

Issue

Attempting to install CUDA on the VM will succeed, resulting in a potential conflict with the NVIDIA GPU driver included in the VM image.

Explanation

The configuration file to prevent driver installs is not working. This will be resolved in a later release of the VM image.

2.8. Version 19.03.1

Image Name

NVIDIA-GPU-Cloud-Image-19.03.0-2019.03.18

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 18.04 LTS
  • NVIDIA Driver:  418.40.04
  • Docker CE:   18.09.2-ce
  • NVIDIA Container Runtime for Docker: (nvidia-docker2) v2.0.3

Key Changes

  • Updated the NVIDIA Driver to 418.40.04
  • Updated Docker to 18.09.2-ce

Known Issues

There are no known issues in this release.

Version 19.02.1

Image Name

NVIDIA-GPU-Cloud-Image-19.02.1-2019.02.11

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 16.04 LTS
  • NVIDIA Driver:  410.104
  • Docker CE:   18.09.1-ce
  • NVIDIA Container Runtime for Docker: (nvidia-docker2) v2.0.3

Key Changes

  • Updated the NVIDIA Driver to 410.104
  • Updated Docker to 18.09.1-ce

Known Issues

There are no known issues in this release.

2.9. Version 19.01.1

Image Name

NVIDIA-GPU-Cloud-Image-19.01.1-2019.01.08

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 16.04 LTS
  • NVIDIA Driver:  410.79
  • Docker CE:   18.06.1
  • NVIDIA Container Runtime for Docker: (nvidia-docker2) v2.0.3

Known Issues

There are no known issues in this release.

2.10. Version 18.11.1

Image Name

NVIDIA-GPU-Cloud-Image-18.11.1-2018.11.21

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 16.04 LTS
  • NVIDIA Driver:  410.79
  • Docker CE:   18.06.1
  • NVIDIA Container Runtime for Docker: (nvidia-docker2) v2.0.3

Key Changes

  • Updated the NVIDIA driver to 410.79.

Known Issues

There are no known issues in this release.

Version 18.09.1

Image Name

NVIDIA-GPU-Cloud-Image-18.09.1

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 16.04 LTS
  • NVIDIA Driver:  410.48
  • Docker CE:   18.06.1
  • NVIDIA Container Runtime for Docker: (nvidia-docker2) v2.0.3

Key Changes

  • Updated the NVIDIA driver to 410.48.
  • Updated Docker CE to 18.06.1

Known Issues

There are no known issues in this release.

2.12. Version 18.08.1

Image Name

NVIDIA-GPU-Cloud-Image-18.08.1

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 16.04 LTS
  • NVIDIA Driver:  396.44
  • Docker CE:   18.06-ce
  • NVIDIA Container Runtime for Docker: (nvidia-docker2) v2.0.3

Key Changes

  • Updated the NVIDIA driver to 396.44.
  • Updated Docker CE to 18.06

Known Issues

There are no known issues in this release.

2.14. Version 18.07.1

Image Name

NVIDIA-GPU-Cloud-Image-18.07.1-2018.07.16

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 16.04 LTS
  • NVIDIA Driver:  396.37
  • Docker CE:   18.03.1-ce
  • NVIDIA Container Runtime for Docker: (nvidia-docker2) v2.0.3

Key Changes

  • Updated the NVIDIA driver to 396.37.

Known Issues

There are no known issues in this release.

Version 18.06.1

Image Name

NVIDIA-GPU-Cloud-Image-18.06.1-2018.06.14

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 16.04 LTS
  • NVIDIA Driver:  396.26
  • Docker CE:   18.03.1-ce
  • NVIDIA Container Runtime for Docker: (nvidia-docker2) v2.0.3

Key Changes

  • Updated the NVIDIA driver to 396.26.

Known Issues

There are no known issues in this release.

Version 18.05.1

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 16.04 LTS
  • NVIDIA Driver:  384.125
  • Docker CE:   18.03.1-ce
  • NVIDIA Container Runtime for Docker: (nvidia-docker2) v2.0.3

Key Changes

  • Includes Ubuntu 16.04 security updates
  • Updated Docker CE to version 18.03.1-ce

Known Issues

There are no known issues in this release.

2.16. Version 18.04.1

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 16.04 LTS
  • NVIDIA Driver:  384.125
  • Docker CE:   18.03.0-ce
  • NVIDIA Container Runtime for Docker: (nvidia-docker2) v2.0.3

Key Changes

  • Updated the NVIDIA Driver to version 384.125
  • Updated Docker CE to version 18.03.0-ce

Known Issues

There are no known issues in this release.

2.17. Version 18.03.0

Contents of the NVIDIA GPU Cloud Virtual Machine Image

  • Ubuntu Server: 16.04 LTS
  • NVIDIA Driver:  384.111
  • Docker CE:   17.12.1-ce
  • NVIDIA Container Runtime: (nvidia-docker2) v2.0.3

Known Issues

There are no known issues in this release.

Notices

Notice

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