Overview
This is a repackaged open source software wherein additional charges apply for extended support with a 24 hour response time.
The Deep Learning AMI DLAMI on Ubuntu 16.04 LTS is optimized for deep learning applications, providing a powerful and flexible platform for developing and deploying machine learning models. This AMI comes pre-configured with a rich set of deep learning frameworks, including TensorFlow, PyTorch, and Apache MXNet, enabling developers to easily build and train neural networks right out of the box.
Key Features:
- Pre-installed Frameworks: Start immediately with popular frameworks that are fully optimized for the EC2 environment.
- NVIDIA GPU Support: Leverage high-performance GPU options for accelerated training, reducing time required for model development and experimentation.
- Customizable Environment: Modify and adjust the runtime environment to fit specific project requirements or preferences, promoting flexibility in development.
- Comprehensive Documentation: Access to detailed documentation and tutorials to accelerate the learning curve and facilitate effective use of the AMI.
Benefits:
- Time-efficient: Rapidly set up deep learning environments without the need for complex installation processes.
- Cost-effective: Pay only for what you use, scalable resources to match your workloads, ensuring you manage costs effectively.
- Production-ready: Suitable for deploying applications into production, with built-in features to enhance reliability and performance.
Use Cases:
- Research & Development: Ideal for researchers and data scientists looking to prototype and test machine learning algorithms.
- Education: Perfect for institutions providing deep learning courses, offering students hands-on experience with state-of-the-art technology.
- Business Applications: Utilize deep learning for advanced analytics, customer segmentation, and predictive modeling in various industries.
Unlock the potential of deep learning with the Deep Learning AMI DLAMI on Ubuntu 16, designed to accelerate your projects while maintaining a robust and user-friendly experience.
Try our most popular AMIs on AWS EC2
- Ubuntu 24.04 AMI on AWS EC2
- Ubuntu 22.04 AMI on AWS EC2
- Ubuntu 20.04 AMI on AWS EC2
- Ubuntu 18.04 AMI on AWS EC2
- CentOS 9 AMI on AWS EC2
- CentOS 8 AMI on AWS EC2
- CentOS 7 AMI on AWS EC2
- Debian 12 AMI on AWS EC2
- Debian 11 AMI on AWS EC2
- Debian 10 AMI on AWS EC2
- Debian 9 AMI on AWS EC2
- Red Hat Enterprise Linux 9 (RHEL 9) AMI on AWS EC2
- Red Hat Enterprise Linux 8 (RHEL 8) AMI on AWS EC2
- Red Hat Enterprise Linux 7 (RHEL 7) AMI on AWS EC2
- Oracle Linux 9 AMI on AWS EC2
- Oracle Linux 8 AMI on AWS EC2
- Oracle Linux 7 AMI on AWS EC2
- Amazon Linux 2023 AMI on AWS EC2
- Windows 2022 Server AMI on AWS EC2
- Windows 2019 Server AMI on AWS EC2
- Docker on Ubuntu 20 AMI on AWS EC2
- Docker on CentOS 7 AMI on AWS EC2
Highlights
- The Deep Learning AMI DLAMI Ubuntu 16.04 LTS offers a robust environment pre-configured with popular deep learning frameworks such as TensorFlow, PyTorch, and MXNet. This enables users to quickly set up their projects without the hassle of manual installation. The AMI is optimized for performance, leveraging EC2's powerful GPU instances to accelerate training times, making it ideal for both researchers and industry practitioners.
- This AMI is designed for scalability, allowing teams to easily deploy multiple instances for distributed training across large datasets. It supports comprehensive tools and libraries, including Jupyter notebooks, making it convenient for collaborative development and experimentation. Users can efficiently manage resources and configurations tailored to specific project needs, enhancing productivity and reducing time to market.
- Additionally, the AMI provides access to comprehensive documentation and community support, ensuring users have the resources needed for troubleshooting and optimization. Whether for academic research or production-level AI applications, this Deep Learning AMI enables data scientists and engineers to harness the full potential of cloud computing to drive innovation and achieve advanced results in machine learning.
Details
Typical total price
$2.30/hour
Pricing
- ...
Instance type | Product cost/hour | EC2 cost/hour | Total/hour |
---|---|---|---|
t1.micro | $0.07 | $0.02 | $0.09 |
t2.nano | $0.07 | $0.006 | $0.076 |
t2.micro AWS Free Tier | $0.21 | $0.012 | $0.222 |
t2.small | $0.07 | $0.023 | $0.093 |
t2.medium | $0.14 | $0.046 | $0.186 |
t2.large | $0.14 | $0.093 | $0.233 |
t2.xlarge | $0.28 | $0.186 | $0.466 |
t2.2xlarge | $0.56 | $0.371 | $0.931 |
t3.nano | $0.07 | $0.005 | $0.075 |
t3.micro AWS Free Tier | $0.07 | $0.01 | $0.08 |
Additional AWS infrastructure costs
Type | Cost |
---|---|
EBS General Purpose SSD (gp2) volumes | $0.10/per GB/month of provisioned storage |
Vendor refund policy
The instance can be terminated at anytime to stop incurring charges
Legal
Vendor terms and conditions
Content disclaimer
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
update
Additional details
Usage instructions
SSH to the instance and login as 'ubuntu' using the key specified at launch.
OS commands via SSH: SSH as user 'ubuntu' to the running instance and use sudo to run commands requiring root access.
More on using Deep Learning AMI with Conda: https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-conda.html
Resources
Vendor resources
Support
Vendor support
Email support for this AMI is available through the following: https://supportedimages.com/support/ OR support@supportedimages.com
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.