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Amazon Deep Learning AMIs

Amazon Deep Learning AMIs


What is Amazon Deep Learning AMIs?

AMIs are Amazon Machine Images, virtual appliance deployed on EC2. The AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at scale. Users can launch Amazon EC2…

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What is Amazon Deep Learning AMIs?

AMIs are Amazon Machine Images, virtual appliance deployed on EC2. The AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at scale. Users can launch Amazon EC2 instances pre-installed with deep…

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Product Details

What is Amazon Deep Learning AMIs?

Amazon Deep Learning AMIs Technical Details

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Reviews and Ratings



(1-2 of 2)
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Score 10 out of 10
Vetted Review
Verified User
We use AWS EC2 for training machine learning models. For spinning up these instances, we use prebuilt AMIs specific for Deep Learning. These AMIs help a lot in setting up the environment very easily without any worries of installing CUDA, Nvidia Drivers, and then specific libraries like Pytorch, and TensorFlow. Support for various Conda environments is also available. The amazing part of this is that it provides support for different types of machines like Ubuntu, Linux, Amazon OS, etc.
  • Setting up environment
  • Support for different types of machines
  • Perfect for Machine Learning / Deep Learning use cases
  • Nvidia / Cuda / Conda support easily
  • Simpler documentation of different types of AMIs
  • Clearly listing out different types of machines as I got confused and spinned up an AMI in Amazon Linux machine instead of Ubuntu
  • Support for latest version of libraries, to avoid manually updating them after launch
1. Best for quickly setting up an instance with pre-installed libraries.
2. Ideal for people in Deep Learning space who struggle with Cuda / Nvidia driver installations.

Not suitable:
1. People who want to install custom libraries or different version of those.
2. In these cases, updating the version of libraries many times leads to version mismatch which can cause many errors.
  • Nvidia / Cuda drivers
  • Conda environment
  • Support for Ubuntu (multiple versions)
  • Saves a lot of Infra Costs
  • Saves a lot of time in handling environment issues
  • Easy to start a new instance
Score 8 out of 10
Vetted Review
Verified User
I work within a university setting within the school of computing where we use Amazon Deep Learning AMI as a cloud based alternative for deep learning in practical labs. Though we have high end hardware within our institution, not all students have access to this all of the time and as we have 100+ students on a Data Science/ Machine Learning Program this can cause issues with fair accessibility to equipment. This is where AMI comes into play, as we use this service to allow students to run complicated and intensive algorithms on significant data sets. This removes the need for expensive and inaccessible hardware in our lab spaces and gives our students the flexibility needed when learning about the intricacies of Deep Learning.
  • You can get several common packages including keras, pytorch and tensorflow setup within an environment ready to code on any AWS instance which saves time
  • Great for virtual applications that helps communicate between various pieces of software
  • Not need to worry about compatibility or any major aspects of setup e.g. GPU configuration
  • Some aspects of the User Interface are quite confusing and activating packages can be a bit convoluted
  • It can be a bit confusing to switch between frameworks for novice users
Amazon AMIs has been very useful for the quick setup and implementation of deep learning for data analysis which is something I have used the service for in my own research. We commonly use the service to enable students to run intensive deep learning algorithms for their assessments. This service works well in this scenario as it allows students to quickly set up a suitable environment and get started with little hassle.
If you are looking to run simple, surface level deep learning algorithms (kind of contradictory statement I know) then AMI is more complicated than most will need. When it comes to teaching the basics of Machine Learning, this kind of system is unnecessary and there are other alternatives which can be used. That being said this service is a must if you are looking to run complex deep learning via the cloud.
  • It has made our Data Science/ Machine Learning Courses easier to manage/ need less human input therefore allowing us to increase the cohort size for this degree
  • It has unified a lot of technologies reducing the load on our IT team
Both of these services provide similar functionality and from my experience both are top class services which cover most of your needs. I think ultimately it comes down to what you need each service for. For example Amazon DL AMIs allows for clustering by default meaning I am able to run several clustering algorithms without a problem whereas IBM Watson Studio doesn't provide this functionality. They both provide a wide range of default packages such as Amazon providing caffe-2 and IBM providing sci-kitlearn. My main point is that both are very good services which have very similar functionality, you just need to think about the costs, suitability of features and integration with other services you are using.
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