AWS Deep Learning AMI
E814037
AWS Deep Learning AMI is an Amazon Web Services virtual machine image preconfigured with popular deep learning frameworks, tools, and drivers to simplify building and training machine learning models in the cloud.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Amazon Machine Image | 2 |
| AWS Deep Learning AMI canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T9674966 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
Target entity: AWS Deep Learning AMI Context triple: [MXNet, integratedInto, AWS Deep Learning AMI]
-
A.
Amazon SageMaker
Amazon SageMaker is a fully managed cloud service that enables developers and data scientists to build, train, and deploy machine learning models at scale.
-
B.
Amazon Linux
Amazon Linux is a Linux distribution provided by Amazon Web Services, optimized for running applications on AWS cloud infrastructure with long-term support and security updates.
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C.
Amazon EMR
Amazon EMR is a managed big data platform on AWS that simplifies running large-scale data processing frameworks like Apache Hadoop and Spark on elastic cloud clusters.
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D.
Azure Machine Learning
Azure Machine Learning is a cloud-based service from Microsoft for building, training, deploying, and managing machine learning models at scale on Azure.
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E.
NVIDIA AI Workflows
NVIDIA AI Workflows are pre-built, end-to-end AI pipelines from NVIDIA that streamline the development, deployment, and scaling of AI applications across common enterprise use cases.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Target entity: AWS Deep Learning AMI Target entity description: AWS Deep Learning AMI is an Amazon Web Services virtual machine image preconfigured with popular deep learning frameworks, tools, and drivers to simplify building and training machine learning models in the cloud.
-
A.
Amazon SageMaker
Amazon SageMaker is a fully managed cloud service that enables developers and data scientists to build, train, and deploy machine learning models at scale.
-
B.
Amazon Linux
Amazon Linux is a Linux distribution provided by Amazon Web Services, optimized for running applications on AWS cloud infrastructure with long-term support and security updates.
-
C.
Amazon EMR
Amazon EMR is a managed big data platform on AWS that simplifies running large-scale data processing frameworks like Apache Hadoop and Spark on elastic cloud clusters.
-
D.
Azure Machine Learning
Azure Machine Learning is a cloud-based service from Microsoft for building, training, deploying, and managing machine learning models at scale on Azure.
-
E.
NVIDIA AI Workflows
NVIDIA AI Workflows are pre-built, end-to-end AI pipelines from NVIDIA that streamline the development, deployment, and scaling of AI applications across common enterprise use cases.
- F. None of above. chosen
Statements (57)
| Predicate | Object |
|---|---|
| instanceOf |
Amazon Machine Image
ⓘ
cloud computing product ⓘ virtual machine image ⓘ |
| category |
deep learning infrastructure
ⓘ
machine learning infrastructure ⓘ |
| deploymentModel |
on-demand EC2 instances
ⓘ
reserved EC2 instances ⓘ spot EC2 instances ⓘ |
| developedBy | Amazon Web Services NERFINISHED ⓘ |
| distributionChannel |
AWS Management Console
NERFINISHED
ⓘ
AWS Marketplace NERFINISHED ⓘ |
| hasFeature |
AWS CLI preinstalled
ⓘ
Amazon CloudWatch integration ⓘ Docker support ⓘ Jupyter notebook support ⓘ conda environments ⓘ optimized for NVIDIA GPUs ⓘ preconfigured deep learning frameworks ⓘ preinstalled CUDA ⓘ preinstalled GPU drivers ⓘ preinstalled cuDNN ⓘ preinstalled machine learning libraries ⓘ |
| includesTool |
Anaconda
NERFINISHED
ⓘ
Git NERFINISHED ⓘ Jupyter Notebook NERFINISHED ⓘ JupyterLab NERFINISHED ⓘ NVIDIA drivers NERFINISHED ⓘ |
| integratesWith |
AWS Identity and Access Management
NERFINISHED
ⓘ
Amazon CloudWatch NERFINISHED ⓘ Amazon EBS NERFINISHED ⓘ Amazon S3 NERFINISHED ⓘ |
| licenseModel | pay-as-you-go with EC2 instance pricing ⓘ |
| optimizedFor | NVIDIA CUDA-enabled GPUs NERFINISHED ⓘ |
| partOf | Amazon Web Services ecosystem NERFINISHED ⓘ |
| providedBy | Amazon Web Services NERFINISHED ⓘ |
| purpose |
build deep learning applications
ⓘ
deploy deep learning models ⓘ train machine learning models ⓘ |
| runsOn |
Amazon EC2 GPU instances
NERFINISHED
ⓘ
Amazon Elastic Compute Cloud NERFINISHED ⓘ |
| supportsFramework |
Apache MXNet
NERFINISHED
ⓘ
Caffe NERFINISHED ⓘ Chainer NERFINISHED ⓘ Gluon NERFINISHED ⓘ Keras NERFINISHED ⓘ PyTorch NERFINISHED ⓘ TensorFlow NERFINISHED ⓘ Theano NERFINISHED ⓘ |
| supportsHardware |
CPU
ⓘ
GPU ⓘ |
| supportsLanguage |
Julia
NERFINISHED
ⓘ
Python ⓘ R ⓘ |
| useCase |
computer vision model training
ⓘ
distributed training on multiple GPUs ⓘ natural language processing model training ⓘ reinforcement learning experiments ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Subject: AWS Deep Learning AMI Description of subject: AWS Deep Learning AMI is an Amazon Web Services virtual machine image preconfigured with popular deep learning frameworks, tools, and drivers to simplify building and training machine learning models in the cloud.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.