Sold by: CoiledÂ
Overview
Coiled makes it easy to use Python at a large scale in your AWS account safely and efficiently. Built on the open source Dask package, Coiled provides the following capabilities:
- Distributed Dask clusters
- Serverless functions
- CLI jobs
- Jupyter Notebooks
These APIs are simple and Pythonic. They're easy to incorporate into your current workflows with APIs like the following:
$ coiled run echo ''Hello, world'' # creates VM, runs the program, shuts down VM
Or
@coiled.function(memory=''1 TiB'', region=''us-west-2'') def process(filename): ...
Coiled makes it easy to run your favorite libraries like pandas, NumPy, scikit-learn, PyTorch, DuckDB, TensorFlow, Dask, Dagster and more at large scale with easy setup.
To learn more, view the following resources: Overview video https://www.youtube.com/watch?v=WyBXg8R4TN0 Documentation https://docs.coiled.io/user_guide/index.html Get Started https://coiled.io/start Security https://coiled.io/securityÂ
Highlights
- Easy to use APIs you can use from anywhere to drive cloud computations
- Low friction with environment synchronization (packages, credentials, files) for a pleasant development experience
- Smooth setup with a generous free tier of 10,000 CPU-hours (but you still have to pay AWS)
Details
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/unit |
|---|---|
10,000 CPU-hours per month free | $0.00 |
Pricing per CPU-hour after 10,000 hours | $0.05 |
Vendor refund policy
No refunds or cancellations.
Legal
Vendor terms and conditions
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
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.
Updated weekly
By Coiled
By Modal
AI generated from product descriptions
Distributed Computing Framework
Utilizes Dask for scaling Python computations across distributed clusters
Serverless Computation
Supports serverless function execution with configurable memory and region specifications
Cloud Integration
Seamlessly runs popular Python libraries like pandas, NumPy, scikit-learn, PyTorch across cloud infrastructure
Environment Synchronization
Automatically synchronizes packages, credentials, and files across computational environments
Command Line Interface
Provides CLI capabilities for launching and managing computational jobs with simple command syntax
Serverless Compute
Provides serverless compute infrastructure specifically designed for AI, ML, and data processing workloads
GPU Container Deployment
Enables rapid GPU-enabled container deployment with startup times as low as one second
Infrastructure as Code
Supports deploying Python functions to cloud environments with custom container image and hardware specification definitions
Dynamic Resource Scaling
Automatically scales computational resources up to hundreds of GPUs and down to zero based on workload requirements
Cloud Workload Optimization
Supports complex computational tasks including ML inference, fine-tuning, and batch data processing
GPU Acceleration
Includes NVIDIA drivers, CUDA, and cuDNN for high-performance GPU-based deep learning computations
Deep Learning Framework
Pre-installed PyTorch 2.0.1 with support for advanced machine learning model development
Operating System
Built on Amazon Linux 2, providing a lightweight and secure cloud-optimized environment
Machine Learning Libraries
Pre-configured with essential libraries including TorchVision, NumPy, and PyTorch-related packages
Cloud Instance Compatibility
Supports multiple GPU-based EC2 instances for scalable AI training and inference tasks
Standard contract
No
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