Overview
A simple AI/ML modeling tool developed as an extension of JupyterLab, Link ensures a smoother flow and a better experience throughout the model development cycle.
[Key Features]
- Pipeline Creation Create a pipeline with notebook cells to improve code readability and reproducibility.
- Caching Management Store execution results for respective cells to minimize redundant cell executions.
- Remote Resources Optimize the use of server resources by using separate resources for different pipeline components.
- Hyper-Parameter Optimizer Link provides a hyper-parameter feature, which works to find optimal hyper-parameters in an automated manner.
- Version Control Link provides a version control feature, where you can easily view changes to your pipeline and merge conflicts.
Highlights
- Build a pipeline on Jupyter
Details
Typical total price
$0.093/hour
Features and programs
Financing for AWS Marketplace purchases
Pricing
- ...
Instance type | Product cost/hour | EC2 cost/hour | Total/hour |
|---|---|---|---|
t2.nano | $0.00 | $0.006 | $0.006 |
t2.micro AWS Free Tier | $0.00 | $0.012 | $0.012 |
t2.small | $0.00 | $0.023 | $0.023 |
t2.medium | $0.00 | $0.046 | $0.046 |
t2.large Recommended | $0.00 | $0.093 | $0.093 |
t2.xlarge | $0.00 | $0.186 | $0.186 |
t2.2xlarge | $0.00 | $0.371 | $0.371 |
t3.nano | $0.00 | $0.005 | $0.005 |
t3.micro AWS Free Tier | $0.00 | $0.01 | $0.01 |
t3.small | $0.00 | $0.021 | $0.021 |
Additional AWS infrastructure costs
Type | Cost |
|---|---|
EBS General Purpose SSD (gp2) volumes | $0.10/per GB/month of provisioned storage |
Vendor refund policy
no refunds
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
Additional details
Usage instructions
- connect to the launched instance through our MakinaRocks Link AMI with your key-pair.
ssh -i <KEY_PAIR> ubuntu@#.#.#.#
- Activate the conda environment "link" which installed MakinaRocks Link on the instance.
conda activate link
- Run Jupyter lab (you can customize your configuration whatever you want) on the instance. In this case, the port number is 8888.
jupyter lab
- Forward on your local port to your remote EC2 instance port. The local port is 9000 and the remote EC2 instance port is 8888 the below command example.
ssh -L 9000:127.0.0.1:8888 ubuntu@#.#.#.# -i <KEY_PAIR>
- Connect to the Jupyter server on your browser and enter your product key. If you don't have a product key, you can get a product key by following the instructions on this page. https://makinarocks-link.readme.io/docs/aws-ami
- Enjoy MakinaRocks Link with pipelining on Jupyter!
Resources
Vendor resources
Support
Vendor support
Free support for MakinaRocks Link is available through technical support, discussions, and email. Technical support : https://link.makinarocks.ai/technical_support/ Discussions : https://makinarocks-link.readme.io/discuss Email : mailto:link.support@makinarocks.ai
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.