Preparing your NAIRR Pilot Classroom Request
To request access to NAIRR Pilot Classroom resources, you must prepare a description, no longer than three (3) pages, describing your course and requirements for computational resources available through this program. Do not include any proprietary information in proposals.
Eligibility
This call is open to proposals by US based educators and researchers who are teaching undergraduate or graduate courses or shorter duration training sessions to US based students that include subject matter in artificial intelligence and require that students use advanced computational resources as part of their coursework. Courses from any discipline are eligible for this program. Courses and training sessions must not allow participants who are not US based.
Available Resources
Vocareum offers 20,000 concurrent user licenses for its advanced Jupyter-based Notebook, a hosted service that requires no setup. This cloud-based solution provides seamless Learning Management System (LMS) integration, auto-graded assignments, collaborative group work capabilities, built-in virtual tutors, and more. Additionally, it offers users one-click access to externally provided Generative AI and GPU resources. Vocareum conducts a comprehensive orientation session and ongoing support for instructors. This session covers key platform features, including LMS integration. Vocareum provides detailed guidance on customizing container images and creating and grading assignments, ensuring users can fully leverage the platform's capabilities from the start.
Allocations
Vocareum has provided 20,000 concurrent user licenses for its advanced Jupyter-based Notebook over two years.
Recommended Use
Educational Institutions: It substantially benefits schools, universities, and training programs that teach artificial intelligence, data science, machine learning, and prompt engineering. Vocareum provides a seamless environment that integrates with existing LMS, making it easy for educators to manage courses, assignments, and grades. Hands-On Learning: The platform supports interactive coding and real-time feedback, making it ideal for students and learners to practice and hone their skills in languages like Python. The auto-grading feature simplifies the evaluation process, allowing instructors to focus more on teaching and less on administrative tasks. Generative AI Integration: Vocareum AI Notebook offers one-click access to leading Generative AI technologies, enabling students and researchers to leverage powerful AI models for their projects. This feature provides hands-on experience with cutting-edge technologies and includes built-in budget controls for cost-effective use. GPU Access and Management: Vocareum AI Notebook provides single-click access to powerful GPUs for training AI models, allowing students to engage in intensive AI tasks without complex setup processes. Cost-effective management options ensure efficient resource utilization. Overall, Vocareum AI Notebook is suited for educational and research settings where ease of use, integration with other tools, and access to powerful AI and computing resources are critical considerations.
Prototype National Research Platform (PNRP) Classroom offers lecturers at non-profit degree granting higher education institutions the opportunity to request Jupyter resources for teaching a class of students as part of the “NAIRR Classroom” pilot. PNRP offers GPU resources for your students and a Jupyter platform for teaching a class that makes heavy use of AI/ML and. Our default platform has nodes each with 8 A10 GPUs, 512GB of RAM, 2 AMD EPYC 7502 CPUs, and 8TB of NVMe. Depending on your needs, we may be able to provision other GPU resources via the NAIRR pilot and make it available to your class via the PNRP Nautilus cluster (https://docs.nationalresearchplatform.org). PNRP provides your class with a set of containers you can customize for the students; an authentication system based on InCommon technology that authenticates your students based on their identity at your institution; and some storage space where you can store the collections of files that your students will need to process as part of your course.
Allocations
PNRP Classroom offers GPU resources for your students and a Jupyter platform for teaching a class that makes heavy use of AI/ML and. Our default platform has nodes each with 8 A10 GPUs, 512GB of RAM, 2 AMD EPYC 7502 CPUs, and 8TB of NVMe. Depending on your needs, we may be able to provision other GPU resources via the NAIRR pilot and make it available to your class.
Recommended Use
Faculty, teaching staff, and teaching assistants will have admin status to create namespaces for your classroom. Namespace admins can create specific namespaces for each student. This can be used to ensure (a) the work of each student is separate and (b) faculty/TA access to each student’s notebook to verify their work. When each student first signs in, the same customized container that you prepared for this course is loaded to implement a standard Jupyter Notebook as a starting point. When they sign out, the work each student has done is saved for that student such that it is available as they left it the next time they sign in again. Two matrix chat support channels will be available: one specifically for the class where faculty/TAs can engage with their students and a second separate matrix chat channel where faculty/TAs can engage with other lecturers, and PNRP staff. In addition, faculty/TA can submit tickets via our ticketing system for class specific issues that need longer to resolve. We expect that you or your teaching assistant will address any class specific questions from your students. PNRP staff will support faculty/TAs in doing so.
NIH Cloud Lab is a cloud-based training environment from the National Institutes of Health (NIH) designed to help biomedical researchers, including undergraduate and graduate students, grow the skills needed to confidently conduct research in the cloud. Enrolled users can access Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure through Cloud Lab, deploying a full range of resources, including: • Central processing unit (CPU) or graphics processing unit (GPU) virtual machines (VMs) • Advanced AI including generative AI and machine learning capabilities • Managed Jupyter notebooks • Bioinformatic workflow managers • Compute clusters • High-speed networking. To support instructors in jumpstarting learning, Cloud Lab provides a variety of NIH and external resources for students to explore – either at their own pace or as part of a course – including non-sensitive datasets, code documentation, and interactive tutorials that demonstrate how to run realistic bioinformatic, data science, and AI workflows in the cloud. Instructors can also use the Cloud Lab environment and resources with their own teaching materials and datasets.
Allocations
Through the NAIRR Classroom program, undergraduate, graduate, or professional training instructors can gain access to Cloud Lab for testing or teaching courses/workshops related to life science. Cloud Lab users will receive up to 90 days of access to AWS, GCP, or Microsoft Azure, plus up to $500 of cloud credits per user.
Recommended Use
Cloud Lab is ideal for any course or workshop where the goal is to help students become more efficient and confident using the cloud for research. Cloud Lab is not a production research environment, nor is it a place to work with sensitive data. Instructors can use Cloud Lab to: • Teach cloud-based computational life science classes, courses, or workshops • Facilitate individual or team-based code-a-thons around bioinformatic workflows, AI, or generative AI use cases • Teach from Cloud Lab’s collections of tutorials on bioinformatics, life science AI, and generative AI for Amazon Web Services, Google Cloud, and Microsoft Azure as part of a course • Sponsor teams to prototype new architectures and evaluate new software and hardware combinations. As an NIH training environment, Cloud Lab is available only for instructors teaching courses and workshops related to life science. The environment is designed to be useful for students at all levels, from undergraduates building foundational knowledge to graduate students learning to prototype the latest cloud technologies. The requested budget for each student (up to $500 each) must be reasonable, and unspent cloud credits will be withdrawn when access to Cloud Lab expires. Instructors may request an extension of the standard 90-day program length for courses longer than 90 days.
Review Criteria and Project Expectations
There are no specific constraints on the courses eligible for this opportunity. Examples include:
- Computer science (or information science, or data science) courses on machine learning, artificial intelligence (AI), or intelligent systems
- Domain science courses or trainings that are incorporating AI into the curriculum
- Independent study courses of any discipline incorporating AI. This opportunity is also available to Instructors who are developing a course or tools for the classroom and need computational resources to complete testing prior to offering their course.
Requests that are outside the scope of this opportunity, notably those for research and development, may be eligible through the NAIRR Pilot researcher and other opportunities. The proposals will be evaluated on the following criteria:
- Alignment with the educational and workforce development focus of this call
- Readiness of the planned course, including institutional support commensurate with what is proposed. The resources offered assume that an instructor, teaching assistant, or other person will be the primary source of technical support for students accessing and using the resources.
- Match of request to the resources offered, and available.
Please note the following parameters and expectations:
- All supported classes will have the name of the primary educator, affiliation, and name of the course posted to the NAIRR Pilot website.
- All primary educators and teaching assistants will be added to a NAIRR Pilot chat channel to foster community.
- All primary educators are expected to go through an exit interview focused on feedback to improve the NAIRR Classroom pilot.
Project Description Outline
To ensure your request can be properly reviewed and awards be directed to the appropriate resource(s), your description should include the sections outlined here.
A. Course Description
- Institution name and course name
- The course contact's name and email address
- Include URL to the course if available
- Course start and end dates
- Number of students expected
- How the computing resources requested support the educational objectives of the course
- Expected method students will access the resources, e.g., JupyterLab, gateway, direct access via SSH
- Describe when will the resource be used, i.e., during class time, project work outside of the class, during quizzes, mid-terms, and finals, or other information to help reviewers understand expectations about on-demand use of resources
B. Estimate of Compute, Storage and Other Resources
To the extent possible, provide an estimate of the scale and type of the resources needed for the course. The information in the Resources section below is available to help you answer this question. Please be as specific as possible in your resource request along with any data you have supporting the request. Your proposal should indicate your preferred resource (if any) and your first alternate choice, should there not be sufficient availability on your preferred resource. Please select only your preferred resource in the online submission form.
- Computing architectures or systems that are most appropriate for the course (e.g. GPUs, large memory, large core counts on shared memory nodes, etc.)
- Estimated computing resource required for each student in terms of core, node, or GPU hours, and memory
- Can this course be supported in a cloud environment?
- Describe the storage needs of the project.
- Does your project require access to any public datasets? If so, please describe these datasets, where they reside, and how you intend to use them.
- Does your project require access to specific software packages? If so, describe this software (including whether it is open source or licensed) and how you intend to use it.
- How much persistent storage per student
- Describe the structure of the data in terms of file counts and file sizes
- If no resources meet your requirements, please explain why.
C. Support Needs
Describe whether collaboration or support from staff at various NAIRR Pilot Resource Providers (e.g. Commercial Cloud Providers, academic research computing centers, data or tool providers) will be essential, helpful, or unnecessary. Estimates of necessary application support are very helpful. Teams should also identify any restrictions that might apply to the project, such as export-controlled code, ITAR restrictions, proprietary data sets, regional location of compute resources, or personal health information (PHI) or HIPAA restrictions. In such cases, please provide information on security, privacy, and access issues.
D. Team and Team Preparedness
Summarize your team's qualifications and readiness to execute the project both in using the methods proposed and the resources requested.
- What is the expected lead time before you can begin using the resource?
- What systems have you recently used and what was the scale of the applications?
- Given that some resources are at federal facilities with restrictions, please provide a list of the team members that will require accounts on resources along with their citizenship.
Supporting Documents
In addition to the main proposal document, please also prepare a CV or other biographical sketch document for the project team lead. Biosketch documents prepared for federal funding agency grant proposals would satisfy this requirement.
Document Formatting
While readability is of greatest importance, documents must satisfy the following minimum requirements. Documents that conform to National Science Foundation proposal format guidelines will satisfy these guidelines.
- Margins: Documents must have 2.5-cm (1-inch) margins at the top, bottom, and sides.
- Fonts and Spacing: The type size used throughout the documents must conform to the following three requirements:
- Use one of the following typefaces identified below:
- Arial 11, Courier New, or Palatino Linotype at a font size of 10 points or larger;
- Times New Roman at a font size of 11 points or larger; or
- Computer Modern family of fonts at a font size of 11 points or larger.
- A font size of less than 10 points may be used for mathematical formulas or equations, figures, table or diagram captions and when using a Symbol font to insert Greek letters or special characters. PIs are cautioned, however, that the text must still be readable.
- Type density must be no more than 15 characters per 2.5 cm (1 inch).
- No more than 6 lines must be within a vertical space of 2.5 cm (1 inch).
- Use one of the following typefaces identified below:
- Page Numbering: Page numbers should be included in each file by the submitter. Page numbering is not provided by the submission system.
- File Format: Only PDF file formats are accepted.