Vendor-neutral lab template #113

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GiteaMirror · 2026-03-22T15:25:46-05:00

GiteaMirror commented

2026-03-22 15:25:46 -05:00

Originally created by @profvjreddi on GitHub (May 30, 2024).

Discussed in https://github.com/harvard-edge/cs249r_book/discussions/232

^{Originally posted by profvjreddi May 30, 2024}
For the labs, there are many different hardware solutions to pick from. we want to have one generic template that everyone follows so that vendors can produce their work according to that. Need to put together something like this that allows everyone to do their own thing but at the same time maintain consistency.

Lab Title

Provide a clear and concise title for the lab.

Objective

Describe the main goal(s) of the lab.
Explain what the student will learn or achieve by completing the lab.
Specify the relevant section or chapter in the book this lab ties into.

Prerequisites

List any necessary background knowledge or skills.
Mention any required software, tools, or prior labs.
Reference the specific reading parts of the book that should be reviewed before starting the lab.

Hardware and Software Requirements

Detail the specific hardware needed for the lab.
Include software versions and any additional dependencies.

Setup Instructions

Step-by-step instructions for setting up the hardware and software.
Include screenshots or diagrams if necessary.
Troubleshooting tips for common setup issues.

ML System Lab Workflow

Data Collection and Preparation

Data Source: Specify where the data is coming from (e.g., public dataset, simulated data, proprietary dataset).
Data Description: Brief description of the data, including its size, format, and key attributes.
Data Preprocessing Steps: List and explain the preprocessing steps required (e.g., cleaning, normalization, augmentation).

Model Training

Model Architecture: Specify the architecture being used (e.g., CNN, RNN, Transformer).
Framework: Mention the ML framework used (e.g., TensorFlow, PyTorch).
Training Configuration: Detail the training configuration (e.g., batch size, learning rate, number of epochs).
Code Snippets: Provide relevant code snippets for training the model.

Model Optimization

Hyperparameters: List the hyperparameters tuned and their optimal values.
Optimization Techniques: Describe any optimization techniques used (e.g., Grid Search, Random Search, Bayesian Optimization).
Performance Metrics: Specify the metrics used to evaluate model performance (e.g., accuracy, F1 score).

Model Evaluation

Evaluation Metrics: Detail the metrics used to evaluate the model (e.g., precision, recall, AUC-ROC).
Validation Strategy: Describe the validation strategy (e.g., k-fold cross-validation, train-test split).
Results: Present the evaluation results, including any visualizations if applicable.

Model Deployment

Deployment Environment: Specify where the model will be deployed (e.g., cloud, edge device).
Deployment Steps: Step-by-step instructions for deploying the model.
Verification: Methods to verify the deployed model is working correctly.

Assessment

Practical tasks to test students' understanding.
Include answers or a separate section for solutions.

Summary

Recap the main points covered in the lab.
Highlight key takeaways.
Mention any further reading in the book that could reinforce the lab's concepts.

References

Cite any external resources or documentation referenced in the lab.
Provide links to additional reading materials or related resources.

Appendix

Any additional information, such as advanced configuration options or extended explanations.

Originally created by @profvjreddi on GitHub (May 30, 2024). ### Discussed in https://github.com/harvard-edge/cs249r_book/discussions/232 <div type='discussions-op-text'> <sup>Originally posted by **profvjreddi** May 30, 2024</sup> For the labs, there are many different hardware solutions to pick from. we want to have one generic template that everyone follows so that vendors can produce their work according to that. Need to put together something like this that allows everyone to do their own thing but at the same time maintain consistency. # Lab Title Provide a clear and concise title for the lab. ## Objective - Describe the main goal(s) of the lab. - Explain what the student will learn or achieve by completing the lab. - Specify the relevant section or chapter in the book this lab ties into. ## Prerequisites - List any necessary background knowledge or skills. - Mention any required software, tools, or prior labs. - Reference the specific reading parts of the book that should be reviewed before starting the lab. ## Hardware and Software Requirements - Detail the specific hardware needed for the lab. - Include software versions and any additional dependencies. ## Setup Instructions - Step-by-step instructions for setting up the hardware and software. - Include screenshots or diagrams if necessary. - Troubleshooting tips for common setup issues. ## ML System Lab Workflow ### Data Collection and Preparation - **Data Source**: Specify where the data is coming from (e.g., public dataset, simulated data, proprietary dataset). - **Data Description**: Brief description of the data, including its size, format, and key attributes. - **Data Preprocessing Steps**: List and explain the preprocessing steps required (e.g., cleaning, normalization, augmentation). ### Model Training - **Model Architecture**: Specify the architecture being used (e.g., CNN, RNN, Transformer). - **Framework**: Mention the ML framework used (e.g., TensorFlow, PyTorch). - **Training Configuration**: Detail the training configuration (e.g., batch size, learning rate, number of epochs). - **Code Snippets**: Provide relevant code snippets for training the model. ### Model Optimization - **Hyperparameters**: List the hyperparameters tuned and their optimal values. - **Optimization Techniques**: Describe any optimization techniques used (e.g., Grid Search, Random Search, Bayesian Optimization). - **Performance Metrics**: Specify the metrics used to evaluate model performance (e.g., accuracy, F1 score). ### Model Evaluation - **Evaluation Metrics**: Detail the metrics used to evaluate the model (e.g., precision, recall, AUC-ROC). - **Validation Strategy**: Describe the validation strategy (e.g., k-fold cross-validation, train-test split). - **Results**: Present the evaluation results, including any visualizations if applicable. ### Model Deployment - **Deployment Environment**: Specify where the model will be deployed (e.g., cloud, edge device). - **Deployment Steps**: Step-by-step instructions for deploying the model. - **Verification**: Methods to verify the deployed model is working correctly. ## Assessment - Practical tasks to test students' understanding. - Include answers or a separate section for solutions. ## Summary - Recap the main points covered in the lab. - Highlight key takeaways. - Mention any further reading in the book that could reinforce the lab's concepts. ## References - Cite any external resources or documentation referenced in the lab. - Provide links to additional reading materials or related resources. ## Appendix - Any additional information, such as advanced configuration options or extended explanations. </div>

GiteaMirror added the area: book label 2026-03-22 15:25:46 -05:00

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2026-03-22 15:25:46 -05:00

GiteaMirror referenced this issue

2026-04-11 07:40:58 -05:00

[GH-ISSUE #113] Build step fails #1267

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2026-04-19 11:58:06 -05:00

[GH-ISSUE #113] Build step fails #3887

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2026-04-21 21:15:55 -05:00

[GH-ISSUE #113] Build step fails #5229