#Coding Autograder

[steppedoffaflight]

🫠

[verystressedcollegestudent]

today in sleep deprived shenanigans i accidentally went to the wrong section of a class and didn't fully comprehend it until we were 90% of the way through the daily quiz...

[scholarhect]

there’s a written portion that’s submitted on gradescope, do we also submit our code on gradescope to use their autograder feature, like every other course does? no, there’s an autograder using github actions, meaning there’s tons of files in here i can’t touch because they configure the autograder. i do have to admit running the autograder immediately every time you push a change is kind of cool but i still feel like this is ridiculous

professors who just got their phds will be like “i’ve provided a test.py” and you’ll be like “is it tests that will help me check my progress during development” and they’ll be like “it’s tests” and you run it and get a couple contextless outputs and then it crashes, and you check it out and it’s like

a = f()

# your function should make a grid

print(a)

# checking if b and c are equal

print(b==c)

and this continues into calls to functions you haven’t implemented yet. also b and c aren’t supposed to be equal, so if you ran the script and saw a random ass False print out after the grid, you’re doing alright. literally just False by itself im going to scream

[aroacesigma]

i hate coding. evil. disgraceful <<< guy whos code is definitelyyyyy not not working for some inexplicable reason that he cannot fucking figure out because everything SEEMS TO BE WORKING AND IN ORDER BUT THE AUTOGRADER DOESNT SEEM TO THINK SO

[oplishin]

my comp sci professor is deeply funny for giving the code autograder (infinite retries) a 3 minute waiting time so people don't just spam submit things but i guess i'm just waiting now!!!!

[thegreatgodbird]

My strange code shyly passes the autograder

[bleachersgirl]

crisis averted i do not have to drop out. my code did NOT run but the autograder decided that was worth a 95% anyway

[mousydentist]

i think one thing that genuinely helped me not want to use it was having a professor that, instead of outright banning the use of it, says “dont use it for assignments, but if there’s a concept you’re stuck on feel free to ask for help.” because tbh, it’s pretty obvious when you ask chatgpt to right a full essay, but writing code? not so much. and the problem is, most of the time it works. maybe it’s shit and messy and confusing but it works, and the majority of my classes have autograders that dont care what it looks like as long as it passes tests.

so when a professor outright says “don’t use it it’s completely forbidden,” not only am i extremely tempted, but i’m also trying so hard to refrain from using it that it makes me want to do it even more.

but if the professor instead says use it to understand concepts, or even in my web programming class he ENCOURAGES us to use it for the css because no one in their right mind would memorize everything you can do in css, THATS what helps my understanding. cause now, instead of just taking the L and deciding this topic is one ill never be able to grasp, i can ask chat (which, is really just a very specific web search) and maybe actually be able to understand it.

and THATS what helps my understanding, more than being forbidden from using it, and more than using it to complete assignments. i shudder to think of all the people in my classes who only pass cause of chatgpt actually joining the workforce. because yeah, it works, but working code can only get you so far, and if the future of technology is working code, well. frankly we’re fucked.

it's so fucking frustrating to be in college and know everyone uses chatgpt and to be tempted by it constantly while also knowing intellectually that it doesn't work and it's a bad idea. like, i hang out in the library a lot, and i see people using chatgpt on assignments almost every day. and i know it isn't a good way to learn, because it's not really "artificial intelligence" so much as it is an auto text generator. and it gives you wrong information or badly worded sentences all the time. but every week i stare down assignments i don't want to do and i think man. if only i could type this prompt into a text generator and have it done in 10 minutes flat. and i know it wouldn't work. it wouldn't synthesize information from the text the way professors want, it wouldn't know how to answer questions, it just spits out vaguely related words for a couple paragraphs. but knowing my classmates get their work done in 10 minutes flat with it while i fight every ounce of attention deficit hyperactivity disorder in my body is infuriating.

[programmingandengineering]

CS540 A* Search for the 7-Tile Puzzle State Space & Heuristic Optimization

Assignment Goals Deepen understanding of state space generation. Practice implementation of an efficient search algorithm. Canvas autograder notes Before submitting your code, make sure you remove ALL print statements that are not required by the assignment. Having extra print statements will at best give you an inaccurate grade and at worst will crash the autograder making it so we have to…

[ankitcodinghub]

CS7643: Deep Learning Assignment 3 Solved

• It is your responsibility to make sure that all code and other deliverables are in the correct format and that your submission compiles and runs. We will not manually check your code (this is not feasible given the class size). Thus, non-runnable code in our test environment will directly lead to a score of 0. Also, be sure to clean up print statements, etc. before submitting – the autograder…

[govindhtech]

PyTorch/XLA 2.4: Pallas & developer experience, “eager mode”

PyTorch/XLA 2.4

For deep learning academics and practitioners, the open-source PyTorch machine learning (ML) library and XLA ML compiler provide flexible, powerful model training, fine-tuning, and serving. The PyTorch/XLA team is happy to announce the release of PyTorch/XLA 2.4 today. This version includes several noteworthy enhancements to address issues raised by developers and builds on the previous release. Here, we go over a few of the most recent additions that facilitate using PyTorch/XLA:

Pallas, a proprietary kernel language that supports GPUs and TPUs, has been improved.

Fresh calls to the API

The “eager mode” experiment is introduced.

The TPU command line interface has been updated.

Pallas improvements

Although the XLA compiler can optimize your current models, there are situations in which bespoke kernel code can provide model authors with superior performance. Pallas is a bespoke kernel language that supports TPU and GPUs, so instead of requiring you to use a more complex and lower-level language like C++, you can write more performant code in Python that is closer to the hardware. Pallas is comparable to the Triton library, but it makes porting your model from one machine learning accelerator to another easier because it runs on both TPUs and GPUs.

The latest version of PyTorch/XLA 2.4 brings improvements to Pallas’ functionality and user experience.

Flash Attention is now completely integrated with PyTorch autograd, allowing for automatic gradient calculation.

Integrated assistance for Paged Focus on Inference.

Support for group matrix multiplication using Mega blocks’ block sparse kernels as an Autograd function, eliminating the requirement for backpropagation to be done manually.

API modifications

A few new calls are included in PyTorch/XLA 2.4 to facilitate integration with your current PyTorch workflow, such as:device = torch_xla.device()

And now you can call torch_xla.sync() in place of having to do xm.mark_step(). The developer workflow is enhanced and the process of converting your code to PyTorch/XLA is made simpler by these enhancements.

import torch_xla.core.xla_model as xm device = xm.device()

Try out the eager mode

If you’ve worked with PyTorch/XLA for any length of time, you are aware of the term “lazily executed” models. This implies that before models are sent to be performed on the XLA device target hardware, PyTorch/XLA 2.4 builds the compute graph of operation. Operations are compiled and then instantly carried out on the target hardware with the new eager mode.

The drawback of this feature is that, because each instruction is not conveyed to the TPU immediately by default, TPUs themselves lack a real eager mode. In order to compel the compilation and execution, Google cloud add a “mark step” call to each PyTorch action on TPUs. As a result, eager mode functions, albeit as an emulator rather than a built-in feature.

With this release, Google cloud want for eager mode to be used in your local surroundings rather than in your production environment. Eager mode is intended to simplify local model debugging on your PCs without requiring you to deploy it to a broader fleet of devices, as is the case with most production systems.

CLI to view Cloud TPU information

The nvidia-smi tool, which you can use to troubleshoot your GPU workloads, determine which cores are being used, and check how much memory a particular workload is consuming, may be familiar to you if you’ve previously used Nvidia GPUs. Additionally, a comparable command line tool has been developed for Cloud TPUs that facilitates the retrieval of device and utilization data.

Start using PyTorch/XLA 2.4 right now

The best aspect is that your current code is still compatible with PyTorch/XLA 2.4, despite the fact that it has certain API changes. Additionally, the new API methods will make your future development processes easier. What are you waiting for? Try the most recent version.

The PyTorch logo: Key Features

Prepared for Production

Use TorchScript to switch between eager and graph modes with ease, and TorchServe to quicken the production process.

Dispersed Instruction

The torch.distributed backend enables scalable distributed training and performance optimisation in research and production.

Sturdy Ecosystem

A plethora of tools and frameworks complement PyTorch/XLA 2.4 Improved Pallas and developer experience, “eager mode” and facilitate its development in computer vision, natural language processing, and other fields.

 Cloud Assistance

Major cloud platforms support PyTorch well, enabling easy scaling and frictionless development.

XLA Features

Accelerated Linear Algebra, or XLA, is an open-source machine learning compiler. Models from well-known frameworks like PyTorch, TensorFlow, and JAX are imported into the XLA compiler, which then optimises them for high-performance execution on a variety of hardware platforms, including GPUs, CPUs, and ML accelerators. For instance, employing XLA with 8 Volta V100 GPUs produced a ~7x performance boost and ~5x batch-size improvement over the same GPUs without XLA in a BERT MLPerf submission.

Leading ML hardware and software companies, including as Alibaba, Amazon Web Services, AMD, Apple, Arm, Google, Intel, Meta, and NVIDIA, are working together to develop XLA as part of the OpenXLA initiative. Principal advantages

Construct anywhere: Prominent machine learning frameworks like TensorFlow, PyTorch, and JAX have already incorporated XLA.

Run anywhere: It has pluggable infrastructure to offer support for other backends, such as GPUs, CPUs, and ML accelerators, among other backends.

Optimise and scale performance: It makes use of automated partitioning for model parallelism and production-tested optimization stages to maximize a model’s performance.

Reduce complexity: By utilising MLIR, it combines the greatest features into a single compiler toolchain, saving you from having to handle a variety of domain-specific compilers.

Future-ready: XLA is an open-source project that was developed in conjunction with top ML software and hardware providers. Its goal is to be the industry leader in machine learning.

Read more on Govindhtech.com

[daixiepython]

 EECS 183 Project 4

 EECS 183 Project 4: CoolPics | p4-coolpics  1/28 p4-coolpics EECS 183 Project 4: CoolPics Project Due Friday, March 22 2024, 11:59 pm Direct autograder link In this project, you will create a program that reads in a description of shapes, draws those shapes, and saves the result to a file. You will represent the different shapes using classes. Here are some examples of images created by students in past semesters:  EECS 183 Project 4: CoolPics | p4-coolpics  2/28 By completing this project, you will learn to: Develop an application using multiple classes Divide a C++ program into source and header files Read program input from a file with multiple line formats Write test cases for classes Write member function stubs given their declarations You will apply the following skills you learned in lecture: Lecture 13 Use a streamʼs fail state to detect input format Recover from a stream entering the fail state Read and write to files using streams Lecture 14 Write code using classes Write and use default and non-default constructors Lecture 15 Place class and member function declarations and definitions in the correct files Access public and private portions of a class in the appropriate places代写 EECS 183 Project 4 Write and us getter and setter functions Define and use multiple non-default constructors Lecture 16 Create and use classes that contain member variables that are instances of other classes Lecture 17  EECS 183 Project 4: CoolPics | p4-coolpics  3/28 Overload operator« and operator» to allow classes to be read from and written to streams Write test cases for code structured with classes Getting Started Starter Files Download the starter files using this link and create a project using them in your IDE. You will be working with the following files: File Role What you will do pics.cpp Driver for application Write code here and submit test.cpp Test cases Write code here and submit Circle.cpp, Color.cpp, Graphics.cpp, Line.cpp, Point.cpp, Rectangle.cpp, Triangle.cpp Member function definitions Write code here and submit Circle.h, Color.h, Graphics.h, Line.h, Point.h, Rectangle.h, Triangle.h Class declarations Do not modify! Shape.h, Shape.cpp Provided support code Do not modify! bmp.h, utility.h Provided support code Do not modify! .txt files Input to generate pictures Use these as input for testing pics.cpp .bmp files Ouput from .txt files Use these for testing the output of pics.cpp We suggest writing the code in the following order:

test.cpp (ongoing as you develop each class)

Point.cpp  EECS 183 Project 4: CoolPics | p4-coolpics  4/28

Color.cpp

Graphics.cpp

Line.cpp

Triangle.cpp

Circle.cpp

Rectangle.cpp

pics.cpp Writing Function Stubs The first time you try to run the starter code, you will see many compile errors. They will look something like the following. These errors are due to missing function definitions for most of the class member functions. In previous projects in EECS 183, you were provided with all of the necessary functions for each project. The shell of the function definitons were given and you had to finish implementing them. For this project, you will be required to complete all of the shells of the function definitions. This must be completed for all classes before you will be able to compile your code. Each function declaration must have a corresponding写  EECS 183 function definition once any call to the function exists. This is called a function stub. You must write all of the stubs for each function definition immediately after creating your project in Visual Studio or Xcode. A function stub for the Point class non-default constructor would look like the following, and appear in the file Point.cpp While a function stub for the Point class checkRange function would look like the following: Rectangle.obj : error LNK2001: unresolved external symbol "public: __thiscall Point::Point(int,int)" (??0Point@@QAE@HH@Z) 1 2 3 Point::Point(int xVal, int yVal) { // to do - implement } 1 2 3 4 5 6 int Point::checkRange(int val) { // to do - implement // to do - replace with correct return statement return val; }  EECS 183 Project 4: CoolPics | p4-coolpics  5/28 Submission and Grading Submit your code to the autograder here. You receive 4 submits each day and your best overall submission counts as your score. You will submit 11 files, which must be called Circle.cpp , Color.cpp , Graphics.cpp , Line.cpp , pics.cpp , Point.cpp , Rectange.cpp , Triangle.cpp , test.cpp , data1.txt , and data2.txt T

​  WX：codehelp 

[myprogrammingsolver]

Project 1: Search in Pacman Solution

Introduction In this project, your Pacman agent will find paths through his maze world, both to reach a particular location and to collect food efficiently. You will build general search algorithms and apply them to Pacman scenarios. Evaluation: Your code will be autograded for technical correctness, using the same autograder and test cases you are provided with. Please do not change the names…

[educationalinstitutesindia]

Demystifying the World of Code: An Educational Standpoint

Coding education is essential for success in today's digital world. Learning to code enables individuals to develop in-demand and useful skills that benefit their careers and society. This article provides the following: - An overview of why coding is important. - Methods for learning to code. - The role of teachers - Innovative teaching approaches. - Challenges within coding education.

The Basics of Coding

Coding refers to writing instructions that direct computers to perform specific tasks. Coders use programming languages like Python, JavaScript, and C++ to develop software, websites, and mobile apps. Learning to code allows you to become a programmer and build digital solutions. Coding is essential today because so much of how we live, work, and communicate depends on technology. Programmers create the software and apps that power these technologies. Some of the most useful languages for beginners include Python, JavaScript, Java, and C/C++:

Benefits of Learning to Code

Learning to code provides many benefits. Coding is a highly valued skill, with many high-paying software engineering and web development jobs. The US Bureau of Labor Statistics projects over 22% growth for software developer jobs over the next ten years. Building software and apps requires strong problem-solving skills useful in any field. Programmers learn how to break down problems and create solutions logically. Coding education fosters creativity by teaching how to build new technologies, websites, mobile apps, and software. Programmers can design digital solutions to meet all types of needs. Programming languages demand precise syntax and logic to function. Learning to code strengthens analytical abilities through debugging code, understanding algorithms, and developing software. These mindsets translate broadly.

Methods of Learning to Code

Massive open online courses or MOOCs offer free or affordable coding classes on platforms. These interactive courses allow you to learn on schedule but provide deadlines and accountability. Intensive boot camps typically take 2-6 months. They teach tech skills for a software engineer or web developer job placement. Coding schools also provide structured education and career support. These programs charge tuition but often aid in job placement. They offer a quick path to a career in tech for those who still need a formal degree.

An Efficient Teacher Toolkit

To teach students coding, teachers employ various technological tools to streamline and enhance instruction. For example, auto grading systems automatically grade student code so teachers can provide individualized guidance. Coding platforms give students opportunities for the practical application of skills. Tutorials and online lessons allow for self-paced learning. Team collaboration software facilitates group work. These tools, including autograding, coding environments, videos, and collaboration platforms, comprise a robust toolkit for teachers to teach coding engagingly. By leveraging autograding software, hands-on coding platforms, video tutorials, and team collaboration tools, teachers are equipped with technology to teach students coding and help them develop strong computational thinking abilities.

Innovative Approaches to Coding Education

Students learn to code by working on real-world projects and building software and apps. Project-based learning is an engaging method that provides practical experience. Students can collaborate on group projects or work individually on portfolio projects. Incorporating elements like points, badges, and levels, gamification uses game design in non-game contexts like education. For coding, gamification may involve interactive coding challenges, hackathons, or platforms featuring progress meters and achievement-unlocked messages. Studies show gamification can increase motivation and participation. Group work enables students to learn from each other by exchanging ideas, asking questions, explaining concepts, and problem-solving together. For coding, students may work in pairs or small groups to complete hands-on projects, evaluate each other's code, or study programming language documentation together. Social interactions keep students actively engaged and strengthen understanding. Innovative teaching methods will make learning to code more engaging and effective for students. Approaches like project-based learning, gamification, and collaborative coding education could motivate more people to learn programming skills.

Challenges in Coding Education

While coding education is crucial for the future, there are challenges to address. Coding education can be expensive, whether pursuing degrees, boot camps, or self-study. More affordable high-quality options are needed to reach all students. Underrepresented groups like women, minorities, and people with disabilities face barriers to learning to code and entering tech careers. Inclusive outreach and addressing systemic biases are required. Programming languages, frameworks, and other technologies continually evolve. Coding education must keep up to teach relevant and in-demand skills. Updating courses and resources requires an ongoing investment of time and money. Solutions could include public-private partnerships to fund education, tailoring courses to reach underserved groups, and developing flexible programs that adapt quickly to new technologies. With a focus on access and inclusion, coding education can demystify the world of technology for all.

Conclusion

Coding is important because software powers many of the tools we use daily. Coders help build solutions that improve lives and productivity. Coding also promotes critical thinking skills that are useful across fields. However, learning to code can only be challenging with the right educational support. Read the full article

[codequiry01-blog]

Autograder

The autograder is an easy auxiliary Seattle characteristic that allows instructors to collect and grade student submissions via a web interface. So, if you want to learn how autograder works then make a sure visit to the Codequiry website. moreover, here, you can check your code plagiarism.

[kuiinncedes]

oof not my project partner just ,, doing it on her own 🤡😭