<p>Since AWS was already mentioned here, I feel I also have to mention Azure ;)</p>

<p>There is currently running a 200 USD free trial offer that you can utilize. There is one setback about this offer though. I think it requires a credit card in order to activate it. But the 200$ will last you the 1st month and allow you to spin up some serious hardware during that time.</p>

<p>You can sign up here: <a href="https://azure.microsoft.com/en-us/offers/ms-azr-0044p/" rel="nofollow noreferrer">https://azure.microsoft.com/en-us/offers/ms-azr-0044p/</a> </p>

<p>Besides that there is an offer available (Monthly free credits) if someone has access to a MSDN subscription or you can get a MSDN subscription as part of the MCT (As Professor) benefits.</p>

<p>Azure also includes several features for machine learning and cognitive services out of the box.</p>

<p>Besides that I agree with other that mention it is hard to give assignments that require resources that are not commonly available. </p>

CC BY-SA 3.0
2018-04-09T14:38:49.577Z
The idea of using clould computing (and even Azure specifically) is already covered by Aurora0001's answer. For that reason, I've downvoted.
thesecretmaster
CC BY-SA 3.0
2018-04-10T00:24:02.093Z
Furthermore, you shouldn't rely on the students being eligible for a free trial offer, since they may have already used it up. Rather, you, as the instructor, should apply for an instructional grant from Microsoft.
200_success
CC BY-SA 3.0
2018-04-09T09:29:45.727Z
2018-04-09T09:29:45.727Z
Heiko Hatzfeld
<blockquote>
  <p>How to give assignments that require heavy computational resources?</p>
</blockquote>

<p><strong>Don't.</strong></p>

<p>Most computionally intensive problems can be stripped down to something that's just as instructive but runnable on any normal computer. In particular, for inexact algorithms, you can generally trade off accuracy against computational cost, and often actually get still quite good results with only a fraction of the cost of a publication-level run.</p>

<p>I always like to emphasize that stuff like physics simulations (which is what I work on, and have also done assignments for students) or image classification should in principle, <em>mathematically speaking</em>, have <strong>infinite computational cost</strong>: an image is a function from continuous 2D space to colour space, i.e. the space of images is an infinite-dimensional $L_2([0,1]^2)$ Hilbert space. When doing numerics one quickly (IMO, often <em>too</em> quickly and without proper thought) restricts everything down to a finite-dimensional subspace thereof, but that's to some degree arbitrary – the subspace just needs to be big enough (and suitably constructed) to still represent the features that are important for the use case.</p>

<p>In an image-processing example, you should definitely check how much you can downsample the images that the students should work with and still get somewhat sensible results. If necessary, choose only a smaller set of images with clearer distinguishable features, that the algorithm can pick up quickly.</p>

<p>If the results <em>aren't</em> so great with a stripped-down accuracy, well, that's actually <em>particularly</em> instructive, because this kind of tradeoff is absolutely crucial in real-world applications too. You may have more processing power available in a research of commercial situation, but you'll also have <em>much</em> higher demands in terms of data amount and needed accuracy. So, discuss these points intensively, and make a presentation of how much better the students' results look when run by you in higher resolution on a solidly powered machine.</p>

CC BY-SA 3.0
2018-04-08T22:12:12.687Z
2018-04-08T22:12:12.687Z
leftaroundabout
<p>I have a couple of orthogonal suggestions. </p>

<p>First, and you may have done this yourself, before you give any assignment you should create a reference implementation yourself and test it in the student's environment. If you did this, then just let it be a warning to others. This is true, actually, for any assignment, not just one that might run up agains limitations. </p>

<p>But for the current situation, simply realize that there are several ways for the students to learn, not just through successfully building software for a problem. While you might be able to change the conditions for a future offering of the course, you need to deal with today's students. </p>

<p>One of the most valuable educational experiences is a Retrospective of a project in which you formally explore what worked well and what needs to be changed (or should have been changed). Instead of a working program per student, a paper per student (or student group) can impart the desired learning. Students could examine the results of other students, for example, and reflect (in writing) on the relationship between the code and the outcomes. </p>

<p>However, if you only want an answer to the stated question (how to provide for computationally intensive projects), then you need to either scale down the exercise to make it reasonable, or you need to provide adequate resources <em>somehow</em>. Upgrading all the laptops is probably not in the works, but you could, perhaps, provide access to a more powerful system (or systems) on which the students all work (individually or in groups). </p>

<p>Scaling down the project might work, actually, at an undergraduate level, since the students aren't expected to build commercial quality software, but only enough so that the appropriate learning occurs. If this were a doctoral program, on the other hand, where the students are involved in serious research, then there doesn't seem to be any alternative to finding the resources - perhaps through grant writing or even begging resources from local organizations. </p>

<hr>

<p>Note that <a href="https://rads.stackoverflow.com/amzn/click/B00DY3KQJU" rel="noreferrer">Retrospectives</a> are a fundamental Agile Software Development practice. </p>

CC BY-SA 3.0
2018-04-08T15:26:54.243Z
While I understand the advice given in the first paragraphs, I was already aware of this and think that has been one of the problems. This might be a good time to mention that I'm not the one responsible for designing the assignment, but a concerned grading assistant. Of course, this doesn't make your advice less valuable, merely less useful to me in particular. As for 'scaling down', that would be difficult. Deep learning only gives reasonable results on large datasets, as far as I'm aware. I do think that perhaps 'scaling _out_' might be possible: circumvent the actual computations, somehow.
Discrete lizard
CC BY-SA 3.0
2018-04-09T06:30:22.787Z
@Discretelizard Sure, deep learning only gives good results on initial training on large datasets. Then the industry found a workaround for that and transfer learning is the main way of making working nets. This is completely doable with standard laptops.
DonFusili
CC BY-SA 3.0
2018-04-08T15:21:13.677Z
2018-04-08T15:21:13.677Z
Buffy
<p>Giving students credits for a cloud service like AWS might be useful in this case. Amazon's <a href="https://aws.amazon.com/ec2/pricing/on-demand/" rel="noreferrer">pricing</a> is reasonable for a cloud instance:</p>
<blockquote>
<p>GPU Instances - Current Generation</p>
<ul>
<li>p2.xlarge
<ul>
<li>vCPUs: 4</li>
<li>ECUs: 12</li>
<li>Memory (GiB): 61</li>
<li>Storage (GB): EBS Only</li>
<li>Price: $0.9 per Hour</li>
</ul>
</li>
</ul>
</blockquote>
<p>The cheapest instance (<code>t2.nano</code>) is priced at $0.0058 per Hour, though you'd probably want to look at a more powerful instance to save time.</p>
<p>For $20 per student, you could give them 22 hours of <code>p2.xlarge</code> compute time or 3500 hours of <code>t2.nano</code> time (or something in between this if another option would fit better).</p>
<p>It would be wise to test the time taken for EC2 to run a few epochs yourself first before going and paying for your students to do it; you could then work out what a 'reasonable' amount would be to give them, or if this is feasible at all.</p>
<p>This is unfortunate in some ways, however, as it means that students who work quickly are advantaged more than those who spend hours experimenting. It also means that students that need more time would have to work at their own expense; whether this is permitted by your University's policies or ethical is questionable.</p>
<p>I would anticipate that it'd be easy for students to accidentally waste credit if they weren't familiar with AWS too. For this reason I'd be wary of putting this into practice... but it's better than overloading the students' personal laptops.</p>
<hr />
<p>Michael0x2a has a good point that some services may have free credits which you might be able to tap into; for example, Azure offer <a href="https://azure.microsoft.com/en-gb/free/students/#free-products-section" rel="noreferrer">$100 of credit</a> to students; <a href="https://aws.amazon.com/education/awseducate/" rel="noreferrer">AWS Educate</a> is also pretty generous, with <a href="https://s3.amazonaws.com/awseducate-list/AWS_Educate_Institutions.pdf" rel="noreferrer">member institutions</a> gaining \$200 per instructor plus \$100 per student (non-member institutions get less).</p>

CC BY-SA 3.0
2018-04-08T15:28:29.470Z
The main problem here is of course that the costs may scale badly wrt to the number of students. But this is a good overview of this option, thanks.
Discrete lizard
CC BY-SA 3.0
2018-04-09T01:11:57.467Z
Kindly add the FloydHub as well - one of my student used it recently and its not as powerful as AWS, but maybe fit somewhere for some assignments.
Failed Scientist
CC BY-SA 3.0
2018-04-09T07:48:07.687Z
@TalhaIrfan Please, don't add every single cloud service to this answer.
pipe
CC BY-SA 3.0
2018-04-09T15:38:20.650Z
I second this approach. As a current graduate student, last semester I took a parallel computing class that had all CPU parallelization assignments done (via SSH) on a computing cluster at our university, and all GPU parallelization assignments done on a Google Cloud instance using an Nvidia Tesla K80.  The GCloud credits were more than enough to cover usage time during the course.  I think @Aurora0001 has the most appropriate solution.
rm -rf slash
CC BY-SA 3.0
2018-04-14T11:46:14.883Z
Google Cloud Platform gives $300 for a year for all new users. It also offers some [grants for universities](https://cloud.google.com/edu/).
Frax
CC BY-SA 3.0
2018-04-08T15:17:59.790Z
2018-04-08T19:18:49.143Z
2020-06-18T08:36:57.023Z
Aurora0001
<p>For an undergraduate course in AI, there has been an assignment that uses deep learning on neural networks to do image classification.</p>

<p>However, many students complained about their (university provided) laptops were overheating when running their implementations and only a few were able to run enough training epochs to get meaningful results. (While those devices have a high-end CPU (for laptops, at least), the GPU is rather low-end).</p>

<p>I doubt that the problem was bad implementations, as even the better students had these problems.</p>

<p>All in all, just letting the students run their own implementation doesn't seem to have been a good idea. Are there better methods to give students the assignment of practically implementing a computationally intensive algorithm? How feasible would it be to provide cloud compute resources to the students so they <em>can</em> run their own implementations (I don't think we have a computing cluster available to students at my school)?</p>

CC BY-SA 3.0
2018-04-08T15:31:58.827Z
Should the question be taken from the perspective of a grading TA? (I ask because you did not mention this in the prompt)
Ben I.
CC BY-SA 3.0
2018-04-08T15:34:34.077Z
@BenI. My intention was to ask independent of my personal perspective, as 1) I think this may be an issue some course designers could encounter and hence focusing on my perspective is unhelpful for the general public. 2) I'm aware of how I can use information for course designers to provide suggestions to the course designer. Hence, I don't think any accommodation to my perspective would help. But good that you mention it.
Discrete lizard
CC BY-SA 3.0
2018-04-09T15:16:40.403Z
In grad school, I destroyed two perfectly fine laptops by running computationally-intensive tasks on them that they weren't capable of handling. Please be respectful to your students: if they're telling you that their computers are overheating, there is a very real possibility that some of your students will see their laptops become unusable because of the assignments.
Kevin
CC BY-SA 3.0
2018-04-09T20:15:15.613Z
I added an answer (now deleted) to get a cheap 80 dollar add on too allow running this stuff on extra hardware.  I was downvoted because of the incredible cost.  $80.  Really?
Vorsprung
CC BY-SA 3.0
2018-04-09T20:31:04.787Z
@Vorsprung I was the downvoter, because I don't like the idea of offloading the costs to students, and because that product could be discontinued. If you undelete it, I'd be happy to edit+retract my vote.
thesecretmaster
CC BY-SA 3.0
2018-04-09T23:10:17.570Z
@Vorsprung for someone who is on the breadline yes, \$80 can be an issue. Worst thing could be that those (no matter how few) for whom \$80 is actual money switch to a different course.
Tobia Tesan
CC BY-SA 3.0
2018-04-10T10:17:04.130Z
After doing whatever you find here, don't forget to make sure your IT department knows their laptops have problems...
Criticize SE actions means ban
CC BY-SA 3.0
2018-04-10T10:45:39.283Z
@immibis I think the laptops are fine. If the laptops would actually be damaged, the students would have mentioned it explicitly. And in any case, the students would let IT know if there are problems with the laptops.
Discrete lizard
<project><artificial-intelligence><programming-environment>
How to give assignments that require heavy computational resources?
# How to give assignments that require heavy computational resources?

(Asked by: username_0 on 2018-04-08)

For an undergraduate course in AI, there has been an assignment that uses deep learning on neural networks to do image classification.

However, many students complained about their (university provided) laptops were overheating when running their implementations and only a few were able to run enough training epochs to get meaningful results. (While those devices have a high-end CPU (for laptops, at least), the GPU is rather low-end).

I doubt that the problem was bad implementations, as even the better students had these problems.

All in all, just letting the students run their own implementation doesn't seem to have been a good idea. Are there better methods to give students the assignment of practically implementing a computationally intensive algorithm? How feasible would it be to provide cloud compute resources to the students so they *can* run their own implementations (I don't think we have a computing cluster available to students at my school)?

**Tags:** project, artificial-intelligence, programming-environment

> **Comments:**
> **username_1**: Should the question be taken from the perspective of a grading TA? (I ask because you did not mention this in the prompt)  
> **username_0**: @BenI. My intention was to ask independent of my personal perspective, as 1) I think this may be an issue some course designers could encounter and hence focusing on my perspective is unhelpful for the general public. 2) I'm aware of how I can use information for course designers to provide suggestions to the course designer. Hence, I don't think any accommodation to my perspective would help. But good that you mention it.  
> **username_2**: In grad school, I destroyed two perfectly fine laptops by running computationally-intensive tasks on them that they weren't capable of handling. Please be respectful to your students: if they're telling you that their computers are overheating, there is a very real possibility that some of your students will see their laptops become unusable because of the assignments.  
> **username_3**: I added an answer (now deleted) to get a cheap 80 dollar add on too allow running this stuff on extra hardware.  I was downvoted because of the incredible cost.  $80.  Really?  
> **username_4**: @username_3 I was the downvoter, because I don't like the idea of offloading the costs to students, and because that product could be discontinued. If you undelete it, I'd be happy to edit+retract my vote.  
> **username_5**: @username_3 for someone who is on the breadline yes, \$80 can be an issue. Worst thing could be that those (no matter how few) for whom \$80 is actual money switch to a different course.  
> **username_6**: After doing whatever you find here, don't forget to make sure your IT department knows their laptops have problems...  
> **username_0**: @immibis I think the laptops are fine. If the laptops would actually be damaged, the students would have mentioned it explicitly. And in any case, the students would let IT know if there are problems with the laptops.  

---

## 4 answers

### Answer by username_9 on 2018-04-08. Score: 22

> How to give assignments that require heavy computational resources?

**Don't.**

Most computionally intensive problems can be stripped down to something that's just as instructive but runnable on any normal computer. In particular, for inexact algorithms, you can generally trade off accuracy against computational cost, and often actually get still quite good results with only a fraction of the cost of a publication-level run.

I always like to emphasize that stuff like physics simulations (which is what I work on, and have also done assignments for students) or image classification should in principle, *mathematically speaking*, have **infinite computational cost**: an image is a function from continuous 2D space to colour space, i.e. the space of images is an infinite-dimensional $L\_2([0,1]^2)$ Hilbert space. When doing numerics one quickly (IMO, often *too* quickly and without proper thought) restricts everything down to a finite-dimensional subspace thereof, but that's to some degree arbitrary – the subspace just needs to be big enough (and suitably constructed) to still represent the features that are important for the use case.

In an image-processing example, you should definitely check how much you can downsample the images that the students should work with and still get somewhat sensible results. If necessary, choose only a smaller set of images with clearer distinguishable features, that the algorithm can pick up quickly.

If the results *aren't* so great with a stripped-down accuracy, well, that's actually *particularly* instructive, because this kind of tradeoff is absolutely crucial in real-world applications too. You may have more processing power available in a research of commercial situation, but you'll also have *much* higher demands in terms of data amount and needed accuracy. So, discuss these points intensively, and make a presentation of how much better the students' results look when run by you in higher resolution on a solidly powered machine.


---

### Answer by username_10 on 2018-04-08. Score: 19

I have a couple of orthogonal suggestions.

First, and you may have done this yourself, before you give any assignment you should create a reference implementation yourself and test it in the student's environment. If you did this, then just let it be a warning to others. This is true, actually, for any assignment, not just one that might run up agains limitations.

But for the current situation, simply realize that there are several ways for the students to learn, not just through successfully building software for a problem. While you might be able to change the conditions for a future offering of the course, you need to deal with today's students.

One of the most valuable educational experiences is a Retrospective of a project in which you formally explore what worked well and what needs to be changed (or should have been changed). Instead of a working program per student, a paper per student (or student group) can impart the desired learning. Students could examine the results of other students, for example, and reflect (in writing) on the relationship between the code and the outcomes.

However, if you only want an answer to the stated question (how to provide for computationally intensive projects), then you need to either scale down the exercise to make it reasonable, or you need to provide adequate resources *somehow*. Upgrading all the laptops is probably not in the works, but you could, perhaps, provide access to a more powerful system (or systems) on which the students all work (individually or in groups).

Scaling down the project might work, actually, at an undergraduate level, since the students aren't expected to build commercial quality software, but only enough so that the appropriate learning occurs. If this were a doctoral program, on the other hand, where the students are involved in serious research, then there doesn't seem to be any alternative to finding the resources - perhaps through grant writing or even begging resources from local organizations.

---

Note that [Retrospectives](https://rads.stackoverflow.com/amzn/click/B00DY3KQJU) are a fundamental Agile Software Development practice.

> **Comments:**
> **username_0**: While I understand the advice given in the first paragraphs, I was already aware of this and think that has been one of the problems. This might be a good time to mention that I'm not the one responsible for designing the assignment, but a concerned grading assistant. Of course, this doesn't make your advice less valuable, merely less useful to me in particular. As for 'scaling down', that would be difficult. Deep learning only gives reasonable results on large datasets, as far as I'm aware. I do think that perhaps 'scaling _out_' might be possible: circumvent the actual computations, somehow.  
> **username_11**: @Discretelizard Sure, deep learning only gives good results on initial training on large datasets. Then the industry found a workaround for that and transfer learning is the main way of making working nets. This is completely doable with standard laptops.  

---

### Answer by username_12 on 2018-04-08. Score: 16

Giving students credits for a cloud service like AWS might be useful in this case. Amazon's [pricing](https://aws.amazon.com/ec2/pricing/on-demand/) is reasonable for a cloud instance:

> GPU Instances - Current Generation
>
> * p2.xlarge
>   + vCPUs: 4
>   + ECUs: 12
>   + Memory (GiB): 61
>   + Storage (GB): EBS Only
>   + Price: $0.9 per Hour

The cheapest instance (`t2.nano`) is priced at $0.0058 per Hour, though you'd probably want to look at a more powerful instance to save time.

For $20 per student, you could give them 22 hours of `p2.xlarge` compute time or 3500 hours of `t2.nano` time (or something in between this if another option would fit better).

It would be wise to test the time taken for EC2 to run a few epochs yourself first before going and paying for your students to do it; you could then work out what a 'reasonable' amount would be to give them, or if this is feasible at all.

This is unfortunate in some ways, however, as it means that students who work quickly are advantaged more than those who spend hours experimenting. It also means that students that need more time would have to work at their own expense; whether this is permitted by your University's policies or ethical is questionable.

I would anticipate that it'd be easy for students to accidentally waste credit if they weren't familiar with AWS too. For this reason I'd be wary of putting this into practice... but it's better than overloading the students' personal laptops.

---

Michael0x2a has a good point that some services may have free credits which you might be able to tap into; for example, Azure offer [$100 of credit](https://azure.microsoft.com/en-gb/free/students/#free-products-section) to students; [AWS Educate](https://aws.amazon.com/education/awseducate/) is also pretty generous, with [member institutions](https://s3.amazonaws.com/awseducate-list/AWS_Educate_Institutions.pdf) gaining \$200 per instructor plus \$100 per student (non-member institutions get less).

> **Comments:**
> **username_0**: The main problem here is of course that the costs may scale badly wrt to the number of students. But this is a good overview of this option, thanks.  
> **username_13**: Kindly add the FloydHub as well - one of my student used it recently and its not as powerful as AWS, but maybe fit somewhere for some assignments.  
> **username_14**: @TalhaIrfan Please, don't add every single cloud service to this answer.  
> **username_15**: I second this approach. As a current graduate student, last semester I took a parallel computing class that had all CPU parallelization assignments done (via SSH) on a computing cluster at our university, and all GPU parallelization assignments done on a Google Cloud instance using an Nvidia Tesla K80.  The GCloud credits were more than enough to cover usage time during the course.  I think @username_12 has the most appropriate solution.  
> **username_16**: Google Cloud Platform gives $300 for a year for all new users. It also offers some [grants for universities](https://cloud.google.com/edu/).  

---

### Answer by username_7 on 2018-04-09. Score: 0

Since AWS was already mentioned here, I feel I also have to mention Azure ;)

There is currently running a 200 USD free trial offer that you can utilize. There is one setback about this offer though. I think it requires a credit card in order to activate it. But the 200$ will last you the 1st month and allow you to spin up some serious hardware during that time.

You can sign up here: <https://azure.microsoft.com/en-us/offers/ms-azr-0044p/>

Besides that there is an offer available (Monthly free credits) if someone has access to a MSDN subscription or you can get a MSDN subscription as part of the MCT (As Professor) benefits.

Azure also includes several features for machine learning and cognitive services out of the box.

Besides that I agree with other that mention it is hard to give assignments that require resources that are not commonly available.

> **Comments:**
> **username_4**: The idea of using clould computing (and even Azure specifically) is already covered by username_12's answer. For that reason, I've downvoted.  
> **username_8**: Furthermore, you shouldn't rely on the students being eligible for a free trial offer, since they may have already used it up. Rather, you, as the instructor, should apply for an instructional grant from Microsoft.  

---

