6 Do not submit evalpy to us we already have it with us We have given you

6 do not submit evalpy to us we already have it with

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6. Do not submit eval.py to us – we already have it with us. We have given you access to the file eval.py just to show you how we would be evaluating your code. 7. Make sure that running predict.py does not require us to install any Python libraries which are not available using pip . Libraries that are installable using pip are fine. How- ever, if your prediction routine requires using external code not available using pip , you must supply all that external code to us within your ZIP archive – we should not have to install it. To be sure, we will not install or download any libraries from places like GitHub, personal homepages, or other repositories (even the repository mentioned in the question itself) even if you want us to. Your archive itself should contain all such files. 8. Other than the above restrictions, you are free to import libraries as you wish,e.g. sklearn, scipy, pandas. Files for libraries not available on pip must be provided by you. 9. Once we have installed pip libraries required by your code, we should just be able to call predict.py and get predictions i.e. your prediction code must be self contained. We should not have to create any new directories, move files around or rename them. All of that must be done by you while creating the archive (remember, you are allowed to have as many files and (sub)directories apart from the file predict.py as you want). 10. We do not need your training code for part 5. We do not need any code (training or prediction) that you used to solve parts 1-3. We simply need your prediction code for part 5. Do not increase your submission file size (remember there are marks for submission size too) by including unnecessary files. 11. The system on which we will run your prediction code will not have a GPU . Make sure your code can run simply on a CPU. Thus, use the CPU version of deep learning libraries if you are using them and not the GPU version of those libraries e.g. keras. 12. You may use a GPU (if you can find one) to train your method but your prediction method must still work on a CPU. To be sure, we will not offer your prediction code a GPU even if you ask us to. Do not use any libraries that require GPU access during prediction. There are no restrictions on using GPU for training but we will not provide your code GPU access during prediction. 13. We will test your submitted code on a secret dataset which would have the same features per user as well as same number of labels. 7
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  • Fall '16
  • Piyush Rai
  • Computer file

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