Gaming Laptop

first of all thank you so much to nvidia for believing in me and for sponsoring this video now let's get started so hello everyone today is another big day with my roommate who is a gamer because it was always my dream to play games at home and in my life the only gaming console i had in my childhood was a ps2 and after that i could never afford one but now time has changed and i have a gaming laptop with me with a gamer that i'll be unboxing let's get started it feels so premium i mean it looks really good yeah futuristic looks when everything is in the back yeah but this this is a cool design all the ports are on the back yeah wow so let's talk about the checklist requirement for a student that this laptop lenovo legion 5 pro meets number one 16 gigabytes of ram which is written in the georgia tech's laptop requirement guide check along with the powerful processor which is amd ryzen 7 5800h with 8 cores and 16 threads check a quad hd plus display with the maximum brightness of 500 nits check a high refresh rate for gaming 165 hertz check a gpu for data science and programming which is nvidia geforce rtx 3070 with nvidia reflux technology check and you guys will not believe that this gpu powers around 6000 cuda cores and around 8 gigabytes of graphics memory making it perfect for data science students and on the side a toggle for camera which allows you to disable and enable the webcam check as well which is important for some of the online classes and it comes with a 300 watt charger which is one of the most powerful one i have seen so before i set up my laptop for programming and data science for this year let's talk about nvidia reflux technology which is very important for gaming with the help of my roommate i have been playing rainbow six seas for the past five years every day you learn new things in this game even after five years i learn new things new hiding spots new spawn peaks new runouts so system latency is really important for a game like rainbow six each nvidia reflex just boosts the system latency it reduces it system latency is basically the time it takes for your input to appear on the screen okay so there is this nvidia reflex draw input indicator in this game pre-built so you will see when i click this it will show how fast my screen my input is being recorded so [Music]

now let's finally set up my laptop for programming and data science and you will not believe that data science has become one of the most popular major for international students in the u.s before it used to be in the top 10 now it is slowly and gradually becoming in the top five list for especially for masters undergrad it's still computer science for masters it has become one of the top five majors so that's why it is very important to know the fundamentals of data science and why a gpu a graphic card like nvidia is very important so i can give you a simple example you might have seen multiple lanes in the us when you go on a highway so with the help of cpu you can maybe reach four or five lanes so that means four or five cars can be on the highway simultaneously and reach the destination at some point but with the help of gpu it changes the game now up to 3 000 cores can be in a gpu that means they can be up to 3000 rows or lanes where cars can go so that increase the speed of those mathematical operation it's basically math right so what you do using data science is math so those operations are broken down into parallel processes so that's why you need a gpu so that you can run those processes bit faster much faster using a gpu as compared to a cpu with the cpu there's a limit you might be able to run five cores eight cores but with gpu 2000 to 3000 cores thanks to nvidia graphic card now let me set up my laptop my linux environment on this laptop for programming so earlier i used to use google's cloud platform for data science i used to use google collab which is right here on the screen so it has been useful but there were a lot of limitations while i was in my cyber security class data science class data mining class i have taken multiple classes in terms of data science so let's talk about my cyber security class during that time some of the data sets were so so huge it used to take more than 12 hours to run but google collab has a limitation you cannot train for more than 12 hours simultaneously that was number one number two when the data set size is so huge let's say that i am trying to train sets of images which is 10 gigabytes let's say you're trying to train that this is a laptop and this is a table you're trying to make the computer recognize automatically and you have thousands of photos and the size of the photos can be multiple gigabytes it could be let's say 10 gb right i'll upload it to google drive and use google cloud and train it and then realize my accuracy is 50 and then i have to fix my data do some operations sometimes re-upload the images because if i want better images which can be recognized easily and that will take more time so that's why gpu became more productive option for me because it's local i can train as many times as possible earlier i was forced to go to my library to finish all my data science assignments but now i don't have to i can finish all of my data science related tasks on my powerful laptop thanks to nvidia so let me quickly now show you how we data science students or data science engineers use tensorflow and how it makes a difference with the gpu so let's quickly check if we have tensorflow or not so i'm gonna go to terminal and i'm gonna quickly show you my python three version so python three and let's see if i have tensorflow the most popular library for data science and i find out i don't have it so i have to go to the setup to install it but i'm gonna install it through a unique way which is through docker so let me quickly explain what a docker is through an example of gta 5 so let's say your teacher tells you your professor tells you that you have to complete a mission for gta 4 and gta 5 which is the same mission but on both the games now what you will do is you will first install gta 4 which is 100 gigabytes and then complete the mission then submit and then you will install gta 5 complete the mission and then submit now there's a problem if you have to make a change you will have to go back and forth with the process to make it simple what you can do is you can make a virtual computer and have both gta 4 and gta 5 simultaneously and work on your assignment so let me show you how so first of all i'm gonna make a docker container on the cpu level so these are the simple steps how you make a docker container and how you run tensorflow on docker so i'm gonna copy the command go to terminal exit out of python3 and then paste the command here which i already have so now i'm gonna have python 3 running on a jupyter notebook which is on this url which is on a virtual computer so let's run it quickly and i can see my my python files or my python jupyter notebook files and i can see it now here's a cache tensorflow let's see if tensorflow is already installed or i have to install it so if i see the tensorflow version currently on this block it is 2.60 that is already installed let's see the python version here on the docker so import says print sales dot version so which will tell me my python version which is 3.6.9 and if you go back to terminal and if you check my python version on the system which is locally it's going to be 3.9.5 so there's a difference because tensorflow works on a specific version and luckily tensorflow documentation has everything ready for me and it's the fastest way to use tensorflow now that was on cpu now let's try to run it on gpu that's how nvidia makes a difference i'm gonna read the documentation go to docker go to the gpu side of the docker and then run a different command which is this time docker run on gpu and now i'm going to make a simple change add an option gpus all and this time it will run on a gpu as compared to a cpu so here we go now tensorflow is running actually on a gpu as compared to a cpu i'm gonna paste the new url on my chrome tab so now there are two docker containers or you can say two python running on my computer or two tensorflow libraries running on my computer and i can train simultaneously and compare and here you will find out after training the second sample on gpu it's gonna be at least 10 to 30 times faster than a cpu so that's gonna save a lot of time and since i am dual booting windows 10 and ubuntu i have gta 5 which is being installed and when you buy an nvidia powered laptop or computer you also get a capability to integrate nvidia sdks directly into your computer as well so for example their body track sdk is ready to be used and you can use it for your hackathons and win multiple projects as well so that's my favorite part of nvidia powered computers so overall gpu definitely makes a huge difference when it's powered by nvidia not just towards data science and programming but also towards gaming as well so thank you so much to everyone for watching and it's all because of your love and support that i got this opportunity thank you so much for that