WEBVTT 00:00.080 --> 00:00.960 Welcome back. 00:00.960 --> 00:06.520 In this video we're going to talk about the key features and why Google Colab is so popular. 00:06.720 --> 00:13.680 Also we're going to talk about some limitations to be aware of free access to GPUs and TPUs. 00:13.720 --> 00:15.400 This is the killer feature. 00:15.560 --> 00:21.640 Training complex AI models requires immense computational power, which is expensive. 00:21.800 --> 00:25.120 Colab provides access to those resources for free. 00:25.120 --> 00:31.720 In order to upload the files or run some files, we can go to File Locate. 00:31.720 --> 00:40.800 In drive, you can upload upload the files to your drive and located in drive Google drive open in playground 00:40.800 --> 00:41.440 mode. 00:41.440 --> 00:43.640 Create a new notebook in drive. 00:43.800 --> 00:46.400 Open notebook upload notebook. 00:46.680 --> 00:54.600 So in this course, I prepared those notebooks and other notebooks in the later stages, but I want 00:54.640 --> 00:56.880 from you to follow me step by step. 00:57.160 --> 01:04.610 All those files and notebooks are available in the resources folder, so please upload them step by 01:04.610 --> 01:07.170 step as I am doing here. 01:07.250 --> 01:07.770 Okay. 01:07.810 --> 01:10.010 File upload notebook. 01:10.250 --> 01:11.730 Select the notebook. 01:11.770 --> 01:15.850 Select Basics Variables operators and click open. 01:15.970 --> 01:17.730 Zero configuration required. 01:17.730 --> 01:24.090 You don't need to be a system administrator to set up a Python environment with complex dependencies. 01:24.130 --> 01:25.130 Dependencies. 01:25.130 --> 01:27.010 It's very simple. 01:27.010 --> 01:30.490 So you can see the notebook opened. 01:30.490 --> 01:35.210 And in the next videos we're gonna learn about those step by step. 01:35.250 --> 01:35.570 Okay. 01:35.650 --> 01:43.090 And we're going to run the codes and learn about the basic variables and operators in Python. 01:43.090 --> 01:48.330 You can share your notebooks via a link just like a Google document. 01:48.330 --> 01:56.930 So here from file you can save save a copy GitHub copy in drive, move to the bin, move rename and 01:57.050 --> 01:57.570 others. 01:57.850 --> 01:58.450 Okay. 01:58.490 --> 02:02.170 Also you can go to the edit select all cells. 02:02.370 --> 02:04.170 This is by the way a cell. 02:04.170 --> 02:08.970 So here if I need to create a code this is a cell okay. 02:09.090 --> 02:10.410 You can run them. 02:10.450 --> 02:15.770 Run all the cells for here I'm running 22 cells okay. 02:15.890 --> 02:23.330 So here you can notice that we are executing most common data science and ML libraries like TensorFlow, 02:23.330 --> 02:26.130 PyTorch, OpenCV are pre-installed. 02:26.130 --> 02:30.810 You can install any other library with a simple pip install command. 02:30.850 --> 02:33.730 Don't worry, we're gonna see everything later. 02:33.890 --> 02:42.010 In the next video we're gonna talk about the view tab, table of contents, notebook info, execute 02:42.010 --> 02:46.090 code history, start slash slideshow, and others. 02:46.290 --> 02:47.570 Collapse sections. 02:47.610 --> 02:52.450 Expand sections so you can expand sections by simple click. 02:52.690 --> 02:55.740 And you can notice that we have The. 02:55.860 --> 02:59.580 When we run our application, we run our application. 02:59.580 --> 03:02.220 Here we are executing all the cells. 03:02.380 --> 03:05.900 So here you can stop, you can run again. 03:05.900 --> 03:11.180 And also you can click here and clear output. 03:11.220 --> 03:11.740 Okay. 03:11.860 --> 03:13.020 Let me scroll up. 03:13.300 --> 03:18.020 The free version doesn't provide unlimited compute. 03:18.060 --> 03:24.580 The your virtual machine will disconnect after a period of inactivity typically 90 minutes. 03:24.620 --> 03:30.300 Like for example, if you if you have one hour and a half, you don't make anything. 03:30.300 --> 03:35.980 It will disconnect and has a maximum continuous runtime, typically 12 hours. 03:36.180 --> 03:40.700 Your work is saved, but the computational environment is reset. 03:40.700 --> 03:49.820 Also in the free version, the availability of the best GPUs like a ten and V ten, is not guaranteed 03:49.860 --> 03:56.380 in the free tier and is subject to your usage pattern and overall availability. 03:56.420 --> 03:57.780 Why this is important. 03:57.780 --> 04:00.780 If you go to the runtime, we can run all. 04:00.820 --> 04:03.420 Run before run selection, run cell and below. 04:03.780 --> 04:05.540 Those are not important. 04:05.540 --> 04:11.900 Interrupt execution, restart session, restart session and runtime disconnect and delete runtime. 04:11.940 --> 04:15.060 The most important thing is change runtime. 04:15.060 --> 04:19.940 Here we have runtime type are Julia and Python three. 04:19.980 --> 04:22.980 We are interested in in Python three. 04:23.060 --> 04:27.580 Here we have the accelerators, the hardware accelerators. 04:27.580 --> 04:29.780 You can learn about them here. 04:29.780 --> 04:36.740 The type the types of GPUs and TPUs that are available in Colab vary over time. 04:36.740 --> 04:42.340 This is necessary for Colab to be able to provide access to those resources free of charge. 04:42.460 --> 04:49.020 You can access premium GPUs subject to availability by purchasing one of our paid plans. 04:49.020 --> 05:00.550 If you are planning to execute and make very complex executions later when later when you create a very 05:00.550 --> 05:07.470 big project, if you have a lot of data set, you need to process many things. 05:07.590 --> 05:11.670 You, uh, you should, uh, purchase a GPU. 05:11.710 --> 05:12.070 Okay. 05:12.110 --> 05:16.590 But for now, we are using our free version. 05:16.590 --> 05:25.910 Also, if you have a computer with, uh, with a good GPU, like, uh, 30, 70, 40, 60, 50, 70, 05:25.950 --> 05:30.030 50, 80, 50, 90, those are flagships. 05:30.030 --> 05:34.190 So there is no need to buy any of those plans. 05:34.310 --> 05:40.230 If you would like to access to a specific dedicated hardware, explore using GCP market space. 05:40.230 --> 05:40.710 Colab. 05:40.710 --> 05:43.470 But we don't need all of those things. 05:43.470 --> 05:51.120 I'm just showing you that you can use CPU, T4, GPU, um Click okay. 05:51.600 --> 06:01.040 Uh, you can use this TPU, but A10s and L4, l4 GPU and those are not, uh, accessible only if you 06:01.040 --> 06:03.280 want to access premium GPUs. 06:03.320 --> 06:03.920 Okay. 06:03.960 --> 06:08.040 You can use CPU or GPU runtime version. 06:08.040 --> 06:11.400 We use the latest recommended click save. 06:11.640 --> 06:14.920 Then we need to restart the runtime. 06:14.960 --> 06:20.040 We are restarting the runtime automatically and restart session okay. 06:20.240 --> 06:30.480 Here we have the indication when you are connected we get this green tick and you can see the Ram and 06:30.480 --> 06:31.160 the disk. 06:31.400 --> 06:39.720 We are using one over 12 gigabyte and disk 39 over 112 gigabyte. 06:39.760 --> 06:42.280 Okay, this is the Colab. 06:42.280 --> 06:45.880 Also, you can access tools that command palette settings. 06:46.080 --> 06:54.960 Um, you can set the the editor font size for example 15 monospace font and others. 06:55.000 --> 06:56.520 Okay the help. 06:56.560 --> 06:58.560 Report a bug and others. 06:58.600 --> 07:02.840 Okay, so this is a very simple thing. 07:03.000 --> 07:04.320 We have commands. 07:04.320 --> 07:12.680 We have code, text, runtime and the restart sessions interrupt execution from this bar and the slide 07:12.720 --> 07:16.080 bar to the left we have this table of content. 07:16.240 --> 07:25.760 We got the table of content like a summary for the file we have here the find and replace. 07:25.960 --> 07:31.600 We have this code snippet the secrets and the folder. 07:31.600 --> 07:36.200 Here we're going to upload some of our files. 07:36.200 --> 07:39.880 So please pay attention to this icon okay. 07:39.920 --> 07:41.000 Thank you guys. 07:41.040 --> 07:47.560 Let's learn about the basics and the variables and operators in Python.