Hi, thanks very much for the compliment. Yes I think I might start making more of these videos. I had switched my focus over to AI but miss mechanical modelling too.

Yes you can use venv too.

You're welcome!!

Glad it helped!

There’s a different docker file you can use that includes Jupyter notebook… scroll through the comments a bit and you’ll find the one where I responded to someone else… use that command

You need an Nvidia graphics card for the GPU-enabled version to work.

Had the same issue, my main spur gear was set as 'ground to parent', unset the gear and try it again

Glad it helped!

That’s the version that is officially supported by the version of tensorflow being installed.

@@RELUvance After installing the tensorflow and using jupyter notebook, when I run the following code cell, I get the error - The kernel appears to have died. It will restart automatically. Do you have any solution for this? Code: linear_layer = tf.keras.layers.Dense(units=1, activation = 'linear', ) a1 = linear_layer(X_train[0].reshape(1,1)) print(a1)

Option 2 is the best if you don’t need the absolute latest gpu version (most people).

Glad it worked for you!

I WANT TO SOLVE THIS PROBLE BRO PLZ CAN YOU SLOVE THIS PROBLEM IN NATIVE WINDOWS

In the video I explain how to install both the gpu and cpu versions. Maybe I don’t understand your comment?

@@RELUvance can you solve problem in my next comment

Yes, if you run Jupyter from within the Tensorflow environment you created.

I’m glad it helped!

So glad that it worked for you!

You're welcome!

If you can run with sudo but not without, it sounds like setting up the user profile like I showed in the video didn’t work properly. Did you get any error messages when doing that? Try rebooting the computer too if you haven’t already.

Hi Edgar, are you using the command I used in the video? (pip install "tensorflow<2.11") The actual version number is actually something like 2.10.1 (a third digit). By using <2.11 you will get the last "version" of 2.10 if that makes sense.

Fantastic!

@@RELUvance sir, can you please show how to install Tensorflow+GPU on WSL2 environment without dockers help

Yes, I am planning to make a separate video on that.

Great! Glad it helped!

There are actually many ways to work with docker containers but I tried to keep the video simple and just showed this one example. For example, you can actually instantiate a docker container as a "daemon" with this command: docker run --gpus all -dit --name tf -v $PWD:/working -w /working tensorflow/tensorflow:latest-gpu bash This will cause the container to be persistent even after stopping the process or restarting the computer, and can be re-started by using: docker attach tf (replacing tf with whatever name you used in the preceding command. To run a script immediately instead of launching the container as a shell you can replace "bash" as follows: docker run - -gpus all -it -v $PWD:/working -w /working tensorflow/tensorflow:latest-gpu python [script name here] Hope this helps! As you can see it gets complicated depending on what you want to do, and this was beyond what I wanted to cover in the video. Let me know if you think a separate video on using docker would be helpful, and I might be able to make one.

@@RELUvance thanks for the clarification Also a video on using docker for python scripts via the vscode or any other ide would be helpful

The 2nd method I shows in the video actually has python installed natively in Windows so you should be able to fire up VS and use it directly within windows. For the docker use-case, it may be a bit more challenging because python and tensorflow are inside of a docker container. It's probably still possible, but I haven't explored this myself.

I'm so glad that I could help!! Yes, TensorFlow is one of the most important packages you need to get started, and it's one of the hardest to get working properly... which method did you end up using from the video?

Yeah that’s why I made it… all the other videos are so out of date.

If you are installing them via conda or docker like I showed in the video you can install them both if you want because they will be in separate environments. I would only install the gpu version though if you are planning on running really compute intensive models. Otherwise the cpu version will be fine on its own.

No problem! Hope that it helped you.

I’m glad you enjoyed!

Hi @daedalusrasmus3278 - glad that you found it helpful! Yes, there's a docker version of TensorFlow that uses notebooks as well, but to be honest I haven't tried using VScode with it through docker. To open a notebook version in a docker container, use this: docker run --gpus all -p 8888:8888 -it tensorflow/tensorflow:latest-gpu-jupyter ... and then you can access the notebook from a web browser.

Liked, commented, and commenting here to tell you thank you from the bottom of my heart!

would ykou tell me its help anaconda shell

@@mdabubakarchowdhurysunny2846 sorry, I don’t understand what you are asking

@@RELUvance i want to ask you tensorflow gpu doesn"t install conda environment , would yoiu like to tell us how i used install conda

@mdabubakarchowdhurysunny2846 At 5:21 in the video it explains how to install miniconda, which is a “lite” version of the full anaconda.

Really glad you enjoyed it!!

Glad it helped you!

Thanks for this, I will add this correction to the video description.

Thank you so much!