HI there, I have a question how to start with the courses. Is there an lesson which explains how to set up the environment? Im new in python and i dont know, shall i install it with pipenv, pip or shall i install jupyter notebook or jupyter lab? or do I have to download anything? Usually I work with VSCode and terminal. And so I would appreciate if someone can help me to get started with these courses.
thank you, stranger, in advance
The environments are provided as Jupyter notebooks that are loaded in your browser.
You don’t have to install anything.
But you should attend a Python tutorial course first - the courses all assume you already have essential Python skills.
You can find lots of free tutorials on your favorite video sharing service.
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@TheAubisokanVisitor you might want to look into installing Anaconda (not mini-conda)-- Aside from including many common dependencies it also installs Jupyter Notebooks locally in case you want to take a look at the labs locally.
However, as I am sure others will tell you we are not necessarily responsible for getting the labs to actually run locally as I guess there can be a lot of complicated issues around sufficing the proper dependencies.
Hi Nevermnd, I am used to work with vscode. However there must be a tutorial in that online-academy, where I can see how to setup all the things correct, that I can participate the courses. In the course “RAG with Javascript”, there should be an .env file, where I should paste in my Open AI Keys… when I take a look to the folder, its not there… so why is this not shown? Shall I create a .env folder or not? where do I install the dependencies,… npm or python-dotenv? In the project folder, but how do I do this online? shall I download that? There are so many questions…
@TheAubisokanVisitor unfortunately I cannot tell you anything about that course as I have not taken it.
Further, as mentioned getting the code here to run offline is really not something I’ve invested my time/had an interest in–
But, just as an example, excepting any data files that the program loads which would also be necessary, all your needed libraries are listed at the top. And most include a built-in command to show what version is running.
For example, library uses Tensorflow-- But which one to use locally ?
In the Colab notebook put in a:
print(tf.__version__)
or
print(tf.version.VERSION)
or
print(tf.VERSION)
Depending on the version that is running. Similarly for each of the other libraries and you have your list of required dependencies and versions.
Takes a little leg work.
None of DLAI’s courses include instructions for how to run the materials on your own environment. This is largely because there is an extremely large number of platforms and toolsets and operating systems (and levels of student competency), and providing technical support for all those potential installations is a daunting task.
Earlier ML courses that Andrew created did allow students to install their own tools and run everything locally. It was a tech support nightmare.
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