Understanding Python Environments Simplified
Think of Python environments like different rooms in your house, each designed for a specific purpose. Just as you wouldn't cook in the bathroom or sleep in the kitchen, different Python applications need their own "spaces" or environments because they have unique requirements. Sometimes, these requirements can clash with those of other applications (imagine trying to cook a meal in a bathroom!). To avoid this, you can create separate Python environments.
Separate environments, like separate rooms, keep everything organized and prevent conflicts. For instance, one Python application might need a specific version of a library or dependency, while another requires a different version. Just as you wouldn't store kitchen utensils in the bathroom, you wouldn't want these conflicting requirements to interfere with each other. Each environment is tailored and customized for its application, ensuring it has everything it needs without disrupting others.
To put it another way.. Imagine you have three different apps on your computer - let's call them App1, App2, and App3. They are all in the same Python Environment and all need a package called "SuperTool", but each needs a different version:
- App1 needs SuperTool version 1
- App2 needs SuperTool version 2
- App3 needs SuperTool version 3
In a regular Python environment, you can only have one version of SuperTool installed at a time. So if you install App1, then App2, then App3, here's what happens:
- First, App1 installs SuperTool v1 - everything works great!
- Then App2 comes along and says "I need SuperTool v2" - it replaces v1 with v2. Now App1 breaks because it doesn't have the version it needs anymore.
- Finally, App3 installs and demands SuperTool v3 - it replaces v2. Now both App1 and App2 are broken and you have lots of errors on your screen.
Let's look at two examples to see how Python environments work in real-world applications:
When you install AllTalk standalone, it's akin to adding a new room to your house specifically designed for your AllTalk activities. The setup process, using the atsetup utility, constructs this custom "room" (Python environment alltalk_environment) with all the necessary tools and furnishings (libraries and dependencies) that AllTalk needs to function smoothly, without meddling with the rest of your "house" (computer system). The AllTalk environment is started each time you run start_alltalk or start_environment within the AllTalk folder.
Similarly, installing Text-generation-webui is like setting up another specialized room. Upon installation, it automatically creates its own tailored environment, equipped with everything required for text generation, ensuring a seamless and conflict-free operation. The Text-generation-webui environment is started each time you run start_*your-os-version* or cmd_*your-os-version* within the Text-generation-webui folder.
Just as you might renovate a room or bring in new furniture, you can also update or modify Python environments as needed. Tools like Conda or venv make it easy to manage these environments, allowing you to create, duplicate, activate, or delete them much like how you might manage different rooms in your house for comfort and functionality.
Once you're in the right environment, by activating it, installing or updating dependencies (the tools and furniture of your Python application) is straightforward. Using pip, a package installer for Python, you can easily add what you need. For example, to install all required dependencies listed in a requirements.txt file, you'd use: pip install -r requirements.txt This command tells pip to read the list of required packages and versions from the requirements.txt file and install them in the current environment, ensuring your application has everything it needs to operate. It's like having a shopping list for outfitting a room and ensuring you have all the right items delivered and set up.
Remember, just as it's important to use the right tools for tasks in different rooms of your house, it's crucial to manage your Python environments and dependencies properly to ensure your applications run as intended.
When a Python environment starts up, it changes the command prompt to show the Python environment that it currently running within that terminal/console.
