Conda vs. Pinokio: Choosing the Right Tool for Managing Python Environments
Reducing the Stress of Dependencies & Versions
Python’s power and flexibility make it a favourite for everything from data science to automation, and is especially popular for AI tools including ComfyUI. With that power there is a complexity, particularly when managing multiple environments, dependencies, and package versions. It is all too easy to create conflicts and errors leading to many hours of wasted time.
As tools like ComfyUI become more complex and broader I’m finding that having a couple of different ComfyUI environments, for example one for image generation and one for video generation, allows me to keep installed custom nodes and packages limited to the task at hand to minimise conflicts and improve performance.
I have been running Python environments natively, via venv and using Pinokio, a tool aiming to simplify the process of running multiple environments. More recently due to a specific need I have also used Conda, a broader environment and package management tool.
While Conda and Pinokio both help manage Python environments, they cater to different user needs and philosophies, so I’ll start with a little background on both.
What is Conda?
Conda is an open-source package and environment management system originally developed for Python, though it now supports other languages as well. It’s widely used in scientific computing, data science, and machine learning. It is closely related to (but separate from) Anaconda, which includes a Conda distribution and a graphical Navigator utility.
Key Features
Environment management: Create isolated environments with specific versions of Python and packages.
Binary package handling: Conda installs precompiled binaries, avoiding the need for compilation from source.
Cross-platform: Works on Windows, macOS, and Linux.
Channels: Uses repositories (like conda-forge) to install packages.
Strengths
Excellent for reproducible research and scientific workflows.
Handles complex dependencies better than pip.
Works well in offline environments or controlled deployment pipelines.
Weaknesses
Can be slow when resolving large or complex environments.
The Conda ecosystem is somewhat distinct from the broader Python community (which centres around pip and PyPI).
What is Pinokio?
Pinokio is a newer, GUI-based environment launcher designed to simplify launching complex applications and environments, especially those involving AI, creative tools, and GPU-intensive workflows. Instead of just managing dependencies, Pinokio wraps an entire application or environment into a scriptable, one-click launcher.
Key Features
Graphical interface for launching and managing environments.
Supports custom launch scripts with built-in environment configuration.
Often used to package and distribute AI tools, notebooks, and creative apps (e.g. ComfyUI workflows, Stable Diffusion projects).
Automatically installs dependencies like Python, Git, and extensions.
Strengths
User-friendly, especially for non-programmers or hobbyists.
Great for sharing and running reproducible projects with minimal setup.
Eliminates the need to manually install Python, clone repos, or set environment variables.
Weaknesses
It is not currently a general-purpose package manager like Conda or pip.
In its drive for simplicity, it gives the user less control and transparency which can be limiting.
Still early-stage and under active development.
In the Real World
Neither Conda or Pinokio are virtualised environments, they are environment and package managers, they help to control and manage dependencies but they do not provide full isolation, meaning the core (“host” if you like) machine plays a critical factor in operation.
This dependency is particularly obvious when installing either of them. If you have an existing Python installation on the target machine which is referenced in the PATH variable in Windows then installation can end up a mess with Conda or Pinokio linked back to this existing installation, so you are well advised to remove any existing Python installs.
In a similar way the host machine controls the GPU drivers and other system drivers so any issues there will impact all environments.
Neither Conda nor Pinokio are 100% clean when you exit the environment (as they still run directly on the host), I have had occasional issues after running Pinokio when trying to launch ComfyUI Desktop (outside of Pinokio) which were only resolved by a system reboot.
To be fair I suspect this is GPU driver or memory related with remnants left over when Pinokio exits. In a similar way I have had a Conda environment lock up my entire machine, and as there is no isolation, Conda can consume all system resources leading to spurious errors which are hard to troubleshoot.
Neither of these tools are trying to provide that level of isolation, they are about managing packages and dependencies, reducing conflicts and giving you confidence that you can run multiple environments in known states, which they both do very well.
Which One Should You Use?
Before heading down either route it is worth stopping to consider whether venv (the virtual environment manager within Python) gives you enough. It is not a full environment management tool like Pinokio or Conda and requires manual configuration, but it does give you isolation for packages. If you are not requiring anything too complex this may be the easiest approach.
Pinokio excels with the one click installation of entire workflows but you are limited to what has been packaged (there is a growing list which covers a lot in the AI generation space). It is ideal for the less technical user who wants a simple solution for AI workflows like ComfyUI and Fluxgym.
Conda is more heavyweight but doesn’t come with the range of fully packaged installers like Pinokio meaning there is a higher level of manual configuration required. It is ideal for the power user who requires a broader range of environments and is happy to install packages within the environments.
Final Thoughts
There is no single best solution, it depends on what you need to achieve. Conda, Pinokio and venv all deliver in their specific areas of strength.
I am continuing to use a mix of venv and Pinokio for my main environment as I am really only working with ComfyUI, FluxGym and other related diffusion model applications. Using Pinokio has allowed me to easily separate image generation from LoRA training and video generation, keeping each of those environments focused on their specific task without extra baggage causing problems.
I do have a Conda install on my laptop, although this is to support testing of IPEX (Intel Extension for PyTorch) which according to Intel only works within Conda, with no plans currently to use it on my main machine, maybe that will change over time.
If you are a user who is working across multiple tools and frequently installing (and uninstalling) Python packages to get different applications to work correctly then looking at these options is well worthy of some time.