This repo presents a curated list of Conda environments.
One of the tasks where Conda env has been very useful is managing LLM environments.
To create an environment, copy or download the requirements.yml file you are interested in and run the command:
conda env create -f requirements.ymlAfter creating the env, you can add or modify any package using the conda or pip command.
Windows
| Item | Name | Where to Use |
|---|---|---|
| 1 | adanet | Use AutoMl with AdaNet package |
| 2 | akeras | Use Auto Keras 1.0.16 package |
| 3 | akeras1 | Use Auto Keras 1.1.0 package |
| 4 | aug | Use Augmentation package |
| 5 | autogpt | Use Auto-GPT package |
| 6 | az-conda | Use Azure package |
| 7 | carnd-sim | Self-Driving Car simulator @ Udacity course |
| 8 | carnd-t1 | Self-Driving Car Engineer ND course @ Udacity |
| 9 | cv4 | Use OpenCV4 package |
| 10 | dl4m-gpu | AI for Medicine Specialisation course (GPU) + TensorFlow |
| 11 | dl4m-gpunn | AI for Medicine Specialisation course (GPU) + Pytorch |
| 12 | dl4m1x | AI for Medicine Specialisation course (GPU) + TensorFlow 1.15 |
| 13 | fbase | Use firebase package |
| 14 | gans-pt14 | Use GAN model + Pytorch 1.4 |
| 15 | gpt4all | Use GPT4All model |
| 16 | jupylab | Use JupyLab package |
| 17 | jupyter_ai | Use ChatGPT with Jupyter Lab package |
| 18 | labelimg | Use Labelimg package |
| 19 | lavis | Use Lavis package |
| 20 | ldm | Use Latent Difusion Model package |
| 21 | manning.tf2 | TensorFlow 2 book @ Manning |
| 22 | manning.tfx | TensorFlow book @ Manning |
| 23 | monai | Use Monai model + Pytorch |
| 24 | monai2 | Use the Monai model + Torch |
| 25 | msvision | Use the Microsoft Vision package |
| 26 | ocpp21 | Use ocpp21 package |
| 27 | openvino | Use Openvino package |
| 28 | playtorch | Use PlayTorch package |
| 29 | privateGPT | Use privateGPT model |
| 30 | project | Use Project pacakge |
| 31 | pytorch-env | Use Pytorch + Azure |
| 32 | pyviz | Use PyViz package |
| 33 | sam | Use Segment Anything package |
| 34 | sdv2 | Use Stable Diffusion v2 package |
| 35 | tf2 | Use TensorFlow 2 package (GPU) |
| 36 | tf210 | Use TensorFlow 2.1 package (GPU) |
| 37 | tfod | Use TensorFlow for Object Detection |
| 38 | visgpt | Use Visual GPT package |
| 39 | vissl | Use Vision SOTA Self-Supervised Learning |
| 40 | yo1 | Use to run Yolo5 (Openvino + Onnx) |
| 41 | yolo5 | Use to run Yolo5 model |
| 42 | yolo5-tf | Use TensorFlow 2 to run yolo5 model |
Linux
| Item | Name | Where to Use |
|---|---|---|
| 1 | akeras | Use Auto Keras package |
| 2 | carnd-t1 | Self-Driving Car Engineer ND course @ Udacity |
| 3 | dl4m-gpu | AI for Medicine Specialisation course (GPU) |
| 4 | gluon | Use Gluon package |
| 5 | ldm | Use Latent Difusion Model package |
| 6 | manning.tfx | TensorFlow book @ Manning |
| 7 | monai2 | Use Monai package |
| 8 | ocpp16 | Use ocpp 1.6 package |
| 9 | pyviz | Use PyViz package |
| 10 | sdv2 | Use Stable Diffusion v2 package |
| 11 | tfmaker | Use TensorFlow Maker package |
| 12 | tfod | Use TensorFlow Object Detection package |
| 13 | yolov5 | Use to run Yolov5 model |
Creating requirements files can sometimes be challenging, especially when dealing with libraries that have different version requirements. The curated list of environment files can help alleviate this issue by providing pre-configured environments with compatible versions of libraries and dependencies. Developers can reference these files as a starting point, ensuring a smoother process of creating requirements files tailored to their specific needs.