conda_environments_for_data_science

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

conda_environments_for_data_science

(★ 23)

Environmental configuration files used to aid in committing Data Science and Machine Learning.

conda_environments_for_data_science 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Conda Environments for Data Science

**NOTE: This repository is not being maintained and new environments are being created the [containerised repository](https://github.com/kierenAW/containerised_environments_for_data_science/).

Containerised environments are able to use Conda environments in addition other package management systems and offer a number of advantages over locally run Conda Environments, these include:

- Isolation: Containers provide isolation between applications, preventing dependency conflicts.
- Scalability: Containers are lightweight and can be scaled up or down to meet demands, providing flexibility in resource allocation.
- Dependency Management: Dependencies are encapsulated within the container, reducing conflicts.
- Environment Consistency: Ensures the same environment is used in development, testing, and production.
- Reproducibility and Portability: Containers can be easily transferred between machines or environments, making them portable.
 **

## How to use this repo
This repository contains conda environment configuration files for Data Engineering, Data Science (including Machine Learning), and related projects. The goal is to establish standardised environments that can be easily shared across multiple projects, reducing the number of virtual environments on each computer and facilitating collaboration among team members.

New environments are created when packages are added, removed, or upgraded. Once an environment specification is defined and published in a YAML file, we consider it immutable. Any changes require creating a new environment to ensure environments are reproducible and maintainable.

The naming convention for environments follows the format E (for Environment) followed by a three-digit number in sequence, continuing from the most recent environment. For example, E001 is followed by E002.

## I'm new here where do I start?

If you are part of a team Select an environment that suits your needs,

The answer depends on what you want to achieve and what packages you need so below are some examples and ideas of where to start, most environments contain Jupyter, pandas and numpy

Data collection and handling, this environment contains various libraries to obtain data from a range of sources (make sure to check terms of the 3rd party sites before using)

## Docker Install

If you are setting up the environment in a Docker container you might find these [Docker install instructions](https://docs.docker.com/engine/install/ubuntu/#install-using-the-repository) helpful.

## Conda Environments

The yml folder contains anaconda environment yml files and a read me that describes at a high level their contents.

## How do I add my own changes to an environment?
Once created and published we consider environments to be immutable. When making any alterations or additions please submit the new environment via a pull request with the new environment numbered sequentially from the highest number environment below.

#### I want to do get serious about deep learning with [PyTorch]("https://pytorch.org/")
[E041] provides pytorch

## More info
Each environment is created sequentially numbered to allow for versioning and easy tracking.

e.g. Environment 001 which gets called E001

Most of the environments are created by importing a previous environment and updating and adding additional packages.

## YML Files

The YML files are named as per the environment name followed by the operating system (eg windows) or "generic" if OS specific packages have been removed and the packages should work on both windows and Linux.

## Useful commands

#### Create a Conda Environment with default imports:
conda create --name [env]

#### Create a environment from a yml file
conda env create -f [filename].yml

#### Clone an existing conda environment
conda create --name <new_env> --clone <existing_env>

#### Update all packages in an enviroment
conda update --all

#### Export YML from an existing enviroment
conda env export -n [venv] > [filename].yml

#### Remove an existing conda environment
conda env remove -n [venv]

#### List all discoverable environments:
conda info --envs

#### Add an ENV kernel to Jupyter (when needed)
python -m ipykernel install --user

#### More information can be found at

https://conda.io/docs/user-guide/tasks/manage-environments.html

#### Add Channel
conda config --add channels [channel]

## Using Mamba
conda install mamba -n base -c conda-forge
mamba install [package_name] -c conda-forge

## Windows to Linux Diffrences

#### Regex to find the hashes at the end of the yml files:
=[A-Z]+.*$

Removing the hashes from a yml file aids the imports into Linux where the compiled hash values are different.

#### The following packages appear to be windows specific:
- [msys2-conda-epoch](https://www.msys2.org/)
- m2w64* including:
	* m2w64-gcc-libgfortran
	* m2w64-libwinpthread-git
	* m2w64-gcc-libs
	* m2w64-libwinpthread-git
	* m2w64-gmp
	* m2w64-gcc-libs-core
- [vc](https://github.com/conda/conda/wiki/VC-features)
- [wincertstore](https://pypi.org/project/wincertstore/)
- [winpty](https://github.com/rprichard/winpty)
- [win_inet_pton](https://pypi.org/project/win-inet-pton/)
- [pyreadline](https://pypi.org/project/pyreadline/)
- [pywinpty](https://github.com/spyder-ide/pywinpty)
- [icc_rt - Intel(R) C++ | Visual Fortran Compiler for Windows](https://anaconda.org/intel/icc_rt)

#### The following packages appear to be *nix  specific:
- [libedit](https://anaconda.org/anaconda/libedit)
- [libnghttp2 ](https://anaconda.org/main/libnghttp2)

## Package Conflicts
- torchvision 0.2.2 does not support pillow 7+ due to removal of PILLOW_VERSION. See [Github Issue](https://github.com/pytorch/vision/issues/1712)

## Enviroments

[MinkowskiEngine](https://github.com/NVIDIA/MinkowskiEngine#cuda-11x)
 pip install -U git+https://github.com/NVIDIA/MinkowskiEngine -v --no-deps --install-option="--blas_include_dirs=${CONDA_PREFIX}/include" --install-option="--blas=openblas"

### E044
Environment designed to support machine learning research, including data exploration, pre-processing, model development, training, and deployment. This environment includes libraries such as PyTorch, MLflow (for experiment tracking and reproducibility), NumPy, Pandas, Dask, and Matplotlib, as well as tools for visualisation (Matplotlib) among others

Channels:
 - pytorch
 - nvidia
 - huggingface
 - conda-forge
 - anaconda
 - defaults

To provides:
* python
* pytorch 2.3.0
* mlflow
* numpy
* pillow
* pandas
* dask
* pyarrow
* fastparquet
* pandas-profiling
* xlrd
* sqlite
* matplotlib
* jupyterlab
* jupyter_contrib_nbextensions
* ipywidgets
* widgetsnbextension
* xlrd
* sqlite
* matplotlib
* nodejs
* graphviz
* accelerate
* kornia
* wandb
* matplotlib
* tqdm
* webdataset
* wandb
* munch
* onnxruntime
* einops
* onnx2torch

### E043
Copy of E041 with updated packages and HuggingFace transformers added used with gpt-neo.
To provides:
* pytorch=1.11
* python=3.9
* cuda=11.5
* cudnn=8.3.2
* pip:
	* tokenizers==0.13
	* transformers==4.23

### E042

Created from scratch using channels:
1. conda-forge
1. anaconda

To provide :
* mamba
* python
* openjdk
* ipykernel
* newspaper3k
* numpy
* alpha_vantage
* yfinance
* pandas-datareader
* pandas
* jupyterlab
* matplotlib
* seaborn
* fastparquet
* pandas-profiling
* graphviz
* dask
* nodejs
* sqlite
* plotly
* quandl
* scipy
* xlrd
* h5py
* scikit-image
* scikit-learn
* pillow requests
* youtube-dl
* mlflow
* pyarrow
* beautifulsoup4
* indexed_gzip
* urllib3
* pytrends
* pyautogui
* black
* pyspark=3.2

### E041
Created from scratch using channels:
1. pytorch
1. conda-forge
1. anaconda

To provide :
* pytorch
* torchaudio
* torchvision
* python
* opencv
* mlflow
* ax-platform
* botorch
* gpytorch
* pillow
* tensorboard
* tensorboardx
* databricks-cli
* pycocotools
* indexed_gzip
* numpy
* pandas
* dask
* pyarrow
* fastparquet
* h5py
* pandas-profiling
* xlrd
* sqlite
* matplotlib
* jupyterlab
* scipy
* scikit-learn
* scikit-image
* nodejs
* graphviz
* seaborn
* jupyter_contrib_nbextensions
* ipywidgets
* widgetsnbextension
* openblas-devel

### E040
Created from scratch using channels:
1. pytorch
1. conda-forge
1. anaconda
1. acellera

To provide :
* pytorch=1.10
* torchaudio
* torchvision=0.11
* python=3.9
* mlflow=1.20
* ax-platform
* botorch
* gpytorch
* pillow
* tensorboard
* tensorboardx
* databricks-cli
* pycocotools
* indexed_gzip
* numpy
* pandas
* dask
* pyarrow
* fastparquet
* h5py
* pandas-profiling
* xlrd
* sqlite
* matplotlib
* jupyterlab
* scipy
* scikit-learn
* scikit-image
* nodejs
* graphviz
* seaborn
* jupyter_contrib_nbextensions
* ipywidgets
* widgetsnbextension

### E039
Created from scratch using channels:
1. pytorch
1. conda-forge
1. anaconda

To provide :
* pytorch=1.10
* torchaudio
* torchvision=0.11
* python=3.9
* mlflow=1.20
* pillow
* tensorboard
* tensorboardx
* databricks-cli
* pycocotools
* indexed_gzip
* numpy pandas
* dask pyarrow
* fastparquet
* h5py
* pandas-profiling
* xlrd
* sqlite
* matplotlib
* jupyterlab
* scipy
* scikit-learn
* scikit-image
* nodejs
* graphviz
* seaborn
* jupyter_contrib_nbextensions
* ipywidgets
* widgetsnbextension

### E038
Created from scratch to provide fastai 2.4 and fastbook
Using channels:
1. pytorch
1. conda-forge
1. anaconda
 - mamba
 - python=3.9
 - mlflow=1.18
 - pillow
 - tensorboardx
 - databricks-cli
 - indexed_gzip
 - dask
 - numpy
 - pandas
 - matplotlib
 - jupyterlab
 - scikit-learn
 - sqlite
 - xlrd
 - plotly
 - nodejs
 - graphviz
 - seaborn

### E037
Created from scrach to provide pytorch 1.8.1
Using channels:
1. pytorch
1. conda-forge
1. anaconda

Packages:
 - mamba
 - python=3.9
 - pytorch=1.8.1
 - torchvision=0.9.1
 - mlflow=1.16
 - pillow
 - tensorboard
 - tensorboardx
 - pillow
 - databricks-cli
 - pycocotools
 - indexed_gzip
 - dask
 - pillow
 - pandas-profiling
 - pyarrow
 - numpy
 - pandas
 - matplotlib
 - jupyterlab
 - scikit-learn
 - scikit-image
 - sqlite
 - xlrd
 - plotly
 - nodejs
 - graphviz
 - fastparquet
 - seaborn
 - dask
 - pillow
 - scipy
 - h5py
 - matplotlib
 - jupyter_contrib_nbextensions

### E036
Created from scrach to create QR Codes
conda-forge:
 - segno
 - qrcode-artistic
 - pillow
 - jupyterlab
 - nodejs

### E035
Created from scrach as data handler:
 - [mamba](https://github.com/mamba-org/mamba) (conda-forge)
 - newspaper3k (conda-forge)
 - pyautogui (conda-forge)
 - numpy (conda-forge)
 - pandas (conda-forge)
 - jupyterlab (conda-forge)
 - matplotlib (conda-forge)
 - h5py (conda-forge)
 - scikit-image (conda-forge)
 - scikit-learn (conda-forge)
 - pillow (conda-forge)
 - requests (conda-forge)
 - youtube-dl (conda-forge)
 - mlflow=1.14 (conda-forge)
 - pyarrow (conda-forge)
 - beautifulsoup4 (conda-forge)
 - indexed_gzip (conda-forge)
 - xlrd (conda-forge)
 - quandl (conda-forge)
 - urllib3 (conda-forge)
 - scipy (conda-forge)
 - pytrends (conda-forge)
 - plotly (conda-forge)
 - sqlite (conda-forge)
 - databricks-cli (conda-forge)
 - nodejs (conda-forge)
 - pandas-profiling (conda-forge)
 - graphviz (anaconda)
 - dask (anaconda)
 - fastparquet (anaconda)
 - seaborn

### E034
Created from scrach using mamba top get TF2.4.1 for GPU. 
 - [mamba](https://github.com/mamba-org/mamba)
 - tensorflow-gpu=2.4 (anaconda) (available for linux only as of 2021-03-10)
 - tensorboard=2.4.1 (conda-forge)
 - mlflow=1.14 (conda-forge)
 - pyarrow (conda-forge)
 - mlflow=1.14 (conda-forge)
 - pyarrow (conda-forge)
 - indexed_gzip (conda-forge)
 - xlrd (conda-forge)
 - scipy (conda-forge)
 - plotly (conda-forge)
 - sqlite (conda-forge)
 - databricks-cli (conda-forge)
 - nodejs (anaconda)
 - numpy=1.19.5 (anaconda) (version needed by TF)
 - pandas (anaconda)
 - jupyterlab (anaconda)
 - matplotlib (anaconda)
 - h5py (anaconda)
 - scikit-image (anaconda)
 - scikit-learn (anaconda)
 - pillow (anaconda)

### E033
New env from scratch to provide data wrangling and collection tools
 - graphviz (anaconda)
 - dask (anaconda)
 - pandas-profiling (anaconda)
 - fastparquet (anaconda)
 - seaborn (anaconda)
 - conda-forge (conda-forge)
 - nodejs (conda-forge)
 - newspaper3k (conda-forge)
 - numpy (conda-forge)
 - pandas (conda-forge)
 - jupyterlab (conda-forge)
 - matplotlib (conda-forge)
 - h5py (conda-forge)
 - scikit-image (conda-forge)
 - scikit-learn (conda-forge)
 - pillow (conda-forge)
 - requests (conda-forge)
 - youtube-dl (conda-forge)
 - mlflow=1.13 (conda-forge)
 - pyarrow (conda-forge)
 - beautifulsoup4 (conda-forge)
 - indexed_gzip (conda-forge)
 - xlrd (conda-forge)
 - quandl (conda-forge)
 - urllib3 (conda-forge)
 - scipy (conda-forge)
 - pytrends (conda-forge)
 - quandl (conda-forge)
 - plotly (conda-forge)
 - sqlite (conda-forge)
 - databricks-cli (conda-forge)
 - alpha-vantage (pip)
 - tinysegmenter (pip)

### E032
Built from scratch (similar to E028) to provide:
 - pytorch=1.7
 - torchvision=0.8
 - pytorch-lightning
 - mlflow=1.13
 - pillow=6.2.1
 - pandas-profiling
 - dask
 - pyarrow
 - numpy
 - pandas
 - matplotlib
 - jupyterlab
 - tensorboardx
 - scikit-learn
 - scikit-image
 - scipy
 - h5py
 - sqlite
 - databricks-cli
 - pycocotools (as of 2021-01-15 only available for linux, can be used for computing the evaluation IOU metrics)

### E031
Built from scratch for use with [labelme](https://github.com/wkentaro/labelme)

### E030
Built from scratch to provide:
 - tensorflow-gpu=2.3
 - tensorboard=2.3
 - tensorflow-gpu=2.3 (as of 2021-01-07 tensorflow-gpu=2.3 only available for windows)
 - keras=2.4.3
 - tensorboard
 - scikit-image
 - scikit-learn
 - scipy jupyterlab
 - h5py=2.10
 - dask=2.3
 - pillow=8
 - pandas-profiling
 - pyarrow
 - numpy
 - pandas
 - mlflow=1.13
 - Databricks-cli=0.9
 - matplotlib

### E029
Clone of E026 with xlrd added

### E028
Built from scratch to provide:
 - pytorch=1.7
 - torchvision=0.8
 - pytorch-lightning
 - mlflow=1.12
 - pillow=6.2.1 
 - pandas-profiling
 - dask
 - pyarrow 
 - numpy
 - pandas
 - matplotlib
 - jupyterlab
 - tensorboardx
 - scikit-learn
 - scikit-image
 - scipy
 - h5py
 - sqlite
 - databricks-cli

### E027
Intended for use of fast AI and MLFlow together 
 - fastai=1.0
 - pytorch
 - mlflow=1.12
 - pillow=8
 - pandas-profiling
 - dask
 - pyarrow 
 - numpy
 - pandas
 - matplotlib
 - jupyterlab
 - tensorboardx
 - scikit-learn
 - scikit-image
 - scipy
 - h5py
 - databricks-cli
 - sqlite

### E026
Built from scratch as data handling env to work with [Delta Lake](https://docs.delta.io/latest/quick-start.html) and Apache Spark (Pyspark)
 - Pyarrow
 - Pandas
 - Numpy
 - Jupyterlab
 - Matplotlib
 - H5py
 - Scikit-image
 - Scikit-learn
 - MLFlow=1.12
 - Pillow=8.0
 - youtube-dl
 - pandas-profiling
 - dask
 - beautifulsoup4
 - indexed_gzip
 - urllib3
 - Pyarrow
 - pytrends
 - alpha-vantage
 - quandl
 - plotly
 - scipy
 - scikit-image
 - scikit-learn
 - seaborn
 - newspaper3k

### E025
Built from scratch to provide:
 - Tensorflow CPU=2.3 (tensorflow-gpu 2.2 is highest version available on anaconda channel at time of env creation)
 - Tensorboard=2.3
 - MLFlow=1.11
 - Databricks-cli=0.9
 - Dask=2.3
 - Pillow=8
 - Pandas Profiling
 - Pyarrow
 - Numpy
 - Pandas
 - Jupyterlab
 - Matplotlib
 - H5py
 - Scikit-image
 - Scikit-learn
 - Scipy

### E024
Built from scratch to provide:
 - FastAI=2.1
 - Pytorch=1.6
 - Torchvision 
 - TensorboardX=2.1 
 - MLFlow
 - Databricks-cli
 - Dask 
 - Pillow
 - Pandas Profiling
 - Pyarrow
 - Numpy
 - Pandas
 - Jupyterlab
 - Matplotlib
 - H5py
 - Scikit-image
 - Scikit-learn
 - Scipy

### E023
Built from scratch to provide:
 - Tensorflow GPU 2.1 
 - Tensorboard 2.2
 - Keras GPU 2.3
 - MLFlow  1.11
 - Databricks-cli
 - Jupyterlab 2.2
 - Numpy 1.19
 - Pandas 1.1
 - Matplotlib 3.3
 - Pillow 8.0

### E022
Built from scratch to provide:
 - Pytorch GPU  1.6
 - MLFlow  1.11
 - TensorboardX 2.1
 - Jupyterlab 2.2
 - Databricks-cli
 - Numpy 1.19
 - Pandas 1.1
 - Torchvision 0.7
 - Matplotlib 3.3
 - Pillow 8.0

### E021
Rebuilt based on E020 to provide:
 - Tensorflow GPU  2.1
 - Pytorch GPU  1.3
 - MLFlow

### E020
Based on a clone of E019 with alpha-vantage and qandl packages added, tensorflow 2.1.0 GPU and pytorch CPU.

### E019
Clone of E018 with wandb added via pip

### E018 
Clone of E017 with update to all packages, and pytorch forced to version 1.4 resulting in CUDA tools to 10.1

### E017 
Clone of E015 with packages to aid data acquisition and handling:
  - datapackage see https://github.com/frictionlessdata/datapackage-py 
  - urllib3
  - indexed_gzip
  - beautifulsoup4

### E016
New freshly created ENV to allow TensorFlow 2 GPU to be used.
It supplies:
  - TensorFlow 2
  - Pillow
  - pyarrow
  - pandas-profiling
  - dask
  - scikit-image
  - scikit-learn
  - scipy

### E015
Brand new (not cloned from other) environment with unconstrained versions, this expands on E014 and is currently working with Deoldify.
 - Use pip install ffmpeg-python as the conda version is not working at time of writing
 - jupyterlab
 - pytorch
 - matplotlib
 - xlrd
 - seaborn
 - xlrd
 - plotly
 - numpy
 - pandas-profiling
 - pandas
 - scikit-image
 - scikit-learn
 - scipy
 - pyarrow
 - pillow
 - dask
 - fastai
 - pydotplus
 - py-xgboost
 - cufflinks-py
 - keras-gpu
 - nvidia-ml-py3
 - ffmpeg
 - opencv
 - youtube-dl
 - opencv

### E014
Brand new (not cloned from other) environment with unconstrained versions
 - jupyter
 - jupyterlab
 - pytorch
 - matplotlib
 - xlrd
 - seaborn
 - xlrd
 - plotly
 - numpy
 - pandas-profiling
 - pandas
 - scikit-image
 - scikit-learn
 - scipy
 - pyarrow
 - pillow
 - dask
 - fastai
 - pydotplus
 - py-xgboost
 - pyarrow
 - cufflinks-py
 - keras-gpu
  
### E013
- Clone from E012
- pyarrow
- cufflinks-py

### E012
- Clone from E010
- pydotplus
- py-xgboost
- fastai
- update:
	* dask 2.2 --> 2.3
	
### E011
Intended as a generic environment for basic Analytics and Data Science 
- plotly
- numpy
- pandas
- seaborn
- matplotlib
- jupyterlab
- xlrd
- scikit-image
- scikit-learn
- pillow
- scipy
- dask
- tensorflow (cpu)
- pandas-profiling

### E010
- Clone from E009
- pandas-profiling 2.3
- update:
	* tensorflow to 1.13.1 --> 1.14.0
	* bokeh 1.2.0--> 1.3.4
	* cudnn 7.3.1 --> 7.6.0
	* dask 2.0 --> 2.2
	* jupyterlab 0.35.6 --> 1.0.5
	* numpy 1.16.3 --> 1.16.4
	* pandas 0.24.2 --> 0.25.0
	* scikit-learn 0.21.2 --> 0.21.3
	* scipy 1.2.1 --> 1.3.0
	* pillow 6.0.0 --> 6.1.0

### E009
- All from E008
- plotly
- jupyterlab

### E008
- All from E007
- dask

### E007
- All from E002
- xlrd
- scikit-image

### E006
- Python 3.6
- Seaborn
- xlrd
- Jupyter
- tensorflow=1.6
- pip imports not working so import E006 then run pip install opencv-contrib-python

### E005
- All from E002
- xlrd
- pip imports not working so import E005 then run pip install opencv-contrib-python

### E004
- All from E001
- xlrd

### E003
- All from E002
- pyro-ppl
- pip imports not working so import E003 then run pip install pyro-ppl

### E002
- All from E001
- Pytorch
- Seaborn

### E001
- Jupyter
- TF GPU

conda_environments_for_data_science

conda_environments_for_data_science 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

conda_environments_for_data_science

conda_environments_for_data_science 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README