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Background

Miniconda is a free minimal installer for conda. It is a small, bootstrap version of Anaconda that includes only conda, Python, the packages they depend on, and a small number of other useful packages, including pip, zlib and a few others. Use the conda install command to install 720+ additional conda packages from the Anaconda repository.

Challenges and Solutions

  1. How to install a specific package?

    1. Users can not and should not install packages in existing python environments.

      1. Lists of Existing Anaconda Environments

    2. Users need to create their own python environment to install their own packages.

      1. Create a New Env from Existing Environment.

      2. Create Conda Environment from Scratch.

    3. Users should search for all the available packages before installation.

      1. Search Packages in All Default Channels.

      2. Search Packages in a Specific Channel.

    4. Users could install a specific python package to avoid conflict or add functionality.

      1. Install a Package with a Specific Version and Build Code.

  2. How to solve dependency conflict?

Lists of Existing Anaconda Environments 

There are currently 6 Conda environments supported on the HAL system

Environment NameLocationDescription
base/opt/miniconda3Default conda env with basic python packages.
jupyter/opt/miniconda3/envs/jupyterDefault conda env with jupyter server.
Used for hal-ondemand service only.

deepspeed-v0.3.16

/opt/miniconda3/envs/deepspeed-v0.3.16

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

fastai-v0.1.18

/opt/miniconda3/envs/fastai-v0.1.18

fastai is a deep learning library that provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains.
wmlce-v1.6.2/opt/miniconda3/envs/wmlce-v1.6.2Watson Machine Learning Community Edition is an IBM Cognitive Systems offering that is designed for the rapidly growing and quickly evolving AI category of deep learning. 
This is the only environment still have tensorflow-v1.15
wmlce-v1.7.0/opt/miniconda3/envs/wmlce-v1.7.0Watson Machine Learning Community Edition is an IBM Cognitive Systems offering that is designed for the rapidly growing and quickly evolving AI category of deep learning.
This is the last version of WMLCE, the following module is opence.
opence-v1.0.0

/opt/miniconda3/envs/opence-v1.0.0

Open-CE is a community-driven software distribution for machine learning that runs on standard Linux platforms with NVIDIA GPU technologies.
opence-v1.1.2/opt/miniconda3/envs/opence-v1.1.2Open-CE is a community-driven software distribution for machine learning that runs on standard Linux platforms with NVIDIA GPU technologies.
opence-v1.2.2/opt/miniconda3/envs/opence-v1.2.2Open-CE is a community-driven software distribution for machine learning that runs on standard Linux platforms with NVIDIA GPU technologies.
opence-v1.3.1/opt/miniconda3/envs/opence-v1.3.1Open-CE is a community-driven software distribution for machine learning that runs on standard Linux platforms with NVIDIA GPU technologies.
opence-v1.4.1/opt/miniconda3/envs/opence-v1.4.1Open-CE is a community-driven software distribution for machine learning that runs on standard Linux platforms with NVIDIA GPU technologies.
opence-v1.5.1

opence-v1.5.2		/opt/miniconda3/envs/opence-v1.5.1

/opt/miniconda3/envs/opence-v1.5.2		Open-CE is a community-driven software distribution for machine learning that runs on standard Linux platforms with NVIDIA GPU technologies.
This is the default python environment.
Use opence-v1.5.1 as base, fixed openblas issue caused by openmp.
theano-v1.0.4/opt/miniconda3/envs/theano-v1.0.4Open-CE is a community-driven software distribution for machine learning that runs on standard Linux platforms with NVIDIA GPU technologies.
rapids-v0.11.0 

/opt/miniconda3/envs/rapids

The RAPIDS suite of software libraries, built on CUDA-X AI, gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs.

Create a New Env from Existing Environments

We recommend our users to create a new environment from one of our existing opence environment.

Create a New Env from Existing Env
conda create --name=<new_env> --clone=opence-v1.5.1

The new Conda environment will be located within $HOME/.conda/envs/<new_env>, then users can search and/or install python packages via Conda

Example: Search for a New Package
conda search r-tensorflow

Create Conda Environment from Scratch

Users can also create a new environment from scratch

Create a New Env from Existing Env
conda create --name=<new_env_name>

Search Packages in All Default Channels 

conda search openblas

Search Packages in a Specific Channel 

conda search openblas -c conda-forge

Install a Package with a Specific Version and Build Code

Install a package from the result of the search

(<new_env_name>)[<user_id>@hal-login2 ~]# conda search openblas
...
openblas                      0.3.12      pthreads_hca0ad1f_0  conda-forge         
openblas                      0.3.12      pthreads_hca0ad1f_1  conda-forge         
openblas                      0.3.13      h6ffa863_0           pkgs/main           
openblas                      0.3.13      h6ffa863_1           pkgs/main           
openblas                      0.3.13      openmp_h25a920f_0    conda-forge         
openblas                      0.3.13      pthreads_h92053e5_0  conda-forge
...
(<new_env_name>)[<user_id>@hal-login2 ~]# conda install openblas=0.3.13=openmp_h25a920f_0



Note: If you want to use your own conda env in hal-ondemand, you need to install conda install ipykernel .


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