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There are currently 6 Conda environments supported on HAL system
| Environment Name | Location | Description |
|---|---|---|
| base | /opt/apps/anaconda3 | Default Conda env with basic python packages. |
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 and provides researchers with low-level components that can be mixed and matched to build new approaches. |
| wmlce-v1.6.2 | /opt |
| /anaconda3/envs/wmlce-v1.6.2 |
| Watson 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. | |
| wmlce-v1.7.0 | /opt/ |
| anaconda3/envs/wmlce-v1.7.0 |
| Watson 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. | ||
| 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-v1.1.2 | Open-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-v1.2.2 | Open-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-v1.3.1 | Open-CE is a community-driven software distribution for machine learning that runs on standard Linux platforms with NVIDIA GPU technologies. |
| rapids | /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 Envs
We recommend our users to create a new environment from one of our existing powerai or wmlce opence environment.
| Code Block | ||||
|---|---|---|---|---|
| ||||
conda create --name=<new_env> --clone=wmlceopence-v1.73.01 |
The new Conda environment will be located within $HOME/.conda/envs/<new_env>, then users can search and/or install python packages via Conda
| Code Block | ||||
|---|---|---|---|---|
| ||||
conda search r-tensorflow | ||||
| ||||
conda install r-tensorflow |
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Create Conda Environment from Scratch
Users can also create a new environment from scratch
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