Difference between revisions of "Draft:Software Installation Policies and Guidelines"
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==Generalized vs Specialized vs Domain Specific environments== | ==Generalized vs Specialized vs Domain Specific environments== | ||
| − | * | + | *Generalized environments like R and Python provide a wide range of functionality for data analysis, machine learning, and other scientific computing tasks. These environments have a large number of libraries and packages available for users to work with. |
| − | + | *Specialized environments like PyTorch and TensorFlow are focused on specific machine learning tasks, such as deep learning, and provide specialized tools and libraries to support these tasks. | |
| − | ==Prebuilt Environments for Reference== | + | *Domain-specific collections like GeoPython or DJPytt are environments that are tailored to specific domains, such as geospatial data analysis or data journalism. |
| − | * | + | ==Recommendations on using our prebuilt environments vs building their own== |
| − | ** | + | *Using prebuilt environments can be beneficial for users who want to get started quickly and don't need to make a lot of customizations to the environment. These environments are usually well-maintained and can be updated easily. |
| + | *Building your own environment can be beneficial if you need more control over the environment or want to customize it for your specific needs. However, this can also be more time-consuming and requires more technical expertise. | ||
| + | ===Prebuilt Environments for Reference=== | ||
| + | *There are many prebuilt environments available for popular libraries and frameworks, such as PyTorch. Users can refer to these environments for guidance on how to build their own environment or use prebuilt containers. | ||
| + | **For example, NVIDIA provides a prebuilt PyTorch container that users can use as a starting point for their own environment. | ||
==Availabile Linux Software installed in lapps in ResVault== | ==Availabile Linux Software installed in lapps in ResVault== | ||
*Enter here | *Enter here | ||
Revision as of 04:49, 19 February 2023
Generalized vs Specialized vs Domain Specific environments
- Generalized environments like R and Python provide a wide range of functionality for data analysis, machine learning, and other scientific computing tasks. These environments have a large number of libraries and packages available for users to work with.
- Specialized environments like PyTorch and TensorFlow are focused on specific machine learning tasks, such as deep learning, and provide specialized tools and libraries to support these tasks.
- Domain-specific collections like GeoPython or DJPytt are environments that are tailored to specific domains, such as geospatial data analysis or data journalism.
Recommendations on using our prebuilt environments vs building their own
- Using prebuilt environments can be beneficial for users who want to get started quickly and don't need to make a lot of customizations to the environment. These environments are usually well-maintained and can be updated easily.
- Building your own environment can be beneficial if you need more control over the environment or want to customize it for your specific needs. However, this can also be more time-consuming and requires more technical expertise.
Prebuilt Environments for Reference
- There are many prebuilt environments available for popular libraries and frameworks, such as PyTorch. Users can refer to these environments for guidance on how to build their own environment or use prebuilt containers.
- For example, NVIDIA provides a prebuilt PyTorch container that users can use as a starting point for their own environment.
Availabile Linux Software installed in lapps in ResVault
- Enter here