PyTorch Models (adapted from existing code) and Dockerfile
$25-50 USD / jam
Any (or all) of
A- PyTorch Video Diffusion model, can be a simplified/stripped down version of one of these two codebases applied to moving MNIST. This codebase works RaMVID [login to view URL]
B- datascience / ML Dockerfile
- Dummy variables for user/cluster info
- Can scale number of GPUs
- nvidia CUDA and cuDNN version changeable (FROM an nvidia image is fine)
- conda package management
- only torch necessary, but include lines with comments for other installations
Stripped down/simplified
Project structure should be
- Dockerfile
- [login to view URL]
- [login to view URL]
- [login to view URL]
- [login to view URL]
- [login to view URL]
- code
- [login to view URL]
- other_files
- ...
Notes
- All input variables must be contained in the yaml files and logically ordered
- Other dependencies ideally removed
- [login to view URL] contains lines needed to setup environment (including image creation)
ID Proyek: #36180435
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