semvec/pypi-packages huggingface dataset:
nvidia-cudss-cu12 0.7.1.6
cuDSS - runtime libraries
Keywords: cuda, nvidia, runtime, sparse, linear solver, multi-node, mgmn

# NVIDIA cuDSS library

NVIDIA cuDSS (Preview) is a library of GPU-accelerated linear solvers with sparse matrices. It provides algorithms for solving linear systems of the following type:
`AX=B`

with a sparse matrix A, right-hand side B and unknown solution X (could be a matrix or a vector).

The cuDSS functionality allows flexibility in matrix properties and solver configuration, as well as execution parameters like CUDA streams.
nvidia-cudss-cu13 0.7.1.6
cuDSS - runtime libraries
Keywords: cuda, nvidia, runtime, sparse, linear solver, multi-node, mgmn

# NVIDIA cuDSS library

NVIDIA cuDSS (Preview) is a library of GPU-accelerated linear solvers with sparse matrices. It provides algorithms for solving linear systems of the following type:
`AX=B`

with a sparse matrix A, right-hand side B and unknown solution X (could be a matrix or a vector).

The cuDSS functionality allows flexibility in matrix properties and solver configuration, as well as execution parameters like CUDA streams.
singhlab 0.0.2
General functions useful to members of Rohit Singh lab.

To install:

```bash
git clone git@github.com:rohitsinghlab/singhlab-main.git
cd singhlab-main
pip install .
```

To run sceodesic with 10 different random seeds 

```python
from singhlab.utils.decorators import run_with_seed
from sceodesic import run_sceo

seeds = [123, 444, 33, 1234, 99, 14, 37]
results = {}

adata = sc.read_h5ad('adata.h5ad')

for seed in seeds:
    run_with_seed(seed)(run_sceo)(adata, num_hvgs=300)
    sceo_embeddings = adata.obsm['sceo_embeddings']
    sceo_programs = adata.varm['sceo_programs]
    results[seed] = (sceo_embeddings, sceo_programs)
```

To do a bootstrap for some uncertainty quantification: 

```python
from singhlab.utils.decorators import bootstrap
import numpy as np

# bootstrap parameters 
num_trials = 100
seed = 42

# the function we want to run our bootstrap on
@bootstrap(num_trials, seed)
def get_mean(data):
    return np.mean(data)
 
# Example data
data = np.array([5, 7, 8, 6, 9, 10, 12, 7, 6, 8])

# automatically applies bootstrap
boot_means = get_mean(data)

# Percentile CI (95%)
lower, upper = np.percentile(boot_means, [2.5, 97.5])

print(f"Bootstrap mean CI: [{lower:.2f}, {upper:.2f}]")
```