semvec/pypi-packages huggingface dataset:
lamini-rocm-smi-exporter 0.0.1a5
Export rocm-smi metrics as prometheus metrics
Requires Python: >=3.10

# rocm-smi-exporter

Export rocm-smi metrics as prometheus metrics

## Docker

```
docker build . -t powerml/rocm-smi-exporter

# Login with powerml repo in 1password and push to it
docker login
docker push powerml/rocm-smi-exporter

# -v /opt:/opt is needed to make rocm runtime library available to container
docker run -d --rm --name=smi-exporter -p 9001:9001 \
--device=/dev/kfd --device=/dev/dri --group-add video \
-v /opt:/opt powerml/rocm-smi-exporter

# You should see the ROCM_* prefixed metrics
curl localhost:9001/metrics
```

## Pants build

[Pants](https://www.pantsbuild.org/2.21/docs/introduction/welcome-to-pants)
uses explicit `BUILD` files to track source files' dependencies and builds.

Pants is hermetic, means that the entire build environment is specified in
[pants.toml](pants.toml), which is copied from
[example-python](https://github.com/pantsbuild/example-python).

## Extra

* [Add args to systemd service](https://superuser.com/a/728962)
  * The python code accepts `--port` and other arguments
  * If needed, set its value when launching systemd service
impeller 0.1.2
Impeller is a package for spatial transcriptomics imputation using path-based graph neural networks.
Requires Python: >=3.6

# Impeller

Impeller is a sophisticated package designed for imputing spatial transcriptomics data using path-based graph neural networks. It supports a variety of datasets and integrates powerful processing and machine learning techniques.

## Installation

Install the Impeller package using pip with the following command:

```bash
pip install Impeller
```

## Usage
The Impeller package simplifies the process of downloading, processing, and training models on spatial transcriptomics data. Follow the steps below to utilize the package effectively.

### Download Example Data
The package supports three datasets: '10XVisium', 'Stereoseq', and 'SlideseqV2'. Begin by downloading the dataset of your choice:

```python
from Impeller import download_example_data

# Replace '10XVisium' with 'Stereoseq' or 'SlideseqV2' to download other datasets
download_example_data('10XVisium')
```

### Load and Process Data
Once the data is downloaded, you can load and process it for analysis:
```python 
from Impeller import load_and_process_example_data

# Ensure to specify the same dataset you downloaded
data, val_mask, test_mask, x, original_x = load_and_process_example_data('10XVisium')
```

### Train Model
After loading and processing the data, you can initialize the model's arguments and start training:
```python 
from Impeller import create_args, train

args = create_args()
test_l1_distance, test_cosine_sim, test_rmse = train(args, data, val_mask, test_mask, x, original_x)
print(f"Final L1 distance: {test_l1_distance}, Cosine similarity: {test_cosine_sim}, RMSE: {test_rmse}.")
```

### Only Inference
You can use Impeller to perform inference on your custom data (you'll need to provide a mask indicating which genes in which cells you want to impute). Below is a demo for inference only (replace the following adata and inference_mask with your own data):
```python 
from Impeller import load_example_data, process_inference_data

adata, _, inference_mask = load_example_data(example_dataset = '10XVisium')
data = process_inference_data(adata)

from Impeller import inference

Impeller_imputed_data = inference(args, data, inference_mask)
```

### Naive Baseline
We also provide a simple baseline implementation using K-Nearest Neighbors (KNN):
```python 
from Impeller import SpatialKNNImputer

knn_imputer = SpatialKNNImputer(adata, n_neighbors=5)
knn_imputed_data = knn_imputer.impute(inference_mask)
```