semvec/pypi-packages huggingface dataset:
furiosa-registry 0.9.1
FuriosaAI model registry
Requires Python: ~=3.8

Furiosa Registry
----------------

Furiosa Registry defines `Model` which allows client to communicate with several types of registries.

## Getting started

**Install**

```sh
pip install furiosa-registry
```

**List up available models**

```python
import furiosa.common.thread as synchronous
import furiosa.registry as registry


# Repository where to load models from.
repository = "https://github.com/furiosa-ai/furiosa-artifacts"

# List up the available artifacts.
models: List[registry.Model] = synchronous(registry.list)(repository)

for model in models:
    print(model.name)
```

**Load models**

```python
import furiosa.common.thread as synchronous
import furiosa.registry as registry


# Repository where to load models from.
repository = "https://github.com/furiosa-ai/furiosa-artifacts"

models: registry.Model = synchronous(registry.load)(uri=repository, name="MLCommonsResNet50")

# Access the model
print(model.name)
print(model.version)
print(model.metadata.description)
```

**Print documentation about a model**

```python
import furiosa.common.thread as synchronous
import furiosa.registry as registry


# Repository where to load models from.
repository = "https://github.com/furiosa-ai/furiosa-artifacts"

print(synchronous(registry.help)(repository, "MLCommonsResNet50"))
```

## Development

**Generate artfiact JSON schema from pydantic model definition.**

When you changed model schema, you can generate modified schema via

```sh
python -c 'from furiosa.registry import Model;\
            print(Model.schema_json(indent=2), file=open("model_schema.json", "w"))'
```

See `model_schema.json`
codetf 19.4.6
call of duty



# Installation


To install requirements: `python -m pip install requirements.txt`

To save requirements: `python -m pip list --format=freeze --exclude-editable -f https://download.pytorch.org/whl/torch_stable.html > requirements.txt`

* Note we use Python 3.9.4 for our experiments

The code in augerino_lib is an extension (with our modification) from the original Augerino code https://github.com/g-benton/learning-invariances. The code in data_augmentation/my_training.py is a modification of the pytorch example in https://github.com/pytorch/examples/tree/master/imagenet.

# Running the code 

For remaining experiments: 

Navigate to the corresponding directory, then execute: `python run.py -m` with the corresponding `config.yaml` file (which stores experiment configs).

# License

Consult License.md