semvec/pypi-packages huggingface dataset:
vertex_tensorboard_logger 0.1.1
logger to log information to vertex ai experiments and it's tensorboards
Requires Python: <3.11,>=3.10

# Vertex AI Logger

Logger for monitoring and visualizing machine learning training processes within Vertex AI.

## Installation
```pip install vertex_tensorboard_logger```

## Setup

1. Authenticate with Google Cloud:
```bash
gcloud auth application-default login
```

2. Set Environment Variables:

 Set the necessary environment variables for your Google Cloud project:

3. Add GCP Secrets to Access Google cloud projects: 


## Usage

1. Initialize the Logger:
```python
from vertex_tensorboard_logger import VertexAILogger

factory = VertexAILogger(
    experiment_name="your_experiment_name"
)
```

2. Start a Experiment Run:
```python
experiment_logger = factory.vertex_experiment_logger()
experiment_logger.start_run(run_name="your_run_name", existing_run=False)
```

3. Log Metrics, Parameters, and Time Series Data:
```python
# Log metrics
metrics = {"accuracy": 0.95}
experiment_logger.log_metrics(metrics)

# Log hyperparameters
params = {"learning_rate": 0.001, "num_train_epochs": 3, "warmup_ratio": 0.1, "weight_decay": 0.01}
experiment_logger.log_params(params)

# Log time series metrics
time_series_metrics = {"accuracy": 0.95, "F1-score": 0.85}
experiment_logger.log_time_series_metrics(time_series_metrics, step=1)
```

4. End the Experiment Run:
```python
experiment_logger.end_run()
```

5. Stream TensorBoard Logs:
```python
tensorboard_logger = factory.vertex_tensorboard_logger(log_dir="path_to_log_directory")
tensorboard_logger.stream_tensorboard_logs()
# Training Code
tensorboard_logger.stop_logging()
```

6. Upload TensorBoard Logs:
```python
tensorboard_logger.upload_tensorboard_logs()
```
privacera-shield 1.1.13
Privacera AI Governance (PAIG) Shield Plugin Library
Requires Python: >=3.8.1

# Privacera AI Goverance Shield Library

The `privacera_shield` library allows you to seamlessly integrate Privacera AI Governance into your Langchain projects. 

Register an account with Privacera AI Governance at https://privacera.ai.
etag-generator 0.9.1
Given a JSON object (python dict), generate a stable value suitable as an ETag
Requires Python: >=3.8
Keywords: etag, json

# etag_generator

Given a python list or dictionary that will represents a JSON return value, generate an ETag that is guaranteed to be stable.

Will generate an etag from most data commonly used types:
* dict
* list
* string
* int / float

Works to create stable identifiers even with complex, multi-level objects.

Does not have the smarts to handle circular references.

Currently doesn't deal with the following data types:
* tuple
* classes
* boolean
* none
* other stuff that might exist that isn't in the supported list

## installation

pip install etag_generator

## usage

```python
from etag_generator import generate_etag

etag = generate_etag({"any_object":"value"})
```