semvec/pypi-packages huggingface dataset:
inferex 0.1.2
Inferex CLI - Init, deploy and manage your projects on Inferex infrastructure
Requires Python: >=3.8,<4.0

# Inferex CLI

Deploy and manage your AI projects on Inferex infrastructure.

[See our online documentation for a tutorial.](https://docs.inferex.com/)

## Installation

```bash
pip install inferex
```

You can invoke "inferex --help" for a list of commands. Each command may have
subcommands, which can be called with "--help" as well.

Version 0.1.2:

```bash
Usage: inferex [OPTIONS] COMMAND [ARGS]...

  Inferex CLI is a tool that enables AI companies to rapidly deploy pipelines.
  Init, deploy, and manage your projects with Inferex. Invoke "inferex --help"
  for a list of commands.

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  deploy      🚀 Deploy a project.
  deployment  🌎 Manage Inferex deployments.
  pipelines   📞 List pipelines for a deployment.
  init        ✨ Initializes a new project.
  login       🔑 Fetch api key via username & password authentication.
  logs        📃 Get logs of an Inferex deployment
  projects    📁 Manage Inferex projects.
  reset       ❌ Deletes the token.json file created at login.
```

## CLI - Basic usage

1. Create or navigate to the project folder you wish to deploy. You may copy an
   example project folder from the examples folder ("face_detection",
   "sentiment_analysis", etc). Each example has inferex.yaml, pipeline.py, and
   requirements.txt files.

1. Run the "inferex login" command to log in with your inferex account
   and save your token locally.

1. Run "inferex deploy". This will create a tar archive of your project folder
   and send it to the server for processing.

That's it! `inferex deployments` will list your deployed projects and their URLs.
dbmeta 0.0.12
Inspect database schema, tables and columns and returns a metadata object

# dbmeta v0.0.11
## Database metadata extractor

Generate a Metadata object containing information about the database.

### Install
```bash
pip install dbmeta
```

### Use
```python
import dbmeta

db_type = 'postgres'
db_url = 'postgres://postgres:postgres@localhost:5432'
metadata = dbmeta.gen_metadata(db_type, db_url)
```

## Excluding schema or tables

Postgres system tables and schema are already excluded from the introspection.
To exclude other tables or schema use the `exclude` class

```python
from dbmeta import exclude

exclude.schema = ["hdb_catalog"]
exclude.tables = ["migrations"]

...
```

## Classes

### Metadata

```python
class Metadata:
    schema: List[Schema]
    tables: List[Table]
    columns: List[Column]
    user_defined: List[UserDefined]

```


### Schema

```python
class Schema(DatabaseItem):
    catalog_name: str
    schema_name: str
    schema_owner: str
    tables: List[Table]
```
### Table

```python
class Table(DatabaseItem):
    table_catalog: str
    table_schema: str
    table_name: str
    table_type: str
    columns: List[Column]
```

### Column
```python
class Column(DatabaseItem):
    udt_name: str
    table_catalog: str
    table_schema: str
    table_name: str
    column_name: str
    ordinal_position: int
    column_default: str | None
    is_nullable: Literal["YES"] | Literal["NO"]
    data_type: str
    character_maximum_length: int | None
    is_updatable: bool

```

### User Defined Types

```python
class UserDefined(DatabaseItem):
    schema: str
    name: str
    internal_name: str
    elements: List[str | int]

```