semvec/pypi-packages huggingface dataset:
osearch 0.1.0
Search nested lists and dicts
License: MIT
Keywords: search,list,dictionary,objects,pretty,print,json

# Osearch - The tool for finding information in nested dicts/lists

When you are working with imported JSON in python, it's often hard to get an overview of the datastructure. I created `osearch` to help you with this. It allows you to search for strings or integers in huge nested structures.
Here is an example of a messy test structure:
```
tstObj = [
    1,
    2,
    3,
    "listval2",
    "listval5",
    {
        "dict1": "dictval1",
        "dict2": "dictval2",
        "dict3": 3,
        "dict4": ["item1", "A long string here", 1000, "another string"],
        100: 222,
    },
]
```
first we import the `osearch` lib: 
```
from osearch import osearch
```
Now we can do some funny searches for strings containing the part "val":
```
>> osearch(tstObj, "val", method="contains", prettyprint=True)
[3]='listval2'
[4]='listval5'
[5]['dict1']='dictval1'
[5]['dict2']='dictval2'
```

Or we could search for an integer:
```
>> osearch(tstObj, 100, prettyprint=True)
[5][100]
```

Or maybe even a regular expression search that is non-case sensitive. Here we search for all occurances of keys/values that ends in val1 or val5:
```
>> osearch(tstObj, r"val[15]$", method="regex-case", prettyprint=True)
[4]='listval5'
[5]['dict1']='dictval1'
```
Have fun

/ Alex Skov Jensen
agoge 0.1.3
Machine Learning infra
License: MIT
Keywords: pytorch,machine learning

# Agoge

An opinionated ML development environment

Train pytorch models, hyperparameter tune them with single loc change.


## Libraries

### Pytorch

[Pytorch](https://pytorch.org/) is a Python first machine learning library 

### Ray

[Ray](https://ray-project.github.io/) Provides easy experiment scaling + hyper parameter optimisation

### Weights and Biases

Agoge uses WandB to monitor model training. It's super easy to setup, just go to the [wandb website](https://www.wandb.com/) and sign up for an account. Then follow the instructions to set up

## Static Components

These components should not need to be customised for model specific use cases

### Train Worker

Setups all the required components to train a model

### Inference Worker

Setups all the required components for inference. Also attempts to download model weights if they are not found locally.

### Data Handler

Loads the dataset and handles the dataset split

## User Provided Components

These components need to be inherited by project specific classes

### Model

Provides some convenience functions around loading models. This class will hold all model specific code and is used by the train worker and inference workers

### Solver

Override the `solve` method with the code required to train your model

### Dataset

Any dataset that is compatiable with the [Pytorch map style dataset model](https://pytorch.org/docs/stable/data.html#map-style-datasets)


# Disclaimer

This code is subject to change. I will try not to break anything but can't promise. File an issue if an update breaks your code