Metadata-Version: 2.1
Name: hydra-ml
Version: 0.3.7
Summary: A cloud-agnostic ML Platform
Home-page: https://github.com/georgianpartners/hydra
Author: Hydra Development Team
Author-email: faisal.anees@georgian.io
License: UNKNOWN
Description: # hydra
        A cloud-agnostic Machine Learning Platform that will enable Data Scientists to run multiple experiments, perform hyper parameter optimization, evaluate results and serve models (batch/realtime) while still maintaining a uniform development UX across cloud environments 
        
        ## Installation
        To install Hydra using PyPI, run the following command
        ```
        $ pip install hydra-ml
        ```
        To install Hydra using GitHub source, first clone Hydra using `git` :
        ```
        $ git clone https://github.com/georgianpartners/hydra
        ```
        Then in the `hydra` repository that you cloned, run
        ```
        $ python setup.py install
        ```
        Check the current hydra version by running
        ```
        $ hydra --version
        ```
        
        ## Documentation
        
        ### Prerequisites
        
        1. Github Token generation
            - Follow this guide : https://docs.github.com/en/free-pro-team@latest/github/authenticating-to-github/creating-a-personal-access-token
            - Add token to your enviroment variable by running 
            ```
            $ export GITHUB_TOKEN=<Fill your github token here>
            ```
        2. Setting up your Cloud's CLI tool locally
            - GCP : https://cloud.google.com/sdk/gcloud
            - AWS : https://docs.aws.amazon.com/polly/latest/dg/setup-aws-cli.html
            - Azure : https://docs.microsoft.com/en-us/cli/azure/authenticate-azure-cli
            
        ### Getting started
        
        ----------------------
        
        #### `hydra`
        
        Entrypoint for Hydra CLI
        
        `hydra [flags]`
        
        ##### Examples
        
        ```
        $ hydra --version
        $ hydra --help
        ```
        
        ##### Options
        
        ```
          --version  Show hydra version
          --help     Show usage guide
        ```
        ----------------------
        
        #### `hydra train`
        
        Submit a training job to the selected cloud platform. You need to run this from inside a git hosted repository that
        contains your model code and a conda yaml file `environment.yml` . The command takes a number of options to tailor your
        training job. These options can also be provided via a `yaml` file 
        
        `hydra train [flags]`
        
        ##### Examples
        
        ```
        $ hydra train -m catboost_model.py --cloud gcp --cpu_count 8 --memory_size 20
        $ hydra train -m catboost_model.py --cloud gcp --cpu_count 8 --memory_size 20 --options '{"iterations": 100, "depth": 20}'
        $ hydra train -y catboost_model_configs.yaml
        ```
        
        `catboost_model_configs.yaml` looks like this :
        ```
        train:
          model_path: 'catboost_model.py'
          cloud: "gcp"
          cpu_count: 8
          memory_size: 16
          gpu_count: 1
          gpu_type: 'NVIDIA_TESLA_P4'
          region: 'us-west2'
          image_tag: 'batch'
          options:
            - project_name: "hydra-gcp-test-291317-aiplatform"
              bucket_name: "hydra-gcp-test-291317-aiplatform"
              blob_path: "hmnist/hmnist_64_64_L.csv"
              batch_size: 1
              epoch: 5
            - project_name: "hydra-gcp-test-291317-aiplatform"
              bucket_name: "hydra-gcp-test-291317-aiplatform"
              blob_path: "hmnist/hmnist_64_64_L.csv"
              batch_size: [1, 2, 3]
              epoch: [1, 2, 3]
        ```
        
        ##### Options
        
        ```
          -y, --yaml_path TEXT            Path to YAML file that contains preset options
          -m, --model_path TEXT           Path to file containing model code
          --cloud [fast_local|local|aws|gcp|azure]
          --github_token TEXT
          --cpu_count INTEGER RANGE       Number of CPU cores required
          --memory_size INTEGER RANGE     GB of RAM required
          --gpu_count INTEGER RANGE       Number of accelerator GPUs
          --gpu_type TEXT                 Accelerator GPU type
          --region TEXT                   Region of cloud server location
          -t, --image_tag TEXT            Docker image tag name
          -u, --image_url TEXT            Url to the docker image on cloud
          -o, --options TEXT              Environmental variables for the script
        
        ```
        
        ##### Options inherited from parent commands
        
        ```
          --help   Show usage guide for command
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Description-Content-Type: text/markdown
