Metadata-Version: 2.1
Name: gradient_utils
Version: 0.1.0
Summary: Gradient Utils
Home-page: https://github.com/Paperspace/gradient-utils
Author: Paperspace Co.
Author-email: info@paperspace.com
License: UNKNOWN
Description: ![GitHubSplash](https://user-images.githubusercontent.com/585865/65443342-e630d300-ddfb-11e9-9bcd-de1d2033ea60.png)
        
        Gradient Utils
        =================
        
        ![PyPI](https://img.shields.io/pypi/v/gradient)
        [![codecov](https://codecov.io/gh/Paperspace/gradient-cli/branch/master/graph/badge.svg)](https://codecov.io/gh/Paperspace/gradient-utils)
        
        <br>
        
        **Get started:** [Create Account](https://www.paperspace.com/account/signup?gradient=true) • [Install CLI](https://docs.paperspace.com/gradient/get-started/install-the-cli) • [Tutorials](https://docs.paperspace.com/gradient/tutorials) • [Docs](https://docs.paperspace.com/gradient)
        
        **Resources:** [Website](https://gradient.paperspace.com/) • [Blog](https://blog.paperspace.com/) • [Support](https://support.paperspace.com/hc/en-us) • [Contact Sales](https://use.paperspace.com/contact-sales)
        
        <br>
        
        Gradient is an an end-to-end MLOps platform that enables individuals and organizations to quickly develop, train, and deploy Deep Learning models.  The Gradient software stack runs on any infrastructure e.g. AWS, GCP, on-premise and low-cost [Paperspace GPUs](https://gradient.paperspace.com/instances).  Leverage automatic versioning, distributed training, built-in graphs & metrics, hyperparameter search, GradientCI, 1-click Jupyter Notebooks, our Python SDK, and more. 
        
        This is an SDK for performing Machine Learning with Gradientº, it can be installed in addition to [gradient-cli](https://github.com/Paperspace/gradient-cli).
        
        # Requirements
        
        This SDK requires Python 3.5+.
        
        To install it, run:
        
        ```bash
        pip install gradient-utils
        ```
        
        # Usage
        
        ## Multinode Helper Functions
        
        ### Multinode GRPC Tensorflow
        
        **Set the TF_CONFIG environment variable**
        For multi-worker training, you need to set the `TF_CONFIG` environment variable for each binary running in your cluster. Set the value of `TF_CONFIG` to a JSON string that specifies each task within the cluster, including each task's address and role within the cluster. We've provided a Kubernetes template in the tensorflow/ecosystem repo which sets `TF_CONFIG` for your training tasks.
        
        **get_tf_config()**
        
        Function to set value of `TF_CONFIG` when run on machines within Paperspace infrastructure.
        
        It can raise a `ConfigError` exception with message if there's a problem with its configuration in a particular machine.
        
        **_Usage example:_**
        
        ```python
        from gradient_utils import get_tf_config
        
        get_tf_config()
        ```
        
        ## Hyperparameter Tuning
        
        Currently, Gradientº only supports _Hyperopt_ for Hyperparameter Tuning.
        
        **hyper_tune()**
        
        Function to run hyperparameter tuning.
        
        It accepts the following arguments:
        
        - `train_model`
          User model to tune.
        - `hparam_def`
          User definition (scope) of search space.
          To set this value, refer to [hyperopt documentation](https://github.com/hyperopt/hyperopt).
        - `algo`
          Search algorithm.
          _Default_: `tpe.suggest` (from hyperopt).
        - `max_ecals`
          Maximum number of function evaluations to allow before returning.
          _Default_: `25`.
        - `func`
          Function to be run by hyper tune.
          _Default_: `fmin` (from hyperopt). _Do not change this value if you do not know what you are doing!_
        
        It returns a dict with information about the tuning process.
        
        It can raise a `ConfigError` exception with message if there's no connection to MongoDB.
        
        **Note:** _You do not need to worry about setting your MongoDB version; it will be set within Paperspace infrastructure for hyperparameter tuning._
        
        **Usage example:**
        
        ```python
        from gradient_utils import hyper_tune
        
        # Prepare model and search scope
        
        # minimal version
        argmin1 = hyper_tune(model, scope)
        
        # pass more arguments
        argmin2 = hyper_tune(model, scope, algo=tpe.suggest, max_evals=100)
        ```
        
        ## Utility Functions
        
        **get_mongo_conn_str()**
        
        Function to check and construct MongoDB connection string.
        
        It returns a connection string to MongoDB.
        
        It can raise a `ConfigError` exception with message if there's a problem with any values used to prepare the MongoDB connection string.
        
        Usage example:
        
        ```python
        from gradient_utils import get_mongo_conn_str
        
        conn_str = get_mongo_conn_str()
        ```
        
        **data_dir()**
        
        Function to retrieve path to job space.
        
        Usage example:
        
        ```python
        from gradient_utils import data_dir
        
        job_space = data_dir()
        ```
        
        **model_dir()**
        
        Function to retrieve path to model space.
        
        Usage example:
        
        ```python
        from gradient_utils import model_dir
        
        model_path = model_dir(model_name)
        ```
        
        **export_dir()**
        
        Function to retrieve path for model export.
        
        Usage example:
        
        ```python
        from gradient_utils import export_dir
        
        model_path = export_dir(model_name)
        ```
        
        **worker_hosts()**
        
        Function to retrieve information about worker hosts.
        
        Usage example:
        
        ```python
        from gradient_utils import worker_hosts
        
        model_path = worker_hosts()
        ```
        
        **ps_hosts()**
        
        Function to retrieve information about Paperspace hosts.
        
        Usage example:
        
        ```python
        from gradient_utils import ps_hosts
        
        model_path = ps_hosts()
        ```
        
        **task_index()**
        
        Function to retrieve information about task index.
        
        Usage example:
        
        ```python
        from gradient_utils import task_index
        
        model_path = task_index()
        ```
        
        **job_name()**
        
        Function to retrieve information about job name.
        
        Usage example:
        
        ```python
        from gradient_utils import job_name
        
        model_path = job_name()
        ```
        
        # MetricsLogger
        Prometheus wrapper for logging custom metrics
        
        Usage example:
        
        ```python
        from gradient_utils import MetricsLogger
        m_logger = MetricsLogger()
        m_logger.add_gauge("some_metric_1")
        m_logger["some_metric_1"].set(3)
        m_logger["some_metric_1"].inc()
        
        m_logger.add_gauge("some_metric_2")
        m_logger["some_metric_2"].set_to_current_time()
        
        m_logger.push_metrics()
        ```
        
Keywords: gradient utils sdk
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Provides-Extra: dev
