Metadata-Version: 2.1
Name: tracker_ml
Version: 0.0.6
Summary: Machine learning file and parameter version control SDK for www.tracker.ml
Home-page: https://github.com/sage-t/tracker_ml
Author: Sage Thomas
Author-email: sage.thomas@outlook.com
License: UNKNOWN
Download-URL: https://github.com/sage-t/tracker_ml/archive/v0.0.5.tar.gz
Description: # tracker_ml
        
        www.tracker.ml SDK and CLI
        
        ## Install
        
        `pip install tracker-ml`
        
        ## Build
        
        Install build tools
        ```
        python -m pip install --user --upgrade setuptools wheel
        ```
        
        Create distribution archives
        ```
        python setup.py sdist bdist_wheel
        ```
        
        Install from archives
        ```
        python -m pip install dist/tracker_ml-X.X.X.tar.gz
        ```
        
        ## Python SDK
        
        ### Use
        
        First, initialize tracker using the CLI and update the files you want to track. Then import 
        `tracker_ml.tml` anywhere and everywhere. Use is easy:
        
        ```python
        import tracker_ml.tml as tml
        
        tml.login("username", "password")
        tml.model("Logistic Regression")
        
        # <machine learning code>
        
        # record int, float, or str
        tml.record("accuracy", 0.42)
        tml.record("model", "Logistic Regression")
        
        # record multiple values under the same key
        tml.mrecord("epoch", 1)
        tml.mrecord("epoch", 2)
        tml.mrecord("epoch", 3)
        
        # data will be saved locally and to the API on exit
        ```
        
        All changes since the previous run and all recorded values will be automatically saved. The CLI
        can be used to view/compare trials and undo changes.
        
        
        ## Command Line Interface
        
        tracker.ml command line interface for locally tracking/reverting file changes and tracking results 
        for each change. Similar to git, but works with the SDK to track every time a new model is 
        trained/tested.
        
        
        ### Use
        
        Use the help command. (Not all commands displayed work yet)
        
        ```
        $ tracker --help
        $ tracker status --help
        ```
        
        Initialize in the project root. 
        
        ```
        $ tracker init -u <username> -p <password> -n <project name>
        ```
        
        Add file(s)/directory(s) that will be saved every run. 
        
        ```
        $ tracker add .
        ```
        
        Stop recording file(s)/directory(s) that would be saved every run. 
        
        ```
        $ tracker remove .
        ```
        
        View past trials and sort them
        
        ```
        $ tracker status
         Total trials: 4
         Sorted by: id
        
          Id  |  Accuracy  |         Model
        ------------------------------------------
          4   |     63     |  Logistic Regression
          3   |     74     |  Logistic Regression
          2   |     50     |  Logistic Regression
          1   |     92     |  Logistic Regression
        $ python tracker.py status -k accuracy -l 2 -r
         Total trials: 4
         Reverse sorted by: accuracy
         Only displaying 2 results
        
          Id  |  Accuracy  |         Model
        ------------------------------------------
          2   |     50     |  Logistic Regression
          4   |     63     |  Logistic Regression
        ```
        
Keywords: MACHINE,LEARNING,VERSION,TRACKING,TRACKER
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Description-Content-Type: text/markdown
