Metadata-Version: 2.1
Name: track-ml
Version: 0.1.1
Summary: Experiment tracking module
Home-page: https://github.com/richardliaw/track
Author: RISE
License: MIT License
Description: # track
        
        ## Installation
        
        Just use:
        
        ```
        pip install track-ml
        ```
        
        Right now this requires python 3.
        
        ## Usage
        
        Report various metrics of interest, with automatically configured and persisted logging.
        
        ```python
        import track 
        
        def training_function(param1=0.01, param2=10):
            local = "~/track/myproject"
            remote = "s3://my-track-bucket/myproject"
            with track.trial(local, remote, param_map={"param1": param1, "param2": param2}):
                model = create_model()
                for epoch in range(100):
                    model.train()
                    loss = model.get_loss()
                    track.metric(iteration=epoch, loss=loss)
                    track.debug("epoch {} just finished with loss {}", epoch, loss)
                    model.save(os.path.join(track.trial_dir(), "model{}.ckpt".format(epoch)))
        ```
                
        Inspect existing experiments
        
        ```bash
        $ python -m track.trials --local_dir ~/track/myproject trial_id "start_time>2018-06-28" param2
        trial_id    start_time                param2
        8424fb387a 2018-06-28 11:17:28.752259 10
        ```
        
        Plot results
        
        ```python
        import track
        import matplotlib
        matplotlib.use('Agg')
        import matplotlib.pyplot as plt
        
        proj = track.Project("~/track/myproject", "s3://my-track-bucket/myproject")
        most_recent = proj.ids["start_time"].idxmax()
        most_recent_id = proj.ids["trial_id"].iloc[[most_recent]]
        res = proj.results(most_recent_id)
        plt.plot(res[["iteration", "loss"]].dropna())
        plt.savefig("loss.png")
        ```
        
        Recover saved artifacts
        
        ```python
        model.load(proj.fetch_artifact(most_recent_id[0], 'model10.ckpt'))
        model.serve_predictions()
        ```
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
