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        "description": "# track\n\n## Installation\n\nJust use:\n\n```\npip install track-ml\n```\n\nRight now this requires python 3.\n\n## Usage\n\nReport various metrics of interest, with automatically configured and persisted logging.\n\n```python\nimport track \n\ndef training_function(param1=0.01, param2=10):\n    local = \"~/track/myproject\"\n    remote = \"s3://my-track-bucket/myproject\"\n    with track.trial(local, remote, param_map={\"param1\": param1, \"param2\": param2}):\n        model = create_model()\n        for epoch in range(100):\n            model.train()\n            loss = model.get_loss()\n            track.metric(iteration=epoch, loss=loss)\n            track.debug(\"epoch {} just finished with loss {}\", epoch, loss)\n            model.save(os.path.join(track.trial_dir(), \"model{}.ckpt\".format(epoch)))\n```\n\nInspect existing experiments\n\n```bash\n$ python -m track.trials --local_dir ~/track/myproject trial_id \"start_time>2018-06-28\" param2\ntrial_id    start_time                param2\n8424fb387a 2018-06-28 11:17:28.752259 10\n```\n\nPlot results\n\n```python\nimport track\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\nproj = track.Project(\"~/track/myproject\", \"s3://my-track-bucket/myproject\")\nmost_recent = proj.ids[\"start_time\"].idxmax()\nmost_recent_id = proj.ids[\"trial_id\"].iloc[[most_recent]]\nres = proj.results(most_recent_id)\nplt.plot(res[[\"iteration\", \"loss\"]].dropna())\nplt.savefig(\"loss.png\")\n```\n\nRecover saved artifacts\n\n```python\nmodel.load(proj.fetch_artifact(most_recent_id[0], 'model10.ckpt'))\nmodel.serve_predictions()\n```\n\n\n",
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        "license": "MIT License",
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        "name": "track-ml",
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