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Use with Lasagne, Keras, Tensorflow, Torch, Theano, and basically everything else.\n\n- [Installation](#installation)\n- [Logging data](#getting-started)\n- [Python API](#python-api)\n- [POST endpoint](#post-endpoint)\n- [Usage notes](#usage-notes)\n- [Contributing](#contributing)\n- [Misc](#misc)\n\n![alt text](https://raw.githubusercontent.com/rewonc/pastalog/master/screenshots/main-big.gif \"Pastalog demo\")\n\n\n## Installation\n\n#### Easiest method for python \n\nThe python package `pastalog` has a node.js server packaged inside python module, as well as helper functions for logging data.\n\nYou need node.js 5+:\n\n`brew install node`  \n\n(If you don't have homebrew, download an installer from https://nodejs.org/en/)\n\n```bash\npip install pastalog\npastalog --install\npastalog --serve 8120\n# - Open up http://localhost:8120/ to see the server in action.\n```\n\n#### Just node.js server (useful if you don't want the python API)\n\n```bash\ngit clone https://github.com/rewonc/pastalog \u0026\u0026 cd pastalog\nnpm install\nnpm run build\nnpm start -- --port 8120\n# - Open up http://localhost:8120/ to see the server in action.\n```\n\n## Logging data\n\nOnce you have a server running, you can start logging your progress.\n\n\n#### Using Python module\n\n```python\nfrom pastalog import Log\n\nlog_a = Log('http://localhost:8120', 'modelA')\n\n# start training\n\nlog_a.post('trainLoss', value=2.7, step=1)\nlog_a.post('trainLoss', value=2.15, step=2)\nlog_a.post('trainLoss', value=1.32, step=3)\nlog_a.post('validLoss', value=1.56, step=3)\nlog_a.post('validAccuracy', value=0.15, step=3)\n\nlog_a.post('trainLoss', value=1.31, step=4)\nlog_a.post('trainLoss', value=1.28, step=5)\nlog_a.post('trainLoss', value=1.11, step=6)\nlog_a.post('validLoss', value=1.20, step=6)\nlog_a.post('validAccuracy', value=0.18, step=6)\n\n```\nVoila! You should see something like the below:\n\n![alt text](https://raw.githubusercontent.com/rewonc/pastalog/master/screenshots/first_steps.jpg \"Example 1\")\n\n\nNow, train some more models:\n\n```python\nlog_b = Log('http://localhost:8120', 'modelB')\nlog_c = Log('http://localhost:8120', 'modelC')\n\n# ...\n\nlog_b.post('trainLoss', value=2.7, step=1)\nlog_b.post('trainLoss', value=2.0, step=2)\nlog_b.post('trainLoss', value=1.4, step=3)\nlog_b.post('validLoss', value=2.6, step=3)\nlog_b.post('validAccuracy', value=0.14, step=3)\n\nlog_c.post('trainLoss', value=2.7, step=1)\nlog_c.post('trainLoss', value=2.0, step=2)\nlog_c.post('trainLoss', value=1.4, step=3)\nlog_c.post('validLoss', value=2.6, step=3)\nlog_c.post('validAccuracy', value=0.18, step=3)\n\n```\nGo to localhost:8120 and view your logs updating in real time.\n\n\n#### Using the Torch wrapper (Lua)\n\nUse the Torch interface, available here:  https://github.com/Kaixhin/torch-pastalog.  Thanks to Kaixhin for putting it together.\n\n\n\n#### Using a POST request\n\nSee more details in the [POST endpoint section](#post-endpoint)\n```bash\ncurl -H \"Content-Type: application/json\" -X POST -d '{\"modelName\":\"model1\",\"pointType\":\"validLoss\", \"pointValue\": 2.5, \"globalStep\": 1}' http://localhost:8120/data\n```\n\n\n## Python API\n\n##### `pastalog.Log(server_path, model_name)`\n\n\n- `server_path`: The host/port (e.g. `http://localhost:8120`) \n- `model_name`: The name of the model as you want it displayed (e.g. `resnet_48_A_V5`).\n\nThis returns a Log object with one method:\n\n##### `Log.post(series_name, value, step)`\n\n- `series_name`: typically the type of metric (e.g. `validLoss`, `trainLoss`, `validAccuracy`). \n- `value`: the value of the metric (e.g. `1.56`, `0.20`, etc.)\n- `step`: whatever quantity you want to plot on the x axis. If you run for 10 epochs of 100 batches each, you could pass to `step` the number of batches have been seen already (0..1000).\n\n\u003e **Note**: If you want to compare models across batch sizes, a good approach is to pass to `step` the fractional number of times the model has seen the data (number of epochs). In that case, you will have a fairer comparison between a model with batchsize 50 and another with batchsize 100, for example.\n\n## POST endpoint\n\nIf you want to use pastalog but don't want to use the Python interface or the Torch interface, you can just send POST requests to the Pastalog server and everything will work the same. The data should be json and encoded like so:\n\n`{\"modelName\":\"model1\",\"pointType\":\"validLoss\", \"pointValue\": 2.5, \"globalStep\": 1}`\n\n`modelName`, `pointType`, `pointValue`, `globalStep` correspond with `model_name`, `series_name`, `value`, `step` above.\n\nAn example with `curl`:\n\n```bash\ncurl -H \"Content-Type: application/json\" -X POST -d '{\"modelName\":\"model1\",\"pointType\":\"validLoss\", \"pointValue\": 2.5, \"globalStep\": 1}' http://localhost:8120/data\n```\n\n\n## Usage notes\n\n#### Automatic candlesticking\n\n![alt text](https://raw.githubusercontent.com/rewonc/pastalog/master/screenshots/candlestick.jpg \"Candlestick\")\n\nOnce you start viewing a lot of points (typically several thousand), the app will automatically convert them into candlesticks for improved visibility and rendering performance. Each candlestick takes a \"batch\" of points on the x axis and shows aggregate statistics for the y points of that batch:\n\n- Top of line: `max`\n- Top of box: `third quartile`\n- Solid square in middle: `median`\n- Bottom of box: `first quartile`\n- Bottom of line: `min`\n\nThis tends to be much more useful to visualize than a solid mass of dots. Computationally, it makes the app a lot faster than one which renders each point.\n\n\n#### Panning and zooming\n\nDrag your mouse to pan.  Either scroll up or down to zoom in or out. \n\nNote: you can also pinch in/out on your trackpad to zoom.\n\n#### Toggling visibility of lines\n\nSimply click the name of any model under 'series.'  To toggle everything from a certain model (e.g. `modelA`, or to toggle an entire type of points (e.g. `validLoss`), simply click those names in the legend to the right.\n\n#### Deleting logs\n\nClick the `x` next to the name of the series.  If you confirm deletion, this will remove it on the server and remove it from your view. \n\nNote: if you delete a series, then add more points under the same, it will act as if it is a new series.\n\n#### Backups\n\nYou should backup your logs on your own and should not trust this library to store important data. Pastalog does keep track of what it sees, though, inside a file called `database.json` and a directory called `database/`, inside the root directory of the package, in case you need to access it.\n\n\n## Contributing\n\nAny contributors are welcome.\n\n```bash\n# to install\ngit clone https://github.com/rewonc/pastalog\ncd pastalog\nnpm install\n\n# build + watch\nnpm run build:watch\n\n# dev server + watch\nnpm run dev\n\n# tests\nnpm test\n\n# To prep the python module\nnpm run build\n./package_python.sh\n\n```\n\n## Misc\n\n#### License\n\nMIT License (MIT)\n\nCopyright (c) 2016 Rewon Child\n\n#### Thanks\n\nThis is named `pastalog` because I like to use [lasagne](http://lasagne.readthedocs.org/en/latest/). Props to those guys for a great library!","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frewonc%2Fpastalog","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frewonc%2Fpastalog","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frewonc%2Fpastalog/lists"}