{
    "info": {
        "author": "Ge Yang",
        "author_email": "yangge1987@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "Programming Language :: Python :: 3"
        ],
        "description": "ML-Logger, A Simple and Scalable Logging Utility With a Beautiful Visualization Dashboard\n=========================================================================================\n\n|Downloads|\n\nML-Logger makes it easy to:\n\n-  save data locally and remotely, as **binary**, in a transparent\n   ``pickle`` file, with the same API and zero configuration.\n-  write from 500+ worker containers to a single instrumentation server\n-  visualize ``matplotlib.pyplot`` figures from a remote server locally\n   with ``logger.savefig('my_figure.png?raw=true')``\n\nAnd ml-logger does all of these with *minimal configuration* \u2014 you can\nuse the same logging code-block both locally and remotely with no code-block change.\n\nML-logger is highly performant \u2013 the remote writes are asynchronous. For\nthis reason it doesn\u2019t slow down your training even with 100+ metric\nkeys.\n\nWhy did we built this, you might ask? Because we want to make it easy\nfor people in ML to use the same logging code-block in all of they projects,\nso that it is easy to get started with someone else\u2019s baseline.\n\nGetting Started!\n----------------\n\nTo **install** ``ml_logger``, do:\n\n.. code-block:: bash\n\n   pip install ml-logger\n\nNow you can rock!\n\n.. code-block:: python\n\n   from ml_logger import logger\n   logger.configure('/tmp/ml-logger-debug')\n   # ~> logging data to /tmp/ml-logger-debug\n\nLog key/value pairs, and metrics:\n\n.. code-block:: python\n\n   for i in range(1):\n       logger.log(metrics={'some_val/smooth': 10, 'status': f\"step ({i})\"}, reward=20, timestep=i)\n       ### flush the data, otherwise the value would be overwritten with new values in the next iteration.\n       logger.flush()\n   # outputs ~>\n   # \u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n   # \u2502       reward       \u2502             20             \u2502\n   # \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n   # \u2502      timestep      \u2502             0              \u2502\n   # \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n   # \u2502  some val/smooth   \u2502             10             \u2502\n   # \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n   # \u2502       status       \u2502          step (0)          \u2502\n   # \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n   # \u2502      timestamp     \u2502'2018-11-04T11:37:03.324824'\u2502\n   # \u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n\nLogging to a Server\n~~~~~~~~~~~~~~~~~~~\n\n**Skip this if you just want to log locally.** When training in\nparallel, you want to kickstart an logging server (Instrument Server).\nTo do so, run:\n\n.. code-block:: bash\n\n   python -m ml_logger.server --log-dir /home/yourname/ml-logger-debug --host 0.0.0.0 --port 8081\n\nUse ssh tunnel if you are running on a managed cluster.\n\nAllowing Non-local Requests\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe default ``host`` is set to ``127.0.0.1``. This would prevent\nexternal requests from being accepted. To allow requests from a\nnon-localhost client, set ``host`` to ``0.0.0.0``.\n\nFull Readme on the Server\n~~~~~~~~~~~~~~~~~~~~~~~~~\n\n``ml-logger`` uses ``params-proto`` to declaratively define the cli\ninterface. To view the help document, you can simply type\n\n.. code-block:: bash\n\n   python -m ml_logger.server --help\n\nAsynchronously log the summary of LOTs of training metrics\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nA common scenario is you only want to upload averaged statistics of your\nmetrics. A pattern that @jachiam uses is the following:\n``store_metrics()``, ``peak_stored_metrics()``, and\n``log_metrics_summary()``\n\n.. code-block:: python\n\n   # You log lots of metrics during training.\n   for i in range(100):\n       logger.store_metrics(metrics={'some_val/smooth': 10}, some=20, timestep=i)\n   # you can peak what's inside the cache and print out a table like this: \n   logger.peek_stored_metrics(len=4)\n   # outputs ~>\n   #      some      |   timestep    |some_val/smooth\n   # \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u253f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u253f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\n   #       20       |       0       |      10\n   #       20       |       1       |      10\n   #       20       |       2       |      10\n   #       20       |       3       |      10\n\n   # The metrics are stored in-memory. Now we need to actually log the summaries:\n   logger.log_metrics_summary(silent=True)\n   # outputs ~> . (data is now logged to the server)\n\nTable of Contents\n-----------------\n\n-  logging ``matplotlib.pyplot`` figures on an headless server\n-  [documentation under construction]\n\nHow to Develop\n--------------\n\nFirst clone repo, install dev dependencies, and install the module under\nevaluation mode.\n\n.. code-block:: bash\n\n   git clone https://github.com/episodeyang/ml_logger.git\n   cd ml_logger && cd ml_logger && pip install -r requirements-dev.txt\n   pip install -e .\n\nTesting local-mode (without a server)\n-------------------------------------\n\nYou should be inside ml_logger/ml_logger folder\n\n.. code-block:: bash\n\n   pwd # ~> ml_logger/ml_logger\n   make test\n\nTesting with a server (You need to do both for an PR)\n-----------------------------------------------------\n\nTo test with a live server, first run (in a separate console)\n\n::\n\n   python -m ml_logger.server --log-dir /tmp/ml-logger-debug\n\nor do:\n\n.. code-block:: bash\n\n   make start-test-server\n\nThen run this test script with the option:\n\n.. code-block:: bash\n\n   python -m pytest tests --capture=no --log-dir http://0.0.0.0:8081\n\nor do\n\n.. code-block:: bash\n\n   make test-with-server\n\nYour PR should have both of these two tests working. ToDo: add CI to\nthis repo.\n\nTo Publish\n~~~~~~~~~~\n\nYou need ``twine``, ``rst-lint`` etc, which are included in the\n``requirements-dev.txt`` file.\n\n--------------\n\nLogging Matplotlib pyplots\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nConfiguring The Experiment Folder\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code-block:: python\n\n   from ml_logger import logger, Color, percent\n   from datetime import datetime\n\n   now = datetime.now()\n   logger.configure(log_directory=\"/tmp/ml-logger-demo\", f\"deep_Q_learning/{now:%Y%m%d-%H%M%S}\")\n\nThis is a singleton pattern similar to ``matplotlib.pyplot``. However,\nyou could also use the logger constructor\n\n.. code-block:: python\n\n   from ml_logger import ML_Logger\n\n   logger = ML_Logger(log_directory=\"/tmp/ml-logger-demo\", f\"deep_Q_learning/{now:%Y%m%d-%H%M%S}\")\n\nLogging Text, and Metrics\n~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code-block:: python\n\n   logger.log({\"some_var/smooth\": 10}, some=Color(0.85, 'yellow', percent), step=3)\n\ncolored output: (where the values are yellow)\n\n.. code-block:: text\n\n   \u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n   \u2502  some var/smooth   \u2502         10         \u2502\n   \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n   \u2502        some        \u2502       85.0%        \u2502\n   \u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n\nLogging Matplotlib Figures\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nWe have optimized ML-Logger, so it supports any format that ``pyplot``\nsupports. To save a figure locally or remotely,\n\n.. code-block:: python\n\n   import matplotlib.pyplot as plt\n   import numpy as np\n\n   xs = np.linspace(-5, 5)\n\n   plt.plot(xs, np.cos(xs), label='Cosine Func')\n   logger.savefig('cosine_function.pdf')\n\nLogging Videos\n~~~~~~~~~~~~~~\n\nIt is especially hard to visualize RL training sessions on a remote\ncomputer. With ML-Logger this is easy, and super fast. We optimized the\nserialization and transport process, so that a large stack of video\ntensor gets first compressed by ``ffmepg`` before getting sent over the\nwire.\n\nThe compression rate (and speed boost) can be 2000:1.\n\n.. code-block:: python\n\n   import numpy as np\n\n   def im(x, y):\n       canvas = np.zeros((200, 200))\n       for i in range(200):\n           for j in range(200):\n               if x - 5 < i < x + 5 and y - 5 < j < y + 5:\n                   canvas[i, j] = 1\n       return canvas\n\n   frames = [im(100 + i, 80) for i in range(20)]\n\n   logger.log_video(frames, \"test_video.mp4\")\n\nSaving PyTorch Modules\n~~~~~~~~~~~~~~~~~~~~~~\n\nPyTorch has a very nice module saving and loading API that has inspired\nthe one in ``Keras``. We make it easy to save this state dictionary\n(``state_dict``) to a server, and load it. This way you can load from\n100+ of your previous experiments, without having to download those\nweights to your code-block repository.\n\n.. code-block:: python\n\n   # save a module\n   logger.save_module(FastCNN=cnn)\n\n   # load a module\n   state_dict, = logger.load_pkl(f\"modules/{0:04d}_Test.pkl\")\n\nSaving Tensorflow Models\n~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe format tensorflow uses to save the models is opaque. I prefer to\nsave model weights in ``pickle`` as a dictionary. This way the weight\nfiles are transparent. ML_Logger offers easy helper functions to save\nand load from checkpoints saved in this format:\n\n.. code-block:: python\n\n   ## To save checkpoint\n   from ml_logger import logger\n   import tensorflow as tf\n\n   logger.configure(log_directory=\"/tmp/ml-logger-demos\")\n\n   x = tf.get_variable('x', shape=[], initializer=tf.constant_initializer(0.0))\n   y = tf.get_variable('y', shape=[], initializer=tf.constant_initializer(10.0))\n   c = tf.Variable(1000)\n\n   sess = tf.InteractiveSession()\n   sess.run(tf.global_variables_initializer())\n\n   trainables = tf.trainable_variables()\n   logger.save_variables(trainables, path=\"variables.pkl\", namespace=\"checkpoints\")\n\nwhich creates a file ``checkpoints/variables.pkl`` under\n``/tmp/ml-logger-demos``.\n\nVisualization\n-------------\n\nAn idea visualization dashboard would be 1. **Fast, instantaneous.** On\nan AWS headless server? View the plots as if they are on your local\ncomputer. 2. **Searchable, performantly.** So that you don\u2019t have to\nremember where an experiment is from last week. 3. **Answer Questions,\nfrom 100+ Experiments.** We make available Google\u2019s internal\nhyperparameter visualization tool, on your own computer.\n\nSearching for Hyper Parameters\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nExperiments are identified by the ``metrics.pkl`` file. You can log\nmultiple times to the same ``metrics.pkl`` file, and the later parameter\nvalues overwrites earlier ones with the same key. We enforce namespace\nin this file, so each key/value argument you pass into the\n``logger.log_parameters`` function call has to be a dictionary.\n\n.. code-block:: python\n\n   Args = dict(\n       learning_rate=10,\n       hidden_size=200\n   )\n   logger.log_parameters(Args=Args)\n\nHow to launch the Vis App\n~~~~~~~~~~~~~~~~~~~~~~~~~\n\n**This requires node.js and yarn dev environment** at the moment. We\nwill streamline this process without these requirements soon.\n\n0. download this repository\n1. go to ``ml-vis-app`` folder\n2. Install the dev dependencies\n\n   1. install node: `Installation <https://nodejs.org/en/download/>`__\n   2. install yarn:\n      `Installation <https://yarnpkg.com/lang/en/docs/install/#mac-stable>`__\n   3. install the dependencies of this visualization app:\n\n      1. ``yarn install``\n\n3. in that folder, run ``yarn``.\n\n**The IP address of the server is currently hard\ncode-blockd**\\ `here <https://github.com/episodeyang/ml_logger/blob/master/ml-vis-app/src/App.js#L11>`__\\ **.**\nTo use this with your own instrumentation server, over-write this line.\nI\u2019m planning on making this configuration more accessible.\n\nFull Logging API\n----------------\n\n.. code-block:: python\n\n   from ml_logger import logger, Color, percent\n\n   logger.log_params(G=dict(some_config=\"hey\"))\n   logger.log(some=Color(0.1, 'yellow'), step=0)\n   logger.log(some=Color(0.28571, 'yellow', lambda v: \"{:.5f}%\".format(v * 100)), step=1)\n   logger.log(some=Color(0.85, 'yellow', percent), step=2)\n   logger.log({\"some_var/smooth\": 10}, some=Color(0.85, 'yellow', percent), step=3)\n   logger.log(some=Color(10, 'yellow'), step=4)\n\ncolored output: (where the values are yellow)\n\n.. code-block:: text\n\n   \u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n   \u2502        some        \u2502        0.1         \u2502\n   \u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n   \u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n   \u2502        some        \u2502     28.57100%      \u2502\n   \u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n   \u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n   \u2502        some        \u2502       85.0%        \u2502\n   \u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n   \u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n   \u2502  some var/smooth   \u2502         10         \u2502\n   \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n   \u2502        some        \u2502       85.0%        \u2502\n   \u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n\nIn your project files, do:\n\n.. code-block:: python\n\n   from params_proto import cli_parse\n   from ml_logger import logger\n\n\n   @cli_parse\n   class Args:\n       seed = 1\n       D_lr = 5e-4\n       G_lr = 1e-4\n       Q_lr = 1e-4\n       T_lr = 1e-4\n       plot_interval = 10\n       log_dir = \"http://54.71.92.65:8081\"\n       log_prefix = \"https://github.com/episodeyang/ml_logger/blob/master/runs\"\n\n   logger.configure(log_directory=\"http://some.ip.address.com:2000\", prefix=\"your-experiment-prefix!\")\n   logger.log_params(Args=vars(Args))\n   logger.log_file(__file__)\n\n\n   for epoch in range(10):\n       logger.log(step=epoch, D_loss=0.2, G_loss=0.1, mutual_information=0.01)\n       logger.log_key_value(epoch, 'some string key', 0.0012)\n       # when the step index updates, logger flushes all of the key-value pairs to file system/logging server\n\n   logger.flush()\n\n   # Images\n   face = scipy.misc.face()\n   face_bw = scipy.misc.face(gray=True)\n   logger.log_image(index=4, color_image=face, black_white=face_bw)\n   image_bw = np.zeros((64, 64, 1))\n   image_bw_2 = scipy.misc.face(gray=True)[::4, ::4]\n\n   logger.log_image(i, animation=[face] * 5)\n\nThis version of logger also prints out a tabular printout of the data\nyou are logging to your ``stdout``. - can silence ``stdout`` per key\n(per ``logger.log`` call) - can print with color:\n``logger.log(timestep, some_key=green(some_data))`` - can print with\ncustom formatting:\n``logger.log(timestep, some_key=green(some_data, percent))`` where\n``percent`` - uses the correct ``unix`` table characters (please stop\nusing ``|`` and ``+``. **Use ``\u2502``, ``\u253c`` instead**)\n\nA typical print out of this logger look like the following:\n\n.. code-block:: python\n\n   from ml_logger import ML_Logger\n\n   logger = ML_Logger(log_directory=f\"/mnt/bucket/deep_Q_learning/{datetime.now(%Y%m%d-%H%M%S.%f):}\")\n\n   logger.log_params(G=vars(G), RUN=vars(RUN), Reporting=vars(Reporting))\n\noutputs the following\n\n.. code-block:: text\n\n   \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n                 G               \n   \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n              env_name            \u2502 MountainCar-v0      \n                seed              \u2502 None                \n         stochastic_action        \u2502 True                \n            conv_params           \u2502 None                \n            value_params          \u2502 (64,)               \n           use_layer_norm         \u2502 True                \n            buffer_size           \u2502 50000               \n         replay_batch_size        \u2502 32                  \n         prioritized_replay       \u2502 True                \n               alpha              \u2502 0.6                 \n             beta_start           \u2502 0.4                 \n              beta_end            \u2502 1.0                 \n       prioritized_replay_eps     \u2502 1e-06               \n         grad_norm_clipping       \u2502 10                  \n              double_q            \u2502 True                \n            use_dueling           \u2502 False               \n        exploration_fraction      \u2502 0.1                 \n             final_eps            \u2502 0.1                 \n            n_timesteps           \u2502 100000              \n           learning_rate          \u2502 0.001               \n               gamma              \u2502 1.0                 \n           learning_start         \u2502 1000                \n           learn_interval         \u2502 1                   \n   target_network_update_interval \u2502 500                 \n   \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n                RUN              \n   \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n           log_directory          \u2502 /mnt/slab/krypton/machine_learning/ge_dqn/2017-11-20/162048.353909-MountainCar-v0-prioritized_replay(True)\n             checkpoint           \u2502 checkpoint.cp       \n              log_file            \u2502 output.log          \n   \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n             Reporting           \n   \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n        checkpoint_interval       \u2502 10000               \n           reward_average         \u2502 100                 \n           print_interval         \u2502 10                  \n   \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\n   \u2552\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2564\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2555\n   \u2502      timestep      \u2502        1999        \u2502\n   \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n   \u2502      episode       \u2502         10         \u2502\n   \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n   \u2502    total reward    \u2502       -200.0       \u2502\n   \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n   \u2502 total reward/mean  \u2502       -200.0       \u2502\n   \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n   \u2502  total reward/max  \u2502       -200.0       \u2502\n   \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n   \u2502time spent exploring\u2502       82.0%        \u2502\n   \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n   \u2502    replay beta     \u2502        0.41        \u2502\n   \u2558\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2567\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u255b\n\nTODO:\n-----\n\nVisualization (Preview):boom:\n-----------------------------\n\nIn addition, ml-logger also comes with a powerful visualization\ndashboard that beats tensorboard in every aspect.\n\n.. figure:: https://github.com/episodeyang/ml_logger/blob/master/figures/ml_visualization_dashboard_preview.png?raw=true\n   :alt: ml visualization dashboard\n\n   ml visualization dashboard\n\nAn Example Log from ML-Logger\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nA common pain that comes after getting to launch ML training jobs on AWS\nis a lack of a good way to manage and visualize your data. So far, a\ncommon practice is to upload your experiment data to aws s3 or google\ncloud buckets. Then one quickly realizes that downloading data from s3\ncan be slow. s3 does\n\nnot offer diffsync like gcloud-cli\u2019s ``g rsync``. This makes it hard to\nsync a large collection of data that is constantly appended to.\n\nSo far the best way we have found for organizing experimental data is to\nhave a centralized instrumentation server. Compared with managing your\ndata on S3, a centralized instrumentation server makes it much easier to\nmove experiments around, run analysis that is co-located with your data,\nand hosting visualization dashboards on the same machine. To download\ndata locally, you can use ``sshfs``, ``smba``, ``rsync`` or a variety of\nremote disks. All faster than s3.\n\nML-Logger is the logging utility that allows you to do this. To make\nML_logger easy to use, we made it so that you can use ml-logger with\nzero configuration, logging to your local hard-drive by default. When\nthe logging directory field\n``logger.configure(log_directory= <your directory>)`` is an http end\npoint, the logger will instantiate a fast, future based logging client\nthat launches http requests in a separate thread. 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