Neptune 3.0 (#27) (#28)

* adjusted to neptune-client
* dropped tests

README.md

@@ -1,10 +1,6 @@
 # neptune-contrib
 [![Build Status](https://travis-ci.org/neptune-ml/neptune-contrib.svg?branch=master)](https://travis-ci.org/neptune-ml/neptune-contrib)
 
-# Note
-This lib is compatible with `neptune-cli` and some part may not work with `neptune-client`.
-It will be updated for `neptune-client` soon (ETA `4.7.2019`).
-
 # Documentation
 See [neptune-contrib documentation site](https://neptune-contrib.readthedocs.io)
 

docs/conf.py

@@ -27,9 +27,9 @@
 author = 'Neptune Dev Team'
 
 # The short X.Y version
-version = '0.2'
+version = '0.4'
 # The full version, including alpha/beta/rc tags
-release = '0.2.0'
+release = '0.4.0'
 
 # -- General configuration ---------------------------------------------------
 

docs/examples/examples_index.rst

@@ -1,12 +1,10 @@
 .. toctree::
    Interactive experiment run comparison <interactive_compare_experiments>
    Hyper parameter comparison <explore_hyperparams_skopt>
-   Run skopt/hyperopt hyperparameter sweep <run_hyperparameter_search>
    Log model diagnostics <log_model_diagnostics>
    Monitor lightGBM training <monitor_lgbm>
    Monitor fast.ai training <monitor_fastai>
    Log matplotlib charts to neptune <log_matplotlib>
-   Work with local notebooks <local_notebooks>
    Visualize project progress <project_progress>
    Sync experiments with Neptune via json file <sync_with_json>
    Telegram bot <telegram_bot>

docs/examples/interactive_compare_experiments.ipynb

@@ -186,7 +186,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "neptunecontrib_py36",
+   "display_name": "scraping",
    "language": "python",
    "name": "python3"
   },

docs/examples/log_model_diagnostics.ipynb

@@ -45,7 +45,8 @@
    "source": [
     "import neptune\n",
     "\n",
-    "ctx = neptune.Context()"
+    "neptune.init(project_qualified_name='USER_NAME/PROJECT_NAME')\n",
+    "neptune.create_experiment()"
    ]
   },
   {
@@ -63,7 +64,7 @@
    "source": [
     "from neptunecontrib.monitoring.reporting import send_binary_classification_report\n",
     "\n",
-    "send_binary_classification_report(ctx, y_test, y_test_pred, threshold=0.5)"
+    "send_binary_classification_report(y_test, y_test_pred, threshold=0.5)"
    ]
   },
   {
@@ -85,7 +86,7 @@
    "source": [
     "from neptunecontrib.monitoring.reporting import send_confusion_matrix\n",
     "\n",
-    "send_confusion_matrix(ctx, y_test, y_test_pred[:, 1] > 0.5)"
+    "send_confusion_matrix(y_test, y_test_pred[:, 1] > 0.5)"
    ]
   },
   {
@@ -154,6 +155,40 @@
     "send_prediction_distribution(y_test, y_test_pred[:, 1])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stop Neptune experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "neptune.stop()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also put everything in the `with` block.\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with neptune.create_experiment():\n",
+    "    send_prediction_distribution(y_test, y_test_pred[:, 1])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

docs/examples/monitor_fastai.ipynb

@@ -42,8 +42,11 @@
    "source": [
     "import neptune\n",
     "from neptunecontrib.monitoring.fastai import NeptuneMonitor\n",
-    "ctx = neptune.Context()\n",
-    "monitor = NeptuneMonitor(ctx=ctx)"
+    "\n",
+    "neptune.init(project_qualified_name='USER_NAME/PROJECT_NAME')\n",
+    "\n",
+    "with neptune.create_experiment():\n",
+    "    monitor = NeptuneMonitor()"
    ]
   },
   {
@@ -59,10 +62,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "learn = create_cnn(data, models.resnet18, \n",
-    "                   metrics=accuracy, \n",
-    "                   callbacks=[neptune_monitor])\n",
-    "learn.fit_one_cycle(20, 1e-2)"
+    "    learn = create_cnn(data, models.resnet18, \n",
+    "                      metrics=accuracy, \n",
+    "                      callbacks=[neptune_monitor])\n",
+    "    learn.fit_one_cycle(20, 1e-2)"
    ]
   },
   {
@@ -94,7 +97,7 @@
     "import neptune\n",
     "from neptunecontrib.monitoring.fastai import NeptuneMonitor\n",
     "\n",
-    "ctx = neptune.Context()\n",
+    "neptune.init(project_qualified_name='USER_NAME/PROJECT_NAME')\n",
     "\n",
     "mnist = untar_data(URLs.MNIST_TINY)\n",
     "tfms = get_transforms(do_flip=False)\n",
@@ -106,15 +109,16 @@
     "        .databunch()\n",
     "        .normalize(imagenet_stats))\n",
     "\n",
-    "neptune_monitor = NeptuneMonitor(ctx=ctx)\n",
-    "learn = create_cnn(data, models.resnet18, metrics=accuracy, callbacks=[neptune_monitor])\n",
-    "learn.fit_one_cycle(20, 1e-2)"
+    "with neptune.create_experiment():\n",
+    "    neptune_monitor = NeptuneMonitor()\n",
+    "    learn = create_cnn(data, models.resnet18, metrics=accuracy, callbacks=[neptune_monitor])\n",
+    "    learn.fit_one_cycle(20, 1e-2)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "neptunecontrib py36",
+   "display_name": "scraping",
    "language": "python",
    "name": "python3"
   },

docs/examples/monitor_lgbm.ipynb

@@ -49,7 +49,10 @@
     "import neptune\n",
     "from neptunecontrib.monitoring.lightgbm import neptune_monitor\n",
     "\n",
-    "ctx = neptune.Context()\n",
+    "neptune.init(project_qualified_name='USER_NAME/PROJECT_NAME')\n",
+    "\n",
+    "neptune.create_experiment()\n",
+    "\n",
     "monitor = neptune_monitor()"
    ]
   },
@@ -75,6 +78,39 @@
     "       )"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stop Neptune experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "neptune.stop()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also put everything in the `with` block:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with neptune.create_experiment():\n",
+    "    ..."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -101,8 +137,11 @@
     "import lightgbm as lgb\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.datasets import load_wine\n",
+    "import neptune\n",
     "from neptunecontrib.monitoring.lightgbm import neptune_monitor\n",
     "\n",
+    "neptune.init(project_qualified_name='USER_NAME/PROJECT_NAME')\n",
+    "\n",
     "data = load_wine()\n",
     "X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.1)\n",
     "lgb_train = lgb.Dataset(X_train, y_train)\n",
@@ -116,21 +155,22 @@
     "              'feature_fraction': 0.9\n",
     "              }\n",
     "\n",
-    "monitor = neptune_monitor()\n",
+    "with neptune.create_experiment():\n",
+    "    monitor = neptune_monitor()\n",
     "\n",
-    "gbm = lgb.train(params,\n",
-    "        lgb_train,\n",
-    "        num_boost_round=500,\n",
-    "        valid_sets=[lgb_train, lgb_eval],\n",
-    "        valid_names=['train','valid'],\n",
-    "        callbacks=[monitor],\n",
-    "       )"
+    "    gbm = lgb.train(params,\n",
+    "            lgb_train,\n",
+    "            num_boost_round=500,\n",
+    "            valid_sets=[lgb_train, lgb_eval],\n",
+    "            valid_names=['train','valid'],\n",
+    "            callbacks=[monitor],\n",
+    "           )"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "scraping",
    "language": "python",
    "name": "python3"
   },