Merge pull request #178 from cliveseldon/misc_master_tests

Rerun examples and add aio deploy_local/remote
SeldonIO · Aug 2, 2021 · 9e3028c · 9e3028c
2 parents 29e08c4 + e674fee
commit 9e3028c
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diff --git a/conda/tempo-examples.yaml b/conda/tempo-examples.yaml
@@ -22,3 +22,5 @@ dependencies:
       - pytest-asyncio==0.14.0
       - pandas==1.0.1
       - numpyro==0.6.0
+      - jax==0.2.10
+      - jaxlib==0.1.62
diff --git a/docs/api/tempo.insights.rst b/docs/api/tempo.insights.rst
@@ -12,6 +12,7 @@ Submodules
 .. toctree::
    :maxdepth: 6
 
+   tempo.insights.cloudevents
    tempo.insights.manager
    tempo.insights.worker
    tempo.insights.wrapper
diff --git a/docs/api/tempo.rst b/docs/api/tempo.rst
@@ -21,6 +21,7 @@ Subpackages
    tempo.kfserving
    tempo.seldon
    tempo.serve
+   tempo.state
 
 Submodules
 ----------

diff --git a/docs/examples/Makefile b/docs/examples/Makefile
@@ -8,7 +8,7 @@ all: $(EXAMPLES_MD)
 .ipynb.md:
 	jupyter nbconvert \
 		$< \
-		--ClearOutputPreprocessor.enabled=True \
+		--ClearOutputPreprocessor.enabled=False \
 		--to markdown \
 		--output $(notdir $@)
 

diff --git a/docs/examples/asyncio/README.ipynb b/docs/examples/asyncio/README.ipynb
@@ -40,12 +40,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "alert-surge",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[01;34m.\u001b[00m\r\n",
+      "├── \u001b[01;34martifacts\u001b[00m\r\n",
+      "│   ├── \u001b[01;34mclassifier\u001b[00m\r\n",
+      "│   ├── \u001b[01;34msklearn\u001b[00m\r\n",
+      "│   └── \u001b[01;34mxgboost\u001b[00m\r\n",
+      "└── \u001b[01;34msrc\u001b[00m\r\n",
+      "    ├── constants.py\r\n",
+      "    ├── data.py\r\n",
+      "    ├── tempo.py\r\n",
+      "    └── train.py\r\n",
+      "\r\n",
+      "5 directories, 4 files\r\n"
+     ]
+    }
+   ],
    "source": [
     "!tree -P \"*.py\"  -I \"__init__.py|__pycache__\" -L 2"
    ]
@@ -65,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "equipped-silence",
    "metadata": {},
    "outputs": [],
@@ -79,24 +98,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "reported-hurricane",
    "metadata": {
-    "code_folding": [
-     0
-    ]
+    "code_folding": []
    },
    "outputs": [],
    "source": [
     "# %load src/train.py\n",
-    "import joblib\n",
     "import os\n",
     "\n",
+    "import joblib\n",
     "from sklearn.linear_model import LogisticRegression\n",
-    "from xgboost import XGBClassifier\n",
-    "\n",
-    "from src.data import IrisData\n",
     "from src.constants import SKLearnFolder, XGBoostFolder\n",
+    "from src.data import IrisData\n",
+    "from xgboost import XGBClassifier\n",
     "\n",
     "\n",
     "def train_sklearn(data: IrisData):\n",
@@ -118,10 +134,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "lesser-reply",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[18:05:52] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/clive/anaconda3/envs/tempo-examples/lib/python3.7/site-packages/xgboost/sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
+      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
+     ]
+    }
+   ],
    "source": [
     "from src.data import IrisData\n",
     "from src.train import train_sklearn, train_xgboost\n",
@@ -145,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "smoking-tribe",
    "metadata": {},
    "outputs": [],
@@ -155,22 +187,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "regular-balance",
    "metadata": {
-    "code_folding": [
-     0
-    ]
+    "code_folding": []
    },
    "outputs": [],
    "source": [
     "# %load src/tempo.py\n",
     "import numpy as np\n",
+    "from src.constants import ClassifierFolder, SKLearnFolder, XGBoostFolder\n",
     "\n",
     "from tempo import ModelFramework, PipelineModels\n",
-    "from tempo.aio import pipeline, Model\n",
-    "\n",
-    "from src.constants import ClassifierFolder, SKLearnFolder, XGBoostFolder\n",
+    "from tempo.aio import Model, pipeline\n",
     "\n",
     "SKLearnModel = Model(\n",
     "    name=\"test-iris-sklearn\",\n",
@@ -214,20 +243,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "considered-terminology",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "artifacts/classifier/conda.yaml\r\n"
+     ]
+    }
+   ],
    "source": [
-    "!cat artifacts/classifier/conda.yaml"
+    "!ls artifacts/classifier/conda.yaml"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "rural-mathematics",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting packages...\n",
+      "Packing environment at '/home/clive/anaconda3/envs/tempo-330c15d8-a189-45a6-abc3-a27f39b6a7c5' to '/home/clive/work/mlops/fork-tempo/docs/examples/asyncio/artifacts/classifier/environment.tar.gz'\n",
+      "[########################################] | 100% Completed | 11.2s\n"
+     ]
+    }
+   ],
    "source": [
     "import tempo\n",
     "\n",
@@ -246,40 +293,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "56037cc4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tempo.aio import deploy\n",
-    "remote_model = deploy(classifier)"
+    "from tempo.aio import deploy_local\n",
+    "remote_model = deploy_local(classifier)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "cb489575",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2.], dtype=float32)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "await remote_model.predict(np.array([[1, 2, 3, 4]]))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "dc6c0829",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.]\n",
+      "[[0.97329617 0.02412145 0.00258233]]\n"
+     ]
+    }
+   ],
    "source": [
     "print(await remote_model.predict(np.array([[0, 0, 0,0]])))\n",
     "print(await remote_model.predict(np.array([[5.964,4.006,2.081,1.031]])))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "6fa56a63",
    "metadata": {},
    "outputs": [],