From 3d94e14d0fb814277a3f79753c2eb9331805a52b Mon Sep 17 00:00:00 2001 From: Josh Date: Mon, 23 May 2022 10:49:47 -0400 Subject: [PATCH 1/2] adding for customer workshop --- time-series/ecg-workshop.ipynb | 1468 +++++++++++++++++++++++++++-- time-series/prediction-drift.json | 30 + time-series/prediction.json | 194 ++++ 3 files changed, 1622 insertions(+), 70 deletions(-) create mode 100644 time-series/prediction-drift.json create mode 100644 time-series/prediction.json diff --git a/time-series/ecg-workshop.ipynb b/time-series/ecg-workshop.ipynb index 8237f44..615bd01 100644 --- a/time-series/ecg-workshop.ipynb +++ b/time-series/ecg-workshop.ipynb @@ -29,11 +29,136 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: seldon-deploy-sdk==1.4.1 in 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/Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from transformers<5.0.0,>=2.10.0->alibi-detect==0.6.1) (2022.1.18)\n", + "Requirement already satisfied: tqdm>=4.27 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from transformers<5.0.0,>=2.10.0->alibi-detect==0.6.1) (4.62.3)\n", + "Requirement already satisfied: sacremoses in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from transformers<5.0.0,>=2.10.0->alibi-detect==0.6.1) (0.0.47)\n", + "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from transformers<5.0.0,>=2.10.0->alibi-detect==0.6.1) (0.4.0)\n", + "Requirement already satisfied: pyyaml>=5.1 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from transformers<5.0.0,>=2.10.0->alibi-detect==0.6.1) (5.4.1)\n", + "Requirement already satisfied: click in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from sacremoses->transformers<5.0.0,>=2.10.0->alibi-detect==0.6.1) (8.0.3)\n", + "Installing collected packages: grpcio, scikit-learn, alibi-detect\n", + " Attempting uninstall: grpcio\n", + " Found existing installation: grpcio 1.44.0\n", + " Uninstalling grpcio-1.44.0:\n", + " Successfully uninstalled grpcio-1.44.0\n", + " Attempting uninstall: scikit-learn\n", + " Found existing installation: scikit-learn 1.0.2\n", + " Uninstalling scikit-learn-1.0.2:\n", + " Successfully uninstalled scikit-learn-1.0.2\n", + " Attempting uninstall: alibi-detect\n", + " Found existing installation: alibi-detect 0.6.2\n", + " Uninstalling alibi-detect-0.6.2:\n", + " Successfully uninstalled alibi-detect-0.6.2\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "imbalanced-learn 0.9.0 requires scikit-learn>=1.0.1, but you have scikit-learn 0.24.2 which is incompatible.\n", + "grpcio-reflection 1.34.1 requires grpcio>=1.34.1, but you have grpcio 1.32.0 which is incompatible.\u001b[0m\n", + "Successfully installed alibi-detect-0.6.1 grpcio-1.32.0 scikit-learn-0.24.2\n", + "Requirement already satisfied: seaborn in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (0.11.2)\n", + "Requirement already satisfied: pandas>=0.23 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from seaborn) (1.3.5)\n", + "Requirement already satisfied: matplotlib>=2.2 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from seaborn) (3.5.1)\n", + "Requirement already satisfied: numpy>=1.15 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from seaborn) (1.19.5)\n", + "Requirement already satisfied: scipy>=1.0 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from seaborn) (1.7.3)\n", + "Requirement already satisfied: packaging>=20.0 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (21.3)\n", + "Requirement already satisfied: pillow>=6.2.0 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (8.4.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (4.29.1)\n", + "Requirement already satisfied: cycler>=0.10 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (3.0.7)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n", + "Requirement already satisfied: pytz>=2017.3 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2021.3)\n", + "Requirement already satisfied: six>=1.5 in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.15.0)\n", + "Requirement already satisfied: dill in /Users/josh/opt/anaconda3/envs/manufactoring-workshop/lib/python3.7/site-packages (0.3.4)\n" + ] + } + ], "source": [ "# Colab has a load of packages pre-loaded into the environment. Installing the additional ones we require here.\n", "!pip install seldon-deploy-sdk==1.4.1\n", @@ -44,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": { "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a", @@ -83,7 +208,7 @@ "from alibi_detect.utils.saving import save_detector, load_detector\n", "\n", "# Seldon Deploy SDK \n", - "from seldon_deploy_sdk import Configuration, ApiClient, SeldonDeploymentsApi, DriftDetectorApi\n", + "from seldon_deploy_sdk import Configuration, ApiClient, SeldonDeploymentsApi, DriftDetectorApi, ModelMetadataServiceApi\n", "from seldon_deploy_sdk.auth import OIDCAuthenticator\n", "\n", "# For loading serialised objects\n", @@ -106,12 +231,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "id": "X_1YBFJ-UJu-", "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-05-20 08:38:42-- https://storage.googleapis.com/seldon-koz-ecg/data/mitbih_test.csv.zip\n", + "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.176.208, 142.251.40.240, 142.251.32.112, ...\n", + "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.176.208|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 17444159 (17M) [application/zip]\n", + "Saving to: ‘mitbih_test.csv.zip’\n", + "\n", + "mitbih_test.csv.zip 100%[===================>] 16.64M 7.16MB/s in 2.3s \n", + "\n", + "2022-05-20 08:38:44 (7.16 MB/s) - ‘mitbih_test.csv.zip’ saved [17444159/17444159]\n", + "\n", + "--2022-05-20 08:38:45-- https://storage.googleapis.com/seldon-koz-ecg/data/mitbih_train.csv.zip\n", + "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.176.208, 142.251.40.240, 142.251.32.112, ...\n", + "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.176.208|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 69854187 (67M) [application/zip]\n", + "Saving to: ‘mitbih_train.csv.zip’\n", + "\n", + "mitbih_train.csv.zi 100%[===================>] 66.62M 7.87MB/s in 8.7s \n", + "\n", + "2022-05-20 08:38:54 (7.62 MB/s) - ‘mitbih_train.csv.zip’ saved [69854187/69854187]\n", + "\n", + "Archive: mitbih_test.csv.zip\n", + " inflating: mitbih_test.csv \n", + "Archive: mitbih_train.csv.zip\n", + " inflating: mitbih_train.csv \n" + ] + } + ], "source": [ "!wget https://storage.googleapis.com/seldon-koz-ecg/data/mitbih_test.csv.zip\n", "!wget https://storage.googleapis.com/seldon-koz-ecg/data/mitbih_train.csv.zip\n", @@ -148,11 +306,312 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "id": "3OXXmt7VwDDx" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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"source": [ "train_df.iloc[0]" ] @@ -187,11 +668,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "id": "j7Gu18OLwDDy" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 72471\n", + "4 6431\n", + "2 5788\n", + "1 2223\n", + "3 641\n", + "Name: 187, dtype: int64\n" + ] + } + ], "source": [ "train_df[187]=train_df[187].astype(int)\n", "label_counts=train_df[187].value_counts()\n", @@ -207,11 +701,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "id": "7jZlk7R_wDDy" }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "\n", + "[5 rows x 188 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "sample_df = rebalanced_df.groupby(187, group_keys=False).apply(lambda train_df: train_df.sample(n=1))\n", "sample_df" @@ -282,9 +1003,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "def add_gaussian_noise(signal):\n", " noise = np.random.normal(0, 0.3, 186)\n", @@ -344,7 +1091,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "id": "pieTfZXMwDD4" }, @@ -359,7 +1106,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "id": "qtPdefhxwDD4" }, @@ -415,9 +1162,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Copying gs://tom-seldon-examples/time-series-workshop/1/saved_model.pb...\n", + "Copying gs://tom-seldon-examples/time-series-workshop/1/variables/variables.data-00000-of-00001...\n", + "Copying gs://tom-seldon-examples/time-series-workshop/1/variables/variables.index...\n", + "\\ [3 files][ 1.8 MiB/ 1.8 MiB] \n", + "Operation completed over 3 objects/1.8 MiB. \n", + "Copying gs://tom-seldon-examples/time-series-workshop/history.dill...\n", + "/ [1 files][ 484.0 B/ 484.0 B] \n", + "Operation completed over 1 objects/484.0 B. \n" + ] + } + ], "source": [ "!mkdir pretrained_model\n", "!gsutil cp -r gs://tom-seldon-examples/time-series-workshop/1 pretrained_model\n", @@ -426,11 +1188,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-05-20 09:16:10.652835: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n", + "2022-05-20 09:16:10.653101: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], "source": [ "model = load_model(\"pretrained_model/1\")\n", "\n", @@ -447,7 +1219,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "id": "paZkZN3ewDD5" }, @@ -491,12 +1263,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "id": "GoV3FPc4wDD5", "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-05-20 09:16:43.142782: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 95.80%\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "def plot_confusion_matrix(cm, \n", " classes,\n", @@ -559,7 +1383,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Deploying the Model\n", + "# Deploying the Model\n", "We now want to serve our model on the Seldon infrastructure.\n", "\n", "For this we can use the pre-built Tensorflow container image, which allows us to rapidly deploy our newly trained model using TFServing. \n", @@ -582,19 +1406,30 @@ { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir pretrained_model\n", + "!gsutil cp -r gs://tom-seldon-examples/time-series-workshop/1 pretrained_model\n", + "!gsutil cp gs://tom-seldon-examples/time-series-workshop/history.dill history.dill" + ] + }, + { + "cell_type": "code", + "execution_count": 84, "metadata": { "scrolled": true }, "outputs": [], "source": [ - "SD_IP = \"34.141.146.222\"\n", + "SD_IP = \"34.73.137.78\"\n", "\n", "config = Configuration()\n", "config.host = f\"http://{SD_IP}/seldon-deploy/api/v1alpha1\"\n", "config.oidc_client_id = \"sd-api\"\n", "config.oidc_server = f\"http://{SD_IP}/auth/realms/deploy-realm\"\n", "config.oidc_client_secret = \"sd-api-secret\"\n", - "config.auth_method = 'client_credentials'\n", + "config.auth_method = \"client_credentials\"\n", "\n", "def auth():\n", " auth = OIDCAuthenticator(config)\n", @@ -605,24 +1440,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ - "YOUR_NAME = \"YOUR-NAME\" # Replace with your name\n", - "MODEL_NAME = \"MODEL-NAME\" # Replace with your model name\n", + "YOUR_NAME = \"josh\" # Replace with your name\n", + "MODEL_NAME = \"ecg-things\" # Replace with your model name\n", "\n", "DEPLOYMENT_NAME = f\"{YOUR_NAME}-{MODEL_NAME}\"\n", - "MODEL_LOCATION = f\"gs://tom-seldon-examples/time-series-workshop/\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NAMESPACE = \"seldon-gitops\"\n", + "MODEL_LOCATION = f\"gs://tom-seldon-examples/time-series-workshop/\"\n", + "\n", + "NAMESPACE = \"seldon-demos\"\n", "\n", "PREPACKAGED_SERVER = \"TENSORFLOW_SERVER\"\n", "\n", @@ -630,8 +1458,15 @@ "MEMORY_REQUESTS = \"1Gi\"\n", "\n", "CPU_LIMITS = \"0.1\"\n", - "MEMORY_LIMITS = \"1Gi\"\n", - "\n", + "MEMORY_LIMITS = \"1Gi\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ "mldeployment = {\n", " \"kind\": \"SeldonDeployment\",\n", " \"metadata\": {\n", @@ -696,11 +1531,178 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 81, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'api_version': 'machinelearning.seldon.io/v1alpha2',\n", + " 'kind': 'SeldonDeployment',\n", + " 'metadata': {'annotations': None,\n", + " 'cluster_name': None,\n", + " 'creation_timestamp': None,\n", + " 'deletion_grace_period_seconds': None,\n", + " 'deletion_timestamp': None,\n", + " 'finalizers': None,\n", + " 'generate_name': None,\n", + " 'generation': None,\n", + " 'labels': {'fluentd': 'true'},\n", + " 'managed_fields': None,\n", + " 'name': 'josh-ecg-things',\n", + " 'namespace': 'seldon-demos',\n", + " 'owner_references': None,\n", + " 'resource_version': None,\n", + " 'self_link': None,\n", + " 'uid': None},\n", + " 'spec': {'annotations': {'seldon.io/engine-seldon-log-messages-externally': 'true'},\n", + " 'name': 'josh-ecg-things',\n", + " 'oauth_key': None,\n", + " 'oauth_secret': None,\n", + " 'predictors': [{'annotations': None,\n", + " 'component_specs': [{'hpa_spec': None,\n", + " 'keda_spec': None,\n", + " 'metadata': {'annotations': None,\n", + " 'cluster_name': None,\n", + " 'creation_timestamp': '2022-05-23T14:10:35Z',\n", + " 'deletion_grace_period_seconds': None,\n", + " 'deletion_timestamp': None,\n", + " 'finalizers': None,\n", + " 'generate_name': None,\n", + " 'generation': None,\n", + " 'labels': None,\n", + " 'managed_fields': None,\n", + " 'name': None,\n", + " 'namespace': None,\n", + " 'owner_references': None,\n", + " 'resource_version': None,\n", + " 'self_link': None,\n", + " 'uid': None},\n", + " 'pdb_spec': None,\n", + " 'replicas': None,\n", + " 'spec': {'active_deadline_seconds': None,\n", + " 'affinity': None,\n", + " 'automount_service_account_token': None,\n", + " 'containers': [{'args': None,\n", + " 'command': None,\n", + " 'env': None,\n", + " 'env_from': None,\n", + " 'image': None,\n", + " 'image_pull_policy': None,\n", + " 'lifecycle': None,\n", + " 'liveness_probe': None,\n", + " 'name': 'josh-ecg-things-container',\n", + " 'ports': None,\n", + " 'readiness_probe': None,\n", + " 'resources': {'limits': {'cpu': '100m',\n", + " 'memory': '1Gi'},\n", + " 'requests': {'cpu': '100m',\n", + " 'memory': '1Gi'}},\n", + " 'security_context': None,\n", + " 'startup_probe': None,\n", + " 'stdin': None,\n", + " 'stdin_once': None,\n", + " 'termination_message_path': None,\n", + " 'termination_message_policy': None,\n", + " 'tty': None,\n", + " 'volume_devices': None,\n", + " 'volume_mounts': None,\n", + " 'working_dir': None}],\n", + " 'dns_config': None,\n", + " 'dns_policy': None,\n", + " 'enable_service_links': None,\n", + " 'ephemeral_containers': None,\n", + " 'host_aliases': None,\n", + " 'host_ipc': None,\n", + " 'host_network': None,\n", + " 'host_pid': None,\n", + " 'hostname': None,\n", + " 'image_pull_secrets': None,\n", + " 'init_containers': None,\n", + " 'node_name': None,\n", + " 'node_selector': None,\n", + " 'overhead': None,\n", + " 'preemption_policy': None,\n", + " 'priority': None,\n", + " 'priority_class_name': None,\n", + " 'readiness_gates': None,\n", + " 'restart_policy': None,\n", + " 'runtime_class_name': None,\n", + " 'scheduler_name': None,\n", + " 'security_context': None,\n", + " 'service_account': None,\n", + " 'service_account_name': None,\n", + " 'set_hostname_as_fqdn': None,\n", + " 'share_process_namespace': None,\n", + " 'subdomain': None,\n", + " 'termination_grace_period_seconds': None,\n", + " 'tolerations': None,\n", + " 'topology_spread_constraints': None,\n", + " 'volumes': None}}],\n", + " 'engine_resources': {'limits': None,\n", + " 'requests': None},\n", + " 'explainer': None,\n", + " 'graph': {'children': None,\n", + " 'endpoint': {'grpc_port': None,\n", + " 'http_port': None,\n", + " 'service_host': None,\n", + " 'service_port': None,\n", + " 'type': 'REST'},\n", + " 'env_secret_ref_name': None,\n", + " 'implementation': 'TENSORFLOW_SERVER',\n", + " 'logger': {'mode': 'all', 'url': None},\n", + " 'methods': None,\n", + " 'model_uri': 'gs://tom-seldon-examples/time-series-workshop/',\n", + " 'name': 'josh-ecg-things-container',\n", + " 'parameters': None,\n", + " 'service_account_name': None,\n", + " 'storage_initializer_image': None,\n", + " 'type': None},\n", + " 'labels': None,\n", + " 'name': 'default',\n", + " 'replicas': 1,\n", + " 'shadow': None,\n", + " 'ssl': None,\n", + " 'svc_orch_spec': {'env': None,\n", + " 'replicas': None,\n", + " 'resources': None},\n", + " 'traffic': 100}],\n", + " 'protocol': 'seldon',\n", + " 'replicas': None,\n", + " 'server_type': None,\n", + " 'transport': None},\n", + " 'status': {'address': None,\n", + " 'annotations': None,\n", + " 'conditions': None,\n", + " 'deployment_status': None,\n", + " 'description': None,\n", + " 'observed_generation': None,\n", + " 'replicas': None,\n", + " 'service_status': None,\n", + " 'state': None}}" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "deployment_api = SeldonDeploymentsApi(auth())\n", + "SD_IP = \"34.73.137.78\"\n", + "\n", + "config = Configuration()\n", + "config.host = f\"http://{SD_IP}/seldon-deploy/api/v1alpha1\"\n", + "config.oidc_client_id = \"sd-api\"\n", + "config.oidc_server = f\"http://{SD_IP}/auth/realms/deploy-realm\"\n", + "config.oidc_client_secret = \"sd-api-secret\"\n", + "config.auth_method = \"client_credentials\"\n", + "\n", + "auth = OIDCAuthenticator(config)\n", + "config.id_token = auth.authenticate()\n", + "api_client = ApiClient(configuration=config, authenticator=auth)\n", + "\n", + "\n", + "deployment_api = SeldonDeploymentsApi(api_client)\n", "deployment_api.create_seldon_deployment(namespace=NAMESPACE, mldeployment=mldeployment)" ] }, @@ -913,6 +1915,188 @@ "```" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding a Prediction Schema & Metadata\n", + "Seldon Deploy has a model catalog where all deployed models are automatically registered. The model catalog can store custom metadata as well as prediction schemas for your models. \n", + "\n", + "Metadata promotes lineage from across different machine learning systems, aids knowledge transfer between teams, and allows for faster deployment. Meanwhile, prediction schemas allow Seldon Deploy to automatically profile tabular data into histograms, allowing for filtering on features to explore trends. \n", + "\n", + "In order to effectively construct a prediction schema Seldon has the [ML Prediction Schema](https://github.com/SeldonIO/ml-prediction-schema) project. The first step is to determine your datatypes. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "prediction_schema = {\n", + " \"requests\": [\n", + " {\n", + " \"name\": \"Input\",\n", + " \"type\": \"REAL\",\n", + " \"data_type\": \"FLOAT\"\n", + " }\n", + " ],\n", + " \"responses\": [\n", + " {\n", + " \"name\": \"Arrhythmias\",\n", + " \"type\": \"PROBA\",\n", + " \"dataType\": \"FLOAT\",\n", + " \"nCategories\": \"5\",\n", + " \"categoryMap\": {},\n", + " \"schema\": [\n", + " {\n", + " \"name\": \"Non-ecotic beats (normal beat)\",\n", + " \"dataType\": \"FLOAT\"\n", + " },\n", + " {\n", + " \"name\": \"Supraventricular ectopic beats\",\n", + " \"dataType\": \"FLOAT\"\n", + " },\n", + " {\n", + " \"name\": \"Ventricular ectopic beats\",\n", + " \"dataType\": \"FLOAT\"\n", + " },\n", + " {\n", + " \"name\": \"Fusion Beats\",\n", + " \"dataType\": \"FLOAT\"\n", + " },\n", + " {\n", + " \"name\": \"Unknown Beats\",\n", + " \"dataType\": \"FLOAT\"\n", + " }\n", + " ],\n", + " \"shape\": []\n", + " }\n", + " ]\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You then add the prediction schema to the wider model catalog metadata. This includes information such as the model storage location, the name, who authored the model etc. The metadata tags and metrics which can be associated with a model are freeform and can therefore be determined based upon the use case which is being developed. " + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'URI': 'gs://tom-seldon-examples/time-series-workshop/',\n", + " 'name': 'Hello-josh-ecg-things-model',\n", + " 'version': 'v1.1',\n", + " 'artifactType': 'TENSORFLOW',\n", + " 'taskType': 'ECG Classification',\n", + " 'tags': {'auto_created': 'true', 'author': 'josh'},\n", + " 'metrics': {},\n", + " 'project': 'default'}" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_catalog_metadata = {\n", + " \"URI\": MODEL_LOCATION,\n", + " \"name\": f\"Hello-{DEPLOYMENT_NAME}-model\",\n", + " \"version\": \"v1.1\",\n", + " \"artifactType\": \"TENSORFLOW\",\n", + " \"taskType\": \"ECG Classification\",\n", + " \"tags\": {\n", + " \"auto_created\": \"true\",\n", + " \"author\": f\"{YOUR_NAME}\"\n", + " },\n", + " \"metrics\": {},\n", + " \"project\": \"default\",\n", + " \n", + " }\n", + "\n", + "model_catalog_metadata" + ] + }, + { + "cell_type": "markdown", + "id": "b17a25c1", + "metadata": {}, + "source": [ + "Next, using the metadata API you can add this to the model which you have just created in Seldon." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "742bc19f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metadata_api = ModelMetadataServiceApi(auth())\n", + "metadata_api.model_metadata_service_update_model_metadata(model_catalog_metadata)" + ] + }, + { + "cell_type": "markdown", + "id": "2b9c4b87", + "metadata": {}, + "source": [ + "You can then list the metadata via the API, or view it in the UI, to confirm that it has been successfully added to the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "eb60524f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'models': [{'artifact_type': 'TENSORFLOW',\n", + " 'creation_time': datetime.datetime(2022, 5, 20, 14, 53, 10, 205020, tzinfo=tzutc()),\n", + " 'metrics': {},\n", + " 'name': 'josh-ecg-things-model',\n", + " 'prediction_schema': None,\n", + " 'project': 'default',\n", + " 'tags': {'author': 'josh', 'auto_created': 'true'},\n", + " 'task_type': 'ECG Classification',\n", + " 'uri': 'gs://tom-seldon-examples/time-series-workshop/',\n", + " 'version': 'v1.1'}],\n", + " 'next_page_token': ''}" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metadata_response = metadata_api.model_metadata_service_list_model_metadata(uri=MODEL_LOCATION)\n", + "metadata_response" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -939,7 +2123,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -955,9 +2139,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drift? No\n" + ] + } + ], "source": [ "preds = dd.predict(X_train)\n", "labels = [\"No\", \"Yes\"]\n", @@ -976,9 +2168,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drift? Yes\n" + ] + } + ], "source": [ "new_preds = dd.predict(label_dfs[2].values)\n", "\n", @@ -994,9 +2194,86 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 78, "metadata": {}, "outputs": [], + "source": [ + "import json\n", + "type(label_dfs[2][0].values)\n", + "lists = label_dfs[2][0].values.tolist()\n", + "json_str = json.dumps(lists)\n", + "with open('drift-preds.json', 'w') as outfile:\n", + " outfile.write(json_str)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.87, 0. , 1. , ..., 0.02, 1. , 0.74])" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "label_dfs[2][0].values" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': {'is_drift': 1,\n", + " 'distance': array([0.41, 0.26, 0.18, 0.32, 0.28, 0.28, 0.28, 0.29, 0.29, 0.29, 0.3 ,\n", + " 0.3 , 0.29, 0.28, 0.28, 0.27, 0.26, 0.26, 0.26, 0.25, 0.25, 0.24,\n", + " 0.24, 0.24, 0.23, 0.23, 0.22, 0.22, 0.21, 0.21, 0.2 , 0.2 , 0.2 ,\n", + " 0.19, 0.19, 0.19, 0.2 , 0.22, 0.24, 0.26, 0.29, 0.3 , 0.31, 0.32,\n", + " 0.32, 0.33, 0.33, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34, 0.34,\n", + " 0.34, 0.34, 0.33, 0.33, 0.32, 0.32, 0.31, 0.31, 0.3 , 0.3 , 0.29,\n", + " 0.29, 0.29, 0.29, 0.29, 0.29, 0.28, 0.28, 0.29, 0.28, 0.28, 0.27,\n", + " 0.26, 0.25, 0.25, 0.25, 0.25, 0.26, 0.26, 0.26, 0.27, 0.27, 0.27,\n", + " 0.28, 0.28, 0.29, 0.29, 0.3 , 0.3 , 0.3 , 0.31, 0.31, 0.31, 0.31,\n", + " 0.31, 0.32, 0.32, 0.32, 0.33, 0.34, 0.35, 0.35, 0.36, 0.36, 0.37,\n", + " 0.38, 0.37, 0.38, 0.38, 0.39, 0.39, 0.39, 0.39, 0.4 , 0.4 , 0.4 ,\n", + " 0.41, 0.41, 0.41, 0.42, 0.42, 0.43, 0.43, 0.43, 0.44, 0.44, 0.44,\n", + " 0.44, 0.44, 0.45, 0.45, 0.45, 0.45, 0.45, 0.46, 0.46, 0.46, 0.46,\n", + " 0.46, 0.46, 0.47, 0.47, 0.47, 0.47, 0.47, 0.47, 0.47, 0.48, 0.48,\n", + " 0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48, 0.48,\n", + " 0.48, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49,\n", + " 0.49, 0.49, 0.49, 0.49, 0.5 , 0.5 , 0.5 , 0.49, 0.49, 0.5 ],\n", + " dtype=float32),\n", + " 'p_val': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " dtype=float32),\n", + " 'threshold': 0.00026881720430107527},\n", + " 'meta': {'name': 'KSDrift', 'detector_type': 'offline', 'data_type': None}}" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "new_preds" ] @@ -1012,9 +2289,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Directory drift_detector does not exist and is now created.\n" + ] + } + ], "source": [ "save_detector(dd, \"drift_detector\")" ] @@ -1030,11 +2315,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Updates are available for some Google Cloud CLI components. To install them,\n", + "please run:\n", + " $ gcloud components update\n", + "\n", + "Copying file://drift_detector/KSDrift.pickle [Content-Type=application/octet-stream]...\n", + "Copying file://drift_detector/meta.pickle [Content-Type=application/octet-stream]...\n", + "| [2 files][124.2 MiB/124.2 MiB] 8.5 MiB/s \n", + "Operation completed over 2 objects/124.2 MiB. \n" + ] + } + ], "source": [ - "!gsutil cp -r drift_detector gs://tom-seldon-examples/time-series-workshop//" + "!gsutil cp -r drift_detector gs://tom-seldon-examples/time-series-workshop/pretrained-drift/" ] }, { @@ -1046,16 +2348,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ "# TO DO: Add code to deploy the drift detector!\n", - "DD_URI = f'gs://tom-seldon-examples/time-series-workshop/{YOUR_NAME}/drift_detector/'\n", + "DD_URI = f'gs://tom-seldon-examples/time-series-workshop/pretrained-drift/'\n", "DD_NAME = \"ks-drift\"\n", "\n", "dd_config = {'config': {'basic': \n", - " {'drift_batch_size': '5',\n", + " {'drift_batch_size': '2',\n", " 'storage_uri': DD_URI},\n", " 'deployment': {'protocol': 'seldon.http'}\n", " },\n", @@ -1068,9 +2370,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'config': {'basic': {'drift_batch_size': '2',\n", + " 'env_secret_ref': None,\n", + " 'storage_uri': 'gs://tom-seldon-examples/time-series-workshop/pretrained-drift/'},\n", + " 'deployment': {'event_source': 'io.seldon.serving.josh-ecg-things-ks-drift',\n", + " 'event_type': 'io.seldon.serving.inference.drift',\n", + " 'http_port': '8080',\n", + " 'model_name': 'ks-drift',\n", + " 'prom_scraping': True,\n", + " 'protocol': 'seldon.http',\n", + " 'reply_url': 'http://seldon-request-logger.seldon-logs.svc.cluster.local:80',\n", + " 'user_permission': 8888}},\n", + " 'deployment_kind': 'SeldonDeployment',\n", + " 'deployment_name': 'josh-ecg-things',\n", + " 'detector_type': 'drift',\n", + " 'name': 'ks-drift',\n", + " 'namespace': 'seldon-demos'}" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dd_api = DriftDetectorApi(auth())\n", "\n", diff --git a/time-series/prediction-drift.json b/time-series/prediction-drift.json new file mode 100644 index 0000000..68178b4 --- /dev/null +++ b/time-series/prediction-drift.json @@ -0,0 +1,30 @@ +{ + "data": { + "ndarray": [ + [ + + + + [0.87], [1.0], [0.94], [0.89], [0.88], [0.89], [0.89], [0.73], [0.43], [0.24], [0.17], + [0.16], [0.13], [0.12], [0.11], [0.1 ], [0.09], [0.09], [0.06], [0.05], [0.03], [0.02], + [0.01], [0.0], [0.0], [0.0], [0.0], [0.03], [0.04], [0.08], [0.11], [0.16], [0.2 ], + [0.24], [0.25], [0.26], [0.26], [0.27], [0.27], [0.27], [0.26], [0.25], [0.26], [0.27], + [0.27], [0.28], [0.28], [0.29], [0.29], [0.33], [0.37], [0.43], [0.51], [0.62], [0.7 ], + [0.79], [0.86], [0.91], [0.84], [0.79], [0.75], [0.74], [0.76], [0.74], [0.59], [0.36], + [0.25], [0.2 ], [0.16], [0.15], [0.12], [0.12], [0.11], [0.08], [0.07], [0.06], [0.04], + [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], + [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], + [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], + [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], + [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], + [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], + [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], + [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], + [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], + [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], + [2.0] + ] + + ] +} +} \ No newline at end of file diff --git a/time-series/prediction.json b/time-series/prediction.json new file mode 100644 index 0000000..a527cf0 --- /dev/null +++ b/time-series/prediction.json @@ -0,0 +1,194 @@ +{ + "data": { + "ndarray": [ + [ + [1.0], + [0.76], + [0.11], + [0.0], + [0.08], + [0.08], + [0.07], + [0.05], + [0.05], + [0.04], + [0.03], + [0.03], + [0.04], + [0.03], + [0.04], + [0.04], + [0.04], + [0.05], + [0.05], + [0.05], + [0.07], + [0.07], + [0.08], + [0.1], + [0.12], + [0.13], + [0.17], + [0.2], + [0.21], + [0.24], + [0.25], + [0.26], + [0.29], + [0.27], + [0.27], + [0.24], + [0.21], + [0.17], + [0.16], + [0.12], + [0.12], + [0.11], + [0.11], + [0.1], + [0.11], + [0.1], + [0.11], + [0.1], + [0.11], + [0.11], + [0.12], + [0.11], + [0.12], + [0.11], + [0.12], + [0.11], + [0.11], + [0.11], + [0.12], + [0.11], + [0.11], + [0.1], + [0.1], + [0.09], + [0.09], + [0.08], + [0.08], + [0.08], + [0.08], + [0.07], + [0.08], + [0.07], + [0.08], + [0.07], + [0.07], + [0.07], + [0.07], + [0.07], + [0.09], + [0.1], + [0.11], + [0.11], + [0.12], + [0.12], + [0.11], + [0.1], + [0.1], + [0.1], + [0.09], + [0.07], + [0.07], + [0.05], + [0.06], + [0.05], + [0.06], + [0.05], + [0.06], + [0.05], + [0.05], + [0.0], + [0.01], + [0.19], + [0.68], + [0.98], + [0.62], + [0.04], + [0.01], + [0.09], + [0.07], + [0.07], + [0.05], + [0.04], + [0.04], + [0.03], + [0.04], + [0.04], + [0.05], + [0.04], + [0.05], + [0.04], + [0.05], + [0.05], + [0.06], + [0.07], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0], + [0.0] + ] + ] + } +} \ No newline at end of file From 7596ec13771f4b8368ae4e771852441c6f72ac7a Mon Sep 17 00:00:00 2001 From: Josh Date: Mon, 23 May 2022 11:40:58 -0400 Subject: [PATCH 2/2] we got everything working --- time-series/ecg-workshop.ipynb | 371 +++------------------------------ 1 file changed, 32 insertions(+), 339 deletions(-) diff --git a/time-series/ecg-workshop.ipynb b/time-series/ecg-workshop.ipynb index 615bd01..9f00333 100644 --- a/time-series/ecg-workshop.ipynb +++ b/time-series/ecg-workshop.ipynb @@ -1531,7 +1531,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -1564,7 +1564,7 @@ " 'keda_spec': None,\n", " 'metadata': {'annotations': None,\n", " 'cluster_name': None,\n", - " 'creation_timestamp': '2022-05-23T14:10:35Z',\n", + " 'creation_timestamp': '2022-05-23T14:56:20Z',\n", " 'deletion_grace_period_seconds': None,\n", " 'deletion_timestamp': None,\n", " 'finalizers': None,\n", @@ -1682,27 +1682,13 @@ " 'state': None}}" ] }, - "execution_count": 81, + "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "SD_IP = \"34.73.137.78\"\n", - "\n", - "config = Configuration()\n", - "config.host = f\"http://{SD_IP}/seldon-deploy/api/v1alpha1\"\n", - "config.oidc_client_id = \"sd-api\"\n", - "config.oidc_server = f\"http://{SD_IP}/auth/realms/deploy-realm\"\n", - "config.oidc_client_secret = \"sd-api-secret\"\n", - "config.auth_method = \"client_credentials\"\n", - "\n", - "auth = OIDCAuthenticator(config)\n", - "config.id_token = auth.authenticate()\n", - "api_client = ApiClient(configuration=config, authenticator=auth)\n", - "\n", - "\n", - "deployment_api = SeldonDeploymentsApi(api_client)\n", + "deployment_api = SeldonDeploymentsApi(auth())\n", "deployment_api.create_seldon_deployment(namespace=NAMESPACE, mldeployment=mldeployment)" ] }, @@ -1716,271 +1702,19 @@ "* Username: admin@seldon.io\n", "* Password: 12341234\n", "\n", - "We can then test our new deployment using the following request: \n", - "```\n", - "{\n", - "\t\"data\": {\n", - "\t\t\"ndarray\": [\n", - "\t\t\t[\n", - "\t\t\t\t[1.0],\n", - "\t\t\t\t[0.76],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.08],\n", - "\t\t\t\t[0.08],\n", - "\t\t\t\t[0.07],\n", - "\t\t\t\t[0.05],\n", - "\t\t\t\t[0.05],\n", - "\t\t\t\t[0.04],\n", - "\t\t\t\t[0.03],\n", - "\t\t\t\t[0.03],\n", - "\t\t\t\t[0.04],\n", - "\t\t\t\t[0.03],\n", - "\t\t\t\t[0.04],\n", - "\t\t\t\t[0.04],\n", - "\t\t\t\t[0.04],\n", - "\t\t\t\t[0.05],\n", - "\t\t\t\t[0.05],\n", - "\t\t\t\t[0.05],\n", - "\t\t\t\t[0.07],\n", - "\t\t\t\t[0.07],\n", - "\t\t\t\t[0.08],\n", - "\t\t\t\t[0.1],\n", - "\t\t\t\t[0.12],\n", - "\t\t\t\t[0.13],\n", - "\t\t\t\t[0.17],\n", - "\t\t\t\t[0.2],\n", - "\t\t\t\t[0.21],\n", - "\t\t\t\t[0.24],\n", - "\t\t\t\t[0.25],\n", - "\t\t\t\t[0.26],\n", - "\t\t\t\t[0.29],\n", - "\t\t\t\t[0.27],\n", - "\t\t\t\t[0.27],\n", - "\t\t\t\t[0.24],\n", - "\t\t\t\t[0.21],\n", - "\t\t\t\t[0.17],\n", - "\t\t\t\t[0.16],\n", - "\t\t\t\t[0.12],\n", - "\t\t\t\t[0.12],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.1],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.1],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.1],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.12],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.12],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.12],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.12],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.1],\n", - "\t\t\t\t[0.1],\n", - "\t\t\t\t[0.09],\n", - "\t\t\t\t[0.09],\n", - "\t\t\t\t[0.08],\n", - "\t\t\t\t[0.08],\n", - "\t\t\t\t[0.08],\n", - "\t\t\t\t[0.08],\n", - "\t\t\t\t[0.07],\n", - "\t\t\t\t[0.08],\n", - "\t\t\t\t[0.07],\n", - "\t\t\t\t[0.08],\n", - "\t\t\t\t[0.07],\n", - "\t\t\t\t[0.07],\n", - "\t\t\t\t[0.07],\n", - "\t\t\t\t[0.07],\n", - "\t\t\t\t[0.07],\n", - "\t\t\t\t[0.09],\n", - "\t\t\t\t[0.1],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.12],\n", - "\t\t\t\t[0.12],\n", - "\t\t\t\t[0.11],\n", - "\t\t\t\t[0.1],\n", - "\t\t\t\t[0.1],\n", - "\t\t\t\t[0.1],\n", - "\t\t\t\t[0.09],\n", - "\t\t\t\t[0.07],\n", - "\t\t\t\t[0.07],\n", - "\t\t\t\t[0.05],\n", - "\t\t\t\t[0.06],\n", - "\t\t\t\t[0.05],\n", - "\t\t\t\t[0.06],\n", - "\t\t\t\t[0.05],\n", - "\t\t\t\t[0.06],\n", - "\t\t\t\t[0.05],\n", - "\t\t\t\t[0.05],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.01],\n", - "\t\t\t\t[0.19],\n", - "\t\t\t\t[0.68],\n", - "\t\t\t\t[0.98],\n", - "\t\t\t\t[0.62],\n", - "\t\t\t\t[0.04],\n", - "\t\t\t\t[0.01],\n", - "\t\t\t\t[0.09],\n", - "\t\t\t\t[0.07],\n", - "\t\t\t\t[0.07],\n", - "\t\t\t\t[0.05],\n", - "\t\t\t\t[0.04],\n", - "\t\t\t\t[0.04],\n", - "\t\t\t\t[0.03],\n", - "\t\t\t\t[0.04],\n", - "\t\t\t\t[0.04],\n", - "\t\t\t\t[0.05],\n", - "\t\t\t\t[0.04],\n", - "\t\t\t\t[0.05],\n", - "\t\t\t\t[0.04],\n", - "\t\t\t\t[0.05],\n", - "\t\t\t\t[0.05],\n", - "\t\t\t\t[0.06],\n", - "\t\t\t\t[0.07],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0],\n", - "\t\t\t\t[0.0]\n", - "\t\t\t]\n", - "\t\t]\n", - "\t}\n", - "}\n", - "```" + "We can then test our new deployment using the request here --> You can download the file and upload it\n", + "\n", + "[Link to Prediction](https://github.com/SeldonIO/deploy-workshops/blob/ecg-deploy-model-catalog/time-series/prediction.json)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Adding a Prediction Schema & Metadata\n", + "## Adding a Metadata to the model catalog\n", "Seldon Deploy has a model catalog where all deployed models are automatically registered. The model catalog can store custom metadata as well as prediction schemas for your models. \n", "\n", - "Metadata promotes lineage from across different machine learning systems, aids knowledge transfer between teams, and allows for faster deployment. Meanwhile, prediction schemas allow Seldon Deploy to automatically profile tabular data into histograms, allowing for filtering on features to explore trends. \n", - "\n", - "In order to effectively construct a prediction schema Seldon has the [ML Prediction Schema](https://github.com/SeldonIO/ml-prediction-schema) project. The first step is to determine your datatypes. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "prediction_schema = {\n", - " \"requests\": [\n", - " {\n", - " \"name\": \"Input\",\n", - " \"type\": \"REAL\",\n", - " \"data_type\": \"FLOAT\"\n", - " }\n", - " ],\n", - " \"responses\": [\n", - " {\n", - " \"name\": \"Arrhythmias\",\n", - " \"type\": \"PROBA\",\n", - " \"dataType\": \"FLOAT\",\n", - " \"nCategories\": \"5\",\n", - " \"categoryMap\": {},\n", - " \"schema\": [\n", - " {\n", - " \"name\": \"Non-ecotic beats (normal beat)\",\n", - " \"dataType\": \"FLOAT\"\n", - " },\n", - " {\n", - " \"name\": \"Supraventricular ectopic beats\",\n", - " \"dataType\": \"FLOAT\"\n", - " },\n", - " {\n", - " \"name\": \"Ventricular ectopic beats\",\n", - " \"dataType\": \"FLOAT\"\n", - " },\n", - " {\n", - " \"name\": \"Fusion Beats\",\n", - " \"dataType\": \"FLOAT\"\n", - " },\n", - " {\n", - " \"name\": \"Unknown Beats\",\n", - " \"dataType\": \"FLOAT\"\n", - " }\n", - " ],\n", - " \"shape\": []\n", - " }\n", - " ]\n", - "}\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You then add the prediction schema to the wider model catalog metadata. This includes information such as the model storage location, the name, who authored the model etc. The metadata tags and metrics which can be associated with a model are freeform and can therefore be determined based upon the use case which is being developed. " + "Metadata promotes lineage from across different machine learning systems, aids knowledge transfer between teams, and allows for faster deployment. Meanwhile, prediction schemas allow Seldon Deploy to automatically profile tabular data into histograms, allowing for filtering on features to explore trends. \n" ] }, { @@ -2168,7 +1902,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 113, "metadata": {}, "outputs": [ { @@ -2194,41 +1928,7 @@ }, { "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "type(label_dfs[2][0].values)\n", - "lists = label_dfs[2][0].values.tolist()\n", - "json_str = json.dumps(lists)\n", - "with open('drift-preds.json', 'w') as outfile:\n", - " outfile.write(json_str)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.87, 0. , 1. , ..., 0.02, 1. , 0.74])" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "label_dfs[2][0].values" - ] - }, - { - "cell_type": "code", - "execution_count": 49, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -2269,7 +1969,7 @@ " 'meta': {'name': 'KSDrift', 'detector_type': 'offline', 'data_type': None}}" ] }, - "execution_count": 49, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -2289,17 +1989,9 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 116, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Directory drift_detector does not exist and is now created.\n" - ] - } - ], + "outputs": [], "source": [ "save_detector(dd, \"drift_detector\")" ] @@ -2315,28 +2007,22 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 117, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "\n", - "Updates are available for some Google Cloud CLI components. To install them,\n", - "please run:\n", - " $ gcloud components update\n", - "\n", "Copying file://drift_detector/KSDrift.pickle [Content-Type=application/octet-stream]...\n", "Copying file://drift_detector/meta.pickle [Content-Type=application/octet-stream]...\n", - "| [2 files][124.2 MiB/124.2 MiB] 8.5 MiB/s \n", + "- [2 files][124.2 MiB/124.2 MiB] 8.3 MiB/s \n", "Operation completed over 2 objects/124.2 MiB. \n" ] } ], "source": [ - "!gsutil cp -r drift_detector gs://tom-seldon-examples/time-series-workshop/pretrained-drift/" + "!gsutil cp -r drift_detector gs://tom-seldon-examples/time-series-workshop/pretrained-drift/josh" ] }, { @@ -2348,12 +2034,12 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 120, "metadata": {}, "outputs": [], "source": [ "# TO DO: Add code to deploy the drift detector!\n", - "DD_URI = f'gs://tom-seldon-examples/time-series-workshop/pretrained-drift/'\n", + "DD_URI = f'gs://tom-seldon-examples/time-series-workshop/pretrained-drift/josh'\n", "DD_NAME = \"ks-drift\"\n", "\n", "dd_config = {'config': {'basic': \n", @@ -2370,7 +2056,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 121, "metadata": {}, "outputs": [ { @@ -2378,7 +2064,7 @@ "text/plain": [ "{'config': {'basic': {'drift_batch_size': '2',\n", " 'env_secret_ref': None,\n", - " 'storage_uri': 'gs://tom-seldon-examples/time-series-workshop/pretrained-drift/'},\n", + " 'storage_uri': 'gs://tom-seldon-examples/time-series-workshop/pretrained-drift/josh'},\n", " 'deployment': {'event_source': 'io.seldon.serving.josh-ecg-things-ks-drift',\n", " 'event_type': 'io.seldon.serving.inference.drift',\n", " 'http_port': '8080',\n", @@ -2394,7 +2080,7 @@ " 'namespace': 'seldon-demos'}" ] }, - "execution_count": 88, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" } @@ -2408,10 +2094,17 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can then kick off Drift by submitting a few requests using the regular prediction and the drift prediction\n", + "\n", + "[Drift Prediction](https://raw.githubusercontent.com/SeldonIO/deploy-workshops/ecg-deploy-model-catalog/time-series/prediction-drift.json)" + ] + }, + { + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [] } ],