diff --git a/examples/Building-and-deploying-multi-stage-RecSys/01-Building-Recommender-Systems-with-Merlin.ipynb b/examples/Building-and-deploying-multi-stage-RecSys/01-Building-Recommender-Systems-with-Merlin.ipynb index 3dbe42dc5..9a0038917 100644 --- a/examples/Building-and-deploying-multi-stage-RecSys/01-Building-Recommender-Systems-with-Merlin.ipynb +++ b/examples/Building-and-deploying-multi-stage-RecSys/01-Building-Recommender-Systems-with-Merlin.ipynb @@ -146,7 +146,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-06-29 19:20:02.816099: I tensorflow/core/platform/cpu_feature_guard.cc:194] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX\n", + "2023-06-29 19:49:32.836544: I tensorflow/core/platform/cpu_feature_guard.cc:194] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "/usr/local/lib/python3.8/dist-packages/merlin/dtypes/mappings/torch.py:43: UserWarning: PyTorch dtype mappings did not load successfully due to an error: No module named 'torch'\n", " warn(f\"PyTorch dtype mappings did not load successfully due to an error: {exc.msg}\")\n" @@ -167,12 +167,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-06-29 19:20:07.245419: I tensorflow/core/platform/cpu_feature_guard.cc:194] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX\n", + "2023-06-29 19:49:37.094972: I tensorflow/core/platform/cpu_feature_guard.cc:194] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-06-29 19:20:08.267091: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n", - "2023-06-29 19:20:08.267138: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1621] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 24576 MB memory: -> device: 0, name: Quadro RTX 8000, pci bus id: 0000:15:00.0, compute capability: 7.5\n", - "2023-06-29 19:20:08.268109: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n", - "2023-06-29 19:20:08.268137: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1621] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 24576 MB memory: -> device: 1, name: Quadro RTX 8000, pci bus id: 0000:2d:00.0, compute capability: 7.5\n", + "2023-06-29 19:49:38.134481: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n", + "2023-06-29 19:49:38.134526: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1621] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 24576 MB memory: -> device: 0, name: Quadro RTX 8000, pci bus id: 0000:15:00.0, compute capability: 7.5\n", + "2023-06-29 19:49:38.135533: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n", + "2023-06-29 19:49:38.135562: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1621] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 24576 MB memory: -> device: 1, name: Quadro RTX 8000, pci bus id: 0000:2d:00.0, compute capability: 7.5\n", "/usr/local/lib/python3.8/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] @@ -235,7 +235,7 @@ "DATA_FOLDER = os.environ.get(\"DATA_FOLDER\", \"/workspace/data/\")\n", "# set up the base dir for feature store\n", "BASE_DIR = os.environ.get(\n", - " \"BASE_DIR\", \"/raid/workshared/merlin/examples/Building-and-deploying-multi-stage-RecSys/\"\n", + " \"BASE_DIR\", \"/Merlin/examples/Building-and-deploying-multi-stage-RecSys/\"\n", ")" ] }, @@ -424,7 +424,7 @@ " \n", " 6\n", " 7\n", - " 530\n", + " 590\n", " 1\n", " 1\n", " 1\n", @@ -433,11 +433,11 @@ " 1\n", " 1\n", " 1\n", - " 154\n", - " 264\n", - " 28\n", - " 2023-06-29 19:20:20.311986\n", - " 2023-06-29 19:20:20.314307\n", + " 171\n", + " 293\n", + " 31\n", + " 2023-06-29 19:49:50.300270\n", + " 2023-06-29 19:49:50.303330\n", " \n", " \n", "\n", @@ -445,16 +445,16 @@ ], "text/plain": [ " user_id user_shops user_profile user_group user_gender user_age \\\n", - "6 7 530 1 1 1 1 \n", + "6 7 590 1 1 1 1 \n", "\n", " user_consumption_1 user_consumption_2 user_is_occupied user_geography \\\n", "6 1 1 1 1 \n", "\n", " user_intentions user_brands user_categories datetime \\\n", - "6 154 264 28 2023-06-29 19:20:20.311986 \n", + "6 171 293 31 2023-06-29 19:49:50.300270 \n", "\n", " created \n", - "6 2023-06-29 19:20:20.314307 " + "6 2023-06-29 19:49:50.303330 " ] }, "execution_count": 11, @@ -549,48 +549,48 @@ " 1\n", " 1\n", " 1\n", - " 2023-06-29 19:20:20.413296\n", - " 2023-06-29 19:20:20.414521\n", + " 2023-06-29 19:49:50.410715\n", + " 2023-06-29 19:49:50.412307\n", " \n", " \n", " 1\n", " 2\n", - " 7\n", - " 457\n", - " 158\n", - " 73\n", - " 2023-06-29 19:20:20.413296\n", - " 2023-06-29 19:20:20.414521\n", + " 6\n", + " 412\n", + " 142\n", + " 66\n", + " 2023-06-29 19:49:50.410715\n", + " 2023-06-29 19:49:50.412307\n", " \n", " \n", " 2\n", " 3\n", - " 13\n", - " 914\n", - " 315\n", - " 146\n", - " 2023-06-29 19:20:20.413296\n", - " 2023-06-29 19:20:20.414521\n", + " 12\n", + " 824\n", + " 284\n", + " 132\n", + " 2023-06-29 19:49:50.410715\n", + " 2023-06-29 19:49:50.412307\n", " \n", " \n", " 3\n", " 4\n", - " 20\n", - " 1371\n", - " 473\n", - " 219\n", - " 2023-06-29 19:20:20.413296\n", - " 2023-06-29 19:20:20.414521\n", + " 18\n", + " 1236\n", + " 426\n", + " 197\n", + " 2023-06-29 19:49:50.410715\n", + " 2023-06-29 19:49:50.412307\n", " \n", " \n", " 4\n", " 5\n", - " 26\n", - " 1828\n", - " 630\n", - " 292\n", - " 2023-06-29 19:20:20.413296\n", - " 2023-06-29 19:20:20.414521\n", + " 24\n", + " 1648\n", + " 568\n", + " 263\n", + " 2023-06-29 19:49:50.410715\n", + " 2023-06-29 19:49:50.412307\n", " \n", " \n", "\n", @@ -599,17 +599,17 @@ "text/plain": [ " item_id item_category item_shop item_brand item_intention \\\n", "0 1 1 1 1 1 \n", - "1 2 7 457 158 73 \n", - "2 3 13 914 315 146 \n", - "3 4 20 1371 473 219 \n", - "4 5 26 1828 630 292 \n", + "1 2 6 412 142 66 \n", + "2 3 12 824 284 132 \n", + "3 4 18 1236 426 197 \n", + "4 5 24 1648 568 263 \n", "\n", " datetime created \n", - "0 2023-06-29 19:20:20.413296 2023-06-29 19:20:20.414521 \n", - "1 2023-06-29 19:20:20.413296 2023-06-29 19:20:20.414521 \n", - "2 2023-06-29 19:20:20.413296 2023-06-29 19:20:20.414521 \n", - "3 2023-06-29 19:20:20.413296 2023-06-29 19:20:20.414521 \n", - "4 2023-06-29 19:20:20.413296 2023-06-29 19:20:20.414521 " + "0 2023-06-29 19:49:50.410715 2023-06-29 19:49:50.412307 \n", + "1 2023-06-29 19:49:50.410715 2023-06-29 19:49:50.412307 \n", + "2 2023-06-29 19:49:50.410715 2023-06-29 19:49:50.412307 \n", + "3 2023-06-29 19:49:50.410715 2023-06-29 19:49:50.412307 \n", + "4 2023-06-29 19:49:50.410715 2023-06-29 19:49:50.412307 " ] }, "execution_count": 15, @@ -890,13 +890,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "9/9 [==============================] - 10s 275ms/step - loss: 8.9538 - recall_at_10: 0.0055 - ndcg_at_10: 0.0038 - regularization_loss: 0.0000e+00 - loss_batch: 8.8710 - val_loss: 8.9181 - val_recall_at_10: 0.0165 - val_ndcg_at_10: 0.0109 - val_regularization_loss: 0.0000e+00 - val_loss_batch: 8.5802\n" + "9/9 [==============================] - 11s 275ms/step - loss: 8.9538 - recall_at_10: 0.0101 - ndcg_at_10: 0.0067 - regularization_loss: 0.0000e+00 - loss_batch: 8.8711 - val_loss: 8.9179 - val_recall_at_10: 0.0212 - val_ndcg_at_10: 0.0155 - val_regularization_loss: 0.0000e+00 - val_loss_batch: 8.5806\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 26, @@ -1038,13 +1038,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "5/5 [==============================] - 5s 312ms/step - loss: 0.6931 - auc: 0.4991 - regularization_loss: 0.0000e+00 - loss_batch: 0.6932 - val_loss: 0.6931 - val_auc: 0.4983 - val_regularization_loss: 0.0000e+00 - val_loss_batch: 0.6931\n" + "5/5 [==============================] - 5s 305ms/step - loss: 0.6932 - auc: 0.5005 - regularization_loss: 0.0000e+00 - loss_batch: 0.6932 - val_loss: 0.6931 - val_auc: 0.5029 - val_regularization_loss: 0.0000e+00 - val_loss_batch: 0.6931\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 31, @@ -1067,7 +1067,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "00447c12-ea80-4d98-ab47-cc1a982a6958", "metadata": {}, "outputs": [], @@ -1093,7 +1093,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "e62f65f8-e8f1-447e-9500-5960807c36f2", "metadata": {}, "outputs": [], @@ -1107,17 +1107,86 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "e02f0957-6665-400a-80c0-60b307466caf", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
item_idoutput_1
453945[0.012117806822061539, -0.02241620607674122, 0...
454948[0.012117806822061539, -0.02241620607674122, 0...
455956[0.012117806822061539, -0.02241620607674122, 0...
4561437[0.012117806822061539, -0.02241620607674122, 0...
4571469[0.012117806822061539, -0.02241620607674122, 0...
\n", + "
" + ], + "text/plain": [ + " item_id output_1\n", + "453 945 [0.012117806822061539, -0.02241620607674122, 0...\n", + "454 948 [0.012117806822061539, -0.02241620607674122, 0...\n", + "455 956 [0.012117806822061539, -0.02241620607674122, 0...\n", + "456 1437 [0.012117806822061539, -0.02241620607674122, 0...\n", + "457 1469 [0.012117806822061539, -0.02241620607674122, 0..." + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "item_embeddings.tail()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "66d7271e-0ea6-4568-ac5a-04089735f542", "metadata": {}, "outputs": [], @@ -1144,7 +1213,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "4ee27d67-e35a-42c5-8025-ed73f35c8e13", "metadata": {}, "outputs": [], @@ -1195,7 +1264,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "48a5927c-840d-410c-8f5b-bebce4f79640", "metadata": {}, "outputs": [], @@ -1246,10 +1315,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "57133c1e-18d9-4ccb-9704-cdebd271985e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: seedir in /usr/local/lib/python3.8/dist-packages (0.4.2)\n", + "Requirement already satisfied: natsort in /usr/local/lib/python3.8/dist-packages (from seedir) (8.4.0)\n" + ] + } + ], "source": [ "# install seedir\n", "!pip install seedir" @@ -1257,10 +1335,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "986d53ea-c946-4046-a390-6d3b8801d280", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "feast_repo/\n", + "├─README.md\n", + "├─__init__.py\n", + "└─feature_repo/\n", + " ├─__init__.py\n", + " ├─__pycache__/\n", + " │ ├─__init__.cpython-38.pyc\n", + " │ ├─example_repo.cpython-38.pyc\n", + " │ └─test_workflow.cpython-38.pyc\n", + " ├─data/\n", + " │ ├─item_features.parquet\n", + " │ └─user_features.parquet\n", + " ├─feature_store.yaml\n", + " ├─item_features.py\n", + " ├─test_workflow.py\n", + " └─user_features.py\n" + ] + } + ], "source": [ "import seedir as sd\n", "\n", diff --git a/examples/Building-and-deploying-multi-stage-RecSys/02-Deploying-multi-stage-RecSys-with-Merlin-Systems.ipynb b/examples/Building-and-deploying-multi-stage-RecSys/02-Deploying-multi-stage-RecSys-with-Merlin-Systems.ipynb index 3d1d417b0..15f0060d3 100644 --- a/examples/Building-and-deploying-multi-stage-RecSys/02-Deploying-multi-stage-RecSys-with-Merlin-Systems.ipynb +++ b/examples/Building-and-deploying-multi-stage-RecSys/02-Deploying-multi-stage-RecSys-with-Merlin-Systems.ipynb @@ -99,7 +99,7 @@ " _descriptor.FieldDescriptor(\n", "/usr/local/lib/python3.8/dist-packages/cudf/utils/metadata/orc_column_statistics_pb2.py:30: DeprecationWarning: Call to deprecated create function Descriptor(). Note: Create unlinked descriptors is going to go away. Please use get/find descriptors from generated code or query the descriptor_pool.\n", " _INTEGERSTATISTICS = _descriptor.Descriptor(\n", - "2023-06-29 19:13:17.254704: I tensorflow/core/platform/cpu_feature_guard.cc:194] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX\n", + "2023-06-29 19:50:56.885234: I tensorflow/core/platform/cpu_feature_guard.cc:194] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "/usr/local/lib/python3.8/dist-packages/tensorflow/core/framework/tensor_shape_pb2.py:18: DeprecationWarning: Call to deprecated create function FileDescriptor(). Note: Create unlinked descriptors is going to go away. Please use get/find descriptors from generated code or query the descriptor_pool.\n", " DESCRIPTOR = _descriptor.FileDescriptor(\n", @@ -186,10 +186,10 @@ "output_type": "stream", "text": [ "/raid/workshared/merlin/examples/Building-and-deploying-multi-stage-RecSys/feast_repo/feature_repo\n", - "Created entity \u001b[1m\u001b[32mitem_id\u001b[0m\n", "Created entity \u001b[1m\u001b[32muser_id\u001b[0m\n", - "Created feature view \u001b[1m\u001b[32mitem_features\u001b[0m\n", + "Created entity \u001b[1m\u001b[32mitem_id\u001b[0m\n", "Created feature view \u001b[1m\u001b[32muser_features\u001b[0m\n", + "Created feature view \u001b[1m\u001b[32mitem_features\u001b[0m\n", "\n", "Created sqlite table \u001b[1m\u001b[32mfeast_repo_item_features\u001b[0m\n", "Created sqlite table \u001b[1m\u001b[32mfeast_repo_user_features\u001b[0m\n", @@ -228,10 +228,10 @@ "text": [ "Materializing \u001b[1m\u001b[32m2\u001b[0m feature views from \u001b[1m\u001b[32m1995-01-01 01:01:01+00:00\u001b[0m to \u001b[1m\u001b[32m2025-01-01 01:01:01+00:00\u001b[0m into the \u001b[1m\u001b[32msqlite\u001b[0m online store.\n", "\n", - "\u001b[1m\u001b[32mitem_features\u001b[0m:\n", - "100%|███████████████████████████████████████████████████████████| 450/450 [00:00<00:00, 5815.84it/s]\n", "\u001b[1m\u001b[32muser_features\u001b[0m:\n", - "100%|███████████████████████████████████████████████████████████| 448/448 [00:00<00:00, 1758.64it/s]\n" + "100%|███████████████████████████████████████████████████████████| 460/460 [00:00<00:00, 2521.27it/s]\n", + "\u001b[1m\u001b[32mitem_features\u001b[0m:\n", + "100%|███████████████████████████████████████████████████████████| 458/458 [00:00<00:00, 3335.12it/s]\n" ] } ], @@ -338,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "0b6cc5bf-d07c-4963-a748-6e2b4827ee36", "metadata": {}, "outputs": [ @@ -346,7 +346,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "WARNING clustering 450 points to 32 centroids: please provide at least 1248 training points\n" + "WARNING clustering 458 points to 32 centroids: please provide at least 1248 training points\n" ] } ], @@ -354,7 +354,7 @@ "from merlin.systems.dag.ops.faiss import QueryFaiss, setup_faiss \n", "\n", "item_embeddings = pd.read_parquet(os.path.join(BASE_DIR, \"item_embeddings.parquet\"))\n", - "setup_faiss(item_embeddings, faiss_index_path)" + "setup_faiss(item_embeddings, faiss_index_path, embedding_column=\"output_1\")" ] }, { @@ -367,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "3bc00e04-c70c-4882-9952-66f4dbb97bdc", "metadata": {}, "outputs": [], @@ -385,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "3decbe7b-03e3-4978-baac-03f6a0b078c9", "metadata": {}, "outputs": [ @@ -393,9 +393,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Materializing \u001b[1m\u001b[32m1\u001b[0m feature views to \u001b[1m\u001b[32m2023-06-29 19:14:10+00:00\u001b[0m into the \u001b[1m\u001b[32msqlite\u001b[0m online store.\n", + "Materializing \u001b[1m\u001b[32m1\u001b[0m feature views to \u001b[1m\u001b[32m2023-06-29 19:51:06+00:00\u001b[0m into the \u001b[1m\u001b[32msqlite\u001b[0m online store.\n", "\n", - "\u001b[1m\u001b[32muser_features\u001b[0m from \u001b[1m\u001b[32m2025-01-01 01:01:01+00:00\u001b[0m to \u001b[1m\u001b[32m2023-06-29 19:14:10+00:00\u001b[0m:\n" + "\u001b[1m\u001b[32muser_features\u001b[0m from \u001b[1m\u001b[32m2025-01-01 01:01:01+00:00\u001b[0m to \u001b[1m\u001b[32m2023-06-29 19:51:06+00:00\u001b[0m:\n" ] }, { @@ -419,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "f11299b6-20d4-4687-bb0e-b855a9bcb9eb", "metadata": {}, "outputs": [], @@ -441,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "21139caa-3a51-42e6-b006-21a92c95f1bc", "metadata": {}, "outputs": [ @@ -451,7 +451,7 @@ "" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -466,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "47c2d9b1-51dc-4549-977d-d7941ee6486c", "metadata": {}, "outputs": [ @@ -474,10 +474,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-06-29 19:14:11.423802: I tensorflow/core/platform/cpu_feature_guard.cc:194] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX\n", + "2023-06-29 19:51:07.269579: I tensorflow/core/platform/cpu_feature_guard.cc:194] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-06-29 19:14:14.615977: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1621] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 24576 MB memory: -> device: 0, name: Quadro RTX 8000, pci bus id: 0000:15:00.0, compute capability: 7.5\n", - "2023-06-29 19:14:14.616886: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1621] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 24576 MB memory: -> device: 1, name: Quadro RTX 8000, pci bus id: 0000:2d:00.0, compute capability: 7.5\n", + "2023-06-29 19:51:10.430459: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1621] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 24576 MB memory: -> device: 0, name: Quadro RTX 8000, pci bus id: 0000:15:00.0, compute capability: 7.5\n", + "2023-06-29 19:51:10.431356: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1621] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 24576 MB memory: -> device: 1, name: Quadro RTX 8000, pci bus id: 0000:2d:00.0, compute capability: 7.5\n", "WARNING:absl:Found untraced functions such as restored_function_body, restored_function_body, restored_function_body, restored_function_body, restored_function_body while saving (showing 5 of 52). These functions will not be directly callable after loading.\n" ] }, @@ -485,14 +485,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmpqzazhnjq/assets\n" + "INFO:tensorflow:Assets written to: /tmp/tmpdalflmaz/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmpqzazhnjq/assets\n" + "INFO:tensorflow:Assets written to: /tmp/tmpdalflmaz/assets\n" ] } ], @@ -517,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "id": "b270f663-0ae1-4356-acd4-5f8c986abf4d", "metadata": {}, "outputs": [ @@ -525,9 +525,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Materializing \u001b[1m\u001b[32m1\u001b[0m feature views to \u001b[1m\u001b[32m2023-06-29 19:14:18+00:00\u001b[0m into the \u001b[1m\u001b[32msqlite\u001b[0m online store.\n", + "Materializing \u001b[1m\u001b[32m1\u001b[0m feature views to \u001b[1m\u001b[32m2023-06-29 19:51:14+00:00\u001b[0m into the \u001b[1m\u001b[32msqlite\u001b[0m online store.\n", "\n", - "\u001b[1m\u001b[32mitem_features\u001b[0m from \u001b[1m\u001b[32m2025-01-01 01:01:01+00:00\u001b[0m to \u001b[1m\u001b[32m2023-06-29 19:14:18+00:00\u001b[0m:\n" + "\u001b[1m\u001b[32mitem_features\u001b[0m from \u001b[1m\u001b[32m2025-01-01 01:01:01+00:00\u001b[0m to \u001b[1m\u001b[32m2023-06-29 19:51:14+00:00\u001b[0m:\n" ] }, { @@ -550,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "id": "0d0a4531-665c-48a1-98a9-216c955449b7", "metadata": {}, "outputs": [], @@ -569,7 +569,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "id": "eb0ef434-03a5-4a36-afb9-e19a43243c64", "metadata": {}, "outputs": [], @@ -604,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "id": "ce31723e-af4d-4827-bb60-3a9fafcd9da6", "metadata": {}, "outputs": [ @@ -619,14 +619,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmp6epm9p86/assets\n" + "INFO:tensorflow:Assets written to: /tmp/tmpqdd_jn5e/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:tensorflow:Assets written to: /tmp/tmp6epm9p86/assets\n" + "INFO:tensorflow:Assets written to: /tmp/tmpqdd_jn5e/assets\n" ] } ], @@ -644,7 +644,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "id": "7f65598b-e3e7-4238-a73e-19d00c3deb26", "metadata": {}, "outputs": [], @@ -676,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "id": "b28c452f-543c-45a4-9995-130ca6919669", "metadata": {}, "outputs": [], @@ -695,7 +695,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "id": "9c8b7b94-5559-4587-a272-4d9de2d53dd1", "metadata": {}, "outputs": [], @@ -709,7 +709,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "id": "6c64d686-aed5-42f8-b517-482b4237c69f", "metadata": {}, "outputs": [ @@ -743,7 +743,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "id": "89182219-40a6-458c-af0e-7a8e83f364aa", "metadata": {}, "outputs": [ @@ -872,7 +872,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "id": "d08a8975-9c32-467b-99ec-df66319f854b", "metadata": {}, "outputs": [ @@ -905,20 +905,20 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "id": "74ec62f2-5935-45c6-8058-e1cdade6f80f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'ordered_ids': array([[ 52, 102, 42, 204, 312, 117, 414, 258, 14, 450]], dtype=int32),\n", - " 'ordered_scores': array([[0.5010059 , 0.5018582 , 0.5001918 , 0.50212526, 0.5004832 ,\n", - " 0.5006511 , 0.50049436, 0.5014268 , 0.5005215 , 0.5017036 ]],\n", + "{'ordered_ids': array([[100, 168, 324, 79, 361, 294, 267, 289, 397, 189]], dtype=int32),\n", + " 'ordered_scores': array([[0.5016385 , 0.50176895, 0.5017176 , 0.5024097 , 0.5018236 ,\n", + " 0.5018286 , 0.50162375, 0.5015677 , 0.50175667, 0.5014358 ]],\n", " dtype=float32)}" ] }, - "execution_count": 26, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }