From b5b560679933033bdc302bcf0bc62c753cb1158c Mon Sep 17 00:00:00 2001 From: Alivia Jiang Date: Tue, 24 Jun 2025 14:12:01 -0700 Subject: [PATCH] update the script output --- .../multi_node_xgb.ipynb | 167 +++----- .../xgb_classifier_nb/single_node_xgb.ipynb | 404 ++++++++++++------ 2 files changed, 321 insertions(+), 250 deletions(-) diff --git a/samples/ml/ml_jobs/distributed_xgb_classifier_nb/multi_node_xgb.ipynb b/samples/ml/ml_jobs/distributed_xgb_classifier_nb/multi_node_xgb.ipynb index 855f7a55..bb5c5dd2 100644 --- a/samples/ml/ml_jobs/distributed_xgb_classifier_nb/multi_node_xgb.ipynb +++ b/samples/ml/ml_jobs/distributed_xgb_classifier_nb/multi_node_xgb.ipynb @@ -22,7 +22,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "46e5fad4-748b-4390-b4fa-748a10547835", "metadata": {}, "outputs": [], @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "7fb47093-9b23-492d-a135-5722729a0c7a", "metadata": {}, "outputs": [ @@ -66,7 +66,7 @@ "[Row(status='DEMO_POOL_CPU already exists, statement succeeded.')]" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "5f328bfd", "metadata": {}, "outputs": [ @@ -98,7 +98,7 @@ "[Row(status='Statement executed successfully.')]" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "84804c16-a359-4e5b-9037-207cc9675b75", "metadata": {}, "outputs": [], @@ -143,10 +143,18 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "efd72bf3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "'num_instances' is deprecated and will be removed in a future release. Use 'target_instances' instead.\n" + ] + } + ], "source": [ "from snowflake.ml.jobs import remote\n", "\n", @@ -186,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "3eb1ad66-851f-4fdb-887c-4dcaaf5bf2c5", "metadata": {}, "outputs": [ @@ -194,7 +202,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "MLJOB_99440CD7_F620_468E_B52D_B2872C52BAFE\n", + "HEADLESS_STARTER_DB.HEADLESS_DEMO.MLJOB_3152B8EE_A391_4340_9152_D54A58365C1B\n", "PENDING\n" ] } @@ -206,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "1909e552-d289-4bf4-8840-926d99295acb", "metadata": {}, "outputs": [ @@ -214,105 +222,44 @@ "name": "stdout", "output_type": "stream", "text": [ + "2025-06-24 21:08:17,038\tINFO job_manager.py:528 -- Runtime env is setting up.\n", + "/opt/conda/lib/python3.10/site-packages/snowflake/snowpark/session.py:38: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources\n", "\n", - "'micromamba' is running as a subprocess and can't modify the parent shell.\n", - "Thus you must initialize your shell before using activate and deactivate.\n", - "\n", - "To initialize the current bash shell, run:\n", - " $ eval \"$(micromamba shell hook --shell bash)\"\n", - "and then activate or deactivate with:\n", - " $ micromamba activate\n", - "To automatically initialize all future (bash) shells, run:\n", - " $ micromamba shell init --shell bash --root-prefix=~/micromamba\n", - "If your shell was already initialized, reinitialize your shell with:\n", - " $ micromamba shell reinit --shell bash\n", - "Otherwise, this may be an issue. In the meantime you can run commands. See:\n", - " $ micromamba run --help\n", + "2025-06-24 21:08:20,186\tINFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n", "\n", - "Supported shells are {bash, zsh, csh, xonsh, cmd.exe, powershell, fish}.\n", - "Creating log directories...\n", - " * Starting periodic command scheduler cron\n", - " ...done.\n", - "2025-04-24 23:51:15,079 - WARNING - SnowflakeLoginOptions() is in private preview since 0.2.0. Do not use it in production. \n", - "2025-04-24 23:51:15,080 - INFO - Snowflake Connector for Python Version: 3.13.2, Python Version: 3.10.15, Platform: Linux-5.4.181-99.354.amzn2.x86_64-x86_64-with-glibc2.31\n", - "2025-04-24 23:51:15,080 - INFO - Connecting to GLOBAL Snowflake domain\n", - "2025-04-24 23:51:15,081 - INFO - This connection is in OCSP Fail Open Mode. TLS Certificates would be checked for validity and revocation status. Any other Certificate Revocation related exceptions or OCSP Responder failures would be disregarded in favor of connectivity.\n", - "2025-04-24 23:51:16,443 - INFO - Snowpark Session information: \n", - "\"version\" : 1.25.0,\n", - "\"python.version\" : 3.10.15,\n", - "\"python.connector.version\" : 3.13.2,\n", - "\"python.connector.session.id\" : 239723776519818,\n", - "\"os.name\" : Linux\n", + "83f38934a778457abfaff63217cfc17e: Received raw arguments: XGBTrainArgs(model_type=, dataset=, input_cols=['FEATURE_1', 'FEATURE_2', 'FEATURE_3', 'FEATURE_4', 'FEATURE_5', 'FEATURE_6', 'FEATURE_7', 'FEATURE_8', 'FEATURE_9', 'FEATURE_10', 'FEATURE_11', 'FEATURE_12', 'FEATURE_13', 'FEATURE_14', 'FEATURE_15', 'FEATURE_16', 'FEATURE_17', 'FEATURE_18', 'FEATURE_19', 'FEATURE_20', 'FEATURE_21', 'FEATURE_22', 'FEATURE_23', 'FEATURE_24', 'FEATURE_25', 'FEATURE_26', 'FEATURE_27', 'FEATURE_28', 'FEATURE_29', 'FEATURE_30', 'FEATURE_31', 'FEATURE_32', 'FEATURE_33', 'FEATURE_34', 'FEATURE_35', 'FEATURE_36', 'FEATURE_37', 'FEATURE_38', 'FEATURE_39', 'FEATURE_40', 'FEATURE_41', 'FEATURE_42', 'FEATURE_43', 'FEATURE_44', 'FEATURE_45', 'FEATURE_46', 'FEATURE_47', 'FEATURE_48', 'FEATURE_49', 'FEATURE_50', 'FEATURE_51', 'FEATURE_52', 'FEATURE_53', 'FEATURE_54', 'FEATURE_55', 'FEATURE_56', 'FEATURE_57', 'FEATURE_58', 'FEATURE_59', 'FEATURE_60', 'FEATURE_61', 'FEATURE_62', 'FEATURE_63', 'FEATURE_64', 'FEATURE_65', 'FEATURE_66', 'FEATURE_67', 'FEATURE_68', 'FEATURE_69', 'FEATURE_70', 'FEATURE_71', 'FEATURE_72', 'FEATURE_73', 'FEATURE_74', 'FEATURE_75', 'FEATURE_76', 'FEATURE_77', 'FEATURE_78', 'FEATURE_79', 'FEATURE_80', 'FEATURE_81', 'FEATURE_82', 'FEATURE_83', 'FEATURE_84', 'FEATURE_85', 'FEATURE_86', 'FEATURE_87', 'FEATURE_88', 'FEATURE_89', 'FEATURE_90', 'FEATURE_91', 'FEATURE_92', 'FEATURE_93', 'FEATURE_94', 'FEATURE_95', 'FEATURE_96', 'FEATURE_97', 'FEATURE_98', 'FEATURE_99'], label_col='TARGET_1', params={'n_estimators': 100, 'objective': 'reg:pseudohubererror', 'tree_method': 'hist', 'eta': 0.0001, 'subsample': 0.5, 'max_depth': 50, 'max_leaves': 1000, 'max_bin': 63}, eval_set=None, num_workers=-1, num_cpu_per_worker=-1, use_gpu=False, output_model_path='/tmp/ray-jobs/83f38934a778457abfaff63217cfc17e.pkl', eval_results_path='/tmp/ray-jobs/83f38934a778457abfaff63217cfc17e_eval_results.pkl', verbose_eval=None, model=None)\n", + "Info - 2025-06-24 21:08:20.411685 - Number of nodes active for training: 3 nodes\n", + "Info - 2025-06-24 21:08:20.413140 - Scaling Config: {'num_workers': 3, 'resources_per_worker': {'CPU': 2.0}, 'use_gpu': False}\n", + "Info - 2025-06-24 21:08:20.413212 - Materializing Data ...\n", + "Info - 2025-06-24 21:08:20.546457 - Loading data from Snowpark Dataframe from query id 01bd4054-0004-bd5b-0001-3b8704ce79f6\n", "\n", - "2025-04-24 23:51:59,122 - INFO - Closing session: 239723776519818\n", - "2025-04-24 23:51:59,122 - INFO - Canceling all running queries\n", - "2025-04-24 23:51:59,179 - INFO - closed\n", - "2025-04-24 23:51:59,207 - INFO - No async queries seem to be running, deleting session\n", - "2025-04-24 23:51:59,236 - INFO - Closed session: 239723776519818\n", - "Head Instance Index: 0\n", - "Head Instance IP: 10.244.63.201\n", - "2025-04-24 23:52:00,795\tINFO usage_lib.py:441 -- Usage stats collection is disabled.\n", - "2025-04-24 23:52:00,795\tINFO scripts.py:767 -- \u001b[37mLocal node IP\u001b[39m: \u001b[1m10.244.63.201\u001b[22m\n", - "2025-04-24 23:52:02,233\tSUCC scripts.py:804 -- \u001b[32m--------------------\u001b[39m\n", - "2025-04-24 23:52:02,234\tSUCC scripts.py:805 -- \u001b[32mRay runtime started.\u001b[39m\n", - "2025-04-24 23:52:02,234\tSUCC scripts.py:806 -- \u001b[32m--------------------\u001b[39m\n", - "2025-04-24 23:52:02,234\tINFO scripts.py:808 -- \u001b[36mNext steps\u001b[39m\n", - "2025-04-24 23:52:02,234\tINFO scripts.py:811 -- To add another node to this Ray cluster, run\n", - "2025-04-24 23:52:02,234\tINFO scripts.py:814 -- \u001b[1m ray start --address='10.244.63.201:12001'\u001b[22m\n", - "2025-04-24 23:52:02,234\tINFO scripts.py:823 -- To connect to this Ray cluster:\n", - "2025-04-24 23:52:02,234\tINFO scripts.py:825 -- \u001b[35mimport\u001b[39m\u001b[26m ray\n", - "2025-04-24 23:52:02,234\tINFO scripts.py:826 -- ray\u001b[35m.\u001b[39m\u001b[26minit(_node_ip_address\u001b[35m=\u001b[39m\u001b[26m\u001b[33m'10.244.63.201'\u001b[39m\u001b[26m)\n", - "2025-04-24 23:52:02,234\tINFO scripts.py:838 -- To submit a Ray job using the Ray Jobs CLI:\n", - "2025-04-24 23:52:02,234\tINFO scripts.py:839 -- \u001b[1m RAY_ADDRESS='http://10.244.63.201:12003' ray job submit --working-dir . -- python my_script.py\u001b[22m\n", - "2025-04-24 23:52:02,234\tINFO scripts.py:848 -- See https://docs.ray.io/en/latest/cluster/running-applications/job-submission/index.html \n", - "2025-04-24 23:52:02,234\tINFO scripts.py:852 -- for more information on submitting Ray jobs to the Ray cluster.\n", - "2025-04-24 23:52:02,234\tINFO scripts.py:857 -- To terminate the Ray runtime, run\n", - "2025-04-24 23:52:02,234\tINFO scripts.py:858 -- \u001b[1m ray stop\u001b[22m\n", - "2025-04-24 23:52:02,234\tINFO scripts.py:861 -- To view the status of the cluster, use\n", - "2025-04-24 23:52:02,234\tINFO scripts.py:862 -- \u001b[1mray status\u001b[22m\u001b[26m\n", - "2025-04-24 23:52:02,234\tINFO scripts.py:866 -- To monitor and debug Ray, view the dashboard at \n", - "2025-04-24 23:52:02,234\tINFO scripts.py:867 -- \u001b[1m10.244.63.201:12003\u001b[22m\u001b[26m\n", - "2025-04-24 23:52:02,235\tINFO scripts.py:874 -- \u001b[4mIf connection to the dashboard fails, check your firewall settings and network configuration.\u001b[24m\n", - "Running command: python /mnt/app/mljob_launcher.py /mnt/app/func.py --script_main_func func\n", - "SnowflakeLoginOptions() is in private preview since 0.2.0. Do not use it in production. \n", - "DataConnector.from_dataframe() is in private preview since 1.6.0. Do not use it in production. \n", - "DataConnector.from_sql() is in private preview since 1.7.3. Do not use it in production. \n", - "2025-04-24 23:52:06,935\tINFO worker.py:1601 -- Connecting to existing Ray cluster at address: 10.244.63.201:12001...\n", - "2025-04-24 23:52:06,953\tINFO worker.py:1777 -- Connected to Ray cluster. View the dashboard at \u001b[1m\u001b[32m10.244.63.201:12003 \u001b[39m\u001b[22m\n", - "2025-04-24 23:52:07,627\tINFO job_manager.py:528 -- Runtime env is setting up.\n", + "Info - 2025-06-24 21:08:24.398563 - Finished executing data load query.\n", + "Info - 2025-06-24 21:08:25.764243 - Loaded data into ray dataset.\n", + "Info - 2025-06-24 21:08:25.769470 - Starting training job...\n", "\n", - "2025-04-24 23:52:11,193\tINFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n", - "e03fba9706734fa68123110586f16f19: Received raw arguments: XGBTrainArgs(model_type=, dataset=, input_cols=['FEATURE_1', 'FEATURE_2', 'FEATURE_3', 'FEATURE_4', 'FEATURE_5', 'FEATURE_6', 'FEATURE_7', 'FEATURE_8', 'FEATURE_9', 'FEATURE_10', 'FEATURE_11', 'FEATURE_12', 'FEATURE_13', 'FEATURE_14', 'FEATURE_15', 'FEATURE_16', 'FEATURE_17', 'FEATURE_18', 'FEATURE_19', 'FEATURE_20', 'FEATURE_21', 'FEATURE_22', 'FEATURE_23', 'FEATURE_24', 'FEATURE_25', 'FEATURE_26', 'FEATURE_27', 'FEATURE_28', 'FEATURE_29', 'FEATURE_30', 'FEATURE_31', 'FEATURE_32', 'FEATURE_33', 'FEATURE_34', 'FEATURE_35', 'FEATURE_36', 'FEATURE_37', 'FEATURE_38', 'FEATURE_39', 'FEATURE_40', 'FEATURE_41', 'FEATURE_42', 'FEATURE_43', 'FEATURE_44', 'FEATURE_45', 'FEATURE_46', 'FEATURE_47', 'FEATURE_48', 'FEATURE_49', 'FEATURE_50', 'FEATURE_51', 'FEATURE_52', 'FEATURE_53', 'FEATURE_54', 'FEATURE_55', 'FEATURE_56', 'FEATURE_57', 'FEATURE_58', 'FEATURE_59', 'FEATURE_60', 'FEATURE_61', 'FEATURE_62', 'FEATURE_63', 'FEATURE_64', 'FEATURE_65', 'FEATURE_66', 'FEATURE_67', 'FEATURE_68', 'FEATURE_69', 'FEATURE_70', 'FEATURE_71', 'FEATURE_72', 'FEATURE_73', 'FEATURE_74', 'FEATURE_75', 'FEATURE_76', 'FEATURE_77', 'FEATURE_78', 'FEATURE_79', 'FEATURE_80', 'FEATURE_81', 'FEATURE_82', 'FEATURE_83', 'FEATURE_84', 'FEATURE_85', 'FEATURE_86', 'FEATURE_87', 'FEATURE_88', 'FEATURE_89', 'FEATURE_90', 'FEATURE_91', 'FEATURE_92', 'FEATURE_93', 'FEATURE_94', 'FEATURE_95', 'FEATURE_96', 'FEATURE_97', 'FEATURE_98', 'FEATURE_99'], label_col='TARGET_1', params={'n_estimators': 100, 'objective': 'reg:pseudohubererror', 'tree_method': 'hist', 'eta': 0.0001, 'subsample': 0.5, 'max_depth': 50, 'max_leaves': 1000, 'max_bin': 63}, eval_set=None, num_workers=-1, num_cpu_per_worker=-1, use_gpu=False, output_model_path='/tmp/ray-jobs/e03fba9706734fa68123110586f16f19.pkl', eval_results_path='/tmp/ray-jobs/e03fba9706734fa68123110586f16f19_eval_results.pkl', verbose_eval=None, model=None)\n", - "Info - Training XGBoost using CPU single node\n", + "\u001b[36m(TrainTrainable pid=622)\u001b[0m /opt/conda/lib/python3.10/site-packages/snowflake/snowpark/session.py:38: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + "\u001b[36m(TrainTrainable pid=622)\u001b[0m import pkg_resources\n", "\n", - "2025-04-24 23:52:14,987\tINFO streaming_executor.py:108 -- Starting execution of Dataset. Full logs are in /tmp/ray/session_2025-04-24_23-52-00_796201_46/logs/ray-data\n", - "2025-04-24 23:52:14,987\tINFO streaming_executor.py:109 -- Execution plan of Dataset: InputDataBuffer[Input] -> TaskPoolMapOperator[ReadResultSetDataSource]\n", + "\u001b[36m(DistributedXGBoostTrainer pid=622)\u001b[0m Started distributed worker processes: \n", + "\u001b[36m(DistributedXGBoostTrainer pid=622)\u001b[0m - (node_id=fd022abd4d64d5018f9a6df9f04e7670e378af9728c8d44aba1c8c6a, ip=10.244.12.203, pid=664) world_rank=0, local_rank=0, node_rank=0\n", + "\u001b[36m(DistributedXGBoostTrainer pid=622)\u001b[0m - (node_id=98eb04ceb0a5ca449b11bd090039b3d9d855eea6b0f6d6d34c620430, ip=10.244.12.75, pid=348) world_rank=1, local_rank=0, node_rank=1\n", + "\u001b[36m(DistributedXGBoostTrainer pid=622)\u001b[0m - (node_id=5d49574e0383c7f66c78ad4cf45588b5ac6c347e82a29203a474cf3e, ip=10.244.13.75, pid=309) world_rank=2, local_rank=0, node_rank=2\n", "\n", - "✔️ Dataset execution finished in 5.70 seconds: : 0.00 row [00:05, ? row/s] \n", - "✔️ Dataset execution finished in 5.70 seconds: : 0.00 row [00:05, ? row/s]\n", - "✔️ Dataset execution finished in 5.70 seconds: : 0.00 row [00:05, ? row/s]\n", - "✔️ Dataset execution finished in 5.70 seconds: : 0.00 row [00:05, ? row/s]\n", + "\u001b[36m(SplitCoordinator pid=702)\u001b[0m /opt/conda/lib/python3.10/site-packages/snowflake/snowpark/session.py:38: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + "\u001b[36m(SplitCoordinator pid=702)\u001b[0m import pkg_resources\n", "\n", - "2025-04-24 23:52:20,693\tINFO streaming_executor.py:108 -- Starting execution of Dataset. Full logs are in /tmp/ray/session_2025-04-24_23-52-00_796201_46/logs/ray-data\n", - "2025-04-24 23:52:20,693\tINFO streaming_executor.py:109 -- Execution plan of Dataset: InputDataBuffer[Input] -> TaskPoolMapOperator[ReadResultSetDataSource]\n", + "\u001b[36m(ReadResultSetDataSource->SplitBlocks(2) pid=387, ip=10.244.12.75)\u001b[0m /opt/conda/lib/python3.10/site-packages/snowflake/snowpark/session.py:38: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + "\u001b[36m(ReadResultSetDataSource->SplitBlocks(2) pid=387, ip=10.244.12.75)\u001b[0m import pkg_resources\n", "\n", - "✔️ Dataset execution finished in 6.10 seconds: 25%|██▌ | 251k/1.00M [00:06<00:19, 38.2k row/s] \n", - "✔️ Dataset execution finished in 6.10 seconds: 100%|██████████| 1.00M/1.00M [00:06<00:00, 287k row/s]\n", - "✔️ Dataset execution finished in 6.10 seconds: 100%|██████████| 1.00M/1.00M [00:06<00:00, 287k row/s]\n", + "\u001b[36m(ReadResultSetDataSource->SplitBlocks(2) pid=805)\u001b[0m /opt/conda/lib/python3.10/site-packages/snowflake/snowpark/session.py:38: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + "\u001b[36m(ReadResultSetDataSource->SplitBlocks(2) pid=805)\u001b[0m import pkg_resources\n", "\n", - "✔️ Dataset execution finished in 6.10 seconds: 100%|██████████| 1.00M/1.00M [00:06<00:00, 164k row/s]\n", + "\u001b[36m(ReadResultSetDataSource->SplitBlocks(2) pid=383, ip=10.244.13.75)\u001b[0m /opt/conda/lib/python3.10/site-packages/snowflake/snowpark/session.py:38: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + "\u001b[36m(ReadResultSetDataSource->SplitBlocks(2) pid=383, ip=10.244.13.75)\u001b[0m import pkg_resources\n", "\n", - "Training completed\n", - "User job completed. Signaling workers to shut down...\n", - "2025-04-24 23:53:08,545\tINFO worker.py:1601 -- Connecting to existing Ray cluster at address: 10.244.63.201:12001...\n", - "2025-04-24 23:53:08,555\tINFO worker.py:1777 -- Connected to Ray cluster. View the dashboard at \u001b[1m\u001b[32m10.244.63.201:12003 \u001b[39m\u001b[22m\n", - "2025-04-24 23:53:08,573 - INFO - Found 2 worker nodes\n", - "2025-04-24 23:53:08,573 - INFO - Creating new shutdown signal actor: Failed to look up actor with name 'ShutdownSignal'. This could because 1. You are trying to look up a named actor you didn't create. 2. The named actor died. 3. You did not use a namespace matching the namespace of the actor.\n", - "2025-04-24 23:53:09,179 - INFO - Shutdown requested: {'status': 'shutdown_requested', 'timestamp': 1745538789.1788423, 'host': 'job-0'}\n", - "2025-04-24 23:53:09,180 - INFO - Waiting up to 15s for workers to acknowledge shutdown signal...\n", - "2025-04-24 23:53:14,207 - INFO - All 2 workers acknowledged shutdown. Completed in 5.03s\n", - "Head node job completed. Exiting.\n", - "\n" + "Info - 2025-06-24 21:10:09.168513 - Training job completed\n", + "UserWarning: [21:10:09] WARNING: /home/conda/feedstock_root/build_artifacts/xgboost-split_1738395417126/work/src/c_api/c_api.cc:1374: Saving model in the UBJSON format as default. You can use file extension: `json`, `ubj` or `deprecated` to choose between formats.\n" ] } ], @@ -323,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "6c46b2f8", "metadata": {}, "outputs": [ @@ -331,7 +278,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/homebrew/anaconda3/envs/snowml310/lib/python3.10/site-packages/xgboost/core.py:158: UserWarning: [16:53:20] WARNING: /Users/runner/work/xgboost/xgboost/src/gbm/../common/error_msg.h:80: If you are loading a serialized model (like pickle in Python, RDS in R) or\n", + "WARNING:snowflake.snowpark:MLJob.result() is in private preview since 1.8.2. Do not use it in production. \n", + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/xgboost/core.py:158: UserWarning: [14:10:35] WARNING: /Users/runner/work/xgboost/xgboost/src/gbm/../common/error_msg.h:80: If you are loading a serialized model (like pickle in Python, RDS in R) or\n", "configuration generated by an older version of XGBoost, please export the model by calling\n", "`Booster.save_model` from that version first, then load it back in current version. See:\n", "\n", @@ -345,11 +293,12 @@ { "data": { "text/plain": [ - "array([ 7.242013, 9.110336, 18.514343, 14.750366, 18.905142, 11.804218,\n", - " 17.774406, 17.400677, 7.676889, 14.249159], dtype=float32)" + "array([11.8159 , 10.611345 , 9.717881 , 18.790493 , 7.9805217,\n", + " 16.480486 , 15.571457 , 14.789684 , 12.37405 , 12.086709 ],\n", + " dtype=float32)" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -369,9 +318,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.10", "language": "python", - "name": "python3" + "name": "py310" }, "language_info": { "codemirror_mode": { @@ -383,7 +332,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.15" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/samples/ml/ml_jobs/xgb_classifier_nb/single_node_xgb.ipynb b/samples/ml/ml_jobs/xgb_classifier_nb/single_node_xgb.ipynb index 03f6a343..30edc87b 100644 --- a/samples/ml/ml_jobs/xgb_classifier_nb/single_node_xgb.ipynb +++ b/samples/ml/ml_jobs/xgb_classifier_nb/single_node_xgb.ipynb @@ -22,7 +22,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], @@ -146,7 +146,16 @@ "execution_count": 5, "id": "f6ba7a8b-a944-4149-a46e-48196350b70e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-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" + ] + } + ], "source": [ "import json\n", "import os\n", @@ -369,14 +378,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loading data... done! Loaded 10000000 rows, elapsed=21.933s\n", - "Training model... done! Elapsed=16.225s\n", - "Evaluating model... done! Elapsed=3.842s\n", + "Loading data... done! Loaded 100000 rows, elapsed=3.174s\n", + "Training model... done! Elapsed=0.227s\n", + "Evaluating model... done! Elapsed=0.035s\n", "\n", "Model Performance Metrics:\n", - "Accuracy: 0.5003\n", - "ROC AUC: 0.5000\n", - "Saving model to disk... done! Elapsed=0.004s\n" + "Accuracy: 0.5014\n", + "ROC AUC: 0.5027\n", + "Saving model to disk... done! Elapsed=0.002s\n" ] } ], @@ -397,8 +406,7 @@ "execution_count": 7, "id": "c2940e5a-4f85-46d9-9b49-aed6ac79fb53", "metadata": {}, - "outputs": [ - ], + "outputs": [], "source": [ "from snowflake.ml.jobs import remote\n", "\n", @@ -423,7 +431,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "MLJOB_D686BEB4_91ED_4DE7_BFD6_3FB38DCAF972\n", + "HEADLESS_STARTER_DB.HEADLESS_DEMO.MLJOB_1A0AE3B9_4A61_463F_948C_0005D4879C46\n", "PENDING\n" ] } @@ -443,114 +451,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "'micromamba' is running as a subprocess and can't modify the parent shell.\n", - "Thus you must initialize your shell before using activate and deactivate.\n", - "\n", - "To initialize the current bash shell, run:\n", - " $ eval \"$(micromamba shell hook --shell bash)\"\n", - "and then activate or deactivate with:\n", - " $ micromamba activate\n", - "To automatically initialize all future (bash) shells, run:\n", - " $ micromamba shell init --shell bash --root-prefix=~/micromamba\n", - "If your shell was already initialized, reinitialize your shell with:\n", - " $ micromamba shell reinit --shell bash\n", - "Otherwise, this may be an issue. In the meantime you can run commands. See:\n", - " $ micromamba run --help\n", - "\n", - "Supported shells are {bash, zsh, csh, xonsh, cmd.exe, powershell, fish}.\n", - "Creating log directories...\n", - " * Starting periodic command scheduler cron\n", - " ...done.\n", - "2025-04-24 23:53:18,741\tINFO usage_lib.py:441 -- Usage stats collection is disabled.\n", - "2025-04-24 23:53:18,742\tINFO scripts.py:767 -- \u001b[37mLocal node IP\u001b[39m: \u001b[1m10.244.64.74\u001b[22m\n", - "2025-04-24 23:53:20,014\tSUCC scripts.py:804 -- \u001b[32m--------------------\u001b[39m\n", - "2025-04-24 23:53:20,015\tSUCC scripts.py:805 -- \u001b[32mRay runtime started.\u001b[39m\n", - "2025-04-24 23:53:20,015\tSUCC scripts.py:806 -- \u001b[32m--------------------\u001b[39m\n", - "2025-04-24 23:53:20,015\tINFO scripts.py:808 -- \u001b[36mNext steps\u001b[39m\n", - "2025-04-24 23:53:20,015\tINFO scripts.py:811 -- To add another node to this Ray cluster, run\n", - "2025-04-24 23:53:20,015\tINFO scripts.py:814 -- \u001b[1m ray start --address='10.244.64.74:12001'\u001b[22m\n", - "2025-04-24 23:53:20,015\tINFO scripts.py:823 -- To connect to this Ray cluster:\n", - "2025-04-24 23:53:20,015\tINFO scripts.py:825 -- \u001b[35mimport\u001b[39m\u001b[26m ray\n", - "2025-04-24 23:53:20,015\tINFO scripts.py:826 -- ray\u001b[35m.\u001b[39m\u001b[26minit(_node_ip_address\u001b[35m=\u001b[39m\u001b[26m\u001b[33m'10.244.64.74'\u001b[39m\u001b[26m)\n", - "2025-04-24 23:53:20,015\tINFO scripts.py:838 -- To submit a Ray job using the Ray Jobs CLI:\n", - "2025-04-24 23:53:20,015\tINFO scripts.py:839 -- \u001b[1m RAY_ADDRESS='http://10.244.64.74:12003' ray job submit --working-dir . -- python my_script.py\u001b[22m\n", - "2025-04-24 23:53:20,015\tINFO scripts.py:848 -- See https://docs.ray.io/en/latest/cluster/running-applications/job-submission/index.html \n", - "2025-04-24 23:53:20,015\tINFO scripts.py:852 -- for more information on submitting Ray jobs to the Ray cluster.\n", - "2025-04-24 23:53:20,015\tINFO scripts.py:857 -- To terminate the Ray runtime, run\n", - "2025-04-24 23:53:20,015\tINFO scripts.py:858 -- \u001b[1m ray stop\u001b[22m\n", - "2025-04-24 23:53:20,015\tINFO scripts.py:861 -- To view the status of the cluster, use\n", - "2025-04-24 23:53:20,015\tINFO scripts.py:862 -- \u001b[1mray status\u001b[22m\u001b[26m\n", - "2025-04-24 23:53:20,015\tINFO scripts.py:866 -- To monitor and debug Ray, view the dashboard at \n", - "2025-04-24 23:53:20,015\tINFO scripts.py:867 -- \u001b[1m10.244.64.74:12003\u001b[22m\u001b[26m\n", - "2025-04-24 23:53:20,015\tINFO scripts.py:874 -- \u001b[4mIf connection to the dashboard fails, check your firewall settings and network configuration.\u001b[24m\n", - "Running command: python /mnt/app/mljob_launcher.py /mnt/app/func.py --script_main_func func\n", - "2025-04-24 23:53:25,253\tINFO worker.py:1601 -- Connecting to existing Ray cluster at address: 10.244.64.74:12001...\n", - "2025-04-24 23:53:25,263\tINFO worker.py:1777 -- Connected to Ray cluster. View the dashboard at \u001b[1m\u001b[32m10.244.64.74:12003 \u001b[39m\u001b[22m\n", - "2025-04-24 23:53:26,768\tINFO streaming_executor.py:108 -- Starting execution of Dataset. Full logs are in /tmp/ray/session_2025-04-24_23-53-18_742719_43/logs/ray-data\n", - "2025-04-24 23:53:26,768\tINFO streaming_executor.py:109 -- Execution plan of Dataset: InputDataBuffer[Input] -> TaskPoolMapOperator[ReadResultSetDataSource]\n", - "Running 0: 0.00 row [00:00, ? row/s]\n", - "Running. Resources: 3/3 CPU, 0/0 GPU, 768.0MB/1.9GB object_store_memory (pending: 0 CPU, 0 GPU): : 0.00 row [00:01, ? row/s]\n", - "- ReadResultSetDataSource: 3 active, 197 queued 🚧, [cpu: 3.0, objects: 768.0MB]: : 0.00 row [00:01, ? row/s]\u001b[A\n", - "Running. Resources: 3/3 CPU, 0/0 GPU, 768.0MB/1.9GB object_store_memory (pending: 0 CPU, 0 GPU): : 0.00 row [00:02, ? row/s]\n", - " \n", - "Running. Resources: 3/3 CPU, 0/0 GPU, 768.0MB/1.9GB object_store_memory (pending: 0 CPU, 0 GPU): : 0.00 row [00:03, ? row/s]\n", - "- ReadResultSetDataSource: 3 active, 197 queued 🚧, [cpu: 3.0, objects: 768.0MB]: : 0.00 row [00:03, ? row/s]\u001b[A\n", - " \n", - "✔️ Dataset execution finished in 3.40 seconds: : 0.00 row [00:03, ? row/s] \n", - "\n", - "- ReadResultSetDataSource: 3 active, 197 queued 🚧, [cpu: 3.0, objects: 768.0MB]: : 0.00 row [00:03, ? row/s]\u001b[A\n", - "- ReadResultSetDataSource: 3 active, 196 queued 🚧, [cpu: 3.0, objects: 384.0MB]: : 0.00 row [00:03, ? row/s]\u001b[A\n", - "- ReadResultSetDataSource: 3 active, 196 queued 🚧, [cpu: 3.0, objects: 384.0MB]: : 0.00 row [00:03, ? row/s]\u001b[A\n", - "- ReadResultSetDataSource: 3 active, 196 queued 🚧, [cpu: 3.0, objects: 384.0MB]: : 0.00 row [00:03, ? row/s]\u001b[A\n", - "- ReadResultSetDataSource: 3 active, 196 queued 🚧, [cpu: 3.0, objects: 384.0MB]: : 0.00 row [00:03, ? row/s]\n", - "2025-04-24 23:53:30,177\tINFO streaming_executor.py:108 -- Starting execution of Dataset. Full logs are in /tmp/ray/session_2025-04-24_23-53-18_742719_43/logs/ray-data\n", - "2025-04-24 23:53:30,177\tINFO streaming_executor.py:109 -- Execution plan of Dataset: InputDataBuffer[Input] -> TaskPoolMapOperator[ReadResultSetDataSource]\n", - "Running 0: 0.00 row [00:00, ? row/s]\n", - "Running. Resources: 2/3 CPU, 0/0 GPU, 7.9MB/1.9GB object_store_memory (pending: 0 CPU, 0 GPU): 4%|▍ | 774k/19.3M [00:01<00:25, 729k row/s]\n", - "- ReadResultSetDataSource: 3 active, 188 queued 🚧, [cpu: 3.0, objects: 11.8MB]: : 0.00 row [00:01, ? row/s]\u001b[A\n", - "- ReadResultSetDataSource: 3 active, 188 queued 🚧, [cpu: 3.0, objects: 11.8MB]: 0%| | 0.00/22.7M [00:01 TaskPoolMapOperator[ReadResultSetDataSource]\n", + "\u001b[36m(ReadResultSetDataSource->SplitBlocks(34) pid=315)\u001b[0m /opt/conda/lib/python3.10/site-packages/snowflake/snowpark/session.py:38: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + "\u001b[36m(ReadResultSetDataSource->SplitBlocks(34) pid=315)\u001b[0m import pkg_resources\n", + "\u001b[36m(ReadResultSetDataSource->SplitBlocks(34) pid=316)\u001b[0m SnowflakeLoginOptions() is in private preview since 0.2.0. Do not use it in production. \n", + "Loading data...Info - 2025-06-24 21:02:22.501542 - Loading data from Snowpark Dataframe from query id 01bd404e-0004-bd5b-0001-3b8704ce776a\n", + "Info - 2025-06-24 21:02:22.687591 - Finished executing data load query.\n", + "Info - 2025-06-24 21:02:24.128130 - Loaded data into ray dataset.\n", + "Info - 2025-06-24 21:02:24.129586 - Loading data into a pandas dataframe.\n", + " done! Loaded 100000 rows, elapsed=6.918s\n", + "Training model... done! Elapsed=0.501s\n", + "Evaluating model... done! Elapsed=0.075s\n", "\n", "Model Performance Metrics:\n", - "Accuracy: 0.4999\n", - "ROC AUC: 0.5002\n", - "Saving model to disk... done! Elapsed=0.526s\n", - "User job completed. Signaling workers to shut down...\n", - "2025-04-24 23:54:32,173\tINFO worker.py:1601 -- Connecting to existing Ray cluster at address: 10.244.64.74:12001...\n", - "2025-04-24 23:54:32,182\tINFO worker.py:1777 -- Connected to Ray cluster. View the dashboard at \u001b[1m\u001b[32m10.244.64.74:12003 \u001b[39m\u001b[22m\n", - "2025-04-24 23:54:32,201 - INFO - No active worker nodes found\n", - "2025-04-24 23:54:32,201 - INFO - No active worker nodes found to signal.\n", - "Head node job completed. Exiting.\n", - "\n" + "Accuracy: 0.5016\n", + "ROC AUC: 0.4986\n", + "Saving model to disk... done! Elapsed=0.339s\n" ] } ], @@ -609,36 +530,237 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "------------------------------------------------------------------------------------------------------------------------\n", - "|\"id\" |\"owner\" |\"status\" |\"created_on\" |\"compute_pool\" |\n", - "------------------------------------------------------------------------------------------------------------------------\n", - "|MLJOB_2B691E83_2D52_49D1_ACC9_DB0C951017FB |ENGINEER |RUNNING |2025-04-24 16:55:48.851000-07:00 |DEMO_POOL_CPU |\n", - "|MLJOB_B6B11A30_EAC9_4BBB_A41F_B47827C8013A |ENGINEER |RUNNING |2025-04-24 16:55:43.606000-07:00 |DEMO_POOL_CPU |\n", - "|MLJOB_75B596F9_5674_4636_B9F9_67E036E67DEA |ENGINEER |RUNNING |2025-04-24 16:55:32.928000-07:00 |DEMO_POOL_CPU |\n", - "|MLJOB_56239D4C_F6FA_4691_8ABC_FCA5BA8E58B6 |ENGINEER |DONE |2025-04-24 16:55:27.958000-07:00 |DEMO_POOL_CPU |\n", - "|MLJOB_6A0DB4D4_29F8_4CFB_BD46_9FB0966CBC5F |ENGINEER |DONE |2025-04-24 16:55:17.525000-07:00 |DEMO_POOL_CPU |\n", - "|MLJOB_BF15DA6D_8192_4831_A92F_A33483A0E8FB |ENGINEER |DONE |2025-04-08 17:19:39.188000-07:00 |DEMO_POOL_CPU |\n", - "|MLJOB_11E30859_AC22_4F1B_A474_0354370924EF |ENGINEER |DONE |2025-04-08 17:14:12.860000-07:00 |DEMO_POOL_CPU |\n", - "|MLJOB_BD8AC0A7_5417_4CCB_A8F7_0755D8E8181D |ENGINEER |DONE |2025-04-08 17:10:24.612000-07:00 |DEMO_POOL_CPU |\n", - "|MLJOB_8C8CA499_1608_4DA2_8540_A2E2F6B2D8EC |ENGINEER |DONE |2025-04-08 17:00:21.249000-07:00 |DEMO_POOL_CPU |\n", - "|MLJOB_86ADF76C_5797_4EDC_9130_B5167FD5537B |ENGINEER |DONE |2025-04-08 16:54:16.596000-07:00 |DEMO_POOL_CPU |\n", - "------------------------------------------------------------------------------------------------------------------------\n", - "\n" - ] + "data": { + "text/html": [ + "
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9MLJOB_837589B6_6D0F_4AC2_89B5_97622C8CCA14DONEJob completed successfully.HEADLESS_STARTER_DBHEADLESS_DEMOSYSADMINDEMO_POOL_CPU12025-06-24 14:02:48.254000-07:002025-06-24 14:03:31.750000-07:00
\n", + "
" + ], + "text/plain": [ + " name status \\\n", + "0 MLJOB_09C3A7E9_531D_4706_8DEB_7F628935174D PENDING \n", + "1 MLJOB_E16CE37E_7DD4_4BDC_96F8_A27BBD2F694E PENDING \n", + "2 MLJOB_BA598B1E_779A_432E_940A_9537546473A7 PENDING \n", + "3 MLJOB_7114313F_2712_413F_B2C6_B24412F808BD PENDING \n", + "4 MLJOB_19749338_0A76_4BF7_9DBA_7CB4C95A8E8B RUNNING \n", + "5 MLJOB_4954F057_5D52_474A_8618_FDD7D5AEDF25 PENDING \n", + "6 MLJOB_E4F1FF2F_90A4_4C6F_B8F3_EDE0C71A0F05 PENDING \n", + "7 MLJOB_58BC4035_78BB_448F_AE03_38CFBFB1D734 PENDING \n", + "8 MLJOB_4BC196AD_D99B_49A3_98EB_13D55A174F95 DONE \n", + "9 MLJOB_837589B6_6D0F_4AC2_89B5_97622C8CCA14 DONE \n", + "\n", + " message database_name schema_name owner \\\n", + "0 HEADLESS_STARTER_DB HEADLESS_DEMO SYSADMIN \n", + "1 HEADLESS_STARTER_DB HEADLESS_DEMO SYSADMIN \n", + "2 HEADLESS_STARTER_DB HEADLESS_DEMO SYSADMIN \n", + "3 HEADLESS_STARTER_DB HEADLESS_DEMO SYSADMIN \n", + "4 HEADLESS_STARTER_DB HEADLESS_DEMO SYSADMIN \n", + "5 HEADLESS_STARTER_DB HEADLESS_DEMO SYSADMIN \n", + "6 HEADLESS_STARTER_DB HEADLESS_DEMO SYSADMIN \n", + "7 HEADLESS_STARTER_DB HEADLESS_DEMO SYSADMIN \n", + "8 Job completed successfully. HEADLESS_STARTER_DB HEADLESS_DEMO SYSADMIN \n", + "9 Job completed successfully. HEADLESS_STARTER_DB HEADLESS_DEMO SYSADMIN \n", + "\n", + " compute_pool target_instances created_time \\\n", + "0 DEMO_POOL_CPU 1 2025-06-24 14:03:40.897000-07:00 \n", + "1 DEMO_POOL_CPU 1 2025-06-24 14:03:35.318000-07:00 \n", + "2 DEMO_POOL_CPU 1 2025-06-24 14:03:29.767000-07:00 \n", + "3 DEMO_POOL_CPU 1 2025-06-24 14:03:24.145000-07:00 \n", + "4 DEMO_POOL_CPU 1 2025-06-24 14:03:18.936000-07:00 \n", + "5 DEMO_POOL_CPU 1 2025-06-24 14:03:09.669000-07:00 \n", + "6 DEMO_POOL_CPU 1 2025-06-24 14:03:04.633000-07:00 \n", + "7 DEMO_POOL_CPU 1 2025-06-24 14:02:59.467000-07:00 \n", + "8 DEMO_POOL_CPU 1 2025-06-24 14:02:53.670000-07:00 \n", + "9 DEMO_POOL_CPU 1 2025-06-24 14:02:48.254000-07:00 \n", + "\n", + " completed_time \n", + "0 NaT \n", + "1 NaT \n", + "2 NaT \n", + "3 NaT \n", + "4 NaT \n", + "5 NaT \n", + "6 NaT \n", + "7 NaT \n", + "8 2025-06-24 14:03:35.249000-07:00 \n", + "9 2025-06-24 14:03:31.750000-07:00 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "from snowflake.ml.jobs import list_jobs\n", "\n", - "list_jobs().show()" + "list_jobs()" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "21e6f178-e80d-4101-82f1-a4287aac4021", "metadata": {}, "outputs": [], @@ -649,9 +771,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.10", "language": "python", - "name": "python3" + "name": "py310" }, "language_info": { "codemirror_mode": { @@ -663,7 +785,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.15" + "version": "3.10.11" } }, "nbformat": 4,