{ "cells": [ { "cell_type": "code", "execution_count": 40, "id": "d615adff-21b2-4f86-9498-26f7fc6588a9", "metadata": { "scrolled": true, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: boto3 in /opt/conda/lib/python3.7/site-packages (1.26.111)\n", "Requirement already satisfied: s3transfer<0.7.0,>=0.6.0 in /opt/conda/lib/python3.7/site-packages (from boto3) (0.6.0)\n", "Requirement already satisfied: botocore<1.30.0,>=1.29.111 in /opt/conda/lib/python3.7/site-packages (from boto3) (1.29.111)\n", "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/conda/lib/python3.7/site-packages (from boto3) (1.0.1)\n", "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /opt/conda/lib/python3.7/site-packages (from botocore<1.30.0,>=1.29.111->boto3) (2.8.2)\n", "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /opt/conda/lib/python3.7/site-packages (from botocore<1.30.0,>=1.29.111->boto3) (1.26.15)\n", "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.30.0,>=1.29.111->boto3) (1.14.0)\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Requirement already satisfied: sagemaker in /opt/conda/lib/python3.7/site-packages (2.146.1)\n", "Requirement already satisfied: protobuf<4.0,>=3.1 in /opt/conda/lib/python3.7/site-packages (from sagemaker) (3.20.3)\n", "Requirement already satisfied: schema in /opt/conda/lib/python3.7/site-packages (from sagemaker) (0.7.5)\n", "Requirement already satisfied: attrs<23,>=20.3.0 in /opt/conda/lib/python3.7/site-packages (from sagemaker) (22.2.0)\n", "Requirement already satisfied: google-pasta in /opt/conda/lib/python3.7/site-packages (from sagemaker) (0.2.0)\n", "Requirement already satisfied: numpy<2.0,>=1.9.0 in /opt/conda/lib/python3.7/site-packages (from sagemaker) (1.21.6)\n", "Requirement already satisfied: boto3<2.0,>=1.26.28 in /opt/conda/lib/python3.7/site-packages (from sagemaker) (1.26.111)\n", "Requirement already satisfied: jsonschema in /opt/conda/lib/python3.7/site-packages (from sagemaker) (3.2.0)\n", "Requirement already satisfied: platformdirs in /opt/conda/lib/python3.7/site-packages (from sagemaker) (3.2.0)\n", "Requirement already satisfied: smdebug-rulesconfig==1.0.1 in /opt/conda/lib/python3.7/site-packages (from sagemaker) (1.0.1)\n", "Requirement already satisfied: protobuf3-to-dict<1.0,>=0.1.5 in /opt/conda/lib/python3.7/site-packages (from sagemaker) (0.1.5)\n", "Requirement already satisfied: importlib-metadata<5.0,>=1.4.0 in /opt/conda/lib/python3.7/site-packages (from sagemaker) (4.13.0)\n", "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from sagemaker) (23.1)\n", "Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from sagemaker) (1.3.5)\n", "Requirement already satisfied: pathos in /opt/conda/lib/python3.7/site-packages (from sagemaker) (0.3.0)\n", "Requirement already satisfied: PyYAML==5.4.1 in /opt/conda/lib/python3.7/site-packages (from sagemaker) (5.4.1)\n", "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/conda/lib/python3.7/site-packages (from boto3<2.0,>=1.26.28->sagemaker) (1.0.1)\n", "Requirement already satisfied: s3transfer<0.7.0,>=0.6.0 in /opt/conda/lib/python3.7/site-packages (from boto3<2.0,>=1.26.28->sagemaker) (0.6.0)\n", "Requirement already satisfied: botocore<1.30.0,>=1.29.111 in /opt/conda/lib/python3.7/site-packages (from boto3<2.0,>=1.26.28->sagemaker) (1.29.111)\n", "Requirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata<5.0,>=1.4.0->sagemaker) (4.5.0)\n", "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata<5.0,>=1.4.0->sagemaker) (3.15.0)\n", "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from protobuf3-to-dict<1.0,>=0.1.5->sagemaker) (1.14.0)\n", "Requirement already satisfied: pyrsistent>=0.14.0 in /opt/conda/lib/python3.7/site-packages (from jsonschema->sagemaker) (0.15.7)\n", "Requirement already satisfied: setuptools in /opt/conda/lib/python3.7/site-packages (from jsonschema->sagemaker) (59.3.0)\n", "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas->sagemaker) (2019.3)\n", "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas->sagemaker) (2.8.2)\n", "Requirement already satisfied: pox>=0.3.2 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker) (0.3.2)\n", "Requirement already satisfied: dill>=0.3.6 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker) (0.3.6)\n", "Requirement already satisfied: ppft>=1.7.6.6 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker) (1.7.6.6)\n", "Requirement already satisfied: multiprocess>=0.70.14 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker) (0.70.14)\n", "Requirement already satisfied: contextlib2>=0.5.5 in /opt/conda/lib/python3.7/site-packages (from schema->sagemaker) (0.6.0.post1)\n", "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /opt/conda/lib/python3.7/site-packages (from botocore<1.30.0,>=1.29.111->boto3<2.0,>=1.26.28->sagemaker) (1.26.15)\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" ] } ], "source": [ "!pip install boto3\n", "!pip install -U sagemaker" ] }, { "cell_type": "code", "execution_count": 41, "id": "c4ab1da9", "metadata": { "tags": [] }, "outputs": [], "source": [ "import sagemaker\n", "from sagemaker.pytorch import PyTorch\n", "import boto3" ] }, { "cell_type": "code", "execution_count": 42, "id": "520c8810", "metadata": { "tags": [] }, "outputs": [], "source": [ "s3_client = boto3.client(\"s3\")\n", "sess = sagemaker.session.Session()\n", "role = sagemaker.get_execution_role()\n", "bucket = sess.default_bucket()\n", "key_prefix = \"vjain-ray-data\"" ] }, { "cell_type": "code", "execution_count": 20, "id": "3aa0face-94c3-42f8-a0f9-fc0f3582ce86", "metadata": { "tags": [] }, "outputs": [], "source": [ "# !wget --no-check-certificate --no-proxy 'http://anonymous@m5-benchmarks.s3.amazonaws.com/data/train/target.parquet'" ] }, { "cell_type": "code", "execution_count": 43, "id": "cad5369f", "metadata": { "tags": [] }, "outputs": [], "source": [ "input_data = sess.upload_data(\"target.parquet\", bucket, key_prefix=f\"{key_prefix}/input\")" ] }, { "cell_type": "code", "execution_count": 44, "id": "dc1bf8ea", "metadata": { "tags": [] }, "outputs": [], "source": [ "subnets=None\n", "security_group_ids=None" ] }, { "cell_type": "code", "execution_count": 46, "id": "9adec521", "metadata": {}, "outputs": [], "source": [ "# Pytorch Image is used to enable distributed GPU training\n", "estimator_gpu = PyTorch(\n", " source_dir=\"src-3.0.0-dev\",\n", " entry_point=\"train_automl_for_time_series.py\",\n", " subnets=subnets,\n", " security_group_ids=security_group_ids,\n", " role=role,\n", " instance_count=1, \n", " instance_type=\"ml.g4dn.xlarge\",\n", " framework_version=\"1.13\",\n", " py_version=\"py39\",\n", " keep_alive_period_in_seconds=1800\n", ")" ] }, { "cell_type": "code", "execution_count": 51, "id": "36ce59d9", "metadata": { "scrolled": true, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:sagemaker.image_uris:image_uri is not presented, retrieving image_uri based on instance_type, framework etc.\n", "INFO:sagemaker:Creating training-job with name: pytorch-training-2023-04-18-19-28-13-653\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2023-04-18 19:28:16 Starting - Found matching resource for reuse\n", "2023-04-18 19:28:16 Downloading - Downloading input data..\u001b[34mbash: cannot set terminal process group (-1): Inappropriate 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sha256=2765c2e1078638584e5df609701a9b92e2d31bf9679889212ec36a94c65d5060\u001b[0m\n", "\u001b[34mStored in directory: /root/.cache/pip/wheels/9e/36/06/4c11e300918011376af149098621ec7ebe06d8256566d43d51\u001b[0m\n", "\u001b[34mBuilding wheel for fugue-sql-antlr (setup.py): started\u001b[0m\n", "\u001b[34mBuilding wheel for fugue-sql-antlr (setup.py): finished with status 'done'\u001b[0m\n", "\u001b[34mCreated wheel for fugue-sql-antlr: filename=fugue_sql_antlr-0.1.6-py3-none-any.whl size=158050 sha256=01187dfe1903d109dc574c7a00fe7c9dc6051e8987e93cdd16e8994a9fd72ed1\u001b[0m\n", "\u001b[34mStored in directory: /root/.cache/pip/wheels/57/b3/01/795c8a9ccaf9574fc3471b0199731c5526cf7c5e127db42a74\u001b[0m\n", "\u001b[34mSuccessfully built gpustat plotly-resampler fugue-sql-antlr\u001b[0m\n", "\u001b[34mInstalling collected packages: trace-updater, sqlglot, py-spy, opencensus-context, nvidia-ml-py, msgpack, duckdb, distlib, dash-table, dash-html-components, dash-core-components, colorful, antlr4-python3-runtime, tensorboardX, sniffio, pyzmq, pyrsistent, pyasn1-modules, prometheus-client, platformdirs, patsy, orjson, nest-asyncio, multidict, itsdangerous, h11, grpcio, googleapis-common-protos, fs, frozenlist, filelock, debugpy, comm, cachetools, blessed, async-timeout, ansi2html, aiorwlock, yarl, virtualenv, uvicorn, jupyter-core, jsonschema, gpustat, google-auth, Flask, anyio, aiosignal, triad, statsmodels, starlette, ray, jupyter-client, google-api-core, dash, aiohttp, opencensus, ipykernel, fugue-sql-antlr, fastapi, aiohttp-cors, adagio, qpd, jupyter-dash, plotly-resampler, fugue, statsforecast\u001b[0m\n", "\u001b[34mSuccessfully installed Flask-2.2.3 adagio-0.2.4 aiohttp-3.8.4 aiohttp-cors-0.7.0 aiorwlock-1.3.0 aiosignal-1.3.1 ansi2html-1.8.0 antlr4-python3-runtime-4.11.1 anyio-3.6.2 async-timeout-4.0.2 blessed-1.20.0 cachetools-5.3.0 colorful-0.5.5 comm-0.1.3 dash-2.9.3 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 debugpy-1.6.7 distlib-0.3.6 duckdb-0.7.1 fastapi-0.95.1 filelock-3.12.0 frozenlist-1.3.3 fs-2.4.16 fugue-0.8.3 fugue-sql-antlr-0.1.6 google-api-core-2.11.0 google-auth-2.17.3 googleapis-common-protos-1.59.0 gpustat-1.1 grpcio-1.54.0 h11-0.14.0 ipykernel-6.22.0 itsdangerous-2.1.2 jsonschema-4.17.3 jupyter-client-8.2.0 jupyter-core-5.3.0 jupyter-dash-0.4.2 msgpack-1.0.5 multidict-6.0.4 nest-asyncio-1.5.6 nvidia-ml-py-11.525.112 opencensus-0.11.2 opencensus-context-0.1.3 orjson-3.8.10 patsy-0.5.3 platformdirs-3.2.0 plotly-resampler-0.8.3.2 prometheus-client-0.16.0 py-spy-0.3.14 pyasn1-modules-0.2.8 pyrsistent-0.19.3 pyzmq-25.0.2 qpd-0.4.1 ray-2.3.1 sniffio-1.3.0 sqlglot-11.5.4 starlette-0.26.1 statsforecast-1.5.0 statsmodels-0.13.5 tensorboardX-2.6 trace-updater-0.0.9.1 triad-0.8.6 uvicorn-0.21.1 virtualenv-20.21.0 yarl-1.8.2\u001b[0m\n", "\u001b[34mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n", "\u001b[34m[notice] A new release of pip is available: 23.0 -> 23.1\u001b[0m\n", "\u001b[34m[notice] To update, run: pip install --upgrade pip\u001b[0m\n", "\u001b[34m2023-04-18 19:29:00,473 sagemaker-training-toolkit INFO Waiting for the process to finish and give a return code.\u001b[0m\n", "\u001b[34m2023-04-18 19:29:00,473 sagemaker-training-toolkit INFO Done waiting for a return code. Received 0 from exiting process.\u001b[0m\n", "\u001b[34m2023-04-18 19:29:00,501 sagemaker-training-toolkit INFO No Neurons detected (normal if no neurons installed)\u001b[0m\n", "\u001b[34m2023-04-18 19:29:00,536 sagemaker-training-toolkit INFO No Neurons detected (normal if no neurons installed)\u001b[0m\n", "\u001b[34m2023-04-18 19:29:00,572 sagemaker-training-toolkit INFO No Neurons detected (normal if no neurons installed)\u001b[0m\n", "\u001b[34m2023-04-18 19:29:00,583 sagemaker-training-toolkit INFO Invoking user script\u001b[0m\n", "\u001b[34mTraining Env:\u001b[0m\n", "\u001b[34m{\n", " \"additional_framework_parameters\": {},\n", " \"channel_input_dirs\": {\n", " \"train\": \"/opt/ml/input/data/train\"\n", " },\n", " \"current_host\": \"algo-1\",\n", " \"current_instance_group\": \"homogeneousCluster\",\n", " \"current_instance_group_hosts\": [\n", " \"algo-1\"\n", " ],\n", " \"current_instance_type\": \"ml.g4dn.xlarge\",\n", " \"distribution_hosts\": [],\n", " \"distribution_instance_groups\": [],\n", " \"framework_module\": \"sagemaker_pytorch_container.training:main\",\n", " \"hosts\": [\n", " \"algo-1\"\n", " ],\n", " \"hyperparameters\": {},\n", " \"input_config_dir\": \"/opt/ml/input/config\",\n", " \"input_data_config\": {\n", " \"train\": {\n", " \"TrainingInputMode\": \"File\",\n", " \"S3DistributionType\": \"FullyReplicated\",\n", " \"RecordWrapperType\": \"None\"\n", " }\n", " },\n", " \"input_dir\": \"/opt/ml/input\",\n", " \"instance_groups\": [\n", " \"homogeneousCluster\"\n", " ],\n", " \"instance_groups_dict\": {\n", " \"homogeneousCluster\": {\n", " \"instance_group_name\": \"homogeneousCluster\",\n", " \"instance_type\": \"ml.g4dn.xlarge\",\n", " \"hosts\": [\n", " \"algo-1\"\n", " ]\n", " }\n", " },\n", " \"is_hetero\": false,\n", " \"is_master\": true,\n", " \"is_modelparallel_enabled\": null,\n", " \"is_smddpmprun_installed\": true,\n", " \"job_name\": \"pytorch-training-2023-04-18-19-28-13-653\",\n", " \"log_level\": 20,\n", " \"master_hostname\": \"algo-1\",\n", " \"model_dir\": \"/opt/ml/model\",\n", " \"module_dir\": \"s3://sagemaker-us-east-2-850751315356/pytorch-training-2023-04-18-19-28-13-653/source/sourcedir.tar.gz\",\n", " \"module_name\": \"train_automl_for_time_series\",\n", " \"network_interface_name\": \"eth0\",\n", " \"num_cpus\": 4,\n", " \"num_gpus\": 1,\n", " \"num_neurons\": 0,\n", " \"output_data_dir\": \"/opt/ml/output/data\",\n", " \"output_dir\": \"/opt/ml/output\",\n", " \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n", " \"resource_config\": {\n", " \"current_host\": \"algo-1\",\n", " \"current_instance_type\": \"ml.g4dn.xlarge\",\n", " \"current_group_name\": \"homogeneousCluster\",\n", " \"hosts\": [\n", " \"algo-1\"\n", " ],\n", " \"instance_groups\": [\n", " {\n", " \"instance_group_name\": \"homogeneousCluster\",\n", " \"instance_type\": \"ml.g4dn.xlarge\",\n", " \"hosts\": [\n", " \"algo-1\"\n", " ]\n", " }\n", " ],\n", " \"network_interface_name\": \"eth0\"\n", " },\n", " \"user_entry_point\": \"train_automl_for_time_series.py\"\u001b[0m\n", "\u001b[34m}\u001b[0m\n", "\u001b[34mEnvironment variables:\u001b[0m\n", "\u001b[34mSM_HOSTS=[\"algo-1\"]\u001b[0m\n", "\u001b[34mSM_NETWORK_INTERFACE_NAME=eth0\u001b[0m\n", "\u001b[34mSM_HPS={}\u001b[0m\n", "\u001b[34mSM_USER_ENTRY_POINT=train_automl_for_time_series.py\u001b[0m\n", "\u001b[34mSM_FRAMEWORK_PARAMS={}\u001b[0m\n", "\u001b[34mSM_RESOURCE_CONFIG={\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.g4dn.xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.g4dn.xlarge\"}],\"network_interface_name\":\"eth0\"}\u001b[0m\n", "\u001b[34mSM_INPUT_DATA_CONFIG={\"train\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}}\u001b[0m\n", "\u001b[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n", "\u001b[34mSM_CHANNELS=[\"train\"]\u001b[0m\n", "\u001b[34mSM_CURRENT_HOST=algo-1\u001b[0m\n", "\u001b[34mSM_CURRENT_INSTANCE_TYPE=ml.g4dn.xlarge\u001b[0m\n", "\u001b[34mSM_CURRENT_INSTANCE_GROUP=homogeneousCluster\u001b[0m\n", "\u001b[34mSM_CURRENT_INSTANCE_GROUP_HOSTS=[\"algo-1\"]\u001b[0m\n", "\u001b[34mSM_INSTANCE_GROUPS=[\"homogeneousCluster\"]\u001b[0m\n", "\u001b[34mSM_INSTANCE_GROUPS_DICT={\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.g4dn.xlarge\"}}\u001b[0m\n", "\u001b[34mSM_DISTRIBUTION_INSTANCE_GROUPS=[]\u001b[0m\n", "\u001b[34mSM_IS_HETERO=false\u001b[0m\n", "\u001b[34mSM_MODULE_NAME=train_automl_for_time_series\u001b[0m\n", "\u001b[34mSM_LOG_LEVEL=20\u001b[0m\n", "\u001b[34mSM_FRAMEWORK_MODULE=sagemaker_pytorch_container.training:main\u001b[0m\n", "\u001b[34mSM_INPUT_DIR=/opt/ml/input\u001b[0m\n", "\u001b[34mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\u001b[0m\n", "\u001b[34mSM_OUTPUT_DIR=/opt/ml/output\u001b[0m\n", "\u001b[34mSM_NUM_CPUS=4\u001b[0m\n", "\u001b[34mSM_NUM_GPUS=1\u001b[0m\n", "\u001b[34mSM_NUM_NEURONS=0\u001b[0m\n", "\u001b[34mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n", "\u001b[34mSM_MODULE_DIR=s3://sagemaker-us-east-2-850751315356/pytorch-training-2023-04-18-19-28-13-653/source/sourcedir.tar.gz\u001b[0m\n", "\u001b[34mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"train\":\"/opt/ml/input/data/train\"},\"current_host\":\"algo-1\",\"current_instance_group\":\"homogeneousCluster\",\"current_instance_group_hosts\":[\"algo-1\"],\"current_instance_type\":\"ml.g4dn.xlarge\",\"distribution_hosts\":[],\"distribution_instance_groups\":[],\"framework_module\":\"sagemaker_pytorch_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"train\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}},\"input_dir\":\"/opt/ml/input\",\"instance_groups\":[\"homogeneousCluster\"],\"instance_groups_dict\":{\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.g4dn.xlarge\"}},\"is_hetero\":false,\"is_master\":true,\"is_modelparallel_enabled\":null,\"is_smddpmprun_installed\":true,\"job_name\":\"pytorch-training-2023-04-18-19-28-13-653\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"s3://sagemaker-us-east-2-850751315356/pytorch-training-2023-04-18-19-28-13-653/source/sourcedir.tar.gz\",\"module_name\":\"train_automl_for_time_series\",\"network_interface_name\":\"eth0\",\"num_cpus\":4,\"num_gpus\":1,\"num_neurons\":0,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.g4dn.xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.g4dn.xlarge\"}],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"train_automl_for_time_series.py\"}\u001b[0m\n", "\u001b[34mSM_USER_ARGS=[]\u001b[0m\n", "\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n", "\u001b[34mSM_CHANNEL_TRAIN=/opt/ml/input/data/train\u001b[0m\n", "\u001b[34mPYTHONPATH=/opt/ml/code:/opt/conda/bin:/opt/conda/lib/python39.zip:/opt/conda/lib/python3.9:/opt/conda/lib/python3.9/lib-dynload:/opt/conda/lib/python3.9/site-packages\u001b[0m\n", "\u001b[34mInvoking script with the following command:\u001b[0m\n", "\u001b[34m/opt/conda/bin/python3.9 train_automl_for_time_series.py\u001b[0m\n", "\u001b[34m2023-04-18 19:29:01,896 root INFO Using NamedTuple = typing._NamedTuple instead.\u001b[0m\n", "\u001b[34m2023-04-18 19:29:02,045 sagemaker-training-toolkit INFO Exceptions not imported for SageMaker TF as Tensorflow is not installed.\u001b[0m\n", "\u001b[34mRequirement already satisfied: pip in /opt/conda/lib/python3.9/site-packages (23.0)\u001b[0m\n", "\u001b[34mCollecting pip\u001b[0m\n", "\u001b[34mDownloading pip-23.1-py3-none-any.whl (2.1 MB)\u001b[0m\n", "\u001b[34m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1/2.1 MB 56.9 MB/s eta 0:00:00\u001b[0m\n", "\u001b[34mInstalling collected packages: pip\u001b[0m\n", "\u001b[34mAttempting uninstall: pip\u001b[0m\n", "\u001b[34mFound existing installation: pip 23.0\u001b[0m\n", "\u001b[34mUninstalling pip-23.0:\u001b[0m\n", "\u001b[34mSuccessfully uninstalled pip-23.0\u001b[0m\n", "\u001b[34mSuccessfully installed pip-23.1\u001b[0m\n", "\u001b[34mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n", "\u001b[34m2023-04-18 19:29:09,332#011INFO usage_lib.py:461 -- Usage stats collection is enabled by default without user confirmation because this terminal is detected to be non-interactive. To disable this, add `--disable-usage-stats` to the command that starts the cluster, or run the following command: `ray disable-usage-stats` before starting the cluster. See https://docs.ray.io/en/master/cluster/usage-stats.html for more details.\u001b[0m\n", "\u001b[34m2023-04-18 19:29:09,332#011INFO scripts.py:710 -- #033[37mLocal node IP#033[39m: #033[1m10.0.137.17#033[22m\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,424#011SUCC scripts.py:747 -- #033[32m--------------------#033[39m\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,424#011SUCC scripts.py:748 -- #033[32mRay runtime started.#033[39m\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,424#011SUCC scripts.py:749 -- #033[32m--------------------#033[39m\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,424#011INFO scripts.py:751 -- #033[36mNext steps#033[39m\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,424#011INFO scripts.py:752 -- To connect to this Ray runtime from another node, run\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,424#011INFO scripts.py:755 -- #033[1m ray start --address='10.0.137.17:9339'#033[22m\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,424#011INFO scripts.py:771 -- Alternatively, use the following Python code:\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,424#011INFO scripts.py:773 -- #033[35mimport#033[39m#033[26m ray\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,424#011INFO scripts.py:777 -- ray#033[35m.#033[39m#033[26minit(address#033[35m=#033[39m#033[26m#033[33m'auto'#033[39m#033[26m)\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,424#011INFO scripts.py:790 -- To see the status of the cluster, use\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,424#011INFO scripts.py:791 -- #033[1mray status#033[22m#033[26m\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,425#011INFO scripts.py:801 -- #033[4mIf connection fails, check your firewall settings and network configuration.#033[24m\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,425#011INFO scripts.py:809 -- To terminate the Ray runtime, run\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,425#011INFO scripts.py:810 -- #033[1m ray stop#033[22m\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,779#011INFO worker.py:1364 -- Connecting to existing Ray cluster at address: 10.0.137.17:9339...\u001b[0m\n", "\u001b[34m2023-04-18 19:29:11,792#011INFO worker.py:1553 -- Connected to Ray cluster.\u001b[0m\n", "\u001b[34mWaiting 60 seconds for 1 nodes to join\u001b[0m\n", "\u001b[34mAll workers present and accounted for\u001b[0m\n", "\u001b[34m{'node:10.0.137.17': 1.0, 'object_store_memory': 4000432128.0, 'memory': 8000864256.0, 'GPU': 1.0, 'CPU': 4.0}\u001b[0m\n", "\u001b[34m== Status ==\u001b[0m\n", "\u001b[34mCurrent time: 2023-04-18 19:29:19 (running for 00:00:04.32)\u001b[0m\n", "\u001b[34mMemory usage on this node: 2.7/15.3 GiB \u001b[0m\n", "\u001b[34mUsing FIFO scheduling algorithm.\u001b[0m\n", "\u001b[34mResources requested: 1.0/4 CPUs, 0/1 GPUs, 0.0/7.45 GiB heap, 0.0/3.73 GiB objects\u001b[0m\n", "\u001b[34mResult logdir: /root/ray_results/cross_validation_2023-04-18_19-29-14\u001b[0m\n", "\u001b[34mNumber of trials: 5/5 (4 PENDING, 1 RUNNING)\u001b[0m\n", "\u001b[34m+------------------------------+----------+-----------------+------------------------+\u001b[0m\n", "\u001b[34m| Trial name | status | loc | model_cls_and_params |\u001b[0m\n", "\u001b[34m|------------------------------+----------+-----------------+------------------------|\u001b[0m\n", "\u001b[34m| cross_validation_4d662_00000 | RUNNING | 10.0.137.17:600 | (, {})}\u001b[0m\n", "\u001b[34m== Status ==\u001b[0m\n", "\u001b[34mCurrent time: 2023-04-18 19:29:50 (running for 00:00:35.12)\u001b[0m\n", "\u001b[34mMemory usage on this node: 3.5/15.3 GiB \u001b[0m\n", "\u001b[34mUsing FIFO scheduling algorithm.\u001b[0m\n", "\u001b[34mResources requested: 4.0/4 CPUs, 0/1 GPUs, 0.0/7.45 GiB heap, 0.0/3.73 GiB objects\u001b[0m\n", "\u001b[34mCurrent best trial: 4d662_00000 with mse_mean=0.6587367653846741 and parameters={'model_cls_and_params': (, {}), 'n_splits': 5, 'test_size': 1, 'freq': 'D', 'metrics': {'mse': , 'mae': }}\u001b[0m\n", "\u001b[34mResult logdir: /root/ray_results/cross_validation_2023-04-18_19-29-14\u001b[0m\n", "\u001b[34mNumber of trials: 5/5 (4 RUNNING, 1 TERMINATED)\u001b[0m\n", "\u001b[34m+------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------+\u001b[0m\n", "\u001b[34m| Trial name | status | loc | model_cls_and_params | iter | total time (s) | mse_mean | mse_std | mae_mean |\u001b[0m\n", "\u001b[34m|------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------|\u001b[0m\n", "\u001b[34m| cross_validation_4d662_00001 | RUNNING | 10.0.137.17:691 | (, {}), 'n_splits': 5, 'test_size': 1, 'freq': 'D', 'metrics': {'mse': , 'mae': }}\u001b[0m\n", "\u001b[34mResult logdir: /root/ray_results/cross_validation_2023-04-18_19-29-14\u001b[0m\n", "\u001b[34mNumber of trials: 5/5 (4 RUNNING, 1 TERMINATED)\u001b[0m\n", "\u001b[34m+------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------+\u001b[0m\n", "\u001b[34m| Trial name | status | loc | model_cls_and_params | iter | total time (s) | mse_mean | mse_std | mae_mean |\u001b[0m\n", "\u001b[34m|------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------|\u001b[0m\n", "\u001b[34m| cross_validation_4d662_00001 | RUNNING | 10.0.137.17:691 | (, {'season_length': 7, 'model': 'ZNA'})}\u001b[0m\n", "\u001b[34mResult for cross_validation_4d662_00001:\u001b[0m\n", "\u001b[34mcutoff_values: [1463443200000000000, 1463529600000000000, 1463616000000000000, 1463702400000000000,\n", " 1463788800000000000]\n", " cv_time: 35.34237718582153\n", " date: 2023-04-18_19-30-00\n", " done: false\n", " experiment_id: 5b85dfd7d5bd455d86cd99b3284c8e6c\n", " hostname: ip-10-0-137-17.us-east-2.compute.internal\n", " iterations_since_restore: 1\n", " mae_mean: 0.7200614809989929\n", " mae_std: 0.3515159785747528\n", " mse_mean: 0.6420520544052124\n", " mse_std: 0.34351789951324463\n", " node_ip: 10.0.137.17\n", " pid: 691\n", " should_checkpoint: true\n", " time_since_restore: 35.36403155326843\n", " time_this_iter_s: 35.36403155326843\n", " time_total_s: 35.36403155326843\n", " timestamp: 1681846200\n", " timesteps_since_restore: 0\n", " training_iteration: 1\n", " trial_id: 4d662_00001\n", " unique_ids:\n", " - FOODS_1_001_CA_1\n", " warmup_time: 0.0037910938262939453\u001b[0m\n", "\u001b[34mTrial cross_validation_4d662_00001 completed.\u001b[0m\n", "\u001b[34m2023-04-18 19:30:00,310#011INFO tensorboardx.py:267 -- Removed the following hyperparameter values when logging to tensorboard: {'model_cls_and_params': (, {'season_length': 6, 'model': 'ZNA'})}\u001b[0m\n", "\u001b[34m== Status ==\u001b[0m\n", "\u001b[34mCurrent time: 2023-04-18 19:30:05 (running for 00:00:50.11)\u001b[0m\n", "\u001b[34mMemory usage on this node: 3.0/15.3 GiB \u001b[0m\n", "\u001b[34mUsing FIFO scheduling algorithm.\u001b[0m\n", "\u001b[34mResources requested: 2.0/4 CPUs, 0/1 GPUs, 0.0/7.45 GiB heap, 0.0/3.73 GiB objects\u001b[0m\n", "\u001b[34mCurrent best trial: 4d662_00001 with mse_mean=0.6420520544052124 and parameters={'model_cls_and_params': (, {'season_length': 6, 'model': 'ZNA'}), 'n_splits': 5, 'test_size': 1, 'freq': 'D', 'metrics': {'mse': , 'mae': }}\u001b[0m\n", "\u001b[34mResult logdir: /root/ray_results/cross_validation_2023-04-18_19-29-14\u001b[0m\n", "\u001b[34mNumber of trials: 5/5 (2 RUNNING, 3 TERMINATED)\u001b[0m\n", "\u001b[34m+------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------+\u001b[0m\n", "\u001b[34m| Trial name | status | loc | model_cls_and_params | iter | total time (s) | mse_mean | mse_std | mae_mean |\u001b[0m\n", "\u001b[34m|------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------|\u001b[0m\n", "\u001b[34m| cross_validation_4d662_00002 | RUNNING | 10.0.137.17:692 | (, {'season_length': 6, 'model': 'ZNA'}), 'n_splits': 5, 'test_size': 1, 'freq': 'D', 'metrics': {'mse': , 'mae': }}\u001b[0m\n", "\u001b[34mResult logdir: /root/ray_results/cross_validation_2023-04-18_19-29-14\u001b[0m\n", "\u001b[34mNumber of trials: 5/5 (2 RUNNING, 3 TERMINATED)\u001b[0m\n", "\u001b[34m+------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------+\u001b[0m\n", "\u001b[34m| Trial name | status | loc | model_cls_and_params | iter | total time (s) | mse_mean | mse_std | mae_mean |\u001b[0m\n", "\u001b[34m|------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------|\u001b[0m\n", "\u001b[34m| cross_validation_4d662_00002 | RUNNING | 10.0.137.17:692 | (, {'season_length': 6, 'model': 'ZNA'}), 'n_splits': 5, 'test_size': 1, 'freq': 'D', 'metrics': {'mse': , 'mae': }}\u001b[0m\n", "\u001b[34mResult logdir: /root/ray_results/cross_validation_2023-04-18_19-29-14\u001b[0m\n", "\u001b[34mNumber of trials: 5/5 (2 RUNNING, 3 TERMINATED)\u001b[0m\n", "\u001b[34m+------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------+\u001b[0m\n", "\u001b[34m| Trial name | status | loc | model_cls_and_params | iter | total time (s) | mse_mean | mse_std | mae_mean |\u001b[0m\n", "\u001b[34m|------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------|\u001b[0m\n", "\u001b[34m| cross_validation_4d662_00002 | RUNNING | 10.0.137.17:692 | (, {'season_length': 6, 'model': 'ZZZ'})}\u001b[0m\n", "\u001b[34m== Status ==\u001b[0m\n", "\u001b[34mCurrent time: 2023-04-18 19:30:24 (running for 00:01:09.52)\u001b[0m\n", "\u001b[34mMemory usage on this node: 2.7/15.3 GiB \u001b[0m\n", "\u001b[34mUsing FIFO scheduling algorithm.\u001b[0m\n", "\u001b[34mResources requested: 1.0/4 CPUs, 0/1 GPUs, 0.0/7.45 GiB heap, 0.0/3.73 GiB objects\u001b[0m\n", "\u001b[34mCurrent best trial: 4d662_00001 with mse_mean=0.6420520544052124 and parameters={'model_cls_and_params': (, {'season_length': 6, 'model': 'ZNA'}), 'n_splits': 5, 'test_size': 1, 'freq': 'D', 'metrics': {'mse': , 'mae': }}\u001b[0m\n", "\u001b[34mResult logdir: /root/ray_results/cross_validation_2023-04-18_19-29-14\u001b[0m\n", "\u001b[34mNumber of trials: 5/5 (1 RUNNING, 4 TERMINATED)\u001b[0m\n", "\u001b[34m+------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------+\u001b[0m\n", "\u001b[34m| Trial name | status | loc | model_cls_and_params | iter | total time (s) | mse_mean | mse_std | mae_mean |\u001b[0m\n", "\u001b[34m|------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------|\u001b[0m\n", "\u001b[34m| cross_validation_4d662_00004 | RUNNING | 10.0.137.17:600 | (, {'season_length': 6, 'model': 'ZNA'}), 'n_splits': 5, 'test_size': 1, 'freq': 'D', 'metrics': {'mse': , 'mae': }}\u001b[0m\n", "\u001b[34mResult logdir: /root/ray_results/cross_validation_2023-04-18_19-29-14\u001b[0m\n", "\u001b[34mNumber of trials: 5/5 (1 RUNNING, 4 TERMINATED)\u001b[0m\n", "\u001b[34m+------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------+\u001b[0m\n", "\u001b[34m| Trial name | status | loc | model_cls_and_params | iter | total time (s) | mse_mean | mse_std | mae_mean |\u001b[0m\n", "\u001b[34m|------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------|\u001b[0m\n", "\u001b[34m| cross_validation_4d662_00004 | RUNNING | 10.0.137.17:600 | (, {'season_length': 7, 'model': 'ZZZ'})}\u001b[0m\n", "\u001b[34m== Status ==\u001b[0m\n", "\u001b[34mCurrent time: 2023-04-18 19:30:31 (running for 00:01:16.09)\u001b[0m\n", "\u001b[34mMemory usage on this node: 2.7/15.3 GiB \u001b[0m\n", "\u001b[34mUsing FIFO scheduling algorithm.\u001b[0m\n", "\u001b[34mResources requested: 0/4 CPUs, 0/1 GPUs, 0.0/7.45 GiB heap, 0.0/3.73 GiB objects\u001b[0m\n", "\u001b[34mCurrent best trial: 4d662_00001 with mse_mean=0.6420520544052124 and parameters={'model_cls_and_params': (, {'season_length': 6, 'model': 'ZNA'}), 'n_splits': 5, 'test_size': 1, 'freq': 'D', 'metrics': {'mse': , 'mae': }}\u001b[0m\n", "\u001b[34mResult logdir: /root/ray_results/cross_validation_2023-04-18_19-29-14\u001b[0m\n", "\u001b[34mNumber of trials: 5/5 (5 TERMINATED)\u001b[0m\n", "\u001b[34m+------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------+\u001b[0m\n", "\u001b[34m| Trial name | status | loc | model_cls_and_params | iter | total time (s) | mse_mean | mse_std | mae_mean |\u001b[0m\n", "\u001b[34m|------------------------------+------------+-----------------+------------------------+--------+------------------+------------+-----------+------------|\u001b[0m\n", "\u001b[34m| cross_validation_4d662_00000 | TERMINATED | 10.0.137.17:600 | (\u001b[0m\n", "\u001b[34mBest model params: {'season_length': 6, 'model': 'ZNA'}\u001b[0m\n", "\u001b[34mBest mse_mean: 0.64205205\u001b[0m\n", "\u001b[34mBest mae_mean: 0.7200615\u001b[0m\n", "\u001b[34mTOTAL TIME TAKEN: 79.51 seconds\u001b[0m\n", "\u001b[34m2023-04-18 19:30:33,102 sagemaker-training-toolkit INFO Waiting for the process to finish and give a return code.\u001b[0m\n", "\u001b[34m2023-04-18 19:30:33,103 sagemaker-training-toolkit INFO Done waiting for a return code. Received 0 from exiting process.\u001b[0m\n", "\u001b[34m2023-04-18 19:30:33,103 sagemaker-training-toolkit INFO Reporting training SUCCESS\u001b[0m\n", "\n", "2023-04-18 19:30:50 Uploading - Uploading generated training model\n", "2023-04-18 19:30:50 Completed - Resource retained for reuse\n", "Training seconds: 152\n", "Billable seconds: 152\n" ] } ], "source": [ "estimator_gpu.fit({\"train\": input_data})" ] }, { "cell_type": "code", "execution_count": null, "id": "a0794c2b-7785-4234-9f7b-c4f7cd8fb858", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "availableInstances": [ { "_defaultOrder": 0, "_isFastLaunch": true, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 4, "name": "ml.t3.medium", "vcpuNum": 2 }, { "_defaultOrder": 1, "_isFastLaunch": false, "category": "General purpose", "gpuNum": 0, "hideHardwareSpecs": false, "memoryGiB": 8, "name": "ml.t3.large", "vcpuNum": 2 }, { "_defaultOrder": 2, "_isFastLaunch": false, 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