{ "cells": [ { "cell_type": "markdown", "id": "327dd0be-8ba8-43e5-b178-eb1db8d3d40a", "metadata": {}, "source": [ "# Training \n", "* Container: codna_pytorch_p39" ] }, { "cell_type": "markdown", "id": "52ecd494-5ac0-4f66-a3cc-340e847e3c46", "metadata": {}, "source": [ "## AutoReload" ] }, { "cell_type": "code", "execution_count": 1, "id": "c07a9ee3-c178-41b6-a212-2a33f65dc377", "metadata": { "tags": [] }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "markdown", "id": "bccc9dda-9dad-4f81-8dd5-53b5623f1d93", "metadata": { "tags": [] }, "source": [ "## 1. parameter store 설정" ] }, { "cell_type": "code", "execution_count": 2, "id": "1d6e737a-557b-44c7-a5c2-f6798bf0c430", "metadata": { "tags": [] }, "outputs": [], "source": [ "import boto3\n", "from utils.ssm import parameter_store" ] }, { "cell_type": "code", "execution_count": 3, "id": "3f9894a8-4249-4fcc-9b44-d5f43f4fd867", "metadata": { "tags": [] }, "outputs": [], "source": [ "strRegionName=boto3.Session().region_name\n", "pm = parameter_store(strRegionName)\n", "prefix = pm.get_params(key=\"PREFIX\")" ] }, { "cell_type": "markdown", "id": "c0674243-6019-4e5e-8563-2fd17ca8ae95", "metadata": { "tags": [] }, "source": [ "## 2.Training-job for preprocessing" ] }, { "cell_type": "code", "execution_count": 4, "id": "e68c1bf7-336d-46b9-940e-9e5b570e1793", "metadata": { "tags": [] }, "outputs": [], "source": [ "import os\n", "import sagemaker\n", "from omegaconf import OmegaConf\n", "from sagemaker.pytorch.estimator import PyTorch\n", "from sagemaker.workflow.execution_variables import ExecutionVariables\n", "from sagemaker.processing import ProcessingInput, ProcessingOutput, FrameworkProcessor" ] }, { "cell_type": "markdown", "id": "e0e9bb6a-d48d-46f7-bb83-a47255503b6f", "metadata": {}, "source": [ "* **Set Up SageMaker Experiment**\n", " - Create or load [SageMaker Experiment](https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html) for the example training job. This will create an experiment trial object in SageMaker." ] }, { "cell_type": "code", "execution_count": 5, "id": "9612750e-acc9-418c-be4f-24ce6a79726b", "metadata": { "tags": [] }, "outputs": [], "source": [ "from time import strftime\n", "from smexperiments.trial import Trial\n", "from smexperiments.experiment import Experiment" ] }, { "cell_type": "code", "execution_count": 6, "id": "c6a12e2b-2456-4419-8c75-e95d2ca6eab4", "metadata": { "tags": [] }, "outputs": [], "source": [ "def create_experiment(experiment_name):\n", " try:\n", " sm_experiment = Experiment.load(experiment_name)\n", " except:\n", " sm_experiment = Experiment.create(experiment_name=experiment_name)" ] }, { "cell_type": "code", "execution_count": 7, "id": "489351d8-9d95-4483-af38-0bd3f9c80add", "metadata": { "tags": [] }, "outputs": [], "source": [ "def create_trial(experiment_name):\n", " create_date = strftime(\"%m%d-%H%M%s\")\n", " sm_trial = Trial.create(trial_name=f'{experiment_name}-{create_date}',\n", " experiment_name=experiment_name)\n", "\n", " job_name = f'{sm_trial.trial_name}'\n", " return job_name" ] }, { "cell_type": "markdown", "id": "332c8e85-ea66-4fbd-bdde-551400b0f872", "metadata": { "tags": [] }, "source": [ "* **Configure the training job**\n", "\n", " - Now we configure the training job, by modifying the `config.yaml` file that is stored in our source code directory.\n", " - We pass relative directory paths for the data based on the SageMaker mount directory on the remote instance." ] }, { "cell_type": "code", "execution_count": 8, "id": "16bcde0a-bcde-4ad3-a0dc-7b844ac1b733", "metadata": { "tags": [] }, "outputs": [], "source": [ "code_dir = \"./code\"\n", "config_dir = \"./code/conf\"\n", "config_path = os.path.join(config_dir, \"config.yaml\")" ] }, { "cell_type": "markdown", "id": "85e3076c-bfae-4589-963a-4f1288761e62", "metadata": {}, "source": [ "* params for training job" ] }, { "cell_type": "code", "execution_count": 19, "id": "c7460084-c1f4-472d-909c-ce723634b26b", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Set to True to enable SageMaker to run locally\n", "local_mode = False\n", "\n", "\n", "if local_mode:\n", " instance_type = \"local_gpu\"\n", " \n", " from sagemaker.local import LocalSession\n", " import os\n", " \n", " sagemaker_session = LocalSession()\n", " sagemaker_session.config = {'local': {'local_code': True}}\n", " \n", " local_data_dir = os.getcwd() + '/data/preprocessing'\n", " pretrained = os.getcwd() + '/pretrained/CTC.nemo'\n", " \n", " data_channels = {\"training\": f\"file://{local_data_dir}\", \"testing\": f\"file://{local_data_dir}\", \"pretrained\": f\"file://{pretrained}\"}\n", " \n", "else:\n", " \n", " instance_type = \"ml.g4dn.8xlarge\" #\"ml.p3.2xlarge\"#\"ml.g4dn.8xlarge\"#\"ml.p3.2xlarge\", 'ml.p3.16xlarge' , ml.g4dn.8xlarge\n", " \n", " sagemaker_session = sagemaker.Session()\n", " data_channels = {\"training\": pm.get_params(key=\"-\".join([prefix, \"PREP-DATA-PATH\"])), \n", " \"testing\": pm.get_params(key=\"-\".join([prefix, \"PREP-DATA-PATH\"])), \n", " \"pretrained\": pm.get_params(key=prefix + \"-PRETRAINED-WEIGHT\")}\n", "\n", "instance_count = 1\n", "do_spot_training = False\n", "max_wait = None\n", "max_run = 1*60*60 \n", "\n", "resume = True\n", " \n", "proc_prefix = \"/opt/ml/processing\"\n", "bucket_name = pm.get_params(key=prefix + \"-BUCKET\")\n", "\n", "output_path = os.path.join(\n", " \"s3://{}\".format(bucket_name),\n", " prefix,\n", " \"training\",\n", " \"model-output\"\n", ")\n", "\n", "code_location = os.path.join(\n", " \"s3://{}\".format(bucket_name),\n", " prefix,\n", " \"training\",\n", " \"backup_codes\"\n", ")\n", "\n", "experiment_name = '-'.join([prefix, \"nemo-experiments\"])\n", "\n", "num_re = \"([0-9\\\\.]+)(e-?[[01][0-9])?\"\n", "metric_definitions = [\n", " {\"Name\": \"train_loss\", \"Regex\": f\"loss={num_re}\"},\n", " {\"Name\": \"wer\", \"Regex\": f\"wer:{num_re}\"}\n", "]\n", "\n", "git_config = {\n", " 'repo': f'https://{pm.get_params(key=\"-\".join([prefix, \"CODE_REPO\"]))}',\n", " 'branch': 'main',\n", " 'username': pm.get_params(key=\"-\".join([prefix, \"CODECOMMIT-USERNAME\"]), enc=True),\n", " 'password': pm.get_params(key=\"-\".join([prefix, \"CODECOMMIT-PWD\"]), enc=True)\n", "} \n", "\n", "kwargs = {}" ] }, { "cell_type": "code", "execution_count": 20, "id": "26003edf-356a-4e1d-82b1-c8085b508b76", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "experiment_name : nemo-asr-nemo-experiments \n", "train_instance_type : ml.g4dn.8xlarge \n", "train_instance_count : 1\n", "data_channels : {'training': 's3://sm-nemo-ramp/nemo-asr/preprocessing/data', 'testing': 's3://sm-nemo-ramp/nemo-asr/preprocessing/data', 'pretrained': 's3://sm-nemo-ramp/nemo-asr/pretrained'}\n", "experiment_name : nemo-asr-nemo-experiments\n", " git_config : {'repo': 'https://git-codecommit.us-east-1.amazonaws.com/v1/repos/nemo-code', 'branch': 'main', 'username': 'dongjin-at-419974056037', 'password': 'wtLv/fP4ESjBDnyW5xgqFPGR0dMTIyK5/8gK6IS1Zsg='}\n" ] } ], "source": [ "print(\"experiment_name : {} \\ntrain_instance_type : {} \\ntrain_instance_count : {}\\ndata_channels : {}\\nexperiment_name : {}\\n git_config : {}\".format(experiment_name, instance_type, instance_count, data_channels, experiment_name, git_config)) " ] }, { "cell_type": "markdown", "id": "5e51e768-c740-42c6-9a74-c09a1b337405", "metadata": {}, "source": [ "* config" ] }, { "cell_type": "code", "execution_count": 21, "id": "b07e1646-aa7f-49e7-996a-d7cbf22e26c7", "metadata": { "tags": [] }, "outputs": [], "source": [ "conf = OmegaConf.load(config_path)\n", "\n", "# Sampling\n", "conf.model.sample_rate = 16000\n", "\n", "# Set Data Locations based on the mounted directory in the SageMaker instance\n", "conf.model.train_ds.manifest_filepath = \"/opt/ml/input/data/training/an4/train_manifest.json\"\n", "conf.model.validation_ds.manifest_filepath = \"/opt/ml/input/data/testing/an4/test_manifest.json\"\n", "# training setup\n", "conf.trainer.accelerator = \"gpu\"\n", "conf.trainer.num_nodes = instance_count\n", "# enable SageMaker DDP\n", "conf.trainer.strategy = None #\"ddp\"\n", "conf.trainer.max_epochs = 2\n", "\n", "# Output directory for our experiment within the SageMaker instance\n", "conf.exp_manager.exp_dir=\"/opt/ml/model/\"\n", "\n", "# Create a Small Variant of the Conformer Model\n", "conf.model.encoder.n_layers = 8\n", "conf.model.n_heads = 4\n", "conf.model.spec_augment.time_masks = 5\n", "\n", "# Set Optimizer parameters\n", "conf.model.optim.lr = 2.0 # by default we using Noam scheduling, the LR is a multiplier\n", "\n", "if resume == False:\n", " # resume flags if crashes occur\n", " conf.exp_manager.resume_if_exists=False \n", " conf.exp_manager.resume_ignore_no_checkpoint=False\n", " conf.init_from_nemo_model = None\n", " \n", "else:\n", " # resume flags if crashes occur\n", " conf.exp_manager.resume_if_exists=True\n", " conf.exp_manager.resume_ignore_no_checkpoint=True\n", " # the pre-trained model we want to fine-tune\n", " conf.init_from_nemo_model = \"/opt/ml/input/data/pretrained/CTC.nemo\"\n", " \n", "\n", "OmegaConf.save(conf, config_path)" ] }, { "cell_type": "markdown", "id": "5604a35c-4057-4a4f-b308-640a9cc54277", "metadata": {}, "source": [ "* Define processing job" ] }, { "cell_type": "code", "execution_count": 13, "id": "3b054583-4b34-4e4f-bd37-a15a11df6add", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "('419974056037.dkr.ecr.us-east-1.amazonaws.com/nemo-test-training', './code')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pm.get_params(key=''.join([prefix, \"-IMAGE-URI\"])), code_dir" ] }, { "cell_type": "code", "execution_count": 22, "id": "1576ea06-1ac4-4e11-8106-671a3f88cc21", "metadata": { "tags": [] }, "outputs": [], "source": [ "est = PyTorch(\n", " entry_point=\"speech_to_text_ctc.py\", # the script we want to run\n", " source_dir=code_dir, # where our conf/script is\n", " git_config=git_config,\n", " role=pm.get_params(key=prefix + \"-SAGEMAKER-ROLE-ARN\"),\n", " instance_type=instance_type,\n", " instance_count=instance_count,\n", " image_uri=pm.get_params(key=''.join([prefix, \"-IMAGE-URI\"])),\n", " # framework_version=\"1.13.1\", # version of PyTorch\n", " # py_version=\"py39\",\n", " volume_size=256,\n", " code_location = code_location,\n", " output_path=output_path,\n", " disable_profiler=True,\n", " debugger_hook_config=False,\n", " hyperparameters={'config-path': 'conf'},\n", " #distribution={\"smdistributed\":{\"dataparallel\":{\"enabled\":True, \"fp16\": True}}},\n", " sagemaker_session=sagemaker_session,\n", " metric_definitions=metric_definitions,\n", " max_run=max_run,\n", " enable_sagemaker_metrics=True,\n", " **kwargs\n", ")" ] }, { "cell_type": "markdown", "id": "9883bc2a-69e5-436d-aef3-67e994c1f553", "metadata": {}, "source": [ "* run" ] }, { "cell_type": "code", "execution_count": 23, "id": "8640b12b-761f-4fbd-9f09-cac4b2709d2f", "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:botocore.credentials:Found credentials from IAM Role: BaseNotebookInstanceEc2InstanceRole\n", "INFO:botocore.credentials:Found credentials from IAM Role: BaseNotebookInstanceEc2InstanceRole\n", "Cloning into '/tmp/tmplptyic8i'...\n", "remote: Counting objects: 20, done. \n", "Already on 'main'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Your branch is up to date with 'origin/main'.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:sagemaker:Creating training-job with name: nemo-asr-nemo-experiments-0322-10521679482352\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2023-03-22 10:52:46 Starting - Starting the training job...\n", "2023-03-22 10:53:00 Starting - Preparing the instances for training......\n", "2023-03-22 10:54:01 Downloading - Downloading input data...\n", "2023-03-22 10:54:26 Training - Downloading the training image..................\n", "2023-03-22 10:57:53 Training - Training image download completed. Training in progress......\u001b[34mbash: cannot set terminal process group (-1): Inappropriate ioctl for device\u001b[0m\n", "\u001b[34mbash: no job control in this shell\u001b[0m\n", "\u001b[34m2023-03-22 10:58:35,669 sagemaker-training-toolkit INFO Imported framework sagemaker_pytorch_container.training\u001b[0m\n", "\u001b[34m2023-03-22 10:58:35,689 sagemaker-training-toolkit INFO No Neurons detected (normal if no neurons installed)\u001b[0m\n", "\u001b[34m2023-03-22 10:58:35,700 sagemaker_pytorch_container.training INFO Block until all host DNS lookups succeed.\u001b[0m\n", "\u001b[34m2023-03-22 10:58:35,702 sagemaker_pytorch_container.training INFO Invoking user training script.\u001b[0m\n", "\u001b[34m2023-03-22 10:58:35,950 sagemaker-training-toolkit INFO No Neurons detected (normal if no neurons installed)\u001b[0m\n", "\u001b[34m2023-03-22 10:58:35,981 sagemaker-training-toolkit INFO No Neurons detected (normal if no neurons installed)\u001b[0m\n", "\u001b[34m2023-03-22 10:58:36,014 sagemaker-training-toolkit INFO No Neurons detected (normal if no neurons installed)\u001b[0m\n", "\u001b[34m2023-03-22 10:58:36,025 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", " \"pretrained\": \"/opt/ml/input/data/pretrained\",\n", " \"testing\": \"/opt/ml/input/data/testing\",\n", " \"training\": \"/opt/ml/input/data/training\"\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.8xlarge\",\n", " \"distribution_hosts\": [],\n", " \"distribution_instance_groups\": [],\n", " \"framework_module\": \"sagemaker_pytorch_container.training:main\",\n", " \"hosts\": [\n", " \"algo-1\"\n", " ],\n", " \"hyperparameters\": {\n", " \"config-path\": \"conf\"\n", " },\n", " \"input_config_dir\": \"/opt/ml/input/config\",\n", " \"input_data_config\": {\n", " \"pretrained\": {\n", " \"TrainingInputMode\": \"File\",\n", " \"S3DistributionType\": \"FullyReplicated\",\n", " \"RecordWrapperType\": \"None\"\n", " },\n", " \"testing\": {\n", " \"TrainingInputMode\": \"File\",\n", " \"S3DistributionType\": \"FullyReplicated\",\n", " \"RecordWrapperType\": \"None\"\n", " },\n", " \"training\": {\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.8xlarge\",\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\": \"nemo-asr-nemo-experiments-0322-10521679482352\",\n", " \"log_level\": 20,\n", " \"master_hostname\": \"algo-1\",\n", " \"model_dir\": \"/opt/ml/model\",\n", " \"module_dir\": \"s3://sm-nemo-ramp/nemo-asr/training/backup_codes/nemo-asr-nemo-experiments-0322-10521679482352/source/sourcedir.tar.gz\",\n", " \"module_name\": \"speech_to_text_ctc\",\n", " \"network_interface_name\": \"eth0\",\n", " \"num_cpus\": 32,\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.8xlarge\",\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.8xlarge\",\n", " \"hosts\": [\n", " \"algo-1\"\n", " ]\n", " }\n", " ],\n", " \"network_interface_name\": \"eth0\"\n", " },\n", " \"user_entry_point\": \"speech_to_text_ctc.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={\"config-path\":\"conf\"}\u001b[0m\n", "\u001b[34mSM_USER_ENTRY_POINT=speech_to_text_ctc.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.8xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.g4dn.8xlarge\"}],\"network_interface_name\":\"eth0\"}\u001b[0m\n", "\u001b[34mSM_INPUT_DATA_CONFIG={\"pretrained\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"testing\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"training\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}}\u001b[0m\n", "\u001b[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n", "\u001b[34mSM_CHANNELS=[\"pretrained\",\"testing\",\"training\"]\u001b[0m\n", "\u001b[34mSM_CURRENT_HOST=algo-1\u001b[0m\n", "\u001b[34mSM_CURRENT_INSTANCE_TYPE=ml.g4dn.8xlarge\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.8xlarge\"}}\u001b[0m\n", "\u001b[34mSM_DISTRIBUTION_INSTANCE_GROUPS=[]\u001b[0m\n", "\u001b[34mSM_IS_HETERO=false\u001b[0m\n", "\u001b[34mSM_MODULE_NAME=speech_to_text_ctc\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=32\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://sm-nemo-ramp/nemo-asr/training/backup_codes/nemo-asr-nemo-experiments-0322-10521679482352/source/sourcedir.tar.gz\u001b[0m\n", "\u001b[34mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"pretrained\":\"/opt/ml/input/data/pretrained\",\"testing\":\"/opt/ml/input/data/testing\",\"training\":\"/opt/ml/input/data/training\"},\"current_host\":\"algo-1\",\"current_instance_group\":\"homogeneousCluster\",\"current_instance_group_hosts\":[\"algo-1\"],\"current_instance_type\":\"ml.g4dn.8xlarge\",\"distribution_hosts\":[],\"distribution_instance_groups\":[],\"framework_module\":\"sagemaker_pytorch_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"config-path\":\"conf\"},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"pretrained\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"testing\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"training\":{\"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.8xlarge\"}},\"is_hetero\":false,\"is_master\":true,\"is_modelparallel_enabled\":null,\"is_smddpmprun_installed\":true,\"job_name\":\"nemo-asr-nemo-experiments-0322-10521679482352\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"s3://sm-nemo-ramp/nemo-asr/training/backup_codes/nemo-asr-nemo-experiments-0322-10521679482352/source/sourcedir.tar.gz\",\"module_name\":\"speech_to_text_ctc\",\"network_interface_name\":\"eth0\",\"num_cpus\":32,\"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.8xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.g4dn.8xlarge\"}],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"speech_to_text_ctc.py\"}\u001b[0m\n", "\u001b[34mSM_USER_ARGS=[\"--config-path\",\"conf\"]\u001b[0m\n", "\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n", "\u001b[34mSM_CHANNEL_PRETRAINED=/opt/ml/input/data/pretrained\u001b[0m\n", "\u001b[34mSM_CHANNEL_TESTING=/opt/ml/input/data/testing\u001b[0m\n", "\u001b[34mSM_CHANNEL_TRAINING=/opt/ml/input/data/training\u001b[0m\n", "\u001b[34mSM_HP_CONFIG-PATH=conf\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 speech_to_text_ctc.py --config-path conf\u001b[0m\n", "\u001b[34m[2023-03-22 10:58:37.863: W smdistributed/modelparallel/torch/nn/predefined_hooks.py:78] Found unsupported HuggingFace version 4.28.0.dev0 for automated tensor parallelism. HuggingFace modules will not be automatically distributed. You can use smp.tp_register_with_module API to register desired modules for tensor parallelism, or directly instantiate an smp.nn.DistributedModule. Supported HuggingFace transformers versions for automated tensor parallelism: ['4.17.0', '4.20.1', '4.21.0']\u001b[0m\n", "\u001b[34m2023-03-22 10:58:37,867 root INFO Using NamedTuple = typing._NamedTuple instead.\u001b[0m\n", "\u001b[34m2023-03-22 10:58:37,892 sagemaker-training-toolkit INFO Exceptions not imported for SageMaker TF as Tensorflow is not installed.\u001b[0m\n", "\u001b[34mos.environ: environ({'SM_INPUT_DIR': '/opt/ml/input', 'PYTHONIOENCODING': 'UTF-8', 'NV_CUDA_COMPAT_PACKAGE': 'cuda-compat-11-7', 'NCCL_DEBUG': 'WARN', 'HOROVOD_VERSION': '0.26.1', 'NCCL_SOCKET_IFNAME': 'eth0', 'SM_USER_ENTRY_POINT': 'speech_to_text_ctc.py', 'SM_RESOURCE_CONFIG': '{\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.g4dn.8xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.g4dn.8xlarge\"}],\"network_interface_name\":\"eth0\"}', 'SM_CURRENT_INSTANCE_TYPE': 'ml.g4dn.8xlarge', 'EFA_VERSION': '1.19.0', 'MASTER_ADDR': 'algo-1', 'SM_MODULE_NAME': 'speech_to_text_ctc', 'HOSTNAME': 'ip-10-0-239-63.ec2.internal', 'CURRENT_HOST': 'algo-1', 'SHLVL': '1', 'LD_LIBRARY_PATH': '/opt/conda/lib/python3.9/site-packages/smdistributed/dataparallel/lib:/opt/amazon/openmpi/lib/:/opt/amazon/efa/lib/:/opt/conda/lib:/usr/local/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/lib', 'HOME': '/root', 'MASTER_PORT': '7777', 'SM_OUTPUT_DATA_DIR': '/opt/ml/output/data', 'TORCH_NVCC_FLAGS': '-Xfatbin -compress-all', 'SM_INPUT_DATA_CONFIG': '{\"pretrained\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"testing\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"training\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}}', 'PYTHONUNBUFFERED': '1', 'TRAINING_JOB_ARN': 'arn:aws:sagemaker:us-east-1:419974056037:training-job/nemo-asr-nemo-experiments-0322-10521679482352', 'SM_FRAMEWORK_PARAMS': '{}', 'SM_CURRENT_HOST': 'algo-1', 'SM_MODEL_DIR': '/opt/ml/model', 'SM_CHANNEL_PRETRAINED': '/opt/ml/input/data/pretrained', 'SAGEMAKER_METRICS_DIRECTORY': '/opt/ml/output/metrics/sagemaker', 'BRANCH_OFI': '1.4.0-aws', 'AWS_CONTAINER_CREDENTIALS_RELATIVE_URI': '/v2/credentials/proxy-e24ade0e40e151b375ae86becc87641e62d7bc0e4fbb008e3c134b778c23c7db-customer', 'SM_FRAMEWORK_MODULE': 'sagemaker_pytorch_container.training:main', 'SM_NUM_CPUS': '32', 'CUDA_VERSION': '11.7.1', 'TORCH_CUDA_ARCH_LIST': '3.7 5.0 7.0+PTX 8.0', 'SM_NUM_GPUS': '1', 'PYTHONDONTWRITEBYTECODE': '1', 'SM_NUM_NEURONS': '0', 'SM_MODULE_DIR': 's3://sm-nemo-ramp/nemo-asr/training/backup_codes/nemo-asr-nemo-experiments-0322-10521679482352/source/sourcedir.tar.gz', 'NVIDIA_REQUIRE_CUDA': 'cuda>=11.7 brand=tesla,driver>=450,driver<451 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=510,driver<511 brand=unknown,driver>=510,driver<511 brand=nvidia,driver>=510,driver<511 brand=nvidiartx,driver>=510,driver<511 brand=geforce,driver>=510,driver<511 brand=geforcertx,driver>=510,driver<511 brand=quadro,driver>=510,driver<511 brand=quadrortx,driver>=510,driver<511 brand=titan,driver>=510,driver<511 brand=titanrtx,driver>=510,driver<511', 'RDMAV_FORK_SAFE': '1', '_': '/opt/conda/bin/train', 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility,compat32,graphics,video', 'SM_LOG_LEVEL': '20', 'SM_CHANNEL_TRAINING': '/opt/ml/input/data/training', 'NV_CUDA_CUDART_VERSION': '11.7.99-1', 'SM_OUTPUT_DIR': '/opt/ml/output', 'SM_TRAINING_ENV': '{\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"pretrained\":\"/opt/ml/input/data/pretrained\",\"testing\":\"/opt/ml/input/data/testing\",\"training\":\"/opt/ml/input/data/training\"},\"current_host\":\"algo-1\",\"current_instance_group\":\"homogeneousCluster\",\"current_instance_group_hosts\":[\"algo-1\"],\"current_instance_type\":\"ml.g4dn.8xlarge\",\"distribution_hosts\":[],\"distribution_instance_groups\":[],\"framework_module\":\"sagemaker_pytorch_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"config-path\":\"conf\"},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"pretrained\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"testing\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"training\":{\"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.8xlarge\"}},\"is_hetero\":false,\"is_master\":true,\"is_modelparallel_enabled\":null,\"is_smddpmprun_installed\":true,\"job_name\":\"nemo-asr-nemo-experiments-0322-10521679482352\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"s3://sm-nemo-ramp/nemo-asr/training/backup_codes/nemo-asr-nemo-experiments-0322-10521679482352/source/sourcedir.tar.gz\",\"module_name\":\"speech_to_text_ctc\",\"network_interface_name\":\"eth0\",\"num_cpus\":32,\"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.8xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.g4dn.8xlarge\"}],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"speech_to_text_ctc.py\"}', 'PATH': '/opt/amazon/openmpi/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin', 'NVARCH': 'x86_64', 'REQUESTS_CA_BUNDLE': '/etc/ssl/certs/ca-certificates.crt', 'DMLC_INTERFACE': 'eth0', 'SM_INSTANCE_GROUPS_DICT': '{\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.g4dn.8xlarge\"}}', 'LD_PRELOAD': '/libchangehostname.so', 'LANG': 'C.UTF-8', 'SM_HPS': '{\"config-path\":\"conf\"}', 'NCCL_IB_DISABLE': '1', 'SM_INSTANCE_GROUPS': '[\"homogeneousCluster\"]', 'CUDNN_VERSION': '8.5.0.96', 'DEBIAN_FRONTEND': 'noninteractive', 'SM_CHANNELS': '[\"pretrained\",\"testing\",\"training\"]', 'SM_INPUT_CONFIG_DIR': '/opt/ml/input/config', 'SM_CURRENT_INSTANCE_GROUP_HOSTS': '[\"algo-1\"]', 'SM_IS_HETERO': 'false', 'AWS_REGION': 'us-east-1', 'OPEN_MPI_PATH': '/opt/amazon/openmpi', 'DLC_CONTAINER_TYPE': 'training', 'SAGEMAKER_JOB_NAME': 'nemo-asr-nemo-experiments-0322-10521679482352', 'SM_OUTPUT_INTERMEDIATE_DIR': '/opt/ml/output/intermediate', 'PWD': '/opt/ml/code', 'TRAINING_JOB_NAME': 'nemo-asr-nemo-experiments-0322-10521679482352', 'LC_ALL': 'C.UTF-8', 'SAGEMAKER_TRAINING_MODULE': 'sagemaker_pytorch_container.training:main', 'OMPI_VERSION': '4.1.1', 'SM_HOSTS': '[\"algo-1\"]', 'SM_DISTRIBUTION_INSTANCE_GROUPS': '[]', 'SM_CHANNEL_TESTING': '/opt/ml/input/data/testing', 'MANUAL_BUILD': '0', 'SAGEMAKER_REGION': 'us-east-1', 'PYTHONPATH': '/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', 'NVIDIA_VISIBLE_DEVICES': 'all', 'NCCL_VERSION': '2.14.3', 'DGLBACKEND': 'pytorch', 'SM_NETWORK_INTERFACE_NAME': 'eth0', 'SM_USER_ARGS': '[\"--config-path\",\"conf\"]', 'CMAKE_PREFIX_PATH': '$(dirname $(which conda))/../', 'SM_CURRENT_INSTANCE_GROUP': 'homogeneousCluster'})\u001b[0m\n", "\u001b[34mHit:1 http://archive.ubuntu.com/ubuntu focal InRelease\u001b[0m\n", "\u001b[34mGet:2 http://security.ubuntu.com/ubuntu focal-security InRelease [114 kB]\u001b[0m\n", "\u001b[34mGet:3 http://archive.ubuntu.com/ubuntu focal-updates InRelease [114 kB]\u001b[0m\n", "\u001b[34mGet:4 http://archive.ubuntu.com/ubuntu focal-backports InRelease [108 kB]\u001b[0m\n", "\u001b[34mGet:5 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 Packages [3026 kB]\u001b[0m\n", "\u001b[34mFetched 3362 kB in 1s (5656 kB/s)\u001b[0m\n", "\u001b[34mReading package lists...\u001b[0m\n", "\u001b[34mReading package lists...\u001b[0m\n", "\u001b[34mBuilding dependency tree...\u001b[0m\n", "\u001b[34mReading state information...\u001b[0m\n", "\u001b[34mlibsndfile1 is already the newest version (1.0.28-7ubuntu0.1).\u001b[0m\n", "\u001b[34mlibsndfile1 set to manually installed.\u001b[0m\n", "\u001b[34mThe following additional packages will be installed:\u001b[0m\n", "\u001b[34mi965-va-driver intel-media-va-driver libaacs0 libaom0 libass9 libavc1394-0\n", " libavcodec58 libavdevice58 libavfilter7 libavformat58 libavresample4\u001b[0m\n", "\u001b[34mlibavutil56 libbdplus0 libbluray2 libbs2b0 libcaca0 libcdio-cdda2\n", " libcdio-paranoia2 libcdio18 libchromaprint1 libcodec2-0.9 libdc1394-22\n", " libflite1 libgme0 libiec61883-0 libigdgmm11 libjack-jackd2-0 liblilv-0-0\n", " libmpg123-0 libmysofa1 libnorm1 libopenal-data libopenal1 libopenjp2-7\u001b[0m\n", "\u001b[34mlibopenmpt0 libopus0 libpgm-5.2-0 libpostproc55 libraw1394-11 librubberband2\n", " libsamplerate0 libsdl2-2.0-0 libserd-0-0 libshine3 libslang2 libsnappy1v5\n", " libsndio7.0 libsodium23 libsord-0-0 libsoxr0 libspeex1 libsratom-0-0\n", " libssh-gcrypt-4 libswresample3 libswscale5 libtheora0 libusb-1.0-0\n", " libva-drm2 libva-x11-2 libva2 libvdpau1 libvidstab1.1 libvpx6 libx264-155\n", " libx265-179 libxcb-shape0 libxss1 libxv1 libxvidcore4 libzmq5 libzvbi-common\u001b[0m\n", "\u001b[34mlibzvbi0 mesa-va-drivers mesa-vdpau-drivers ocl-icd-libopencl1 va-driver-all\n", " vdpau-driver-all\u001b[0m\n", "\u001b[34mSuggested packages:\n", " ffmpeg-doc i965-va-driver-shaders libbluray-bdj jackd2 libportaudio2\n", " opus-tools libraw1394-doc serdi sndiod sordi speex opencl-icd\n", " libvdpau-va-gl1 nvidia-vdpau-driver nvidia-legacy-340xx-vdpau-driver\n", " nvidia-legacy-304xx-vdpau-driver\u001b[0m\n", "\u001b[34mThe following NEW packages will be installed:\u001b[0m\n", "\u001b[34mffmpeg i965-va-driver intel-media-va-driver libaacs0 libaom0 libass9\n", " libavc1394-0 libavcodec58 libavdevice58 libavfilter7 libavformat58\u001b[0m\n", "\u001b[34mlibavresample4 libavutil56 libbdplus0 libbluray2 libbs2b0 libcaca0\n", " libcdio-cdda2 libcdio-paranoia2 libcdio18 libchromaprint1 libcodec2-0.9\u001b[0m\n", "\u001b[34mlibdc1394-22 libflite1 libgme0 libiec61883-0 libigdgmm11 libjack-jackd2-0\u001b[0m\n", "\u001b[34mliblilv-0-0 libmpg123-0 libmysofa1 libnorm1 libopenal-data libopenal1\u001b[0m\n", "\u001b[34mlibopenjp2-7 libopenmpt0 libopus0 libpgm-5.2-0 libpostproc55 libraw1394-11\u001b[0m\n", "\u001b[34mlibrubberband2 libsamplerate0 libsdl2-2.0-0 libserd-0-0 libshine3 libslang2\n", " libsnappy1v5 libsndio7.0 libsodium23 libsord-0-0 libsoxr0 libspeex1\u001b[0m\n", "\u001b[34mlibsratom-0-0 libssh-gcrypt-4 libswresample3 libswscale5 libtheora0\n", " libusb-1.0-0 libva-drm2 libva-x11-2 libva2 libvdpau1 libvidstab1.1 libvpx6\n", " libx264-155 libx265-179 libxcb-shape0 libxss1 libxv1 libxvidcore4 libzmq5\u001b[0m\n", "\u001b[34mlibzvbi-common libzvbi0 mesa-va-drivers mesa-vdpau-drivers\u001b[0m\n", "\u001b[34mocl-icd-libopencl1 va-driver-all vdpau-driver-all\u001b[0m\n", "\u001b[34m0 upgraded, 78 newly installed, 0 to remove and 37 not upgraded.\u001b[0m\n", "\u001b[34mNeed to get 49.2 MB of archives.\u001b[0m\n", "\u001b[34mAfter this operation, 218 MB of additional disk space will be used.\u001b[0m\n", "\u001b[34mGet:1 http://archive.ubuntu.com/ubuntu focal/main amd64 libslang2 amd64 2.3.2-4 [429 kB]\u001b[0m\n", "\u001b[34mGet:2 http://archive.ubuntu.com/ubuntu focal/main amd64 libsodium23 amd64 1.0.18-1 [150 kB]\u001b[0m\n", "\u001b[34mGet:3 http://archive.ubuntu.com/ubuntu focal/main amd64 libusb-1.0-0 amd64 2:1.0.23-2build1 [46.5 kB]\u001b[0m\n", "\u001b[34mGet:4 http://archive.ubuntu.com/ubuntu focal/universe amd64 libaom0 amd64 1.0.0.errata1-3build1 [1160 kB]\u001b[0m\n", "\u001b[34mGet:5 http://archive.ubuntu.com/ubuntu focal/universe amd64 libva2 amd64 2.7.0-2 [53.5 kB]\u001b[0m\n", "\u001b[34mGet:6 http://archive.ubuntu.com/ubuntu focal/universe amd64 libva-drm2 amd64 2.7.0-2 [7044 B]\u001b[0m\n", "\u001b[34mGet:7 http://archive.ubuntu.com/ubuntu focal/universe amd64 libva-x11-2 amd64 2.7.0-2 [11.9 kB]\u001b[0m\n", "\u001b[34mGet:8 http://archive.ubuntu.com/ubuntu focal/main amd64 libvdpau1 amd64 1.3-1ubuntu2 [25.6 kB]\u001b[0m\n", "\u001b[34mGet:9 http://archive.ubuntu.com/ubuntu focal/main amd64 ocl-icd-libopencl1 amd64 2.2.11-1ubuntu1 [30.3 kB]\u001b[0m\n", "\u001b[34mGet:10 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 libavutil56 amd64 7:4.2.7-0ubuntu0.1 [241 kB]\u001b[0m\n", "\u001b[34mGet:11 http://archive.ubuntu.com/ubuntu focal/universe amd64 libcodec2-0.9 amd64 0.9.2-2 [7886 kB]\u001b[0m\n", "\u001b[34mGet:12 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libopenjp2-7 amd64 2.3.1-1ubuntu4.20.04.1 [141 kB]\u001b[0m\n", "\u001b[34mGet:13 http://archive.ubuntu.com/ubuntu focal/main amd64 libopus0 amd64 1.3.1-0ubuntu1 [191 kB]\u001b[0m\n", "\u001b[34mGet:14 http://archive.ubuntu.com/ubuntu focal/universe amd64 libshine3 amd64 3.1.1-2 [23.2 kB]\u001b[0m\n", "\u001b[34mGet:15 http://archive.ubuntu.com/ubuntu focal/main amd64 libsnappy1v5 amd64 1.1.8-1build1 [16.7 kB]\u001b[0m\n", "\u001b[34mGet:16 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libspeex1 amd64 1.2~rc1.2-1.1ubuntu1.20.04.1 [53.2 kB]\u001b[0m\n", "\u001b[34mGet:17 http://archive.ubuntu.com/ubuntu focal/main amd64 libsoxr0 amd64 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libopus0:amd64.\u001b[0m\n", "\u001b[34mPreparing to unpack .../12-libopus0_1.3.1-0ubuntu1_amd64.deb ...\u001b[0m\n", "\u001b[34mUnpacking libopus0:amd64 (1.3.1-0ubuntu1) ...\u001b[0m\n", "\u001b[34mSelecting previously unselected package libshine3:amd64.\u001b[0m\n", "\u001b[34mPreparing to unpack .../13-libshine3_3.1.1-2_amd64.deb ...\u001b[0m\n", "\u001b[34mUnpacking libshine3:amd64 (3.1.1-2) ...\u001b[0m\n", "\u001b[34mSelecting previously unselected package libsnappy1v5:amd64.\u001b[0m\n", "\u001b[34mPreparing to unpack .../14-libsnappy1v5_1.1.8-1build1_amd64.deb ...\u001b[0m\n", "\u001b[34mUnpacking libsnappy1v5:amd64 (1.1.8-1build1) ...\u001b[0m\n", "\u001b[34mSelecting previously unselected package libspeex1:amd64.\u001b[0m\n", "\u001b[34mPreparing to unpack .../15-libspeex1_1.2~rc1.2-1.1ubuntu1.20.04.1_amd64.deb ...\u001b[0m\n", "\u001b[34mUnpacking libspeex1:amd64 (1.2~rc1.2-1.1ubuntu1.20.04.1) ...\u001b[0m\n", "\u001b[34mSelecting previously unselected package 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libpostproc55:amd64 (7:4.2.7-0ubuntu0.1) ...\u001b[0m\n", "\u001b[34mSetting up librubberband2:amd64 (1.8.2-1build1) ...\u001b[0m\n", "\u001b[34mSetting up libjack-jackd2-0:amd64 (1.9.12~dfsg-2ubuntu2) ...\u001b[0m\n", "\u001b[34mSetting up vdpau-driver-all:amd64 (1.3-1ubuntu2) ...\u001b[0m\n", "\u001b[34mSetting up libsord-0-0:amd64 (0.16.4-1) ...\u001b[0m\n", "\u001b[34mSetting up libsratom-0-0:amd64 (0.6.4-1) ...\u001b[0m\n", "\u001b[34mSetting up libswscale5:amd64 (7:4.2.7-0ubuntu0.1) ...\u001b[0m\n", "\u001b[34mSetting up liblilv-0-0:amd64 (0.24.6-1ubuntu0.1) ...\u001b[0m\n", "\u001b[34mSetting up libswresample3:amd64 (7:4.2.7-0ubuntu0.1) ...\u001b[0m\n", "\u001b[34mSetting up libavresample4:amd64 (7:4.2.7-0ubuntu0.1) ...\u001b[0m\n", "\u001b[34mSetting up libavcodec58:amd64 (7:4.2.7-0ubuntu0.1) ...\u001b[0m\n", "\u001b[34mSetting up libchromaprint1:amd64 (1.4.3-3build1) ...\u001b[0m\n", "\u001b[34mSetting up libavformat58:amd64 (7:4.2.7-0ubuntu0.1) ...\u001b[0m\n", "\u001b[34mSetting up libavfilter7:amd64 (7:4.2.7-0ubuntu0.1) ...\u001b[0m\n", "\u001b[34mSetting up libavdevice58:amd64 (7:4.2.7-0ubuntu0.1) ...\u001b[0m\n", "\u001b[34mSetting up ffmpeg (7:4.2.7-0ubuntu0.1) ...\u001b[0m\n", "\u001b[34mProcessing triggers for libc-bin (2.31-0ubuntu9.9) ...\u001b[0m\n", "\u001b[34m[NeMo W 2023-03-22 10:58:52 optimizers:66] Could not import distributed_fused_adam optimizer from Apex\u001b[0m\n", "\u001b[34m[NeMo W 2023-03-22 10:58:54 experimental:27] Module is experimental, not ready for production and is not fully supported. Use at your own risk.\u001b[0m\n", "\u001b[34m[NeMo W 2023-03-22 10:58:55 nemo_logging:349] /opt/conda/lib/python3.9/site-packages/hydra/_internal/hydra.py:119: UserWarning: Future Hydra versions will no longer change working directory at job runtime by default.\n", " See https://hydra.cc/docs/1.2/upgrades/1.1_to_1.2/changes_to_job_working_dir/ for more information.\n", " ret = run_job(\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:55 speech_to_text_ctc:89] Hydra config: name: Conformer-CTC-Char1\n", " model:\n", " sample_rate: 16000\n", " labels:\n", " - ' '\n", " - a\n", " - b\n", " - c\n", " - d\n", " - e\n", " - f\n", " - g\n", " - h\n", " - i\n", " - j\n", " - k\n", " - l\n", " - m\n", " - 'n'\n", " - o\n", " - p\n", " - q\n", " - r\n", " - s\n", " - t\n", " - u\n", " - v\n", " - w\n", " - x\n", " - 'y'\n", " - z\n", " - ''''\n", " log_prediction: true\n", " ctc_reduction: mean_batch\n", " skip_nan_grad: false\n", " train_ds:\n", " manifest_filepath: /opt/ml/input/data/training/an4/train_manifest.json\n", " labels: ${model.labels}\n", " sample_rate: ${model.sample_rate}\n", " batch_size: 16\n", " shuffle: true\n", " num_workers: 8\n", " pin_memory: true\n", " trim_silence: false\n", " max_duration: 16.7\n", " min_duration: 0.1\n", " is_tarred: false\n", " tarred_audio_filepaths: null\n", " shuffle_n: 2048\n", " bucketing_strategy: synced_randomized\n", " bucketing_batch_size: null\n", " validation_ds:\n", " manifest_filepath: /opt/ml/input/data/testing/an4/test_manifest.json\n", " labels: ${model.labels}\n", " sample_rate: ${model.sample_rate}\n", " batch_size: 16\n", " shuffle: false\n", " num_workers: 8\n", " pin_memory: true\n", " test_ds:\n", " manifest_filepath: null\n", " labels: ${model.labels}\n", " sample_rate: ${model.sample_rate}\n", " batch_size: 16\n", " shuffle: false\n", " num_workers: 8\n", " pin_memory: true\n", " preprocessor:\n", " _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor\n", " sample_rate: ${model.sample_rate}\n", " normalize: per_feature\n", " window_size: 0.025\n", " window_stride: 0.01\n", " window: hann\n", " features: 80\n", " n_fft: 512\n", " log: true\n", " frame_splicing: 1\n", " dither: 1.0e-05\n", " pad_to: 0\n", " pad_value: 0.0\n", " spec_augment:\n", " _target_: nemo.collections.asr.modules.SpectrogramAugmentation\n", " freq_masks: 2\n", " time_masks: 5\n", " freq_width: 27\n", " time_width: 0.05\n", " encoder:\n", " _target_: nemo.collections.asr.modules.ConformerEncoder\n", " feat_in: ${model.preprocessor.features}\n", " feat_out: -1\n", " n_layers: 8\n", " d_model: 256\n", " subsampling: striding\n", " subsampling_factor: 4\n", " subsampling_conv_channels: -1\n", " causal_downsampling: false\n", " ff_expansion_factor: 4\n", " self_attention_model: rel_pos\n", " n_heads: 8\n", " att_context_size:\n", " - -1\n", " - -1\n", " att_context_style: regular\n", " xscaling: true\n", " untie_biases: true\n", " pos_emb_max_len: 5000\n", " conv_kernel_size: 31\n", " conv_norm_type: batch_norm\n", " conv_context_size: null\n", " dropout: 0.1\n", " dropout_pre_encoder: 0.1\n", " dropout_emb: 0.0\n", " dropout_att: 0.1\n", " stochastic_depth_drop_prob: 0.0\n", " stochastic_depth_mode: linear\n", " stochastic_depth_start_layer: 1\n", " decoder:\n", " _target_: nemo.collections.asr.modules.ConvASRDecoder\n", " feat_in: null\n", " num_classes: -1\n", " vocabulary: ${model.labels}\n", " interctc:\n", " loss_weights: []\n", " apply_at_layers: []\n", " optim:\n", " name: adamw\n", " lr: 2.0\n", " betas:\n", " - 0.9\n", " - 0.98\n", " weight_decay: 0.001\n", " sched:\n", " name: NoamAnnealing\n", " d_model: ${model.encoder.d_model}\n", " warmup_steps: 10000\n", " warmup_ratio: null\n", " min_lr: 1.0e-06\n", " n_heads: 4\n", " trainer:\n", " devices: -1\n", " num_nodes: 1\n", " max_epochs: 2\n", " max_steps: -1\n", " val_check_interval: 1.0\n", " accelerator: gpu\n", " strategy: null\n", " accumulate_grad_batches: 1\n", " gradient_clip_val: 0.0\n", " precision: 32\n", " log_every_n_steps: 10\n", " enable_progress_bar: true\n", " resume_from_checkpoint: null\n", " num_sanity_val_steps: 0\n", " check_val_every_n_epoch: 1\n", " sync_batchnorm: true\n", " enable_checkpointing: false\n", " logger: false\n", " benchmark: false\n", " exp_manager:\n", " exp_dir: /opt/ml/model/\n", " name: ${name}\n", " create_tensorboard_logger: true\n", " create_checkpoint_callback: true\n", " checkpoint_callback_params:\n", " monitor: val_wer\n", " mode: min\n", " save_top_k: 5\n", " always_save_nemo: true\n", " resume_if_exists: true\n", " resume_ignore_no_checkpoint: true\n", " create_wandb_logger: false\n", " wandb_logger_kwargs:\n", " name: null\n", " project: null\n", " init_from_nemo_model: /opt/ml/input/data/pretrained/CTC.nemo\u001b[0m\n", "\u001b[34mGPU available: True (cuda), used: True\u001b[0m\n", "\u001b[34mTPU available: False, using: 0 TPU cores\u001b[0m\n", "\u001b[34mIPU available: False, using: 0 IPUs\u001b[0m\n", "\u001b[34mHPU available: False, using: 0 HPUs\u001b[0m\n", "\u001b[34m`Trainer(val_check_interval=1.0)` was configured so validation will run at the end of the training epoch..\u001b[0m\n", "\u001b[34m[NeMo W 2023-03-22 10:58:55 exp_manager:710] No version folders would be created under the log folder as 'resume_if_exists' is enabled.\u001b[0m\n", "\u001b[34m[NeMo W 2023-03-22 10:58:55 exp_manager:562] There was no checkpoint folder at checkpoint_dir :/opt/ml/model/Conformer-CTC-Char1/checkpoints. Training from scratch.\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:55 exp_manager:373] Experiments will be logged at /opt/ml/model/Conformer-CTC-Char1\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:55 exp_manager:791] TensorboardLogger has been set up\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:55 collections:193] Dataset loaded with 948 files totalling 0.71 hours\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:55 collections:194] 0 files were filtered totalling 0.00 hours\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:55 collections:193] Dataset loaded with 130 files totalling 0.10 hours\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:55 collections:194] 0 files were filtered totalling 0.00 hours\u001b[0m\n", "\u001b[34m[NeMo W 2023-03-22 10:58:55 audio_to_text_dataset:576] Could not load dataset as `manifest_filepath` was None. Provided config : {'manifest_filepath': None, 'labels': [' ', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', \"'\"], 'sample_rate': 16000, 'batch_size': 16, 'shuffle': False, 'num_workers': 8, 'pin_memory': True}\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:55 features:287] PADDING: 0\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:55 ctc_models:64] \n", " Replacing placeholder number of classes (-1) with actual number of classes - 28\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:55 conv_asr:428] num_classes of ConvASRDecoder is set to the size of the vocabulary: 28.\u001b[0m\n", "\u001b[34m[NeMo W 2023-03-22 10:58:56 modelPT:161] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\n", " Train config : \n", " manifest_filepath: /opt/ml/input/data/training/an4/train_manifest.json\n", " labels:\n", " - ' '\n", " - a\n", " - b\n", " - c\n", " - d\n", " - e\n", " - f\n", " - g\n", " - h\n", " - i\n", " - j\n", " - k\n", " - l\n", " - m\n", " - 'n'\n", " - o\n", " - p\n", " - q\n", " - r\n", " - s\n", " - t\n", " - u\n", " - v\n", " - w\n", " - x\n", " - 'y'\n", " - z\n", " - ''''\n", " sample_rate: 16000\n", " batch_size: 16\n", " shuffle: true\n", " num_workers: 8\n", " pin_memory: true\n", " trim_silence: false\n", " max_duration: 16.7\n", " min_duration: 0.1\n", " is_tarred: false\n", " tarred_audio_filepaths: null\n", " shuffle_n: 2048\n", " bucketing_strategy: synced_randomized\n", " bucketing_batch_size: null\u001b[0m\n", "\u001b[34m[NeMo W 2023-03-22 10:58:56 modelPT:168] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s). \n", " Validation config : \n", " manifest_filepath: /opt/ml/input/data/testing/an4/test_manifest.json\n", " labels:\n", " - ' '\n", " - a\n", " - b\n", " - c\n", " - d\n", " - e\n", " - f\n", " - g\n", " - h\n", " - i\n", " - j\n", " - k\n", " - l\n", " - m\n", " - 'n'\n", " - o\n", " - p\n", " - q\n", " - r\n", " - s\n", " - t\n", " - u\n", " - v\n", " - w\n", " - x\n", " - 'y'\n", " - z\n", " - ''''\n", " sample_rate: 16000\n", " batch_size: 16\n", " shuffle: false\n", " num_workers: 8\n", " pin_memory: true\u001b[0m\n", "\u001b[34m[NeMo W 2023-03-22 10:58:56 modelPT:174] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).\n", " Test config : \n", " manifest_filepath: null\n", " labels:\n", " - ' '\n", " - a\n", " - b\n", " - c\n", " - d\n", " - e\n", " - f\n", " - g\n", " - h\n", " - i\n", " - j\n", " - k\n", " - l\n", " - m\n", " - 'n'\n", " - o\n", " - p\n", " - q\n", " - r\n", " - s\n", " - t\n", " - u\n", " - v\n", " - w\n", " - x\n", " - 'y'\n", " - z\n", " - ''''\n", " sample_rate: 16000\n", " batch_size: 16\n", " shuffle: false\n", " num_workers: 8\n", " pin_memory: true\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:56 features:287] PADDING: 0\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:56 ctc_models:64] \n", " Replacing placeholder number of classes (-1) with actual number of classes - 28\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:56 conv_asr:428] num_classes of ConvASRDecoder is set to the size of the vocabulary: 28.\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:56 save_restore_connector:247] Model EncDecCTCModel was successfully restored from /opt/ml/input/data/pretrained/CTC.nemo.\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:56 modelPT:1221] Model checkpoint restored from nemo file with path : `/opt/ml/input/data/pretrained/CTC.nemo`\u001b[0m\n", "\u001b[34mLOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:58 modelPT:722] Optimizer config = AdamW (\n", " Parameter Group 0\n", " amsgrad: False\n", " betas: [0.9, 0.98]\n", " capturable: False\n", " eps: 1e-08\n", " foreach: None\n", " lr: 2.0\n", " maximize: False\n", " weight_decay: 0.001\n", " )\u001b[0m\n", "\u001b[34m[NeMo I 2023-03-22 10:58:58 lr_scheduler:910] Scheduler \"\" \n", " will be used during training (effective maximum steps = 120) - \n", " Parameters : \n", " (d_model: 256\n", " warmup_steps: 10000\n", " warmup_ratio: null\n", " min_lr: 1.0e-06\n", " max_steps: 120\n", " )\u001b[0m\n", "\u001b[34m| Name | Type | Params\u001b[0m\n", "\u001b[34m------------------------------------------------------------------------\u001b[0m\n", "\u001b[34m0 | preprocessor | AudioToMelSpectrogramPreprocessor | 0 \u001b[0m\n", "\u001b[34m1 | encoder | ConformerEncoder | 14.6 M\u001b[0m\n", "\u001b[34m2 | decoder | ConvASRDecoder | 7.5 K \u001b[0m\n", "\u001b[34m3 | loss | CTCLoss | 0 \u001b[0m\n", "\u001b[34m4 | spec_augmentation | SpectrogramAugmentation | 0 \u001b[0m\n", "\u001b[34m5 | _wer | WER | 0 \u001b[0m\n", "\u001b[34m------------------------------------------------------------------------\u001b[0m\n", "\u001b[34m14.6 M Trainable params\u001b[0m\n", "\u001b[34m0 Non-trainable params\u001b[0m\n", "\u001b[34m14.6 M Total params\u001b[0m\n", "\u001b[34m58.492 Total estimated model params size (MB)\u001b[0m\n", "\u001b[34mTraining: 0it [00:00, ?it/s]\u001b[0m\n", "\u001b[34mTraining: 0%| | 0/69 [00:00\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m )\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m trial_component_training_analytics.dataframe()[['Experiments', 'Trials', \\\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m'wer - Last'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wer - Min'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wer - Max'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wer - Avg'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m 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"\u001b[0;32m~/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages/sagemaker/analytics.py\u001b[0m in \u001b[0;36m_search\u001b[0;34m(self, search_expression, sort_by, sort_order)\u001b[0m\n\u001b[1;32m 734\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 735\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial_components\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMAX_TRIAL_COMPONENTS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 736\u001b[0;31m \u001b[0msearch_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sage_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0msearch_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 737\u001b[0m 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#sort_order=\"Descending\",\n", " #metric_names=[\"validation:auc\"]\n", ")\n", "\n", "trial_component_training_analytics.dataframe()[['Experiments', 'Trials', \\\n", " 'wer - Last', 'wer - Min', 'wer - Max', 'wer - Avg', \\\n", " 'train_loss - Last', 'train_loss - Min', 'train_loss - Max', 'train_loss - Avg']]" ] }, { "cell_type": "code", "execution_count": null, "id": "83314c20-bd21-4da6-9751-1daee3b4a202", "metadata": { "tags": [] }, "outputs": [], "source": [ "from sagemaker.analytics import TrainingJobAnalytics\n", "df = TrainingJobAnalytics( training_job_name=job_name).dataframe()" ] }, { "cell_type": "code", "execution_count": 53, "id": "07453386-2145-4516-b8f1-f3fe08c9b611", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " timestamp metric_name value\n", "0 0.0 train_loss 76.866667\n", "1 0.0 wer 1170.000000" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": null, "id": "673fa193-9ceb-4078-842f-c206b3a71224", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "f5983e73-8957-466d-bc06-f214144a6903", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "b9d2bd46-3533-4e92-919c-232bbc7acc6b", "metadata": {}, "source": [ "## code pipeline trigger" ] }, { "cell_type": "code", "execution_count": 134, "id": "553313b5-3afb-4dc4-ade8-5c1545dc0ea4", "metadata": { "tags": [] }, "outputs": [], "source": [ "import boto3\n", "pipeline_client = boto3.client('codepipeline')" ] }, { "cell_type": "code", "execution_count": null, "id": "5e9f5534-2270-414c-83aa-2e0cc73c09c6", "metadata": {}, "outputs": [], "source": [ "import boto3\n", "pipeline_client = boto3.client('codepipeline')\n", "code_pipeline_name = \"mlops-code-pipeline\"\n", "pipeline_client.start_pipeline_execution(name=code_pipeline_name)" ] } ], "metadata": { "instance_type": "ml.t3.medium", "kernelspec": { "display_name": "conda_pytorch_p39", "language": "python", "name": "conda_pytorch_p39" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.15" } }, "nbformat": 4, "nbformat_minor": 5 }