tutorials/vertex_sdk_launch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8295c7bd-8fc8-4b2f-a656-20b20d2344bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from google.cloud import aiplatform\n",
    "\n",
    "from google.cloud.aiplatform import hyperparameter_tuning as hpt\n",
    "\n",
    "import google.auth"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c111d339-9d41-49c3-bc82-b224ec8f09f5",
   "metadata": {},
   "source": [
    "# Vertex AI hyperparameter optimization\n",
    "\n",
    "You can use our, or your own, docker containers with a version of the CODA library with the Vertex AI platform on GCP to tune hyperparameters for your models. You need to make sure this API is activated on your GCP account and that you use machines that you have quota for.\n",
    "\n",
    "References:\n",
    "\n",
    "- https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview\n",
    "- https://cloud.google.com/vertex-ai/docs/training/using-hyperparameter-tuning#aiplatform_create_hyperparameter_tuning_job_python_package_sample-python_vertex_ai_sdk\n",
    "- https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-hyperparameter-tuning.ipynb\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ba8f28-0f53-4d0c-b48e-bdab59e36e1a",
   "metadata": {},
   "source": [
    "## Login and initialize your session\n",
    "Make sure you fill in your username in the next cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0685d7b0-c904-4efe-90ea-e1478b920fd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "YOUR_USERNAME = 'replace-with-your-gcp-username'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "75b3766a-b7f4-47af-8131-184aea6551ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "credentials, project = google.auth.default()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b889863-5358-45fc-9760-81de3b5d0717",
   "metadata": {},
   "outputs": [],
   "source": [
    "aiplatform.init(\n",
    "    # your Google Cloud Project ID or number\n",
    "    # environment default used is not set\n",
    "    project=project,\n",
    "\n",
    "    # the Vertex AI region you will use\n",
    "    # defaults to us-central1\n",
    "    location='us-central1',\n",
    "\n",
    "    # Google Cloud Storage bucket in same region as location\n",
    "    # used to stage artifacts\n",
    "    staging_bucket=f'gs://{YOUR_USERNAME}-aip-staging-us-central1',\n",
    "\n",
    "    # custom google.auth.credentials.Credentials\n",
    "    # environment default credentials used if not set\n",
    "    credentials=credentials,\n",
    "\n",
    "    # the name of the experiment to use to track\n",
    "    # logged metrics and parameters\n",
    "    experiment='full-hpo',\n",
    "\n",
    "    # description of the experiment above\n",
    "    experiment_description='Full hyperparameter search.'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95863d7b-6aa9-45a5-a655-e65338dc5300",
   "metadata": {},
   "source": [
    "## Set your worker specs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59a0bdfd-6404-46ed-8f63-89fb7018f5a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "worker_pool_specs = [{\n",
    "    ##########################################################\n",
    "    ## Again, make sure you adjust these to fit your quotas ##\n",
    "    ##########################################################\n",
    "    \"machine_spec\": {\n",
    "        \"machine_type\": \"n1-standard-8\",\n",
    "        \"accelerator_type\": \"NVIDIA_TESLA_V100\",\n",
    "        \"accelerator_count\": 1\n",
    "    },\n",
    "    \"replica_count\": 1,\n",
    "    \"disk_spec\": {\n",
    "      \"boot_disk_type\": \"pd-ssd\",\n",
    "      \"boot_disk_size_gb\": 300\n",
    "    },\n",
    "    ##########################################################\n",
    "    ## This machines pull a container when initialized. You ##\n",
    "    ## to set constant args for the training script as part ##\n",
    "    ## of the container spec. Change these to adjust the    ##\n",
    "    ## training process                                     ##\n",
    "    ##########################################################\n",
    "    \"container_spec\": {\n",
    "        \"image_uri\": \"gcr.io/sabeti-encode/boda/train@sha256:8b90fcbe67a3a31b5aca9a983848b5449243dfd9bd8d310f2c84e3f35dd9e960\",\n",
    "        \"args\": [\n",
    "            \"--data_module=MPRA_DataModule\",\n",
    "            \"--datafile_path=gs://syrgoth/data/MPRA_ALL_v3.txt\",\n",
    "            \"--synth_seed=890207\",\n",
    "            \"--synth_val_pct=0.0\",\n",
    "            \"--synth_test_pct=99.98\",\n",
    "            \"--num_workers=8\",\n",
    "            \"--n_outputs=3\",\n",
    "            \"--optimizer=Adam\",\n",
    "            \"--scheduler=CosineAnnealingWarmRestarts\",\n",
    "            \"--scheduler_interval=step\",\n",
    "            \"--checkpoint_monitor=entropy_spearman\",\n",
    "            \"--stopping_mode=max\",\n",
    "            \"--stopping_patience=30\",\n",
    "            \"--gpus=1\",\n",
    "            \"--min_epochs=60\",\n",
    "            \"--max_epochs=200\",\n",
    "            \"--precision=16\",\n",
    "            \"--default_root_dir=/tmp/output/artifacts\",\n",
    "            \"--artifact_path=gs://sgosai-aip-staging-us-central1/aip_hpo_20231230\",\n",
    "        ]\n",
    "    }\n",
    "}]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c7610c-3e88-4c7d-b216-600c4bf6cc89",
   "metadata": {},
   "source": [
    "## Set optimization objective\n",
    "You choose both the metric and goal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "08d98da4-5226-4a2c-a626-9be0e934efae",
   "metadata": {},
   "outputs": [],
   "source": [
    "metric_spec = {'prediction_mean_spearman': 'maximize'}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a32d659d-fca0-4eea-aa1e-ba8570abf603",
   "metadata": {},
   "source": [
    "## Define hyperparameters to optimize\n",
    "Reference: https://cloud.google.com/vertex-ai/docs/reference/rest/v1/StudySpec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f5f11b72-c0bf-4f8d-9935-1e6c372a1f13",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter_spec = {\n",
    "    'model_module': hpt.CategoricalParameterSpec(\n",
    "        values=['BassetBranched', 'BassetVL'],\n",
    "        conditional_parameter_spec = {\n",
    "            'n_branched_layers': hpt.IntegerParameterSpec(min=1, max=5, scale='linear', parent_values=['BassetBranched']),\n",
    "            'branched_activation': hpt.CategoricalParameterSpec(values=['ReLU', 'ELU','ReLU6'], parent_values=['BassetBranched']),\n",
    "            'branched_channels': hpt.IntegerParameterSpec(min=16, max=1024, scale='linear', parent_values=['BassetBranched']),\n",
    "            'branched_dropout_p': hpt.DoubleParameterSpec(min=0.05, max=0.75, scale='linear', parent_values=['BassetBranched']),\n",
    "        }\n",
    "    ),\n",
    "    'graph_module': hpt.CategoricalParameterSpec(\n",
    "        values=['CNNBasicTraining', 'CNNTransferLearning'],\n",
    "        conditional_parameter_spec = {\n",
    "            'parent_weights': hpt.CategoricalParameterSpec(values=['gs://syrgoth/my-model.epoch_5-step_19885.pkl'], parent_values=['CNNTransferLearning']),\n",
    "            'frozen_epochs': hpt.IntegerParameterSpec(min=0, max=60, scale='linear', parent_values=['CNNTransferLearning']),\n",
    "            'linear_activation': hpt.CategoricalParameterSpec(values=['ReLU', 'ELU','ReLU6'], parent_values=['CNNBasicTraining']),\n",
    "            'linear_channels': hpt.IntegerParameterSpec(min=16, max=4096, scale='log', parent_values=['CNNBasicTraining']),\n",
    "            'conv1_channels': hpt.IntegerParameterSpec(min=16, max=2048, scale='log', parent_values=['CNNBasicTraining']),\n",
    "            'conv2_channels': hpt.IntegerParameterSpec(min=16, max=2048, scale='log', parent_values=['CNNBasicTraining']),\n",
    "            'conv3_channels': hpt.IntegerParameterSpec(min=16, max=2048, scale='log', parent_values=['CNNBasicTraining']),\n",
    "            'conv1_kernel_size': hpt.IntegerParameterSpec(min=5, max=25, scale='linear', parent_values=['CNNBasicTraining']),\n",
    "            'conv2_kernel_size': hpt.IntegerParameterSpec(min=5, max=25, scale='linear', parent_values=['CNNBasicTraining']),\n",
    "            'conv3_kernel_size': hpt.IntegerParameterSpec(min=5, max=25, scale='linear', parent_values=['CNNBasicTraining']),\n",
    "            'padded_seq_len': hpt.DiscreteParameterSpec(values=[216], scale='linear', parent_values=['CNNBasicTraining']),\n",
    "            'input_len': hpt.DiscreteParameterSpec(values=[216], scale='linear', parent_values=['CNNBasicTraining']),\n",
    "        }\n",
    "    ),\n",
    "    'batch_size': hpt.IntegerParameterSpec(min=128, max=3072, scale='log'),\n",
    "    'use_reverse_complements': hpt.CategoricalParameterSpec(values=['True', 'False']),\n",
    "    'duplication_cutoff': hpt.DoubleParameterSpec(min=0.5, max=5.0, scale='linear'),\n",
    "    'n_linear_layers': hpt.IntegerParameterSpec(min=1, max=5, scale='linear'),\n",
    "    'linear_dropout_p': hpt.DoubleParameterSpec(min=0.05, max=0.75, scale='linear'),\n",
    "    'loss_criterion': hpt.CategoricalParameterSpec(values=['L1KLmixed', 'MSEKLmixed']),\n",
    "    'beta': hpt.DoubleParameterSpec(min=0.2, max=5.0, scale='log'),\n",
    "    'lr': hpt.DoubleParameterSpec(min=0.0001, max=0.01, scale='log'),\n",
    "    'weight_decay': hpt.DoubleParameterSpec(min=0.00001, max=0.001, scale='log'),\n",
    "    'beta1': hpt.DoubleParameterSpec(min=0.8, max=0.9999, scale='reverse_log'),\n",
    "    'beta2': hpt.DoubleParameterSpec(min=0.8, max=0.9999, scale='reverse_log'),\n",
    "    'amsgrad': hpt.CategoricalParameterSpec(['False', 'True']),\n",
    "    'T_0': hpt.IntegerParameterSpec(min=2048, max=65536, scale='log'),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34416102-7150-45f4-85e7-85fa01ce63a9",
   "metadata": {},
   "source": [
    "## Deploy HPO job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "afc8f2eb-c401-4988-9a1e-655654580fe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_job = aiplatform.CustomJob(\n",
    "    display_name='custom_job',\n",
    "    worker_pool_specs=worker_pool_specs,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9486caab-da2d-4abb-9672-b2f2d7adbc37",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating HyperparameterTuningJob\n",
      "HyperparameterTuningJob created. Resource name: projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320\n",
      "To use this HyperparameterTuningJob in another session:\n",
      "hpt_job = aiplatform.HyperparameterTuningJob.get('projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320')\n",
      "View HyperparameterTuningJob:\n",
      "https://console.cloud.google.com/ai/platform/locations/us-central1/training/659844072021688320?project=482032041325\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_PENDING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
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      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
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      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n",
      "HyperparameterTuningJob projects/482032041325/locations/us-central1/hyperparameterTuningJobs/659844072021688320 current state:\n",
      "JobState.JOB_STATE_RUNNING\n"
     ]
    }
   ],
   "source": [
    "hpt_job = aiplatform.HyperparameterTuningJob(\n",
    "    display_name='hpo-full-02',\n",
    "    custom_job=custom_job,\n",
    "    metric_spec=metric_spec,\n",
    "    parameter_spec=parameter_spec,\n",
    "    max_trial_count=512,\n",
    "    parallel_trial_count=12,\n",
    "    labels={\n",
    "        'hpo_run_id': '2'\n",
    "    },\n",
    ")\n",
    "\n",
    "hpt_job.run()\n",
    "\n",
    "print(hpt_job.resource_name)\n"
   ]
  }
 ],
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