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Google_Cloud_for_Machine_Learning_-_Sungmin_Han__Cloud_Next_.pdf

Sungmin Han
October 15, 2023
36

 Google_Cloud_for_Machine_Learning_-_Sungmin_Han__Cloud_Next_.pdf

Sungmin Han

October 15, 2023
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  1. 2018. Oct BERT 340M parameters 2019. Oct T5 770M parameters

    2019. Feb GPT-2 1.5B parameters 2020. Jun GPT-3 175B parameters 2022. Apr DALL-E 2 3.5B parameters 2021. Jan DALL-E 12B parameters And more..
  2. NVIDIA A100 NVIDIA T4 NVIDIA V100 NVIDIA P100 NVIDIA P4

    NVIDIA K80 HBM2 40GB, 80GB GDDR6 16GB HBM2 16GB HBB2 8GB GDDR5 8GB GDDR5 12GB
  3. GDPR CCPA COPPA SOC1, 2, 3 ISO/IEC 27701 ISO/IEC 27018

    HIPAA General Data Protection Regulation (유럽) California Consumer Privacy Act (USA) Children's Online Privacy Protection Act General / Global System Security Certification General / Global Privacy Certification General / Global Cloud Privacy Certification Health Insurance Portability and Accountability Act
  4. # keras model python path including run_fn(). module_file=os.path.join(MODULE_ROOT, _trainer_module_file) example_gen

    = tfx.components.CsvExampleGen(input_base=data_root) trainer = tfx.extensions.google_cloud_ai_platform.Trainer( module_file=module_file, examples=example_gen.outputs['examples'], train_args=tfx.proto.TrainArgs(num_steps=100), eval_args=tfx.proto.EvalArgs(num_steps=5), ) pusher = tfx.extensions.google_cloud_ai_platform.Pusher( model=trainer.outputs['model'] ) components = [example_gen, trainer, pusher] return tfx.dsl.Pipeline( pipeline_name=pipeline_name, pipeline_root=pipeline_root, components=components)
  5. client_options = { 'api_endpoint': GOOGLE_CLOUD_REGION + '-aiplatform.googleapis.com' } client =

    aiplatform.gapic.PredictionServiceClient(client_options=client_options) instances = [{ 'culmen_length_mm':[0.71], 'culmen_depth_mm':[0.38], 'flipper_length_mm':[0.98], 'body_mass_g': [0.78], }] endpoint = client.endpoint_path( project=GOOGLE_CLOUD_PROJECT, location=GOOGLE_CLOUD_REGION, endpoint=ENDPOINT_ID, ) response = client.predict(endpoint=endpoint, instances=instances) print('species:', np.argmax(response.predictions[0]))