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{"lastUpload":"2021-04-11T06:50:47.971Z","extensionVersion":"v3.4.3"} |
View elatic_inference.py
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predictor = estimator.deploy( | |
initial_instance_count=1, | |
instance_type='ml.p2.xlarge', | |
endpoint_name="no-elastic-inference-test" | |
) | |
predictor = estimator.deploy( | |
initial_instance_count=1, | |
instance_type='ml.c5.large', | |
accelerator_type='ml.eia1.medium', |
View spot_instances.py
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estimator = TensorFlow( | |
entry_point='model.py', # Script that has the model | |
train_instance_type='ml.p3.2xlarge', # The instance type for training | |
train_instance_count=1, # The number of instances to be spawned | |
model_dir='/opt/ml/model', # The location for the trained model | |
hyperparameters={ # Hyperparameters for the model | |
'epochs': 250, | |
'batch_size': 128, | |
'learning_rate': 0.001 | |
}, |
View dagemaker_debugger_rule.py
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from sagemaker.debugger import Rule, rule_configs | |
estimator = TensorFlow( | |
entry_point='model.py', | |
train_instance_type=train_instance_type, | |
train_instance_count=1, | |
model_dir=model_dir, | |
hyperparameters=hyperparameters, | |
role=sagemaker.get_execution_role(), | |
base_job_name='tf-fashion-mnist', |
View smdebug_example.py
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from smdebug.trials import create_trial | |
from smdebug import modes | |
import numpy as np | |
import matplotlib.pyplot as plt | |
# Get the tensors from S3 | |
s3_output_path = estimator.latest_job_debugger_artifacts_path() | |
# Create a Trial https://github.com/awslabs/sagemaker-debugger/blob/master/docs/analysis.md#Trial |
View custom_debugger_hook.py
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debugger_hook_config = DebuggerHookConfig( | |
s3_output_path=f"s3://{bucket}/fashion-mnist/hook", | |
collection_configs=[ | |
CollectionConfig( | |
name="all_tensors", | |
parameters={ | |
"include_regex": ".*", | |
"save_steps": "1, 2, 3" | |
} | |
) |
View estimator_hook.py
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hyperparameters = {'epochs': 10, 'batch_size': 256, 'learning_rate': 0.001} | |
estimator = TensorFlow( | |
entry_point='model.py', | |
train_instance_type='ml.p3.2xlarge', | |
train_instance_count=1, | |
model_dir='/opt/ml/model', | |
hyperparameters=hyperparameters, | |
role=sagemaker.get_execution_role(), | |
base_job_name='tf-fashion-mnist', |
View sagemaker_debugger_hook.py
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from sagemaker.debugger import CollectionConfig, DebuggerHookConfig | |
bucket = sess.default_bucket() | |
collection_config_biases = CollectionConfig(name='biases') | |
collection_config_weights = CollectionConfig(name='weights') | |
collection_config_metrics = CollectionConfig(name='metrics') | |
debugger_hook_config = DebuggerHookConfig( | |
s3_output_path=f"s3://{bucket}/fashion-mnist/hook", |
View deploy.sh
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sam build | |
sam package --output-template-file .aws-sam/packaged.yaml --s3-bucket <a bucket to save the template> --profile <your profile name> | |
sam deploy --template-file .aws-sam/packaged.yaml --stack-name imageTagging --capabilities CAPABILITY_IAM --profile <your profile name> |
View upload_images.py
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import boto3 | |
from boto3.s3.transfer import TransferConfig | |
BUCKET = "<your bucket from SAM template>" | |
REGION = "<your region>" | |
PROFILE = "<your profile>" | |
def upload_to_s3(file_name, key, bucket=BUCKET, region=REGION): |
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