tests/testthat/test-pytorch.R
In DyfanJones/sagemaker-r-mlframework: sagemaker machine learning developed by amazon
# NOTE: This code has been modified from AWS Sagemaker Python:
#https://github.com/aws/sagemaker-python-sdk/blob/master/tests/unit/test_mxnet.py
context("PyTorch")
library(sagemaker.core)
library(sagemaker.common)
library(sagemaker.mlcore)
DATA_DIR = file.path(getwd(), "data")
SCRIPT_PATH =file.path(DATA_DIR, "dummy_script.py")
TAR_FILE <- file.path(DATA_DIR, "test_tar.tgz")
BIN_OBJ <- readBin(con = TAR_FILE, what = "raw", n = file.size(TAR_FILE))
SERVING_SCRIPT_FILE = "another_dummy_script.py"
MODEL_DATA = "s3://some/data.tar.gz"
ENV = list("DUMMY_ENV_VAR"= "dummy_value")
TIMESTAMP = "2017-11-06-14:14:15.672"
TIME = 1507167947
BUCKET_NAME = "mybucket"
INSTANCE_COUNT = 1
INSTANCE_TYPE = "ml.c4.4xlarge"
ACCELERATOR_TYPE = "ml.eia.medium"
IMAGE_URI = "sagemaker-pytorch"
JOB_NAME = sprintf("%s-%s", IMAGE_URI, TIMESTAMP)
ROLE = "Dummy"
REGION = "us-west-2"
GPU = "ml.p2.xlarge"
CPU = "ml.c4.xlarge"
pytorch_inference_version = "1.6"
pytorch_inference_py_version = "py3"
ENDPOINT_DESC = list("EndpointConfigName"="test-endpoint")
ENDPOINT_CONFIG_DESC = list("ProductionVariants"= list(list("ModelName"= "model-1"), list("ModelName"= "model-2")))
LIST_TAGS_RESULT = list("Tags"= list(list("Key"= "TagtestKey", "Value"= "TagtestValue")))
EXPERIMENT_CONFIG = list(
"ExperimentName"= "exp",
"TrialName"= "trial",
"TrialComponentDisplayName"= "tc"
)
sagemaker_session <- function(){
paws_mock <- Mock$new(name = "PawsCredentials", region_name = REGION)
sms <- Mock$new(
name = "Session",
paws_credentials = paws_mock,
paws_region_name=REGION,
config=NULL,
local_mode=FALSE,
s3 = NULL
)
s3_client <- Mock$new()
s3_client$.call_args("put_object")
s3_client$.call_args("get_object", list(Body = BIN_OBJ))
sagemaker_client <- Mock$new()
describe = list("ModelArtifacts"= list("S3ModelArtifacts"= "s3://m/m.tar.gz"))
describe_compilation = list("ModelArtifacts"= list("S3ModelArtifacts"= "s3://m/model_c5.tar.gz"))
sagemaker_client$.call_args("describe_training_job", describe)
sagemaker_client$.call_args("describe_endpoint", ENDPOINT_DESC)
sagemaker_client$.call_args("describe_endpoint_config", ENDPOINT_CONFIG_DESC)
sagemaker_client$.call_args("list_tags", LIST_TAGS_RESULT)
sms$.call_args("default_bucket", BUCKET_NAME)
sms$.call_args("expand_role", ROLE)
sms$.call_args("train", list(TrainingJobArn = "sagemaker-pytorch-dummy"))
sms$.call_args("create_model", "sagemaker-pytorch")
sms$.call_args("endpoint_from_production_variants", "sagemaker-pytorch-endpoint")
sms$.call_args("logs_for_job")
sms$.call_args("wait_for_job")
sms$.call_args("wait_for_compilation_job", describe_compilation)
sms$.call_args("compile_model")
sms$s3 <- s3_client
sms$sagemaker <- sagemaker_client
return(sms)
}
.get_full_cpu_image_uri <- function(version, py_version){
return (ImageUris$new()$retrieve(
"pytorch",
REGION,
version=version,
py_version=py_version,
instance_type=CPU,
image_scope="training")
)
}
.create_train_job <- function(version, py_version){
return(list(
"image_uri"= .get_full_cpu_image_uri(version, py_version),
"input_mode"= "File",
"input_config"= list(
list(
"ChannelName"= "training",
"DataSource"= list(
"S3DataSource"= list(
"S3DataDistributionType"= "FullyReplicated",
"S3DataType"= "S3Prefix")
)
)
),
"role"= ROLE,
"job_name"= JOB_NAME,
"output_config"= list("S3OutputPath"= sprintf("s3://%s/", BUCKET_NAME)),
"resource_config"= list(
"InstanceType"= "ml.c4.4xlarge",
"InstanceCount"= 1,
"VolumeSizeInGB"= 30),
"hyperparameters"= list(
"sagemaker_program"= "dummy_script.py",
"sagemaker_container_log_level"= "INFO",
"sagemaker_job_name"= JOB_NAME,
"sagemaker_submit_directory"= sprintf("s3://%s/%s/source/sourcedir.tar.gz", BUCKET_NAME, JOB_NAME),
"sagemaker_region"= 'us-west-2'),
"stop_condition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"tags"= NULL,
"vpc_config"= NULL,
"metric_definitions"= NULL,
"experiment_config"= NULL,
"debugger_hook_config"= list(
"CollectionConfigurations"= list(),
"S3OutputPath"= sprintf("s3://%s/",BUCKET_NAME))
)
)
}
.get_environment <- function(submit_directory, model_url, image_uri){
return(list(
"Image"= image_uri,
"Environment"= list(
"SAGEMAKER_PROGRAM"= "dummy_script.py",
"SAGEMAKER_SUBMIT_DIRECTORY"= submit_directory,
"SAGEMAKER_CONTAINER_LOG_LEVEL"= "20",
"SAGEMAKER_REGION"= "us-west-2"
),
"ModelDataUrl"= model_url)
)
}
test_that("test create model", {
container_log_level = 'INFO'
source_dir = "s3://mybucket/source"
base_job_name = "job"
sms = sagemaker_session()
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
source_dir=source_dir,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version,
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
container_log_level=container_log_level,
base_job_name=base_job_name
)
pytorch$fit(inputs="s3://mybucket/train", job_name="new_name")
model = pytorch$create_model()
expect_equal(model$sagemaker_session, sms)
expect_equal(model$framework_version, pytorch_inference_version)
expect_equal(model$py_version, pytorch_inference_py_version)
expect_equal(model$entry_point, basename(SCRIPT_PATH))
expect_equal(model$role, ROLE)
expect_equal(model$container_log_level, "20")
expect_equal(model$source_dir, source_dir)
expect_null(model$image_uri)
expect_null(model$vpc_config)
})
test_that("test create model with optional params", {
container_log_level = 'INFO'
source_dir = "s3://mybucket/source"
sms = sagemaker_session()
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
source_dir=source_dir,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version,
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
container_log_level=container_log_level,
base_job_name="job"
)
pytorch$fit(inputs="s3://mybucket/train", job_name="new_name")
new_role = "role"
model_server_workers = 2
vpc_config = list("Subnets"= list("foo"), "SecurityGroupIds"= list("bar"))
model_name = "model-name"
model = pytorch$create_model(
role=new_role,
model_server_workers=model_server_workers,
vpc_config_override=vpc_config,
entry_point=SERVING_SCRIPT_FILE,
env=ENV,
name=model_name
)
expect_equal(model$role, new_role)
expect_equal(model$model_server_workers, model_server_workers)
expect_equal(model$vpc_config, vpc_config)
expect_equal(model$entry_point, SERVING_SCRIPT_FILE)
expect_equal(model$env, ENV)
expect_equal(model$name, model_name)
})
test_that("test create model with custom image", {
container_log_level = 'INFO'
source_dir = "s3://mybucket/source"
custom_image = "pytorch:9000"
base_job_name = "job"
sms = sagemaker_session()
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
source_dir=source_dir,
framework_version="2.0",
py_version="py3",
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
image_uri=custom_image,
container_log_level=container_log_level,
base_job_name=base_job_name
)
pytorch$fit(inputs="s3://mybucket/train", job_name="new_name")
model = pytorch$create_model()
expect_equal(model$sagemaker_session, sms)
expect_equal(model$image_uri, custom_image)
expect_equal(model$entry_point, basename(SCRIPT_PATH))
expect_equal(model$role, ROLE)
expect_equal(model$container_log_level, "20")
expect_equal(model$source_dir, source_dir)
})
test_that("test pytorch", {
skip_if_no_python()
skip_if_no_numpy()
sms = sagemaker_session()
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
framework_version= pytorch_inference_version,
py_version=pytorch_inference_py_version,
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
enable_sagemaker_metrics=FALSE
)
inputs = "s3://mybucket/train"
pytorch$fit(inputs=inputs, experiment_config=EXPERIMENT_CONFIG)
model = pytorch$create_model()
expected_image_uri = ImageUris$new()$retrieve(
"pytorch",
REGION,
version=pytorch_inference_version,
py_version=pytorch_inference_py_version,
instance_type=GPU,
image_scope="inference"
)
actual_environment = model$prepare_container_def(GPU)
submit_directory = actual_environment$Environment$SAGEMAKER_SUBMIT_DIRECTORY
model_url = actual_environment$ModelDataUrl
expected_environment = .get_environment(submit_directory, model_url, expected_image_uri)
expect_equal(actual_environment, expected_environment)
expect_true(grepl("cpu", model$prepare_container_def(CPU)$Image))
predictor = pytorch$deploy(1, GPU)
expect_true(inherits(predictor, "PyTorchPredictor"))
})
test_that("test model", {
skip_if_no_python()
skip_if_no_numpy()
model = PyTorchModel$new(
MODEL_DATA,
role=ROLE,
entry_point=SCRIPT_PATH,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version,
sagemaker_session=sagemaker_session()
)
predictor = model$deploy(1, GPU)
expect_true(inherits(predictor, "PyTorchPredictor"))
})
test_that("test mms model", {
skip_if_no_python()
skip_if_no_numpy()
model = PyTorchModel$new(
MODEL_DATA,
role=ROLE,
entry_point=SCRIPT_PATH,
sagemaker_session=sagemaker_session(),
framework_version="1.2",
py_version="py3"
)
predictor = model$deploy(1, GPU)
expect_equal(model$model_data, MODEL_DATA)
expect_equal(model$uploaded_code$script_name, basename(SCRIPT_PATH))
expect_true(inherits(predictor, "PyTorchPredictor"))
})
test_that("test non mms model", {
skip_if_no_python()
skip_if_no_numpy()
model = PyTorchModel$new(
MODEL_DATA,
role=ROLE,
entry_point=SCRIPT_PATH,
sagemaker_session=sagemaker_session(),
framework_version="1.1",
py_version="py3"
)
predictor = model$deploy(1, GPU)
expect_equal(model$model_data, MODEL_DATA)
expect_equal(model$uploaded_code$script_name, basename(SCRIPT_PATH))
expect_true(inherits(predictor, "PyTorchPredictor"))
})
test_that("test model image accelerator", {
skip_if_no_python()
skip_if_no_numpy()
model = PyTorchModel$new(
MODEL_DATA,
role=ROLE,
entry_point=SCRIPT_PATH,
sagemaker_session=sagemaker_session(),
framework_version="1.3.1",
py_version="py2"
)
expect_error(model$deploy(1, CPU, accelerator_type=ACCELERATOR_TYPE))
})
test_that("test model prepare container def no instance type or image", {
model = PyTorchModel$new(
MODEL_DATA,
role=ROLE,
entry_point=SCRIPT_PATH,
framework_version="1.3.1",
py_version="py3",
sagemaker_session=sagemaker_session()
)
expect_error(model$prepare_container_def())
})
test_that("test attach", {
training_image = sprintf("1.dkr.ecr.us-west-2.amazonaws.com/sagemaker-pytorch:%s-cpu-%s",
pytorch_inference_version, pytorch_inference_py_version)
returned_job_description = list(
"AlgorithmSpecification"= list("TrainingInputMode"= "File", "TrainingImage"= training_image),
"HyperParameters"= list(
"sagemaker_submit_directory"= 's3://some/sourcedir.tar.gz',
"sagemaker_program"= 'iris-dnn-classifier.py',
"sagemaker_s3_uri_training"= 'sagemaker-3/integ-test-data/tf_iris',
"sagemaker_container_log_level"= 'INFO',
"sagemaker_job_name"= 'neo',
"training_steps"= "100",
"sagemaker_region"= 'us-west-2'),
"RoleArn"= "arn:aws:iam::366:role/SageMakerRole",
"ResourceConfig"= list(
"VolumeSizeInGB"= 30,
"InstanceCount"= 1,
"InstanceType"= "ml.c4.xlarge"),
"StoppingCondition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"TrainingJobName"= "neo",
"TrainingJobStatus"= "Completed",
"TrainingJobArn"= "arn:aws:sagemaker:us-west-2:336:training-job/neo",
"OutputDataConfig"= list("KmsKeyId"= "", "S3OutputPath"= "s3://place/output/neo"),
"TrainingJobOutput"= list("S3TrainingJobOutput"= "s3://here/output.tar.gz")
)
sm <- sagemaker_session()
sm$sagemaker$.call_args("describe_training_job", returned_job_description)
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
role=ROLE,
sagemaker_session=sm,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version
)
estimator = pytorch$attach(training_job_name="neo", sagemaker_session=sm)
expect_equal(estimator$latest_training_job, "neo")
expect_equal(estimator$py_version, pytorch_inference_py_version)
expect_equal(estimator$framework_version, pytorch_inference_version)
expect_equal(estimator$role, "arn:aws:iam::366:role/SageMakerRole")
expect_equal(estimator$instance_count, 1)
expect_equal(estimator$max_run, 24 * 60 * 60)
expect_equal(estimator$input_mode, "File")
expect_equal(estimator$base_job_name, "neo")
expect_equal(estimator$output_path, "s3://place/output/neo")
expect_equal(estimator$output_kms_key, "")
expect_equal(estimator$hyperparameters()$training_steps, "100")
expect_equal(estimator$source_dir, "s3://some/sourcedir.tar.gz")
expect_equal(estimator$entry_point, "iris-dnn-classifier.py")
expect_equal(estimator$tags, LIST_TAGS_RESULT$Tags)
})
test_that("test attach wrong framework", {
rjd = list(
"AlgorithmSpecification"= list(
"TrainingInputMode"= "File",
"TrainingImage"= "1.dkr.ecr.us-west-2.amazonaws.com/sagemaker-mxnet-py2-cpu:1.0.4"),
"HyperParameters"= list(
"sagemaker_submit_directory"= 's3://some/sourcedir.tar.gz',
"checkpoint_path"= 's3://other/1508872349',
"sagemaker_program"= 'iris-dnn-classifier.py',
"sagemaker_container_log_level"= 'INFO',
"training_steps"= "100",
"sagemaker_region"= 'us-west-2'),
"RoleArn"= "arn:aws:iam::366:role/SageMakerRole",
"ResourceConfig"= list(
"VolumeSizeInGB"= 30,
"InstanceCount"= 1,
"InstanceType"= "ml.c4.xlarge"),
"StoppingCondition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"TrainingJobName"= "neo",
"TrainingJobStatus"= "Completed",
"TrainingJobArn"= "arn:aws:sagemaker:us-west-2:336:training-job/neo",
"OutputDataConfig"= list("KmsKeyId"= "", "S3OutputPath"= "s3://place/output/neo"),
"TrainingJobOutput"= list("S3TrainingJobOutput"= "s3://here/output.tar.gz")
)
sm <- sagemaker_session()
sm$sagemaker$.call_args("describe_training_job", rjd)
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
role=ROLE,
sagemaker_session=sm,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version
)
expect_error(pytorch$attach(training_job_name="neo", sagemaker_session=sm))
})
test_that("test attach custom image", {
training_image = "pytorch:latest"
returned_job_description = list(
"AlgorithmSpecification"= list("TrainingInputMode"= "File", "TrainingImage"= training_image),
"HyperParameters"=list(
"sagemaker_submit_directory"= 's3://some/sourcedir.tar.gz',
"sagemaker_program"= 'iris-dnn-classifier.py',
"sagemaker_s3_uri_training"= 'sagemaker-3/integ-test-data/tf_iris',
"sagemaker_container_log_level"= 'INFO',
"sagemaker_job_name"= 'neo',
"training_steps"= "100",
"sagemaker_region"= 'us-west-2'),
"RoleArn"= "arn:aws:iam::366:role/SageMakerRole",
"ResourceConfig"= list(
"VolumeSizeInGB"= 30,
"InstanceCount"= 1,
"InstanceType"= "ml.c4.xlarge"),
"StoppingCondition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"TrainingJobName"= "neo",
"TrainingJobStatus"= "Completed",
"TrainingJobArn"= "arn:aws:sagemaker:us-west-2:336:training-job/neo",
"OutputDataConfig"= list("KmsKeyId"= "", "S3OutputPath"= "s3://place/output/neo"),
"TrainingJobOutput"= list("S3TrainingJobOutput"= "s3://here/output.tar.gz")
)
sm <- sagemaker_session()
sm$sagemaker$.call_args("describe_training_job", returned_job_description)
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
role=ROLE,
sagemaker_session=sm,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version
)
estimator = pytorch$attach(training_job_name="neo", sagemaker_session=sm)
expect_equal(estimator$latest_training_job, "neo")
expect_equal(estimator$image_uri, training_image)
expect_equal(estimator$training_image_uri(), training_image)
})
test_that("test estimator py2 warning", {
estimator = PyTorch$new(
entry_point=SCRIPT_PATH,
framework_version="1.2.1",
py_version="py2",
role=ROLE,
sagemaker_session=sagemaker_session(),
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE
)
expect_equal(estimator$py_version, "py2")
})
test_that("test model py2 warning", {
model = PyTorchModel$new(
MODEL_DATA,
role=ROLE,
entry_point=SCRIPT_PATH,
framework_version="1.2.1",
py_version="py2",
sagemaker_session=sagemaker_session()
)
expect_equal(model$py_version, "py2")
})
test_that("test pt enable sm metrics", {
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version,
role=ROLE,
sagemaker_session=sagemaker_session(),
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
enable_sagemaker_metrics=TRUE
)
expect_true(pytorch$enable_sagemaker_metrics)
})
test_that("test mx disable sm metrics", {
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
framework_version="1.2",
py_version=pytorch_inference_py_version,
role=ROLE,
sagemaker_session=sagemaker_session(),
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
enable_sagemaker_metrics=FALSE
)
expect_false(pytorch$enable_sagemaker_metrics)
})
test_that("test pt default sm metrics", {
pt_args = list(
entry_point=SCRIPT_PATH,
py_version=pytorch_inference_py_version,
role=ROLE,
sagemaker_session=sagemaker_session(),
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE)
for (fw_version in c("1.2.0", "1.6.0")){
pytorch = do.call(PyTorch$new, c(pt_args, framework_version = fw_version))
if(package_version(fw_version) < package_version("1.3"))
expect_null(pytorch$enable_sagemaker_metrics)
else
expect_true(pytorch$enable_sagemaker_metrics)
}
})
test_that("test custom image estimator deploy", {
custom_image = "mycustomimage:latest"
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version,
role=ROLE,
sagemaker_session=sagemaker_session(),
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE
)
pytorch$fit(inputs="s3://mybucket/train", job_name="new_name")
model = pytorch$create_model(image_uri=custom_image)
expect_equal(model$image_uri, custom_image)
})
DyfanJones/sagemaker-r-mlframework documentation built on March 18, 2022, 7:41 a.m.
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# NOTE: This code has been modified from AWS Sagemaker Python:
#https://github.com/aws/sagemaker-python-sdk/blob/master/tests/unit/test_mxnet.py
context("PyTorch")
library(sagemaker.core)
library(sagemaker.common)
library(sagemaker.mlcore)
DATA_DIR = file.path(getwd(), "data")
SCRIPT_PATH =file.path(DATA_DIR, "dummy_script.py")
TAR_FILE <- file.path(DATA_DIR, "test_tar.tgz")
BIN_OBJ <- readBin(con = TAR_FILE, what = "raw", n = file.size(TAR_FILE))
SERVING_SCRIPT_FILE = "another_dummy_script.py"
MODEL_DATA = "s3://some/data.tar.gz"
ENV = list("DUMMY_ENV_VAR"= "dummy_value")
TIMESTAMP = "2017-11-06-14:14:15.672"
TIME = 1507167947
BUCKET_NAME = "mybucket"
INSTANCE_COUNT = 1
INSTANCE_TYPE = "ml.c4.4xlarge"
ACCELERATOR_TYPE = "ml.eia.medium"
IMAGE_URI = "sagemaker-pytorch"
JOB_NAME = sprintf("%s-%s", IMAGE_URI, TIMESTAMP)
ROLE = "Dummy"
REGION = "us-west-2"
GPU = "ml.p2.xlarge"
CPU = "ml.c4.xlarge"
pytorch_inference_version = "1.6"
pytorch_inference_py_version = "py3"
ENDPOINT_DESC = list("EndpointConfigName"="test-endpoint")
ENDPOINT_CONFIG_DESC = list("ProductionVariants"= list(list("ModelName"= "model-1"), list("ModelName"= "model-2")))
LIST_TAGS_RESULT = list("Tags"= list(list("Key"= "TagtestKey", "Value"= "TagtestValue")))
EXPERIMENT_CONFIG = list(
"ExperimentName"= "exp",
"TrialName"= "trial",
"TrialComponentDisplayName"= "tc"
)
sagemaker_session <- function(){
paws_mock <- Mock$new(name = "PawsCredentials", region_name = REGION)
sms <- Mock$new(
name = "Session",
paws_credentials = paws_mock,
paws_region_name=REGION,
config=NULL,
local_mode=FALSE,
s3 = NULL
)
s3_client <- Mock$new()
s3_client$.call_args("put_object")
s3_client$.call_args("get_object", list(Body = BIN_OBJ))
sagemaker_client <- Mock$new()
describe = list("ModelArtifacts"= list("S3ModelArtifacts"= "s3://m/m.tar.gz"))
describe_compilation = list("ModelArtifacts"= list("S3ModelArtifacts"= "s3://m/model_c5.tar.gz"))
sagemaker_client$.call_args("describe_training_job", describe)
sagemaker_client$.call_args("describe_endpoint", ENDPOINT_DESC)
sagemaker_client$.call_args("describe_endpoint_config", ENDPOINT_CONFIG_DESC)
sagemaker_client$.call_args("list_tags", LIST_TAGS_RESULT)
sms$.call_args("default_bucket", BUCKET_NAME)
sms$.call_args("expand_role", ROLE)
sms$.call_args("train", list(TrainingJobArn = "sagemaker-pytorch-dummy"))
sms$.call_args("create_model", "sagemaker-pytorch")
sms$.call_args("endpoint_from_production_variants", "sagemaker-pytorch-endpoint")
sms$.call_args("logs_for_job")
sms$.call_args("wait_for_job")
sms$.call_args("wait_for_compilation_job", describe_compilation)
sms$.call_args("compile_model")
sms$s3 <- s3_client
sms$sagemaker <- sagemaker_client
return(sms)
}
.get_full_cpu_image_uri <- function(version, py_version){
return (ImageUris$new()$retrieve(
"pytorch",
REGION,
version=version,
py_version=py_version,
instance_type=CPU,
image_scope="training")
)
}
.create_train_job <- function(version, py_version){
return(list(
"image_uri"= .get_full_cpu_image_uri(version, py_version),
"input_mode"= "File",
"input_config"= list(
list(
"ChannelName"= "training",
"DataSource"= list(
"S3DataSource"= list(
"S3DataDistributionType"= "FullyReplicated",
"S3DataType"= "S3Prefix")
)
)
),
"role"= ROLE,
"job_name"= JOB_NAME,
"output_config"= list("S3OutputPath"= sprintf("s3://%s/", BUCKET_NAME)),
"resource_config"= list(
"InstanceType"= "ml.c4.4xlarge",
"InstanceCount"= 1,
"VolumeSizeInGB"= 30),
"hyperparameters"= list(
"sagemaker_program"= "dummy_script.py",
"sagemaker_container_log_level"= "INFO",
"sagemaker_job_name"= JOB_NAME,
"sagemaker_submit_directory"= sprintf("s3://%s/%s/source/sourcedir.tar.gz", BUCKET_NAME, JOB_NAME),
"sagemaker_region"= 'us-west-2'),
"stop_condition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"tags"= NULL,
"vpc_config"= NULL,
"metric_definitions"= NULL,
"experiment_config"= NULL,
"debugger_hook_config"= list(
"CollectionConfigurations"= list(),
"S3OutputPath"= sprintf("s3://%s/",BUCKET_NAME))
)
)
}
.get_environment <- function(submit_directory, model_url, image_uri){
return(list(
"Image"= image_uri,
"Environment"= list(
"SAGEMAKER_PROGRAM"= "dummy_script.py",
"SAGEMAKER_SUBMIT_DIRECTORY"= submit_directory,
"SAGEMAKER_CONTAINER_LOG_LEVEL"= "20",
"SAGEMAKER_REGION"= "us-west-2"
),
"ModelDataUrl"= model_url)
)
}
test_that("test create model", {
container_log_level = 'INFO'
source_dir = "s3://mybucket/source"
base_job_name = "job"
sms = sagemaker_session()
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
source_dir=source_dir,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version,
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
container_log_level=container_log_level,
base_job_name=base_job_name
)
pytorch$fit(inputs="s3://mybucket/train", job_name="new_name")
model = pytorch$create_model()
expect_equal(model$sagemaker_session, sms)
expect_equal(model$framework_version, pytorch_inference_version)
expect_equal(model$py_version, pytorch_inference_py_version)
expect_equal(model$entry_point, basename(SCRIPT_PATH))
expect_equal(model$role, ROLE)
expect_equal(model$container_log_level, "20")
expect_equal(model$source_dir, source_dir)
expect_null(model$image_uri)
expect_null(model$vpc_config)
})
test_that("test create model with optional params", {
container_log_level = 'INFO'
source_dir = "s3://mybucket/source"
sms = sagemaker_session()
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
source_dir=source_dir,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version,
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
container_log_level=container_log_level,
base_job_name="job"
)
pytorch$fit(inputs="s3://mybucket/train", job_name="new_name")
new_role = "role"
model_server_workers = 2
vpc_config = list("Subnets"= list("foo"), "SecurityGroupIds"= list("bar"))
model_name = "model-name"
model = pytorch$create_model(
role=new_role,
model_server_workers=model_server_workers,
vpc_config_override=vpc_config,
entry_point=SERVING_SCRIPT_FILE,
env=ENV,
name=model_name
)
expect_equal(model$role, new_role)
expect_equal(model$model_server_workers, model_server_workers)
expect_equal(model$vpc_config, vpc_config)
expect_equal(model$entry_point, SERVING_SCRIPT_FILE)
expect_equal(model$env, ENV)
expect_equal(model$name, model_name)
})
test_that("test create model with custom image", {
container_log_level = 'INFO'
source_dir = "s3://mybucket/source"
custom_image = "pytorch:9000"
base_job_name = "job"
sms = sagemaker_session()
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
source_dir=source_dir,
framework_version="2.0",
py_version="py3",
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
image_uri=custom_image,
container_log_level=container_log_level,
base_job_name=base_job_name
)
pytorch$fit(inputs="s3://mybucket/train", job_name="new_name")
model = pytorch$create_model()
expect_equal(model$sagemaker_session, sms)
expect_equal(model$image_uri, custom_image)
expect_equal(model$entry_point, basename(SCRIPT_PATH))
expect_equal(model$role, ROLE)
expect_equal(model$container_log_level, "20")
expect_equal(model$source_dir, source_dir)
})
test_that("test pytorch", {
skip_if_no_python()
skip_if_no_numpy()
sms = sagemaker_session()
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
framework_version= pytorch_inference_version,
py_version=pytorch_inference_py_version,
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
enable_sagemaker_metrics=FALSE
)
inputs = "s3://mybucket/train"
pytorch$fit(inputs=inputs, experiment_config=EXPERIMENT_CONFIG)
model = pytorch$create_model()
expected_image_uri = ImageUris$new()$retrieve(
"pytorch",
REGION,
version=pytorch_inference_version,
py_version=pytorch_inference_py_version,
instance_type=GPU,
image_scope="inference"
)
actual_environment = model$prepare_container_def(GPU)
submit_directory = actual_environment$Environment$SAGEMAKER_SUBMIT_DIRECTORY
model_url = actual_environment$ModelDataUrl
expected_environment = .get_environment(submit_directory, model_url, expected_image_uri)
expect_equal(actual_environment, expected_environment)
expect_true(grepl("cpu", model$prepare_container_def(CPU)$Image))
predictor = pytorch$deploy(1, GPU)
expect_true(inherits(predictor, "PyTorchPredictor"))
})
test_that("test model", {
skip_if_no_python()
skip_if_no_numpy()
model = PyTorchModel$new(
MODEL_DATA,
role=ROLE,
entry_point=SCRIPT_PATH,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version,
sagemaker_session=sagemaker_session()
)
predictor = model$deploy(1, GPU)
expect_true(inherits(predictor, "PyTorchPredictor"))
})
test_that("test mms model", {
skip_if_no_python()
skip_if_no_numpy()
model = PyTorchModel$new(
MODEL_DATA,
role=ROLE,
entry_point=SCRIPT_PATH,
sagemaker_session=sagemaker_session(),
framework_version="1.2",
py_version="py3"
)
predictor = model$deploy(1, GPU)
expect_equal(model$model_data, MODEL_DATA)
expect_equal(model$uploaded_code$script_name, basename(SCRIPT_PATH))
expect_true(inherits(predictor, "PyTorchPredictor"))
})
test_that("test non mms model", {
skip_if_no_python()
skip_if_no_numpy()
model = PyTorchModel$new(
MODEL_DATA,
role=ROLE,
entry_point=SCRIPT_PATH,
sagemaker_session=sagemaker_session(),
framework_version="1.1",
py_version="py3"
)
predictor = model$deploy(1, GPU)
expect_equal(model$model_data, MODEL_DATA)
expect_equal(model$uploaded_code$script_name, basename(SCRIPT_PATH))
expect_true(inherits(predictor, "PyTorchPredictor"))
})
test_that("test model image accelerator", {
skip_if_no_python()
skip_if_no_numpy()
model = PyTorchModel$new(
MODEL_DATA,
role=ROLE,
entry_point=SCRIPT_PATH,
sagemaker_session=sagemaker_session(),
framework_version="1.3.1",
py_version="py2"
)
expect_error(model$deploy(1, CPU, accelerator_type=ACCELERATOR_TYPE))
})
test_that("test model prepare container def no instance type or image", {
model = PyTorchModel$new(
MODEL_DATA,
role=ROLE,
entry_point=SCRIPT_PATH,
framework_version="1.3.1",
py_version="py3",
sagemaker_session=sagemaker_session()
)
expect_error(model$prepare_container_def())
})
test_that("test attach", {
training_image = sprintf("1.dkr.ecr.us-west-2.amazonaws.com/sagemaker-pytorch:%s-cpu-%s",
pytorch_inference_version, pytorch_inference_py_version)
returned_job_description = list(
"AlgorithmSpecification"= list("TrainingInputMode"= "File", "TrainingImage"= training_image),
"HyperParameters"= list(
"sagemaker_submit_directory"= 's3://some/sourcedir.tar.gz',
"sagemaker_program"= 'iris-dnn-classifier.py',
"sagemaker_s3_uri_training"= 'sagemaker-3/integ-test-data/tf_iris',
"sagemaker_container_log_level"= 'INFO',
"sagemaker_job_name"= 'neo',
"training_steps"= "100",
"sagemaker_region"= 'us-west-2'),
"RoleArn"= "arn:aws:iam::366:role/SageMakerRole",
"ResourceConfig"= list(
"VolumeSizeInGB"= 30,
"InstanceCount"= 1,
"InstanceType"= "ml.c4.xlarge"),
"StoppingCondition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"TrainingJobName"= "neo",
"TrainingJobStatus"= "Completed",
"TrainingJobArn"= "arn:aws:sagemaker:us-west-2:336:training-job/neo",
"OutputDataConfig"= list("KmsKeyId"= "", "S3OutputPath"= "s3://place/output/neo"),
"TrainingJobOutput"= list("S3TrainingJobOutput"= "s3://here/output.tar.gz")
)
sm <- sagemaker_session()
sm$sagemaker$.call_args("describe_training_job", returned_job_description)
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
role=ROLE,
sagemaker_session=sm,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version
)
estimator = pytorch$attach(training_job_name="neo", sagemaker_session=sm)
expect_equal(estimator$latest_training_job, "neo")
expect_equal(estimator$py_version, pytorch_inference_py_version)
expect_equal(estimator$framework_version, pytorch_inference_version)
expect_equal(estimator$role, "arn:aws:iam::366:role/SageMakerRole")
expect_equal(estimator$instance_count, 1)
expect_equal(estimator$max_run, 24 * 60 * 60)
expect_equal(estimator$input_mode, "File")
expect_equal(estimator$base_job_name, "neo")
expect_equal(estimator$output_path, "s3://place/output/neo")
expect_equal(estimator$output_kms_key, "")
expect_equal(estimator$hyperparameters()$training_steps, "100")
expect_equal(estimator$source_dir, "s3://some/sourcedir.tar.gz")
expect_equal(estimator$entry_point, "iris-dnn-classifier.py")
expect_equal(estimator$tags, LIST_TAGS_RESULT$Tags)
})
test_that("test attach wrong framework", {
rjd = list(
"AlgorithmSpecification"= list(
"TrainingInputMode"= "File",
"TrainingImage"= "1.dkr.ecr.us-west-2.amazonaws.com/sagemaker-mxnet-py2-cpu:1.0.4"),
"HyperParameters"= list(
"sagemaker_submit_directory"= 's3://some/sourcedir.tar.gz',
"checkpoint_path"= 's3://other/1508872349',
"sagemaker_program"= 'iris-dnn-classifier.py',
"sagemaker_container_log_level"= 'INFO',
"training_steps"= "100",
"sagemaker_region"= 'us-west-2'),
"RoleArn"= "arn:aws:iam::366:role/SageMakerRole",
"ResourceConfig"= list(
"VolumeSizeInGB"= 30,
"InstanceCount"= 1,
"InstanceType"= "ml.c4.xlarge"),
"StoppingCondition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"TrainingJobName"= "neo",
"TrainingJobStatus"= "Completed",
"TrainingJobArn"= "arn:aws:sagemaker:us-west-2:336:training-job/neo",
"OutputDataConfig"= list("KmsKeyId"= "", "S3OutputPath"= "s3://place/output/neo"),
"TrainingJobOutput"= list("S3TrainingJobOutput"= "s3://here/output.tar.gz")
)
sm <- sagemaker_session()
sm$sagemaker$.call_args("describe_training_job", rjd)
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
role=ROLE,
sagemaker_session=sm,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version
)
expect_error(pytorch$attach(training_job_name="neo", sagemaker_session=sm))
})
test_that("test attach custom image", {
training_image = "pytorch:latest"
returned_job_description = list(
"AlgorithmSpecification"= list("TrainingInputMode"= "File", "TrainingImage"= training_image),
"HyperParameters"=list(
"sagemaker_submit_directory"= 's3://some/sourcedir.tar.gz',
"sagemaker_program"= 'iris-dnn-classifier.py',
"sagemaker_s3_uri_training"= 'sagemaker-3/integ-test-data/tf_iris',
"sagemaker_container_log_level"= 'INFO',
"sagemaker_job_name"= 'neo',
"training_steps"= "100",
"sagemaker_region"= 'us-west-2'),
"RoleArn"= "arn:aws:iam::366:role/SageMakerRole",
"ResourceConfig"= list(
"VolumeSizeInGB"= 30,
"InstanceCount"= 1,
"InstanceType"= "ml.c4.xlarge"),
"StoppingCondition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"TrainingJobName"= "neo",
"TrainingJobStatus"= "Completed",
"TrainingJobArn"= "arn:aws:sagemaker:us-west-2:336:training-job/neo",
"OutputDataConfig"= list("KmsKeyId"= "", "S3OutputPath"= "s3://place/output/neo"),
"TrainingJobOutput"= list("S3TrainingJobOutput"= "s3://here/output.tar.gz")
)
sm <- sagemaker_session()
sm$sagemaker$.call_args("describe_training_job", returned_job_description)
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
role=ROLE,
sagemaker_session=sm,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version
)
estimator = pytorch$attach(training_job_name="neo", sagemaker_session=sm)
expect_equal(estimator$latest_training_job, "neo")
expect_equal(estimator$image_uri, training_image)
expect_equal(estimator$training_image_uri(), training_image)
})
test_that("test estimator py2 warning", {
estimator = PyTorch$new(
entry_point=SCRIPT_PATH,
framework_version="1.2.1",
py_version="py2",
role=ROLE,
sagemaker_session=sagemaker_session(),
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE
)
expect_equal(estimator$py_version, "py2")
})
test_that("test model py2 warning", {
model = PyTorchModel$new(
MODEL_DATA,
role=ROLE,
entry_point=SCRIPT_PATH,
framework_version="1.2.1",
py_version="py2",
sagemaker_session=sagemaker_session()
)
expect_equal(model$py_version, "py2")
})
test_that("test pt enable sm metrics", {
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version,
role=ROLE,
sagemaker_session=sagemaker_session(),
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
enable_sagemaker_metrics=TRUE
)
expect_true(pytorch$enable_sagemaker_metrics)
})
test_that("test mx disable sm metrics", {
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
framework_version="1.2",
py_version=pytorch_inference_py_version,
role=ROLE,
sagemaker_session=sagemaker_session(),
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
enable_sagemaker_metrics=FALSE
)
expect_false(pytorch$enable_sagemaker_metrics)
})
test_that("test pt default sm metrics", {
pt_args = list(
entry_point=SCRIPT_PATH,
py_version=pytorch_inference_py_version,
role=ROLE,
sagemaker_session=sagemaker_session(),
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE)
for (fw_version in c("1.2.0", "1.6.0")){
pytorch = do.call(PyTorch$new, c(pt_args, framework_version = fw_version))
if(package_version(fw_version) < package_version("1.3"))
expect_null(pytorch$enable_sagemaker_metrics)
else
expect_true(pytorch$enable_sagemaker_metrics)
}
})
test_that("test custom image estimator deploy", {
custom_image = "mycustomimage:latest"
pytorch = PyTorch$new(
entry_point=SCRIPT_PATH,
framework_version=pytorch_inference_version,
py_version=pytorch_inference_py_version,
role=ROLE,
sagemaker_session=sagemaker_session(),
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE
)
pytorch$fit(inputs="s3://mybucket/train", job_name="new_name")
model = pytorch$create_model(image_uri=custom_image)
expect_equal(model$image_uri, custom_image)
})
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