tests/testthat/test-tensorflow.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/tree/master/tests/unit/sagemaker/tensorflow
context("tensorflow")
library(sagemaker.core)
library(sagemaker.common)
library(sagemaker.mlcore)
DATA_DIR = file.path(getwd(), "data")
SCRIPT_FILE = "dummy_script.py"
SCRIPT_PATH =file.path(DATA_DIR, SCRIPT_FILE)
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.eia1.medium"
IMAGE_URI = "tensorflow-inference:2.0.0-cpu"
PREDICT_INPUT = list("instances"= c(1.0, 2.0, 5.0))
PREDICT_RESPONSE = list("predictions"= list(c(3.5, 4.0, 5.5), c(3.5, 4.0, 5.5)))
CLASSIFY_INPUT = list(
"signature_name"= "tensorflow/serving/classify",
"examples"= list(list("x"= 1.0), list("x"= 2.0))
)
CLASSIFY_RESPONSE = list("result"= list(c(0.4, 0.6), c(0.2, 0.8)))
REGRESS_INPUT = list(
"signature_name"= "tensorflow/serving/regress",
"examples"= list(list("x"= 1.0), list("x"= 2.0))
)
JOB_NAME = "sagemaker-tensorflow-scriptmode-.*"
IMAGE_URI_FORMAT_STRING = "520713654638.dkr.ecr.%s.amazonaws.com/sagemaker-tensorflow-scriptmode:%s-cpu-%s"
DISTRIBUTION_PS_ENABLED = list("parameter_server"=list("enabled"=TRUE))
DISTRIBUTION_MPI_ENABLED = list(
"mpi"=list("enabled"=TRUE, "custom_mpi_options"="options", "processes_per_host"=2)
)
DISTRIBUTION_SM_DDP_ENABLED = list("smdistributed"=list("dataparallel"=list("enabled"=TRUE)))
ROLE = "Dummy"
REGION = "us-west-2"
GPU = "ml.p2.xlarge"
CPU = "ml.c4.xlarge"
tensorflow_inference_version = "2.1.1"
tensorflow_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$.call_args("train", list(TrainingJobArn = "sagemaker-tf-dummy"))
sms$.call_args("create_model", "sagemaker-tf")
sms$.call_args("endpoint_from_production_variants", "sagemaker-tf-endpoint")
sms$.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$sagemaker <- sagemaker_client
return(sms)
}
.build_tf <- function(sagemaker_session, ...){
return(TensorFlow$new(
sagemaker_session=sagemaker_session,
entry_point="dummy.py",
role="dummy-role",
instance_count=1,
instance_type="ml.c4.xlarge",
...
)
)
}
.image_uri <- function(tf_version, py_version){
return(sprintf(IMAGE_URI_FORMAT_STRING, REGION, tf_version, py_version))
}
.hyperparameters <- function(horovod=FALSE, smdataparallel=FALSE){
hps = list(
"sagemaker_submit_directory"= sprintf(
"s3://%s/%s/source/sourcedir.tar.gz", BUCKET_NAME, JOB_NAME),
"sagemaker_program"= "dummy_script.py",
"sagemaker_container_log_level"= 20,
"sagemaker_job_name"= JOB_NAME,
"sagemaker_region"= "us-west-2"
)
if (horovod || smdataparallel)
hps$model_dir = "/opt/ml/model"
else
hps$model_dir = sprintf("s3://%s/%s/model", BUCKET_NAME, JOB_NAME)
return(hps)
}
.create_train_job <- function(tf_version, horovod=FALSE, ps=FALSE, py_version="py2", smdataparallel=FALSE){
conf = list(
"input_config"= list(
list(
"DataSource"= list(
"S3DataSource"= list(
"S3DataType"= "S3Prefix",
"S3Uri" = NULL,
"S3DataDistributionType"= "FullyReplicated"
)
),
"ChannelName"= "training"
)
),
"role"= ROLE,
"output_config"= list("S3OutputPath"= sprintf("s3://%s/",BUCKET_NAME)),
"resource_config"= list(
"InstanceCount"= 1,
"InstanceType"= "ml.c4.4xlarge",
"VolumeSizeInGB"= 30),
"stop_condition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"vpc_config"= NULL,
"input_mode"= "File",
"hyperparameters"= .hyperparameters(horovod, smdataparallel),
"image_uri"= .image_uri(tf_version, py_version)
)
if (!ps)
conf$debugger_hook_config = list(
"S3OutputPath"= sprintf("s3://%s/", BUCKET_NAME),
"CollectionConfigurations"= list()
)
profiler_rule_configs = list(
list(
"RuleConfigurationName"= "ProfilerReport-.*",
"RuleEvaluatorImage"= "895741380848.dkr.ecr.us-west-2.amazonaws.com/sagemaker-debugger-rules:latest",
"RuleParameters"= list("rule_to_invoke"= "ProfilerReport")
)
)
profiler_config = list("S3OutputPath"= sprintf("s3://%s/",BUCKET_NAME))
conf[["profiler_rule_configs"]] = profiler_rule_configs
conf[["profiler_config"]] = profiler_config
return(conf)
}
# ===== test estimator init ====
test_that("test estimator py2 deprecation warning", {
sms <- sagemaker_session()
estimator = .build_tf(sms,framework_version="2.1.1", py_version="py2")
expect_equal(estimator$py_version, "py2")
})
test_that("test py2 version deprecated", {
sms <- sagemaker_session()
expect_error(
.build_tf(sms, framework_version="2.1.2", py_version="py2"))
})
test_that("test py2 version is not deprecated", {
estimator = .build_tf(sagemaker_session(), framework_version="1.15.0", py_version="py2")
expect_equal(estimator$py_version, "py2")
estimator = .build_tf(sagemaker_session(), framework_version="2.0.0", py_version="py2")
expect_equal(estimator$py_version, "py2")
})
test_that("test framework name", {
tf = .build_tf(sagemaker_session(), framework_version="1.15.2", py_version="py3")
expect_equal(attr(tf, "_framework_name"), "tensorflow")
})
test_that("test enable sm metrics", {
tf = .build_tf(
sagemaker_session(),
framework_version="1.15.2",
py_version="py3",
enable_sagemaker_metrics=TRUE
)
expect_true(tf$enable_sagemaker_metrics)
})
test_that("test disable sm metrics", {
tf = .build_tf(
sagemaker_session(),
framework_version="1.15.2",
py_version="py3",
enable_sagemaker_metrics=FALSE
)
expect_false(tf$enable_sagemaker_metrics)
})
test_that("test disable sm metrics if fw ver is less than 1.15", {
tf = .build_tf(
sagemaker_session(),
framework_version="1.14",
py_version="py3",
image_uri="old-image",
)
expect_null(tf$enable_sagemaker_metrics)
})
test_that("test require image uri if fw ver is less than 1.11", {
expect_error(
.build_tf(
sagemaker_session(),
framework_version="1.10",
py_version="py2"
)
)
})
# ===== test estimator attach ====
test_that("test attach", {
training_image = ImageUris$new()$retrieve(
"tensorflow",
region=REGION,
version="1.15",
py_version="py3",
instance_type="ml.c4.xlarge",
image_scope="training")
rjd = list(
"AlgorithmSpecification"= list("TrainingInputMode"= "File", "TrainingImage"= training_image),
"HyperParameters"= list(
"sagemaker_submit_directory"= 's3://some/sourcedir.tar.gz',
"sagemaker_program"= 'iris-dnn-classifier.py',
"sagemaker_container_log_level"= 'INFO',
"sagemaker_job_name"= 'neo'),
"RoleArn"= "arn:aws:iam::366:role/SageMakerRole",
"ResourceConfig"= list(
"VolumeSizeInGB"= 30,
"InstanceCount"= 1,
"InstanceType"= "ml.c4.xlarge"),
"StoppingCondition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"TrainingJobName"= "neo",
"TrainingJobStatus"= "Completed",
"TrainingJobArn"= "arn:aws:sagemaker:us-west-2:336:training-job/neo",
"OutputDataConfig"= list("KmsKeyId"= "", "S3OutputPath"= "s3://place/output/neo"),
"TrainingJobOutput"= list("S3TrainingJobOutput"= "s3://here/output.tar.gz")
)
sm <- sagemaker_session()
sm$sagemaker$.call_args("describe_training_job", rjd)
tf = TensorFlow$new(sagemaker_session = sm,
framework_version="1.15",
py_version="py3",
entry_point="dummy.py",
role="dummy-role",
instance_count=1,
instance_type="ml.c4.xlarge")
estimator = tf$attach(training_job_name="neo", sagemaker_session=sm)
expect_equal(estimator$latest_training_job, "neo")
expect_equal(estimator$py_version, "py3")
expect_equal(estimator$framework_version, "1.15")
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$output_kms_key, "")
expect_true(!is.null(estimator$hyperparameters()))
expect_equal(estimator$source_dir, 's3://some/sourcedir.tar.gz')
expect_equal(estimator$entry_point, 'iris-dnn-classifier.py')
expect_equal(estimator$training_image_uri(), training_image)
})
test_that("test attach old container", {
training_image = "1.dkr.ecr.us-west-2.amazonaws.com/sagemaker-tensorflow-py2-cpu:1.0"
rjd = list(
"AlgorithmSpecification"= list("TrainingInputMode"= "File", "TrainingImage"= training_image),
"HyperParameters"= list(
"sagemaker_submit_directory"= 's3://some/sourcedir.tar.gz',
"sagemaker_program"= 'iris-dnn-classifier.py',
"sagemaker_container_log_level"= 'INFO',
"sagemaker_job_name"= 'neo'),
"RoleArn"= "arn:aws:iam::366:role/SageMakerRole",
"ResourceConfig"= list(
"VolumeSizeInGB"= 30,
"InstanceCount"= 1,
"InstanceType"= "ml.c4.xlarge"),
"StoppingCondition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"TrainingJobName"= "neo",
"TrainingJobStatus"= "Completed",
"TrainingJobArn"= "arn:aws:sagemaker:us-west-2:336:training-job/neo",
"OutputDataConfig"= list("KmsKeyId"= "", "S3OutputPath"= "s3://place/output/neo"),
"TrainingJobOutput"= list("S3TrainingJobOutput"= "s3://here/output.tar.gz")
)
sm <- sagemaker_session()
sm$sagemaker$.call_args("describe_training_job", rjd)
tf = TensorFlow$new(sagemaker_session = sm,
framework_version="1.15",
py_version="py3",
entry_point="dummy.py",
role="dummy-role",
instance_count=1,
instance_type="ml.c4.xlarge")
estimator = tf$attach(training_job_name="neo", sagemaker_session=sm)
expect_equal(estimator$latest_training_job, "neo")
expect_equal(estimator$py_version, "py2")
expect_equal(estimator$framework_version, "1.4")
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$output_kms_key, "")
expect_equal(estimator$source_dir, 's3://some/sourcedir.tar.gz')
expect_equal(estimator$entry_point, 'iris-dnn-classifier.py')
})
test_that("test attach wrong framework", {
returned_job_description = list(
"AlgorithmSpecification"= list(
"TrainingInputMode"= "File",
"TrainingImage"= "1.dkr.ecr.us-west-2.amazonaws.com/sagemaker-mxnet-py2-cpu:1.0"),
"HyperParameters"= list(
"sagemaker_submit_directory"= 's3://some/sourcedir.tar.gz',
"sagemaker_program"= 'iris-dnn-classifier.py',
"sagemaker_container_log_level"= 'logging.INFO'),
"RoleArn"= "arn:aws:iam::366:role/SageMakerRole",
"ResourceConfig"= list(
"VolumeSizeInGB"= 30,
"InstanceCount"= 1,
"InstanceType"= "ml.c4.xlarge"),
"StoppingCondition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"TrainingJobName"= "neo",
"TrainingJobStatus"= "Completed",
"TrainingJobArn"= "arn:aws:sagemaker:us-west-2:336:training-job/neo",
"OutputDataConfig"= list("KmsKeyId"= "", "S3OutputPath"= "s3://place/output/neo"),
"TrainingJobOutput"= list("S3TrainingJobOutput"= "s3://here/output.tar.gz")
)
sm <- sagemaker_session()
sm$sagemaker$.call_args("describe_training_job", returned_job_description)
tf = TensorFlow$new(sagemaker_session = sm,
framework_version="1.15",
py_version="py3",
entry_point="dummy.py",
role="dummy-role",
instance_count=1,
instance_type="ml.c4.xlarge")
expect_error(tf$attach(training_job_name="neo", sagemaker_session=sm),
"neo didn't use image for requested framework")
})
test_that("test attach custom image", {
training_image = "1.dkr.ecr.us-west-2.amazonaws.com/tensorflow_with_custom_binary:1.0"
rjd = list(
"AlgorithmSpecification"= list("TrainingInputMode"= "File", "TrainingImage"= training_image),
"HyperParameters"= list(
"sagemaker_submit_directory"= 's3://some/sourcedir.tar.gz',
"sagemaker_program"= 'iris-dnn-classifier.py',
"sagemaker_container_log_level"= 'INFO',
"sagemaker_job_name"= 'neo'),
"RoleArn"= "arn:aws:iam::366:role/SageMakerRole",
"ResourceConfig"= list(
"VolumeSizeInGB"= 30,
"InstanceCount"= 1,
"InstanceType"= "ml.c4.xlarge"),
"StoppingCondition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"TrainingJobName"= "neo",
"TrainingJobStatus"= "Completed",
"TrainingJobArn"= "arn:aws:sagemaker:us-west-2:336:training-job/neo",
"OutputDataConfig"= list("KmsKeyId"= "", "S3OutputPath"= "s3://place/output/neo"),
"TrainingJobOutput"= list("S3TrainingJobOutput"= "s3://here/output.tar.gz")
)
sm <- sagemaker_session()
sm$sagemaker$.call_args("describe_training_job", rjd)
tf = TensorFlow$new(sagemaker_session = sm,
framework_version="1.15",
py_version="py3",
entry_point="dummy.py",
role="dummy-role",
instance_count=1,
instance_type="ml.c4.xlarge")
estimator = tf$attach(training_job_name="neo", sagemaker_session=sm)
expect_equal(estimator$image_uri, training_image)
expect_equal(estimator$training_image_uri(), training_image)
})
# ===== test estimator ====
############### check this part
test_that("test create model", {
container_log_level = 'INFO'
base_job_name = "job"
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_PATH,
source_dir="s3://mybucket/source",
framework_version=tensorflow_inference_version,
py_version=tensorflow_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,
enable_network_isolation=TRUE,
output_path="s3://mybucket/output")
tf$.current_job_name = "doing something"
model = tf$create_model()
expect_equal(model$sagemaker_session, sms)
expect_equal(model$framework_version, tensorflow_inference_version)
expect_null(model$entry_point)
expect_equal(model$role, ROLE)
expect_equal(model$.container_log_level, 20)
expect_null(model$source_dir)
expect_null(model$vpc_config)
expect_true(model$enable_network_isolation())
})
test_that("test create model_with optional params", {
container_log_level = 'INFO'
source_dir = "job"
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_PATH,
framework_version=tensorflow_inference_version,
py_version=tensorflow_inference_py_version,
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
container_log_level=container_log_level,
base_job_name="job",
source_dir=source_dir,
output_path="s3://mybucket/output")
tf$.current_job_name = "doing something"
new_role = "role"
vpc_config = list("Subnets"= list("foo"), "SecurityGroupIds"= list("bar"))
model_name = "model-name"
model = tf$create_model(
role=new_role,
vpc_config_override=vpc_config,
entry_point=SERVING_SCRIPT_FILE,
name=model_name,
enable_network_isolation=TRUE)
expect_equal(model$role, new_role)
expect_equal(model$vpc_config, vpc_config)
expect_equal(model$entry_point, SERVING_SCRIPT_FILE)
expect_equal(model$name, model_name)
expect_true(model$enable_network_isolation())
})
test_that("test create model with custom image", {
container_log_level = 'INFO'
source_dir = "s3://mybucket/source"
custom_image = "tensorflow:1.0"
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_PATH,
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
image_uri=custom_image,
container_log_level=container_log_level,
base_job_name="job",
source_dir=source_dir)
job_name = "doing something"
tf$fit(inputs="s3://mybucket/train", job_name=job_name)
model = tf$create_model()
expect_equal(model$image_uri, custom_image)
})
test_that("test fit", {
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_FILE,
framework_version="1.11",
py_version="py2",
role=ROLE,
sagemaker_session=sms,
instance_type=INSTANCE_TYPE,
instance_count=1,
source_dir=DATA_DIR)
inputs = "s3://mybucket/train"
tf$fit(inputs=inputs)
expected_train_args = .create_train_job("1.11", py_version = "py2")
expected_train_args$input_config[[1]]$DataSource$S3DataSource$S3Uri = inputs
actual_train_args = sms$train(..return_value = T)
actual_train_args$job_name = NULL
# check if keys are identical
expect_equal(names(actual_train_args), names(expected_train_args))
# check if hyperparameters are created correctly
expected_hyperparameters = expected_train_args$hyperparameters
actual_hyperparameters = actual_train_args$hyperparameters
expected_train_args$hyperparameters = NULL
actual_train_args$hyperparameters = NULL
for (i in names(expected_hyperparameters)){
expect_true(grepl(expected_hyperparameters[[i]], actual_hyperparameters[[i]]))
}
# check if rule configuration name is created correctly
expected_RuleConfigurationName = expected_train_args$profiler_rule_configs[[1]]$RuleConfigurationName
actual_RuleConfigurationName= actual_train_args$profiler_rule_configs[[1]]$RuleConfigurationName
expected_train_args$profiler_rule_configs[[1]]$RuleConfigurationName = NULL
actual_train_args$profiler_rule_configs[[1]]$RuleConfigurationName = NULL
expect_true(grepl(expected_RuleConfigurationName, actual_RuleConfigurationName))
# check if list of parameters is created correctly
expect_equal(expected_train_args,actual_train_args)
})
test_that("test fit ps", {
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_FILE,
framework_version="1.11",
py_version="py2",
role=ROLE,
sagemaker_session=sms,
instance_type=INSTANCE_TYPE,
instance_count=1,
source_dir=DATA_DIR,
distribution=DISTRIBUTION_PS_ENABLED)
inputs = "s3://mybucket/train"
tf$fit(inputs=inputs)
expected_train_args = .create_train_job("1.11", ps=TRUE, py_version="py2")
expected_train_args$input_config[[1]]$DataSource$S3DataSource$S3Uri = inputs
expected_train_args[["hyperparameters"]][[Framework$public_fields$LAUNCH_PS_ENV_NAME]] = TRUE
actual_train_args = sms$train(..return_value = T)
actual_train_args$job_name = NULL
# check if keys are identical
expect_equal(names(actual_train_args), names(expected_train_args))
# check if hyperparameters are created correctly
expected_hyperparameters = expected_train_args$hyperparameters
actual_hyperparameters = actual_train_args$hyperparameters
expected_train_args$hyperparameters = NULL
actual_train_args$hyperparameters = NULL
for (i in names(expected_hyperparameters)){
expect_true(grepl(expected_hyperparameters[[i]], actual_hyperparameters[[i]]))
}
# check if rule configuration name is created correctly
expected_RuleConfigurationName = expected_train_args$profiler_rule_configs[[1]]$RuleConfigurationName
actual_RuleConfigurationName= actual_train_args$profiler_rule_configs[[1]]$RuleConfigurationName
expected_train_args$profiler_rule_configs[[1]]$RuleConfigurationName = NULL
actual_train_args$profiler_rule_configs[[1]]$RuleConfigurationName = NULL
expect_true(grepl(expected_RuleConfigurationName, actual_RuleConfigurationName))
# check if list of parameters is created correctly
expect_equal(expected_train_args,actual_train_args)
})
test_that("test fit mpi", {
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_FILE,
framework_version="1.11",
py_version="py2",
role=ROLE,
sagemaker_session=sms,
instance_type=INSTANCE_TYPE,
instance_count=1,
source_dir=DATA_DIR,
distribution=DISTRIBUTION_MPI_ENABLED)
inputs = "s3://mybucket/train"
tf$fit(inputs=inputs)
expected_train_args = .create_train_job("1.11", horovod=TRUE, py_version="py2")
expected_train_args$input_config[[1]]$DataSource$S3DataSource$S3Uri = inputs
expected_train_args[["hyperparameters"]][[Framework$public_fields$LAUNCH_MPI_ENV_NAME]] = TRUE
expected_train_args[["hyperparameters"]][[Framework$public_fields$MPI_NUM_PROCESSES_PER_HOST]] = "2"
expected_train_args[["hyperparameters"]][[Framework$public_fields$MPI_CUSTOM_MPI_OPTIONS]] = "options"
actual_train_args = sms$train(..return_value = T)
actual_train_args$job_name = NULL
# check if keys are identical
expect_equal(names(actual_train_args), names(expected_train_args))
# check if hyperparameters are created correctly
expected_hyperparameters = expected_train_args$hyperparameters
actual_hyperparameters = actual_train_args$hyperparameters
expected_train_args$hyperparameters = NULL
actual_train_args$hyperparameters = NULL
for (i in names(expected_hyperparameters)){
expect_true(grepl(expected_hyperparameters[[i]], actual_hyperparameters[[i]]))
}
# check if rule configuration name is created correctly
expected_RuleConfigurationName = expected_train_args$profiler_rule_configs[[1]]$RuleConfigurationName
actual_RuleConfigurationName= actual_train_args$profiler_rule_configs[[1]]$RuleConfigurationName
expected_train_args$profiler_rule_configs[[1]]$RuleConfigurationName = NULL
actual_train_args$profiler_rule_configs[[1]]$RuleConfigurationName = NULL
expect_true(grepl(expected_RuleConfigurationName, actual_RuleConfigurationName))
# check if list of parameters is created correctly
expect_equal(expected_train_args,actual_train_args)
})
test_that("test hyperparameters no model dir",{
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_PATH,
framework_version="1.15.2",
py_version="py3",
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
base_job_name=NULL,
image_uri="tensorflow:latest",
model_dir=FALSE)
hyperparameters = tf$hyperparameters()
expect_false("model_dir" %in% names(hyperparameters))
})
test_that("test hyperparameters no model dir",{
custom_image = "tensorflow:latest"
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_PATH,
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
image_uri=custom_image)
expect_equal(custom_image, tf$training_image_uri())
})
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/tree/master/tests/unit/sagemaker/tensorflow
context("tensorflow")
library(sagemaker.core)
library(sagemaker.common)
library(sagemaker.mlcore)
DATA_DIR = file.path(getwd(), "data")
SCRIPT_FILE = "dummy_script.py"
SCRIPT_PATH =file.path(DATA_DIR, SCRIPT_FILE)
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.eia1.medium"
IMAGE_URI = "tensorflow-inference:2.0.0-cpu"
PREDICT_INPUT = list("instances"= c(1.0, 2.0, 5.0))
PREDICT_RESPONSE = list("predictions"= list(c(3.5, 4.0, 5.5), c(3.5, 4.0, 5.5)))
CLASSIFY_INPUT = list(
"signature_name"= "tensorflow/serving/classify",
"examples"= list(list("x"= 1.0), list("x"= 2.0))
)
CLASSIFY_RESPONSE = list("result"= list(c(0.4, 0.6), c(0.2, 0.8)))
REGRESS_INPUT = list(
"signature_name"= "tensorflow/serving/regress",
"examples"= list(list("x"= 1.0), list("x"= 2.0))
)
JOB_NAME = "sagemaker-tensorflow-scriptmode-.*"
IMAGE_URI_FORMAT_STRING = "520713654638.dkr.ecr.%s.amazonaws.com/sagemaker-tensorflow-scriptmode:%s-cpu-%s"
DISTRIBUTION_PS_ENABLED = list("parameter_server"=list("enabled"=TRUE))
DISTRIBUTION_MPI_ENABLED = list(
"mpi"=list("enabled"=TRUE, "custom_mpi_options"="options", "processes_per_host"=2)
)
DISTRIBUTION_SM_DDP_ENABLED = list("smdistributed"=list("dataparallel"=list("enabled"=TRUE)))
ROLE = "Dummy"
REGION = "us-west-2"
GPU = "ml.p2.xlarge"
CPU = "ml.c4.xlarge"
tensorflow_inference_version = "2.1.1"
tensorflow_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$.call_args("train", list(TrainingJobArn = "sagemaker-tf-dummy"))
sms$.call_args("create_model", "sagemaker-tf")
sms$.call_args("endpoint_from_production_variants", "sagemaker-tf-endpoint")
sms$.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$sagemaker <- sagemaker_client
return(sms)
}
.build_tf <- function(sagemaker_session, ...){
return(TensorFlow$new(
sagemaker_session=sagemaker_session,
entry_point="dummy.py",
role="dummy-role",
instance_count=1,
instance_type="ml.c4.xlarge",
...
)
)
}
.image_uri <- function(tf_version, py_version){
return(sprintf(IMAGE_URI_FORMAT_STRING, REGION, tf_version, py_version))
}
.hyperparameters <- function(horovod=FALSE, smdataparallel=FALSE){
hps = list(
"sagemaker_submit_directory"= sprintf(
"s3://%s/%s/source/sourcedir.tar.gz", BUCKET_NAME, JOB_NAME),
"sagemaker_program"= "dummy_script.py",
"sagemaker_container_log_level"= 20,
"sagemaker_job_name"= JOB_NAME,
"sagemaker_region"= "us-west-2"
)
if (horovod || smdataparallel)
hps$model_dir = "/opt/ml/model"
else
hps$model_dir = sprintf("s3://%s/%s/model", BUCKET_NAME, JOB_NAME)
return(hps)
}
.create_train_job <- function(tf_version, horovod=FALSE, ps=FALSE, py_version="py2", smdataparallel=FALSE){
conf = list(
"input_config"= list(
list(
"DataSource"= list(
"S3DataSource"= list(
"S3DataType"= "S3Prefix",
"S3Uri" = NULL,
"S3DataDistributionType"= "FullyReplicated"
)
),
"ChannelName"= "training"
)
),
"role"= ROLE,
"output_config"= list("S3OutputPath"= sprintf("s3://%s/",BUCKET_NAME)),
"resource_config"= list(
"InstanceCount"= 1,
"InstanceType"= "ml.c4.4xlarge",
"VolumeSizeInGB"= 30),
"stop_condition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"vpc_config"= NULL,
"input_mode"= "File",
"hyperparameters"= .hyperparameters(horovod, smdataparallel),
"image_uri"= .image_uri(tf_version, py_version)
)
if (!ps)
conf$debugger_hook_config = list(
"S3OutputPath"= sprintf("s3://%s/", BUCKET_NAME),
"CollectionConfigurations"= list()
)
profiler_rule_configs = list(
list(
"RuleConfigurationName"= "ProfilerReport-.*",
"RuleEvaluatorImage"= "895741380848.dkr.ecr.us-west-2.amazonaws.com/sagemaker-debugger-rules:latest",
"RuleParameters"= list("rule_to_invoke"= "ProfilerReport")
)
)
profiler_config = list("S3OutputPath"= sprintf("s3://%s/",BUCKET_NAME))
conf[["profiler_rule_configs"]] = profiler_rule_configs
conf[["profiler_config"]] = profiler_config
return(conf)
}
# ===== test estimator init ====
test_that("test estimator py2 deprecation warning", {
sms <- sagemaker_session()
estimator = .build_tf(sms,framework_version="2.1.1", py_version="py2")
expect_equal(estimator$py_version, "py2")
})
test_that("test py2 version deprecated", {
sms <- sagemaker_session()
expect_error(
.build_tf(sms, framework_version="2.1.2", py_version="py2"))
})
test_that("test py2 version is not deprecated", {
estimator = .build_tf(sagemaker_session(), framework_version="1.15.0", py_version="py2")
expect_equal(estimator$py_version, "py2")
estimator = .build_tf(sagemaker_session(), framework_version="2.0.0", py_version="py2")
expect_equal(estimator$py_version, "py2")
})
test_that("test framework name", {
tf = .build_tf(sagemaker_session(), framework_version="1.15.2", py_version="py3")
expect_equal(attr(tf, "_framework_name"), "tensorflow")
})
test_that("test enable sm metrics", {
tf = .build_tf(
sagemaker_session(),
framework_version="1.15.2",
py_version="py3",
enable_sagemaker_metrics=TRUE
)
expect_true(tf$enable_sagemaker_metrics)
})
test_that("test disable sm metrics", {
tf = .build_tf(
sagemaker_session(),
framework_version="1.15.2",
py_version="py3",
enable_sagemaker_metrics=FALSE
)
expect_false(tf$enable_sagemaker_metrics)
})
test_that("test disable sm metrics if fw ver is less than 1.15", {
tf = .build_tf(
sagemaker_session(),
framework_version="1.14",
py_version="py3",
image_uri="old-image",
)
expect_null(tf$enable_sagemaker_metrics)
})
test_that("test require image uri if fw ver is less than 1.11", {
expect_error(
.build_tf(
sagemaker_session(),
framework_version="1.10",
py_version="py2"
)
)
})
# ===== test estimator attach ====
test_that("test attach", {
training_image = ImageUris$new()$retrieve(
"tensorflow",
region=REGION,
version="1.15",
py_version="py3",
instance_type="ml.c4.xlarge",
image_scope="training")
rjd = list(
"AlgorithmSpecification"= list("TrainingInputMode"= "File", "TrainingImage"= training_image),
"HyperParameters"= list(
"sagemaker_submit_directory"= 's3://some/sourcedir.tar.gz',
"sagemaker_program"= 'iris-dnn-classifier.py',
"sagemaker_container_log_level"= 'INFO',
"sagemaker_job_name"= 'neo'),
"RoleArn"= "arn:aws:iam::366:role/SageMakerRole",
"ResourceConfig"= list(
"VolumeSizeInGB"= 30,
"InstanceCount"= 1,
"InstanceType"= "ml.c4.xlarge"),
"StoppingCondition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"TrainingJobName"= "neo",
"TrainingJobStatus"= "Completed",
"TrainingJobArn"= "arn:aws:sagemaker:us-west-2:336:training-job/neo",
"OutputDataConfig"= list("KmsKeyId"= "", "S3OutputPath"= "s3://place/output/neo"),
"TrainingJobOutput"= list("S3TrainingJobOutput"= "s3://here/output.tar.gz")
)
sm <- sagemaker_session()
sm$sagemaker$.call_args("describe_training_job", rjd)
tf = TensorFlow$new(sagemaker_session = sm,
framework_version="1.15",
py_version="py3",
entry_point="dummy.py",
role="dummy-role",
instance_count=1,
instance_type="ml.c4.xlarge")
estimator = tf$attach(training_job_name="neo", sagemaker_session=sm)
expect_equal(estimator$latest_training_job, "neo")
expect_equal(estimator$py_version, "py3")
expect_equal(estimator$framework_version, "1.15")
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$output_kms_key, "")
expect_true(!is.null(estimator$hyperparameters()))
expect_equal(estimator$source_dir, 's3://some/sourcedir.tar.gz')
expect_equal(estimator$entry_point, 'iris-dnn-classifier.py')
expect_equal(estimator$training_image_uri(), training_image)
})
test_that("test attach old container", {
training_image = "1.dkr.ecr.us-west-2.amazonaws.com/sagemaker-tensorflow-py2-cpu:1.0"
rjd = list(
"AlgorithmSpecification"= list("TrainingInputMode"= "File", "TrainingImage"= training_image),
"HyperParameters"= list(
"sagemaker_submit_directory"= 's3://some/sourcedir.tar.gz',
"sagemaker_program"= 'iris-dnn-classifier.py',
"sagemaker_container_log_level"= 'INFO',
"sagemaker_job_name"= 'neo'),
"RoleArn"= "arn:aws:iam::366:role/SageMakerRole",
"ResourceConfig"= list(
"VolumeSizeInGB"= 30,
"InstanceCount"= 1,
"InstanceType"= "ml.c4.xlarge"),
"StoppingCondition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"TrainingJobName"= "neo",
"TrainingJobStatus"= "Completed",
"TrainingJobArn"= "arn:aws:sagemaker:us-west-2:336:training-job/neo",
"OutputDataConfig"= list("KmsKeyId"= "", "S3OutputPath"= "s3://place/output/neo"),
"TrainingJobOutput"= list("S3TrainingJobOutput"= "s3://here/output.tar.gz")
)
sm <- sagemaker_session()
sm$sagemaker$.call_args("describe_training_job", rjd)
tf = TensorFlow$new(sagemaker_session = sm,
framework_version="1.15",
py_version="py3",
entry_point="dummy.py",
role="dummy-role",
instance_count=1,
instance_type="ml.c4.xlarge")
estimator = tf$attach(training_job_name="neo", sagemaker_session=sm)
expect_equal(estimator$latest_training_job, "neo")
expect_equal(estimator$py_version, "py2")
expect_equal(estimator$framework_version, "1.4")
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$output_kms_key, "")
expect_equal(estimator$source_dir, 's3://some/sourcedir.tar.gz')
expect_equal(estimator$entry_point, 'iris-dnn-classifier.py')
})
test_that("test attach wrong framework", {
returned_job_description = list(
"AlgorithmSpecification"= list(
"TrainingInputMode"= "File",
"TrainingImage"= "1.dkr.ecr.us-west-2.amazonaws.com/sagemaker-mxnet-py2-cpu:1.0"),
"HyperParameters"= list(
"sagemaker_submit_directory"= 's3://some/sourcedir.tar.gz',
"sagemaker_program"= 'iris-dnn-classifier.py',
"sagemaker_container_log_level"= 'logging.INFO'),
"RoleArn"= "arn:aws:iam::366:role/SageMakerRole",
"ResourceConfig"= list(
"VolumeSizeInGB"= 30,
"InstanceCount"= 1,
"InstanceType"= "ml.c4.xlarge"),
"StoppingCondition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"TrainingJobName"= "neo",
"TrainingJobStatus"= "Completed",
"TrainingJobArn"= "arn:aws:sagemaker:us-west-2:336:training-job/neo",
"OutputDataConfig"= list("KmsKeyId"= "", "S3OutputPath"= "s3://place/output/neo"),
"TrainingJobOutput"= list("S3TrainingJobOutput"= "s3://here/output.tar.gz")
)
sm <- sagemaker_session()
sm$sagemaker$.call_args("describe_training_job", returned_job_description)
tf = TensorFlow$new(sagemaker_session = sm,
framework_version="1.15",
py_version="py3",
entry_point="dummy.py",
role="dummy-role",
instance_count=1,
instance_type="ml.c4.xlarge")
expect_error(tf$attach(training_job_name="neo", sagemaker_session=sm),
"neo didn't use image for requested framework")
})
test_that("test attach custom image", {
training_image = "1.dkr.ecr.us-west-2.amazonaws.com/tensorflow_with_custom_binary:1.0"
rjd = list(
"AlgorithmSpecification"= list("TrainingInputMode"= "File", "TrainingImage"= training_image),
"HyperParameters"= list(
"sagemaker_submit_directory"= 's3://some/sourcedir.tar.gz',
"sagemaker_program"= 'iris-dnn-classifier.py',
"sagemaker_container_log_level"= 'INFO',
"sagemaker_job_name"= 'neo'),
"RoleArn"= "arn:aws:iam::366:role/SageMakerRole",
"ResourceConfig"= list(
"VolumeSizeInGB"= 30,
"InstanceCount"= 1,
"InstanceType"= "ml.c4.xlarge"),
"StoppingCondition"= list("MaxRuntimeInSeconds"= 24 * 60 * 60),
"TrainingJobName"= "neo",
"TrainingJobStatus"= "Completed",
"TrainingJobArn"= "arn:aws:sagemaker:us-west-2:336:training-job/neo",
"OutputDataConfig"= list("KmsKeyId"= "", "S3OutputPath"= "s3://place/output/neo"),
"TrainingJobOutput"= list("S3TrainingJobOutput"= "s3://here/output.tar.gz")
)
sm <- sagemaker_session()
sm$sagemaker$.call_args("describe_training_job", rjd)
tf = TensorFlow$new(sagemaker_session = sm,
framework_version="1.15",
py_version="py3",
entry_point="dummy.py",
role="dummy-role",
instance_count=1,
instance_type="ml.c4.xlarge")
estimator = tf$attach(training_job_name="neo", sagemaker_session=sm)
expect_equal(estimator$image_uri, training_image)
expect_equal(estimator$training_image_uri(), training_image)
})
# ===== test estimator ====
############### check this part
test_that("test create model", {
container_log_level = 'INFO'
base_job_name = "job"
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_PATH,
source_dir="s3://mybucket/source",
framework_version=tensorflow_inference_version,
py_version=tensorflow_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,
enable_network_isolation=TRUE,
output_path="s3://mybucket/output")
tf$.current_job_name = "doing something"
model = tf$create_model()
expect_equal(model$sagemaker_session, sms)
expect_equal(model$framework_version, tensorflow_inference_version)
expect_null(model$entry_point)
expect_equal(model$role, ROLE)
expect_equal(model$.container_log_level, 20)
expect_null(model$source_dir)
expect_null(model$vpc_config)
expect_true(model$enable_network_isolation())
})
test_that("test create model_with optional params", {
container_log_level = 'INFO'
source_dir = "job"
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_PATH,
framework_version=tensorflow_inference_version,
py_version=tensorflow_inference_py_version,
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
container_log_level=container_log_level,
base_job_name="job",
source_dir=source_dir,
output_path="s3://mybucket/output")
tf$.current_job_name = "doing something"
new_role = "role"
vpc_config = list("Subnets"= list("foo"), "SecurityGroupIds"= list("bar"))
model_name = "model-name"
model = tf$create_model(
role=new_role,
vpc_config_override=vpc_config,
entry_point=SERVING_SCRIPT_FILE,
name=model_name,
enable_network_isolation=TRUE)
expect_equal(model$role, new_role)
expect_equal(model$vpc_config, vpc_config)
expect_equal(model$entry_point, SERVING_SCRIPT_FILE)
expect_equal(model$name, model_name)
expect_true(model$enable_network_isolation())
})
test_that("test create model with custom image", {
container_log_level = 'INFO'
source_dir = "s3://mybucket/source"
custom_image = "tensorflow:1.0"
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_PATH,
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
image_uri=custom_image,
container_log_level=container_log_level,
base_job_name="job",
source_dir=source_dir)
job_name = "doing something"
tf$fit(inputs="s3://mybucket/train", job_name=job_name)
model = tf$create_model()
expect_equal(model$image_uri, custom_image)
})
test_that("test fit", {
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_FILE,
framework_version="1.11",
py_version="py2",
role=ROLE,
sagemaker_session=sms,
instance_type=INSTANCE_TYPE,
instance_count=1,
source_dir=DATA_DIR)
inputs = "s3://mybucket/train"
tf$fit(inputs=inputs)
expected_train_args = .create_train_job("1.11", py_version = "py2")
expected_train_args$input_config[[1]]$DataSource$S3DataSource$S3Uri = inputs
actual_train_args = sms$train(..return_value = T)
actual_train_args$job_name = NULL
# check if keys are identical
expect_equal(names(actual_train_args), names(expected_train_args))
# check if hyperparameters are created correctly
expected_hyperparameters = expected_train_args$hyperparameters
actual_hyperparameters = actual_train_args$hyperparameters
expected_train_args$hyperparameters = NULL
actual_train_args$hyperparameters = NULL
for (i in names(expected_hyperparameters)){
expect_true(grepl(expected_hyperparameters[[i]], actual_hyperparameters[[i]]))
}
# check if rule configuration name is created correctly
expected_RuleConfigurationName = expected_train_args$profiler_rule_configs[[1]]$RuleConfigurationName
actual_RuleConfigurationName= actual_train_args$profiler_rule_configs[[1]]$RuleConfigurationName
expected_train_args$profiler_rule_configs[[1]]$RuleConfigurationName = NULL
actual_train_args$profiler_rule_configs[[1]]$RuleConfigurationName = NULL
expect_true(grepl(expected_RuleConfigurationName, actual_RuleConfigurationName))
# check if list of parameters is created correctly
expect_equal(expected_train_args,actual_train_args)
})
test_that("test fit ps", {
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_FILE,
framework_version="1.11",
py_version="py2",
role=ROLE,
sagemaker_session=sms,
instance_type=INSTANCE_TYPE,
instance_count=1,
source_dir=DATA_DIR,
distribution=DISTRIBUTION_PS_ENABLED)
inputs = "s3://mybucket/train"
tf$fit(inputs=inputs)
expected_train_args = .create_train_job("1.11", ps=TRUE, py_version="py2")
expected_train_args$input_config[[1]]$DataSource$S3DataSource$S3Uri = inputs
expected_train_args[["hyperparameters"]][[Framework$public_fields$LAUNCH_PS_ENV_NAME]] = TRUE
actual_train_args = sms$train(..return_value = T)
actual_train_args$job_name = NULL
# check if keys are identical
expect_equal(names(actual_train_args), names(expected_train_args))
# check if hyperparameters are created correctly
expected_hyperparameters = expected_train_args$hyperparameters
actual_hyperparameters = actual_train_args$hyperparameters
expected_train_args$hyperparameters = NULL
actual_train_args$hyperparameters = NULL
for (i in names(expected_hyperparameters)){
expect_true(grepl(expected_hyperparameters[[i]], actual_hyperparameters[[i]]))
}
# check if rule configuration name is created correctly
expected_RuleConfigurationName = expected_train_args$profiler_rule_configs[[1]]$RuleConfigurationName
actual_RuleConfigurationName= actual_train_args$profiler_rule_configs[[1]]$RuleConfigurationName
expected_train_args$profiler_rule_configs[[1]]$RuleConfigurationName = NULL
actual_train_args$profiler_rule_configs[[1]]$RuleConfigurationName = NULL
expect_true(grepl(expected_RuleConfigurationName, actual_RuleConfigurationName))
# check if list of parameters is created correctly
expect_equal(expected_train_args,actual_train_args)
})
test_that("test fit mpi", {
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_FILE,
framework_version="1.11",
py_version="py2",
role=ROLE,
sagemaker_session=sms,
instance_type=INSTANCE_TYPE,
instance_count=1,
source_dir=DATA_DIR,
distribution=DISTRIBUTION_MPI_ENABLED)
inputs = "s3://mybucket/train"
tf$fit(inputs=inputs)
expected_train_args = .create_train_job("1.11", horovod=TRUE, py_version="py2")
expected_train_args$input_config[[1]]$DataSource$S3DataSource$S3Uri = inputs
expected_train_args[["hyperparameters"]][[Framework$public_fields$LAUNCH_MPI_ENV_NAME]] = TRUE
expected_train_args[["hyperparameters"]][[Framework$public_fields$MPI_NUM_PROCESSES_PER_HOST]] = "2"
expected_train_args[["hyperparameters"]][[Framework$public_fields$MPI_CUSTOM_MPI_OPTIONS]] = "options"
actual_train_args = sms$train(..return_value = T)
actual_train_args$job_name = NULL
# check if keys are identical
expect_equal(names(actual_train_args), names(expected_train_args))
# check if hyperparameters are created correctly
expected_hyperparameters = expected_train_args$hyperparameters
actual_hyperparameters = actual_train_args$hyperparameters
expected_train_args$hyperparameters = NULL
actual_train_args$hyperparameters = NULL
for (i in names(expected_hyperparameters)){
expect_true(grepl(expected_hyperparameters[[i]], actual_hyperparameters[[i]]))
}
# check if rule configuration name is created correctly
expected_RuleConfigurationName = expected_train_args$profiler_rule_configs[[1]]$RuleConfigurationName
actual_RuleConfigurationName= actual_train_args$profiler_rule_configs[[1]]$RuleConfigurationName
expected_train_args$profiler_rule_configs[[1]]$RuleConfigurationName = NULL
actual_train_args$profiler_rule_configs[[1]]$RuleConfigurationName = NULL
expect_true(grepl(expected_RuleConfigurationName, actual_RuleConfigurationName))
# check if list of parameters is created correctly
expect_equal(expected_train_args,actual_train_args)
})
test_that("test hyperparameters no model dir",{
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_PATH,
framework_version="1.15.2",
py_version="py3",
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
base_job_name=NULL,
image_uri="tensorflow:latest",
model_dir=FALSE)
hyperparameters = tf$hyperparameters()
expect_false("model_dir" %in% names(hyperparameters))
})
test_that("test hyperparameters no model dir",{
custom_image = "tensorflow:latest"
sms <- sagemaker_session()
tf = TensorFlow$new(
entry_point=SCRIPT_PATH,
role=ROLE,
sagemaker_session=sms,
instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
image_uri=custom_image)
expect_equal(custom_image, tf$training_image_uri())
})
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