breaking: deprecate unused functions from utils and fw_utils

This also deprecates the unused class sagemaker.session.ModelContainer

Author: laurenyu

src/sagemaker/fw_utils.py

@@ -33,15 +33,6 @@
 instantiated with positional or keyword arguments.
 """
 
-EMPTY_FRAMEWORK_VERSION_WARNING = (
-    "No framework_version specified, defaulting to version {}. "
-    "framework_version will be required in SageMaker Python SDK v2."
-)
-LATER_FRAMEWORK_VERSION_WARNING = (
-    "This is not the latest supported version. "
-    "If you would like to use version {latest}, "
-    "please add framework_version={latest} to your constructor."
-)
 PYTHON_2_DEPRECATION_WARNING = (
     "{latest_supported_version} is the latest version of {framework} that supports "
     "Python 2. Newer versions of {framework} will only be available for Python 3."
@@ -54,21 +45,10 @@
     "fully leverage all GPU cores; the parameter server will be configured to run "
     "only one worker per host regardless of the number of GPUs."
 )
-PARAMETER_V2_RENAME_WARNING = (
-    "Parameter {v1_parameter_name} will be renamed to {v2_parameter_name} "
-    "in SageMaker Python SDK v2."
-)
-
-
-EMPTY_FRAMEWORK_VERSION_ERROR = (
-    "framework_version is required for script mode estimator. "
-    "Please add framework_version={} to your constructor to avoid this error."
-)
 
+DEBUGGER_UNSUPPORTED_REGIONS = ("us-gov-west-1", "us-iso-east-1")
 SINGLE_GPU_INSTANCE_TYPES = ("ml.p2.xlarge", "ml.p3.2xlarge")
 
-DEBUGGER_UNSUPPORTED_REGIONS = ["us-gov-west-1", "us-iso-east-1"]
-
 
 def is_version_equal_or_higher(lowest_version, framework_version):
     """Determine whether the ``framework_version`` is equal to or higher than
@@ -294,26 +274,6 @@ def model_code_key_prefix(code_location_key_prefix, model_name, image):
     return "/".join(filter(None, [code_location_key_prefix, model_name or training_job_name]))
 
 
-def empty_framework_version_warning(default_version, latest_version):
-    """
-    Args:
-        default_version:
-        latest_version:
-    """
-    msgs = [EMPTY_FRAMEWORK_VERSION_WARNING.format(default_version)]
-    if default_version != latest_version:
-        msgs.append(later_framework_version_warning(latest_version))
-    return " ".join(msgs)
-
-
-def later_framework_version_warning(latest_version):
-    """
-    Args:
-        latest_version:
-    """
-    return LATER_FRAMEWORK_VERSION_WARNING.format(latest=latest_version)
-
-
 def warn_if_parameter_server_with_multi_gpu(training_instance_type, distribution):
     """Warn the user that training will not fully leverage all the GPU
     cores if parameter server is enabled and a multi-GPU instance is selected.
@@ -363,17 +323,6 @@ def python_deprecation_warning(framework, latest_supported_version):
     )
 
 
-def parameter_v2_rename_warning(v1_parameter_name, v2_parameter_name):
-    """
-    Args:
-        v1_parameter_name: parameter name used in SageMaker Python SDK v1
-        v2_parameter_name: parameter name used in SageMaker Python SDK v2
-    """
-    return PARAMETER_V2_RENAME_WARNING.format(
-        v1_parameter_name=v1_parameter_name, v2_parameter_name=v2_parameter_name
-    )
-
-
 def _region_supports_debugger(region_name):
     """Returns boolean indicating whether the region supports Amazon SageMaker Debugger.
 

src/sagemaker/session.py

@@ -3370,29 +3370,6 @@ def __init__(self, seed):
         self.seed = seed
 
 
-class ModelContainer(object):
-    """Amazon SageMaker Model configurations for inference pipelines.
-
-    Attributes:
-        model_data (str): S3 Model artifact location
-        image_uri (str): Docker image URL in ECR
-        env (dict[str,str]): Environment variable mapping
-    """
-
-    def __init__(self, model_data, image_uri, env=None):
-        """Create a definition of a model which can be part of an Inference Pipeline
-
-        Args:
-            model_data (str): The S3 location of a SageMaker model data ``.tar.gz`` file.
-            image_uri (str): A Docker image URI.
-            env (dict[str, str]): Environment variables to run with ``image_uri`` when hosted in
-                SageMaker (default: None).
-        """
-        self.model_data = model_data
-        self.image_uri = image_uri
-        self.env = env
-
-
 def _create_model_request(
     name, role, container_def=None, tags=None
 ):  # pylint: disable=redefined-outer-name

src/sagemaker/utils.py

@@ -24,10 +24,8 @@
 import tempfile
 import time
 from datetime import datetime
-from functools import wraps
 
 import botocore
-import six
 from six.moves.urllib import parse
 
 
@@ -132,21 +130,6 @@ def sagemaker_short_timestamp():
     return time.strftime("%y%m%d-%H%M")
 
 
-def debug(func):
-    """Print the function name and arguments for debugging.
-
-    Args:
-        func:
-    """
-
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        print("{} args: {} kwargs: {}".format(func.__name__, args, kwargs))
-        return func(*args, **kwargs)
-
-    return wrapper
-
-
 def get_config_value(key_path, config):
     """
     Args:
@@ -178,19 +161,6 @@ def get_short_version(framework_version):
     return ".".join(framework_version.split(".")[:2])
 
 
-def extract_name_from_job_arn(arn):
-    """Returns the name used in the API given a full ARN for a training job or
-    hyperparameter tuning job.
-
-    Args:
-        arn:
-    """
-    slash_pos = arn.find("/")
-    if slash_pos == -1:
-        raise ValueError("Cannot parse invalid ARN: %s" % arn)
-    return arn[(slash_pos + 1) :]
-
-
 def secondary_training_status_changed(current_job_description, prev_job_description):
     """Returns true if training job's secondary status message has changed.
 
@@ -276,55 +246,6 @@ def secondary_training_status_message(job_description, prev_description):
     return "\n".join(status_strs)
 
 
-def generate_tensorboard_url(domain, bucket_paths):
-    """Generate Tensorboard URL for given list of s3 buckets
-
-    Args:
-        domain: JupyterLab app domain
-        bucket_paths: List of S3 bucket paths in format `bucket/path`
-                      or a single string in the same format
-
-    Returns:
-        str: Tensorboard URL
-
-    Raises:
-        AttributeError if invalid inputs are passed
-    """
-
-    def trim_prefix(s, prefix):
-        if s.startswith(prefix):
-            return s[len(prefix) :]
-        return s
-
-    def encode_s3_url(s3_url):
-        if not s3_url:
-            raise AttributeError("bucket_paths element should not be empty")
-        s3_url = trim_prefix(s3_url, S3_PREFIX)
-        return parse.quote_plus("{}{}".format(S3_PREFIX, s3_url))
-
-    if not isinstance(domain, six.string_types):
-        raise AttributeError("domain parameter should be string")
-
-    if len(domain) == 0:
-        raise AttributeError("domain parameter should not be empty")
-
-    if isinstance(bucket_paths, six.string_types):
-        bucket_paths = [bucket_paths]
-    elif not isinstance(bucket_paths, list):
-        raise AttributeError("bucket paths should be a list or a string")
-
-    if len(bucket_paths) == 0:
-        raise AttributeError("bucket_paths parameter should not be empty list")
-
-    domain = trim_prefix(domain, HTTPS_PREFIX)
-    domain = trim_prefix(domain, HTTP_PREFIX)
-
-    s3_urls = map(encode_s3_url, bucket_paths)
-    query = ",".join(s3_urls)
-
-    return "https://{}/tensorboard/default?s3urls={}".format(domain, query)
-
-
 def download_folder(bucket_name, prefix, target, sagemaker_session):
     """Download a folder from S3 to a local path
 

tests/unit/test_pipeline_model.py

@@ -18,7 +18,6 @@
 from sagemaker.model import FrameworkModel
 from sagemaker.pipeline import PipelineModel
 from sagemaker.predictor import Predictor
-from sagemaker.session import ModelContainer
 from sagemaker.sparkml import SparkMLModel
 
 ENTRY_POINT = "blah.py"
@@ -30,8 +29,6 @@
 ROLE = "some-role"
 ENV_1 = {"SAGEMAKER_DEFAULT_INVOCATIONS_ACCEPT": "application/json"}
 ENV_2 = {"SAGEMAKER_DEFAULT_INVOCATIONS_ACCEPT": "text/csv"}
-MODEL_CONTAINER_1 = ModelContainer(image_uri=MODEL_IMAGE_1, model_data=MODEL_DATA_1, env=ENV_1)
-MODEL_CONTAINER_2 = ModelContainer(image_uri=MODEL_IMAGE_2, model_data=MODEL_DATA_2, env=ENV_2)
 ENDPOINT = "some-ep"
 
 

tests/unit/test_utils.py

@@ -103,18 +103,6 @@ def test_unique_name_from_base_truncated():
     )
 
 
-def test_name_from_tuning_arn():
-    arn = "arn:aws:sagemaker:us-west-2:968277160000:hyper-parameter-tuning-job/resnet-sgd-tuningjob-11-07-34-11"
-    name = sagemaker.utils.extract_name_from_job_arn(arn)
-    assert name == "resnet-sgd-tuningjob-11-07-34-11"
-
-
-def test_name_from_training_arn():
-    arn = "arn:aws:sagemaker:us-west-2:968277160000:training-job/resnet-sgd-tuningjob-11-22-38-46-002-2927640b"
-    name = sagemaker.utils.extract_name_from_job_arn(arn)
-    assert name == "resnet-sgd-tuningjob-11-22-38-46-002-2927640b"
-
-
 def test_base_from_name():
     name = "mxnet-training-2020-06-29-15-19-25-475"
     assert "mxnet-training" == sagemaker.utils.base_from_name(name)
@@ -217,105 +205,6 @@ def test_secondary_training_status_message_prev_missing():
     )
 
 
-def test_generate_tensorboard_url_valid_domain_and_bucket_paths():
-    domain = "jupyterlab.us-east-2.abcdefgh.com"
-    bucket_paths = ["bucket1/path1", "bucket2/path2"]
-    expected = "https://{}/tensorboard/default?{}".format(
-        domain, "s3urls=s3%3A%2F%2Fbucket1%2Fpath1,s3%3A%2F%2Fbucket2%2Fpath2"
-    )
-    assert sagemaker.utils.generate_tensorboard_url(domain, bucket_paths) == expected
-
-
-def test_generate_tensorboard_url_valid_domain_and_bucket_paths_with_s3_prefixes():
-    domain = "jupyterlab.us-east-2.abcdefgh.com"
-    bucket_paths = ["s3://bucket1/path1", "s3://bucket2/path2"]
-    expected = "https://{}/tensorboard/default?{}".format(
-        domain, "s3urls=s3%3A%2F%2Fbucket1%2Fpath1,s3%3A%2F%2Fbucket2%2Fpath2"
-    )
-    assert sagemaker.utils.generate_tensorboard_url(domain, bucket_paths) == expected
-
-
-def test_generate_tensorboard_url_valid_domain_and_bucket_paths_single():
-    domain = "jupyterlab.us-east-2.abcdefgh.com"
-    bucket_paths = ["bucket1/path1"]
-    expected = "https://{}/tensorboard/default?{}".format(
-        domain, "s3urls=s3%3A%2F%2Fbucket1%2Fpath1"
-    )
-    assert sagemaker.utils.generate_tensorboard_url(domain, bucket_paths) == expected
-
-
-def test_generate_tensorboard_url_valid_domain_and_bucket_paths_string():
-    domain = "jupyterlab.us-east-2.abcdefgh.com"
-    bucket_paths = "bucket1/path1"
-    expected = "https://{}/tensorboard/default?{}".format(
-        domain, "s3urls=s3%3A%2F%2Fbucket1%2Fpath1"
-    )
-    assert sagemaker.utils.generate_tensorboard_url(domain, bucket_paths) == expected
-
-
-def test_generate_tensorboard_url_valid_domain_with_http_prefix_and_bucket_paths():
-    domain = "http://jupyterlab.us-east-2.abcdefgh.com"
-    bucket_paths = ["bucket1/path1"]
-    expected = "https://{}/tensorboard/default?{}".format(
-        "jupyterlab.us-east-2.abcdefgh.com", "s3urls=s3%3A%2F%2Fbucket1%2Fpath1"
-    )
-    assert sagemaker.utils.generate_tensorboard_url(domain, bucket_paths) == expected
-
-
-def test_generate_tensorboard_url_valid_domain_with_https_prefix_and_bucket_paths():
-    domain = "https://jupyterlab.us-east-2.abcdefgh.com"
-    bucket_paths = ["bucket1/path1"]
-    expected = "https://{}/tensorboard/default?{}".format(
-        "jupyterlab.us-east-2.abcdefgh.com", "s3urls=s3%3A%2F%2Fbucket1%2Fpath1"
-    )
-    assert sagemaker.utils.generate_tensorboard_url(domain, bucket_paths) == expected
-
-
-def test_generate_tensorboard_url_bucket_path_neither_string_nor_list():
-    domain = "jupyterlab.us-east-2.abcdefgh.com"
-    bucket_paths = None
-    try:
-        sagemaker.utils.generate_tensorboard_url(domain, bucket_paths)
-    except AttributeError as error:
-        assert str(error) == "bucket paths should be a list or a string"
-
-
-def test_generate_tensorboard_url_empty_domain():
-    domain = ""
-    bucket_paths = ["bucket1/path1"]
-    try:
-        sagemaker.utils.generate_tensorboard_url(domain, bucket_paths)
-    except AttributeError as error:
-        assert str(error) == "domain parameter should not be empty"
-
-
-def test_generate_tensorboard_url_empty_bucket_paths():
-    domain = "jupyterlab.us-east-2.abcdefgh.com"
-    bucket_paths = []
-    try:
-        sagemaker.utils.generate_tensorboard_url(domain, bucket_paths)
-    except AttributeError as error:
-        assert str(error) == "bucket_paths parameter should not be empty list"
-
-
-def test_generate_tensorboard_url_bucket_paths_with_empty_string():
-    domain = "jupyterlab.us-east-2.abcdefgh.com"
-    bucket_paths = [""]
-    try:
-        sagemaker.utils.generate_tensorboard_url(domain, bucket_paths)
-    except AttributeError as error:
-        assert str(error) == "bucket_paths element should not be empty"
-
-
-def test_generate_tensorboard_url_domain_non_string():
-    domain = None
-    bucket_paths = ["bucket1/path1"]
-    try:
-        sagemaker.utils.generate_tensorboard_url(domain, bucket_paths)
-    except AttributeError as error:
-        assert str(error) == "domain parameter should be string"
-
-
 @patch("os.makedirs")
 def test_download_folder(makedirs):
     boto_mock = Mock(name="boto_session")