Merge branch 'master' into doc_clarify_1.0.9

Author: shreyapandit

CHANGELOG.md

@@ -1,5 +1,17 @@
 # Changelog
 
+## v2.49.1 (2021-07-19)
+
+### Bug Fixes and Other Changes
+
+ * Set flag when debugger is disabled
+ * KMS Key fix for kwargs
+ * Update BiasConfig to accept multiple facet params
+
+### Documentation Changes
+
+ * Update huggingface estimator documentation
+
 ## v2.49.0 (2021-07-15)
 
 ### Features

VERSION

@@ -1 +1 @@
-2.49.1.dev0
+2.49.2.dev0

doc/_static/aws-ux-shortbread/index.js

@@ -0,0 +1,34 @@
+(function (w) {
+w.URLSearchParams = w.URLSearchParams || function (searchString) {
+  var self = this;
+  self.searchString = searchString;
+  self.get = function (name) {
+    var results = new RegExp('[\?&]' + name + '=([^&#]*)').exec(self.searchString);
+    if (results === null) {
+      return null;
+    }
+    else {
+      return decodeURI(results[1]) || 0;
+    }
+  };
+}
+})(window);
+
+const queryString = window.location.search;
+const urlParams = new URLSearchParams(queryString);
+const lang = urlParams.get('lang')
+window.onload = function () {
+var domainName = window.location.hostname;
+
+// remove an instance of shortbread if already exists
+var existingShortbreadEl = document.getElementById("awsccc-sb-ux-c");
+existingShortbreadEl && existingShortbreadEl.remove();        
+
+var shortbread = AWSCShortbread({
+  domain: domainName,
+  language: lang,
+  //queryGeolocation: function (geolocatedIn) { geolocatedIn("EU") },
+});
+
+shortbread.checkForCookieConsent();
+}

doc/_static/aws-ux-shortbread/init.js

@@ -68,7 +68,12 @@
 
 htmlhelp_basename = "%sdoc" % project
 
-html_js_files = ["https://a0.awsstatic.com/s_code/js/1.0/awshome_s_code.js", "js/analytics.js"]
+# For Adobe Analytics
+html_js_files = [
+    "https://a0.awsstatic.com/s_code/js/3.0/awshome_s_code.js",
+    "aws-ux-shortbread/index.js",
+    "aws-ux-shortbread/init.js",
+]
 
 html_context = {"css_files": ["_static/theme_overrides.css"]}
 

doc/conf.py

@@ -15,6 +15,7 @@
 
 from sagemaker.debugger.debugger import (  # noqa: F401
     CollectionConfig,
+    DEBUGGER_FLAG,
     DebuggerHookConfig,
     framework_name,
     get_default_profiler_rule,

src/sagemaker/debugger/__init__.py

@@ -32,6 +32,7 @@
 from sagemaker.utils import build_dict
 
 framework_name = "debugger"
+DEBUGGER_FLAG = "USE_SMDEBUG"
 
 
 def get_rule_container_image_uri(region):

src/sagemaker/debugger/debugger.py

@@ -29,6 +29,7 @@
 from sagemaker.analytics import TrainingJobAnalytics
 from sagemaker.debugger import TensorBoardOutputConfig  # noqa: F401 # pylint: disable=unused-import
 from sagemaker.debugger import (
+    DEBUGGER_FLAG,
     DebuggerHookConfig,
     FrameworkProfile,
     get_default_profiler_rule,
@@ -2269,6 +2270,11 @@ def _validate_and_set_debugger_configs(self):
                     )
                     self.debugger_hook_config = False
 
+        if self.debugger_hook_config is False:
+            if self.environment is None:
+                self.environment = {}
+            self.environment[DEBUGGER_FLAG] = "0"
+
     def _stage_user_code_in_s3(self):
         """Upload the user training script to s3 and return the location.
 

src/sagemaker/estimator.py

@@ -70,10 +70,10 @@ def __init__(
                 ``image_uri`` is provided. The current supported version is ``4.6.1``.
             tensorflow_version (str): TensorFlow version you want to use for
                 executing your model training code. Defaults to ``None``. Required unless
-                ``pytorch_version`` is provided. The current supported version is ``1.6.0``.
+                ``pytorch_version`` is provided. The current supported version is ``2.4.1``.
             pytorch_version (str): PyTorch version you want to use for
                 executing your model training code. Defaults to ``None``. Required unless
-                ``tensorflow_version`` is provided. The current supported version is ``2.4.1``.
+                ``tensorflow_version`` is provided. The current supported versions are ``1.7.1`` and ``1.6.0``.
             source_dir (str): Path (absolute, relative or an S3 URI) to a directory
                 with any other training source code dependencies aside from the entry
                 point file (default: None). If ``source_dir`` is an S3 URI, it must

src/sagemaker/huggingface/estimator.py

@@ -112,8 +112,8 @@ def __init__(
         if "entry_point" in kwargs:
             repack_model = True
             entry_point = kwargs.pop("entry_point", None)
-            source_dir = kwargs.get("source_dir")
-            dependencies = kwargs.get("dependencies")
+            source_dir = kwargs.pop("source_dir", None)
+            dependencies = kwargs.pop("dependencies", None)
             kwargs = dict(**kwargs, output_kms_key=kwargs.pop("model_kms_key", None))
 
             repack_model_step = _RepackModelStep(
@@ -130,13 +130,10 @@ def __init__(
             steps.append(repack_model_step)
             model_data = repack_model_step.properties.ModelArtifacts.S3ModelArtifacts
 
-        # remove kwargs consumed by model repacking step
-        kwargs.pop("entry_point", None)
-        kwargs.pop("source_dir", None)
-        kwargs.pop("dependencies", None)
-        kwargs.pop("output_kms_key", None)
+            # remove kwargs consumed by model repacking step
+            kwargs.pop("output_kms_key", None)
 
-        if model is not None:
+        elif model is not None:
             if isinstance(model, PipelineModel):
                 self.model_list = model.models
                 self.container_def_list = model.pipeline_container_def(inference_instances[0])
@@ -156,7 +153,9 @@ def __init__(
                     entry_point = model_entity.entry_point
                     source_dir = model_entity.source_dir
                     dependencies = model_entity.dependencies
+                    kwargs = dict(**kwargs, output_kms_key=model_entity.model_kms_key)
                     name = model_entity.name or model_entity._framework_name
+
                     repack_model_step = _RepackModelStep(
                         name=f"{name}RepackModel",
                         depends_on=depends_on,
@@ -166,12 +165,16 @@ def __init__(
                         entry_point=entry_point,
                         source_dir=source_dir,
                         dependencies=dependencies,
+                        **kwargs,
                     )
                     steps.append(repack_model_step)
                     model_entity.model_data = (
                         repack_model_step.properties.ModelArtifacts.S3ModelArtifacts
                     )
 
+                    # remove kwargs consumed by model repacking step
+                    kwargs.pop("output_kms_key", None)
+
         register_model_step = _RegisterModelStep(
             name=name,
             estimator=estimator,

src/sagemaker/workflow/step_collections.py

@@ -18,6 +18,7 @@
 import pytest
 
 from sagemaker.debugger.debugger import (
+    DEBUGGER_FLAG,
     DebuggerHookConfig,
     Rule,
     rule_configs,
@@ -748,6 +749,7 @@ def test_mxnet_with_debugger_hook_config_disabled(
         job_description = mx.latest_training_job.describe()
 
         assert job_description.get("DebugHookConfig") is None
+        assert job_description.get("Environment", {}).get(DEBUGGER_FLAG) == "0"
 
 
 def _get_rule_evaluation_statuses(job_description):

tests/integ/test_debugger.py

@@ -23,7 +23,7 @@
     patch,
 )
 
-from sagemaker.debugger import ProfilerConfig
+from sagemaker.debugger import DEBUGGER_FLAG, ProfilerConfig
 from sagemaker.estimator import Estimator
 from sagemaker.tensorflow import TensorFlow
 from sagemaker.inputs import TrainingInput, TransformInput, CreateModelInput
@@ -275,6 +275,7 @@ def test_training_step_tensorflow(sagemaker_session):
                 "sagemaker_distributed_dataparallel_custom_mpi_options": '""',
             },
             "ProfilerConfig": {"S3OutputPath": "s3://my-bucket/"},
+            "Environment": {DEBUGGER_FLAG: "0"},
         },
         "CacheConfig": {"Enabled": True, "ExpireAfter": "PT1H"},
     }