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Naming discrepancy between env in Processing and environment in Estimator #6214

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Naming discrepancy between env in Processing and environment in Estimator#6214

Description

@mikeweltevrede
mikeweltevrede
opened on Aug 26, 2026

Describe the feature you'd like

There is a discrepancy on passing environment variables in Processing and Estimator. The parameter is called env in Processing and environment in Estimator.

I would like these to be aligned. For backwards compatibility sake, this should probably be manifested through a environment_variables parameter, but any solution would work for me.

The problem

The problem is that we cannot have a unified interface to these entities using **kwargs to pass arguments without manually parsing a parameter ourselves.

Current situation van be something like this if we use env for both situations:

defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("env")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", env={"MY_VAR": 42})
model_training(environment="dev", env={"MY_VAR": 67})

Or with a more compatible interface

defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["env"] =env_varsreturnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})

Ideally, we would want it to look like this because both classes accept a environment_variables parameter:

defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})

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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
Naming discrepancy between `env` in Processing and `environment` in Estimator · Issue #6214 · aws/sagemaker-python-sdk · GitHub
      Skip to content
      Naming discrepancy between env in Processing and environment in Estimator #6214
      Open
      Open
      Naming discrepancy between env in Processing and environment in Estimator#6214
      Description
      @mikeweltevrede
      mikeweltevrede
      opened on Aug 26, 2026
      Describe the feature you'd like
      There is a discrepancy on passing environment variables in Processing and Estimator. The parameter is called env in Processing and environment in Estimator.
      I would like these to be aligned. For backwards compatibility sake, this should probably be manifested through a environment_variables parameter, but any solution would work for me.
      The problem
      The problem is that we cannot have a unified interface to these entities using **kwargs to pass arguments without manually parsing a parameter ourselves.
      Current situation van be something like this if we use env for both situations:
      defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("env")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", env={"MY_VAR": 42})
model_training(environment="dev", env={"MY_VAR": 67})
      Or with a more compatible interface
      defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["env"] =env_varsreturnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
      Ideally, we would want it to look like this because both classes accept a environment_variables parameter:
      defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
      Metadata
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, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Naming discrepancy between `env` in Processing and `environment` in Estimator · Issue #6214 · aws/sagemaker-python-sdk · GitHub
          Skip to content
          Naming discrepancy between env in Processing and environment in Estimator #6214
          Open
          Open
          Naming discrepancy between env in Processing and environment in Estimator#6214
          Description
          @mikeweltevrede
          mikeweltevrede
          opened on Aug 26, 2026
          Describe the feature you'd like
          There is a discrepancy on passing environment variables in Processing and Estimator. The parameter is called env in Processing and environment in Estimator.
          I would like these to be aligned. For backwards compatibility sake, this should probably be manifested through a environment_variables parameter, but any solution would work for me.
          The problem
          The problem is that we cannot have a unified interface to these entities using **kwargs to pass arguments without manually parsing a parameter ourselves.
          Current situation van be something like this if we use env for both situations:
          defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("env")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", env={"MY_VAR": 42})
model_training(environment="dev", env={"MY_VAR": 67})
          Or with a more compatible interface
          defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["env"] =env_varsreturnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
          Ideally, we would want it to look like this because both classes accept a environment_variables parameter:
          defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
          Metadata
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            No type
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              None yet
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              No branches or pull requests
              Issue actions
              
, 'i');
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Naming discrepancy between `env` in Processing and `environment` in Estimator · Issue #6214 · aws/sagemaker-python-sdk · GitHub
              Skip to content
              Naming discrepancy between env in Processing and environment in Estimator #6214
              Open
              Open
              Naming discrepancy between env in Processing and environment in Estimator#6214
              Description
              @mikeweltevrede
              mikeweltevrede
              opened on Aug 26, 2026
              Describe the feature you'd like
              There is a discrepancy on passing environment variables in Processing and Estimator. The parameter is called env in Processing and environment in Estimator.
              I would like these to be aligned. For backwards compatibility sake, this should probably be manifested through a environment_variables parameter, but any solution would work for me.
              The problem
              The problem is that we cannot have a unified interface to these entities using **kwargs to pass arguments without manually parsing a parameter ourselves.
              Current situation van be something like this if we use env for both situations:
              defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("env")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", env={"MY_VAR": 42})
model_training(environment="dev", env={"MY_VAR": 67})
              Or with a more compatible interface
              defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["env"] =env_varsreturnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
              Ideally, we would want it to look like this because both classes accept a environment_variables parameter:
              defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
              Metadata
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, 'i');
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
Naming discrepancy between `env` in Processing and `environment` in Estimator · Issue #6214 · aws/sagemaker-python-sdk · GitHub
                  Skip to content
                  Naming discrepancy between env in Processing and environment in Estimator #6214
                  Open
                  Open
                  Naming discrepancy between env in Processing and environment in Estimator#6214
                  Description
                  @mikeweltevrede
                  mikeweltevrede
                  opened on Aug 26, 2026
                  Describe the feature you'd like
                  There is a discrepancy on passing environment variables in Processing and Estimator. The parameter is called env in Processing and environment in Estimator.
                  I would like these to be aligned. For backwards compatibility sake, this should probably be manifested through a environment_variables parameter, but any solution would work for me.
                  The problem
                  The problem is that we cannot have a unified interface to these entities using **kwargs to pass arguments without manually parsing a parameter ourselves.
                  Current situation van be something like this if we use env for both situations:
                  defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("env")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", env={"MY_VAR": 42})
model_training(environment="dev", env={"MY_VAR": 67})
                  Or with a more compatible interface
                  defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["env"] =env_varsreturnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
                  Ideally, we would want it to look like this because both classes accept a environment_variables parameter:
                  defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
                  Metadata
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, 'i');
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// Auto-enable theater mode on YouTube
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function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Naming discrepancy between `env` in Processing and `environment` in Estimator · Issue #6214 · aws/sagemaker-python-sdk · GitHub
                      Skip to content
                      Naming discrepancy between env in Processing and environment in Estimator #6214
                      Open
                      Open
                      Naming discrepancy between env in Processing and environment in Estimator#6214
                      Description
                      @mikeweltevrede
                      mikeweltevrede
                      opened on Aug 26, 2026
                      Describe the feature you'd like
                      There is a discrepancy on passing environment variables in Processing and Estimator. The parameter is called env in Processing and environment in Estimator.
                      I would like these to be aligned. For backwards compatibility sake, this should probably be manifested through a environment_variables parameter, but any solution would work for me.
                      The problem
                      The problem is that we cannot have a unified interface to these entities using **kwargs to pass arguments without manually parsing a parameter ourselves.
                      Current situation van be something like this if we use env for both situations:
                      defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("env")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", env={"MY_VAR": 42})
model_training(environment="dev", env={"MY_VAR": 67})
                      Or with a more compatible interface
                      defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["env"] =env_varsreturnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
                      Ideally, we would want it to look like this because both classes accept a environment_variables parameter:
                      defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
                      Metadata
                      Metadata
                      Assignees
                      No one assigned
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                        No type
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                        No projects
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                          No branches or pull requests
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
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function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Naming discrepancy between `env` in Processing and `environment` in Estimator · Issue #6214 · aws/sagemaker-python-sdk · GitHub
                          Skip to content
                          Naming discrepancy between env in Processing and environment in Estimator #6214
                          Open
                          Open
                          Naming discrepancy between env in Processing and environment in Estimator#6214
                          Description
                          @mikeweltevrede
                          mikeweltevrede
                          opened on Aug 26, 2026
                          Describe the feature you'd like
                          There is a discrepancy on passing environment variables in Processing and Estimator. The parameter is called env in Processing and environment in Estimator.
                          I would like these to be aligned. For backwards compatibility sake, this should probably be manifested through a environment_variables parameter, but any solution would work for me.
                          The problem
                          The problem is that we cannot have a unified interface to these entities using **kwargs to pass arguments without manually parsing a parameter ourselves.
                          Current situation van be something like this if we use env for both situations:
                          defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("env")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", env={"MY_VAR": 42})
model_training(environment="dev", env={"MY_VAR": 67})
                          Or with a more compatible interface
                          defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["env"] =env_varsreturnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
                          Ideally, we would want it to look like this because both classes accept a environment_variables parameter:
                          defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
Naming discrepancy between `env` in Processing and `environment` in Estimator · Issue #6214 · aws/sagemaker-python-sdk · GitHub
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                              Naming discrepancy between env in Processing and environment in Estimator #6214
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                              Naming discrepancy between env in Processing and environment in Estimator#6214
                              Description
                              @mikeweltevrede
                              mikeweltevrede
                              opened on Aug 26, 2026
                              Describe the feature you'd like
                              There is a discrepancy on passing environment variables in Processing and Estimator. The parameter is called env in Processing and environment in Estimator.
                              I would like these to be aligned. For backwards compatibility sake, this should probably be manifested through a environment_variables parameter, but any solution would work for me.
                              The problem
                              The problem is that we cannot have a unified interface to these entities using **kwargs to pass arguments without manually parsing a parameter ourselves.
                              Current situation van be something like this if we use env for both situations:
                              defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("env")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", env={"MY_VAR": 42})
model_training(environment="dev", env={"MY_VAR": 67})
                              Or with a more compatible interface
                              defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["env"] =env_varsreturnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
env_vars=kwargs.pop("environment_variables")
ifenv_vars:
kwargs["environment"] =env_varsreturnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
                              Ideally, we would want it to look like this because both classes accept a environment_variables parameter:
                              defdata_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnProcessing(**kwargs)
defmodel_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)
returnEstimator(**kwargs)
data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
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