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sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0 #6152

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sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0#6152

Description

@hypercubian
hypercubian
opened on Aug 4, 2026

Describe the feature you'd like

sagemaker-train (and sagemaker-serve) declare an unconditional dependency on the full mlflow distribution:

sagemaker-train 1.18.0: mlflow<4.0.0,>=3.0.0

The training integration uses MLflow purely as a tracking client (logging runs and metrics to a configured tracking server), which mlflow-skinny implements completely. The full distribution additionally drags in the tracking server stack and its dependency constraints.

sagemaker-mlflow already made exactly this change in 0.5.0: mlflow-skinny>=2.8 as the base requirement, with full mlflow relegated to an optional full extra. Applying the same pattern to sagemaker-train and sagemaker-serve would make the SDK family consistent.

Why it matters

Full mlflow currently caps cryptography<50. Snyk advisories SNYK-PYTHON-CRYPTOGRAPHY-18516620/21/22 (two high severity) are fixed only in cryptography 50.0.0, so any project consuming sagemaker-train transitively cannot reach the fixed version and must either waive the findings or remove sagemaker-train. Since the mlflow usage is client-only, the cap buys nothing for these packages.

Suggested change

Mirror sagemaker-mlflow 0.5.0: depend on mlflow-skinny, offer full mlflow behind an extra for anyone who genuinely needs the server components.

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sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0 · Issue #6152 · aws/sagemaker-python-sdk · GitHub
      Skip to content
      sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0 #6152
      Open
      Open
      sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0#6152
      Description
      @hypercubian
      hypercubian
      opened on Aug 4, 2026
      Describe the feature you'd like
      sagemaker-train (and sagemaker-serve) declare an unconditional dependency on the full mlflow distribution:
      sagemaker-train 1.18.0: mlflow<4.0.0,>=3.0.0

      The training integration uses MLflow purely as a tracking client (logging runs and metrics to a configured tracking server), which mlflow-skinny implements completely. The full distribution additionally drags in the tracking server stack and its dependency constraints.
      sagemaker-mlflow already made exactly this change in 0.5.0: mlflow-skinny>=2.8 as the base requirement, with full mlflow relegated to an optional full extra. Applying the same pattern to sagemaker-train and sagemaker-serve would make the SDK family consistent.
      Why it matters
      Full mlflow currently caps cryptography<50. Snyk advisories SNYK-PYTHON-CRYPTOGRAPHY-18516620/21/22 (two high severity) are fixed only in cryptography 50.0.0, so any project consuming sagemaker-train transitively cannot reach the fixed version and must either waive the findings or remove sagemaker-train. Since the mlflow usage is client-only, the cap buys nothing for these packages.
      Suggested change
      Mirror sagemaker-mlflow 0.5.0: depend on mlflow-skinny, offer full mlflow behind an extra for anyone who genuinely needs the server components.
      Metadata
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sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0 · Issue #6152 · aws/sagemaker-python-sdk · GitHub
          Skip to content
          sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0 #6152
          Open
          Open
          sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0#6152
          Description
          @hypercubian
          hypercubian
          opened on Aug 4, 2026
          Describe the feature you'd like
          sagemaker-train (and sagemaker-serve) declare an unconditional dependency on the full mlflow distribution:
          sagemaker-train 1.18.0: mlflow<4.0.0,>=3.0.0

          The training integration uses MLflow purely as a tracking client (logging runs and metrics to a configured tracking server), which mlflow-skinny implements completely. The full distribution additionally drags in the tracking server stack and its dependency constraints.
          sagemaker-mlflow already made exactly this change in 0.5.0: mlflow-skinny>=2.8 as the base requirement, with full mlflow relegated to an optional full extra. Applying the same pattern to sagemaker-train and sagemaker-serve would make the SDK family consistent.
          Why it matters
          Full mlflow currently caps cryptography<50. Snyk advisories SNYK-PYTHON-CRYPTOGRAPHY-18516620/21/22 (two high severity) are fixed only in cryptography 50.0.0, so any project consuming sagemaker-train transitively cannot reach the fixed version and must either waive the findings or remove sagemaker-train. Since the mlflow usage is client-only, the cap buys nothing for these packages.
          Suggested change
          Mirror sagemaker-mlflow 0.5.0: depend on mlflow-skinny, offer full mlflow behind an extra for anyone who genuinely needs the server components.
          Metadata
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var __re = new RegExp('^' + ".*" + '
sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0 · Issue #6152 · aws/sagemaker-python-sdk · GitHub
              Skip to content
              sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0 #6152
              Open
              Open
              sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0#6152
              Description
              @hypercubian
              hypercubian
              opened on Aug 4, 2026
              Describe the feature you'd like
              sagemaker-train (and sagemaker-serve) declare an unconditional dependency on the full mlflow distribution:
              sagemaker-train 1.18.0: mlflow<4.0.0,>=3.0.0

              The training integration uses MLflow purely as a tracking client (logging runs and metrics to a configured tracking server), which mlflow-skinny implements completely. The full distribution additionally drags in the tracking server stack and its dependency constraints.
              sagemaker-mlflow already made exactly this change in 0.5.0: mlflow-skinny>=2.8 as the base requirement, with full mlflow relegated to an optional full extra. Applying the same pattern to sagemaker-train and sagemaker-serve would make the SDK family consistent.
              Why it matters
              Full mlflow currently caps cryptography<50. Snyk advisories SNYK-PYTHON-CRYPTOGRAPHY-18516620/21/22 (two high severity) are fixed only in cryptography 50.0.0, so any project consuming sagemaker-train transitively cannot reach the fixed version and must either waive the findings or remove sagemaker-train. Since the mlflow usage is client-only, the cap buys nothing for these packages.
              Suggested change
              Mirror sagemaker-mlflow 0.5.0: depend on mlflow-skinny, offer full mlflow behind an extra for anyone who genuinely needs the server components.
              Metadata
              Metadata
              Assignees
              No one assigned
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, 'i');
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sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0 · Issue #6152 · aws/sagemaker-python-sdk · GitHub
                  Skip to content
                  sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0 #6152
                  Open
                  Open
                  sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0#6152
                  Description
                  @hypercubian
                  hypercubian
                  opened on Aug 4, 2026
                  Describe the feature you'd like
                  sagemaker-train (and sagemaker-serve) declare an unconditional dependency on the full mlflow distribution:
                  sagemaker-train 1.18.0: mlflow<4.0.0,>=3.0.0

                  The training integration uses MLflow purely as a tracking client (logging runs and metrics to a configured tracking server), which mlflow-skinny implements completely. The full distribution additionally drags in the tracking server stack and its dependency constraints.
                  sagemaker-mlflow already made exactly this change in 0.5.0: mlflow-skinny>=2.8 as the base requirement, with full mlflow relegated to an optional full extra. Applying the same pattern to sagemaker-train and sagemaker-serve would make the SDK family consistent.
                  Why it matters
                  Full mlflow currently caps cryptography<50. Snyk advisories SNYK-PYTHON-CRYPTOGRAPHY-18516620/21/22 (two high severity) are fixed only in cryptography 50.0.0, so any project consuming sagemaker-train transitively cannot reach the fixed version and must either waive the findings or remove sagemaker-train. Since the mlflow usage is client-only, the cap buys nothing for these packages.
                  Suggested change
                  Mirror sagemaker-mlflow 0.5.0: depend on mlflow-skinny, offer full mlflow behind an extra for anyone who genuinely needs the server components.
                  Metadata
                  Metadata
                  Assignees
                  No one assigned
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                    No labels
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                    No type
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                    No projects
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                      None yet
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                      No branches or pull requests
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, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0 · Issue #6152 · aws/sagemaker-python-sdk · GitHub
                      Skip to content
                      sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0 #6152
                      Open
                      Open
                      sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0#6152
                      Description
                      @hypercubian
                      hypercubian
                      opened on Aug 4, 2026
                      Describe the feature you'd like
                      sagemaker-train (and sagemaker-serve) declare an unconditional dependency on the full mlflow distribution:
                      sagemaker-train 1.18.0: mlflow<4.0.0,>=3.0.0

                      The training integration uses MLflow purely as a tracking client (logging runs and metrics to a configured tracking server), which mlflow-skinny implements completely. The full distribution additionally drags in the tracking server stack and its dependency constraints.
                      sagemaker-mlflow already made exactly this change in 0.5.0: mlflow-skinny>=2.8 as the base requirement, with full mlflow relegated to an optional full extra. Applying the same pattern to sagemaker-train and sagemaker-serve would make the SDK family consistent.
                      Why it matters
                      Full mlflow currently caps cryptography<50. Snyk advisories SNYK-PYTHON-CRYPTOGRAPHY-18516620/21/22 (two high severity) are fixed only in cryptography 50.0.0, so any project consuming sagemaker-train transitively cannot reach the fixed version and must either waive the findings or remove sagemaker-train. Since the mlflow usage is client-only, the cap buys nothing for these packages.
                      Suggested change
                      Mirror sagemaker-mlflow 0.5.0: depend on mlflow-skinny, offer full mlflow behind an extra for anyone who genuinely needs the server components.
                      Metadata
                      Metadata
                      Assignees
                      No one assigned
                        Labels
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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var __m = "*";
var __re = new RegExp('^' + ".*" + '
sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0 · Issue #6152 · aws/sagemaker-python-sdk · GitHub
                          Skip to content
                          sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0 #6152
                          Open
                          Open
                          sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0#6152
                          Description
                          @hypercubian
                          hypercubian
                          opened on Aug 4, 2026
                          Describe the feature you'd like
                          sagemaker-train (and sagemaker-serve) declare an unconditional dependency on the full mlflow distribution:
                          sagemaker-train 1.18.0: mlflow<4.0.0,>=3.0.0

                          The training integration uses MLflow purely as a tracking client (logging runs and metrics to a configured tracking server), which mlflow-skinny implements completely. The full distribution additionally drags in the tracking server stack and its dependency constraints.
                          sagemaker-mlflow already made exactly this change in 0.5.0: mlflow-skinny>=2.8 as the base requirement, with full mlflow relegated to an optional full extra. Applying the same pattern to sagemaker-train and sagemaker-serve would make the SDK family consistent.
                          Why it matters
                          Full mlflow currently caps cryptography<50. Snyk advisories SNYK-PYTHON-CRYPTOGRAPHY-18516620/21/22 (two high severity) are fixed only in cryptography 50.0.0, so any project consuming sagemaker-train transitively cannot reach the fixed version and must either waive the findings or remove sagemaker-train. Since the mlflow usage is client-only, the cap buys nothing for these packages.
                          Suggested change
                          Mirror sagemaker-mlflow 0.5.0: depend on mlflow-skinny, offer full mlflow behind an extra for anyone who genuinely needs the server components.
                          Metadata
                          Metadata
                          Assignees
                          No one assigned
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
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background: #2d2d44 !important;
border-color: #444 !important;
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::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
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::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
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var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
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if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
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console.log('[Universal Dark Mode] Enabled');
} else {
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console.log('[Universal Dark Mode] Disabled');
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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); }
})();
})();
sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0 · Issue #6152 · aws/sagemaker-python-sdk · GitHub
                              Skip to content
                              sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0 #6152
                              Open
                              Open
                              sagemaker-train should depend on mlflow-skinny, following sagemaker-mlflow 0.5.0#6152
                              Description
                              @hypercubian
                              hypercubian
                              opened on Aug 4, 2026
                              Describe the feature you'd like
                              sagemaker-train (and sagemaker-serve) declare an unconditional dependency on the full mlflow distribution:
                              sagemaker-train 1.18.0: mlflow<4.0.0,>=3.0.0

                              The training integration uses MLflow purely as a tracking client (logging runs and metrics to a configured tracking server), which mlflow-skinny implements completely. The full distribution additionally drags in the tracking server stack and its dependency constraints.
                              sagemaker-mlflow already made exactly this change in 0.5.0: mlflow-skinny>=2.8 as the base requirement, with full mlflow relegated to an optional full extra. Applying the same pattern to sagemaker-train and sagemaker-serve would make the SDK family consistent.
                              Why it matters
                              Full mlflow currently caps cryptography<50. Snyk advisories SNYK-PYTHON-CRYPTOGRAPHY-18516620/21/22 (two high severity) are fixed only in cryptography 50.0.0, so any project consuming sagemaker-train transitively cannot reach the fixed version and must either waive the findings or remove sagemaker-train. Since the mlflow usage is client-only, the cap buys nothing for these packages.
                              Suggested change
                              Mirror sagemaker-mlflow 0.5.0: depend on mlflow-skinny, offer full mlflow behind an extra for anyone who genuinely needs the server components.
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