feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes #2017

Closed
Closed
feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes#2017
Assignees
mollyheamazon
Labels
component: experimentsRelates to the SageMaker Experiements Platformcontributions welcometype: feature request

Description

@brightsparc
brightsparc
opened on Dec 8, 2020

Describe the feature you'd like
Extend the constructors for the TrainingStep and ProcessingStep classes to include an optional experiment_config dictionary which is passed down when creating the job args.

eg: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/workflow/steps.py#L136

How would this feature be used? Please describe.
This feature would allow the caller to pass down an experiment and trial name to a step that is part of a pipeline.

from sagemaker.inputs import TrainingInput
from sagemaker.workflow.steps import TrainingStep
step_train = TrainingStep(
name="AbaloneTrain",
estimator=xgb_train,
inputs={
"train": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"train"
].S3Output.S3Uri,
content_type="text/csv"
),
"validation": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"validation"
].S3Output.S3Uri,
content_type="text/csv"
)
},
experiment_config={
'ExperimentName': 'my-project', 'TrialName': 'my-commit-hash',
'TrialComponentDisplayName': "Training",
},
)

Describe alternatives you've considered
An alternative would be to attach these trial components and tags after the fact, but this would require an additional call.

Additional context
This would provide feature parity with AWS DataScience Step functions SDK.
https://aws-step-functions-data-science-sdk.readthedocs.io/en/stable/sagemaker.html

Activity

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    , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

    Skip to content
    feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes #2017
    Closed
    Closed
    feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes#2017
    Assignees
    mollyheamazon
    Labels
    component: experimentsRelates to the SageMaker Experiements Platformcontributions welcometype: feature request
    Description
    @brightsparc
    brightsparc
    opened on Dec 8, 2020
    Describe the feature you'd like
    
Extend the constructors for the TrainingStep and ProcessingStep classes to include an optional experiment_config dictionary which is passed down when creating the job args.
    eg: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/workflow/steps.py#L136
    How would this feature be used? Please describe.
    
This feature would allow the caller to pass down an experiment and trial name to a step that is part of a pipeline.
    from sagemaker.inputs import TrainingInput
from sagemaker.workflow.steps import TrainingStep
step_train = TrainingStep(
name="AbaloneTrain",
estimator=xgb_train,
inputs={
"train": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"train"
].S3Output.S3Uri,
content_type="text/csv"
),
"validation": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"validation"
].S3Output.S3Uri,
content_type="text/csv"
)
},
experiment_config={
'ExperimentName': 'my-project', 'TrialName': 'my-commit-hash',
'TrialComponentDisplayName': "Training",
},
)

    Describe alternatives you've considered
    
An alternative would be to attach these trial components and tags after the fact, but this would require an additional call.
    Additional context
    
This would provide feature parity with AWS DataScience Step functions SDK.
    https://aws-step-functions-data-science-sdk.readthedocs.io/en/stable/sagemaker.html
    Activity
    Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
    Metadata
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    Assignees
    Labels
    component: experimentsRelates to the SageMaker Experiements Platformcontributions welcometype: feature request
    Type
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    No projects
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      No milestone
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      None yet
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      No branches or pull requests
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

      Skip to content
      feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes #2017
      Closed
      Closed
      feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes#2017
      Assignees
      mollyheamazon
      Labels
      component: experimentsRelates to the SageMaker Experiements Platformcontributions welcometype: feature request
      Description
      @brightsparc
      brightsparc
      opened on Dec 8, 2020
      Describe the feature you'd like
      
Extend the constructors for the TrainingStep and ProcessingStep classes to include an optional experiment_config dictionary which is passed down when creating the job args.
      eg: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/workflow/steps.py#L136
      How would this feature be used? Please describe.
      
This feature would allow the caller to pass down an experiment and trial name to a step that is part of a pipeline.
      from sagemaker.inputs import TrainingInput
from sagemaker.workflow.steps import TrainingStep
step_train = TrainingStep(
name="AbaloneTrain",
estimator=xgb_train,
inputs={
"train": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"train"
].S3Output.S3Uri,
content_type="text/csv"
),
"validation": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"validation"
].S3Output.S3Uri,
content_type="text/csv"
)
},
experiment_config={
'ExperimentName': 'my-project', 'TrialName': 'my-commit-hash',
'TrialComponentDisplayName': "Training",
},
)

      Describe alternatives you've considered
      
An alternative would be to attach these trial components and tags after the fact, but this would require an additional call.
      Additional context
      
This would provide feature parity with AWS DataScience Step functions SDK.
      https://aws-step-functions-data-science-sdk.readthedocs.io/en/stable/sagemaker.html
      Activity
      Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
      Metadata
      Metadata
      Assignees
      Labels
      component: experimentsRelates to the SageMaker Experiements Platformcontributions welcometype: feature request
      Type
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      Projects
      No projects
        Milestone
        No milestone
        Relationships
        None yet
        Development
        No branches or pull requests
        Issue actions
        
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

        Skip to content
        feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes #2017
        Closed
        Closed
        feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes#2017
        Assignees
        mollyheamazon
        Labels
        component: experimentsRelates to the SageMaker Experiements Platformcontributions welcometype: feature request
        Description
        @brightsparc
        brightsparc
        opened on Dec 8, 2020
        Describe the feature you'd like
        
Extend the constructors for the TrainingStep and ProcessingStep classes to include an optional experiment_config dictionary which is passed down when creating the job args.
        eg: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/workflow/steps.py#L136
        How would this feature be used? Please describe.
        
This feature would allow the caller to pass down an experiment and trial name to a step that is part of a pipeline.
        from sagemaker.inputs import TrainingInput
from sagemaker.workflow.steps import TrainingStep
step_train = TrainingStep(
name="AbaloneTrain",
estimator=xgb_train,
inputs={
"train": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"train"
].S3Output.S3Uri,
content_type="text/csv"
),
"validation": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"validation"
].S3Output.S3Uri,
content_type="text/csv"
)
},
experiment_config={
'ExperimentName': 'my-project', 'TrialName': 'my-commit-hash',
'TrialComponentDisplayName': "Training",
},
)

        Describe alternatives you've considered
        
An alternative would be to attach these trial components and tags after the fact, but this would require an additional call.
        Additional context
        
This would provide feature parity with AWS DataScience Step functions SDK.
        https://aws-step-functions-data-science-sdk.readthedocs.io/en/stable/sagemaker.html
        Activity
        Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
        Metadata
        Metadata
        Assignees
        Labels
        component: experimentsRelates to the SageMaker Experiements Platformcontributions welcometype: feature request
        Type
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        Projects
        No projects
          Milestone
          No milestone
          Relationships
          None yet
          Development
          No branches or pull requests
          Issue actions
          
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

          Skip to content
          feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes #2017
          Closed
          Closed
          feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes#2017
          Assignees
          mollyheamazon
          Labels
          component: experimentsRelates to the SageMaker Experiements Platformcontributions welcometype: feature request
          Description
          @brightsparc
          brightsparc
          opened on Dec 8, 2020
          Describe the feature you'd like
          
Extend the constructors for the TrainingStep and ProcessingStep classes to include an optional experiment_config dictionary which is passed down when creating the job args.
          eg: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/workflow/steps.py#L136
          How would this feature be used? Please describe.
          
This feature would allow the caller to pass down an experiment and trial name to a step that is part of a pipeline.
          from sagemaker.inputs import TrainingInput
from sagemaker.workflow.steps import TrainingStep
step_train = TrainingStep(
name="AbaloneTrain",
estimator=xgb_train,
inputs={
"train": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"train"
].S3Output.S3Uri,
content_type="text/csv"
),
"validation": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"validation"
].S3Output.S3Uri,
content_type="text/csv"
)
},
experiment_config={
'ExperimentName': 'my-project', 'TrialName': 'my-commit-hash',
'TrialComponentDisplayName': "Training",
},
)

          Describe alternatives you've considered
          
An alternative would be to attach these trial components and tags after the fact, but this would require an additional call.
          Additional context
          
This would provide feature parity with AWS DataScience Step functions SDK.
          https://aws-step-functions-data-science-sdk.readthedocs.io/en/stable/sagemaker.html
          Activity
          Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
          Metadata
          Metadata
          Assignees
          Labels
          component: experimentsRelates to the SageMaker Experiements Platformcontributions welcometype: feature request
          Type
          No type
          Projects
          No projects
            Milestone
            No milestone
            Relationships
            None yet
            Development
            No branches or pull requests
            Issue actions
            
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

            Skip to content
            feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes #2017
            Closed
            Closed
            feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes#2017
            Assignees
            mollyheamazon
            Labels
            component: experimentsRelates to the SageMaker Experiements Platformcontributions welcometype: feature request
            Description
            @brightsparc
            brightsparc
            opened on Dec 8, 2020
            Describe the feature you'd like
            
Extend the constructors for the TrainingStep and ProcessingStep classes to include an optional experiment_config dictionary which is passed down when creating the job args.
            eg: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/workflow/steps.py#L136
            How would this feature be used? Please describe.
            
This feature would allow the caller to pass down an experiment and trial name to a step that is part of a pipeline.
            from sagemaker.inputs import TrainingInput
from sagemaker.workflow.steps import TrainingStep
step_train = TrainingStep(
name="AbaloneTrain",
estimator=xgb_train,
inputs={
"train": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"train"
].S3Output.S3Uri,
content_type="text/csv"
),
"validation": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"validation"
].S3Output.S3Uri,
content_type="text/csv"
)
},
experiment_config={
'ExperimentName': 'my-project', 'TrialName': 'my-commit-hash',
'TrialComponentDisplayName': "Training",
},
)

            Describe alternatives you've considered
            
An alternative would be to attach these trial components and tags after the fact, but this would require an additional call.
            Additional context
            
This would provide feature parity with AWS DataScience Step functions SDK.
            https://aws-step-functions-data-science-sdk.readthedocs.io/en/stable/sagemaker.html
            Activity
            Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
            Metadata
            Metadata
            Assignees
            Labels
            component: experimentsRelates to the SageMaker Experiements Platformcontributions welcometype: feature request
            Type
            No type
            Projects
            No projects
              Milestone
              No milestone
              Relationships
              None yet
              Development
              No branches or pull requests
              Issue actions
              
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

              Skip to content
              feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes #2017
              Closed
              Closed
              feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes#2017
              Assignees
              mollyheamazon
              Labels
              component: experimentsRelates to the SageMaker Experiements Platformcontributions welcometype: feature request
              Description
              @brightsparc
              brightsparc
              opened on Dec 8, 2020
              Describe the feature you'd like
              
Extend the constructors for the TrainingStep and ProcessingStep classes to include an optional experiment_config dictionary which is passed down when creating the job args.
              eg: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/workflow/steps.py#L136
              How would this feature be used? Please describe.
              
This feature would allow the caller to pass down an experiment and trial name to a step that is part of a pipeline.
              from sagemaker.inputs import TrainingInput
from sagemaker.workflow.steps import TrainingStep
step_train = TrainingStep(
name="AbaloneTrain",
estimator=xgb_train,
inputs={
"train": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"train"
].S3Output.S3Uri,
content_type="text/csv"
),
"validation": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"validation"
].S3Output.S3Uri,
content_type="text/csv"
)
},
experiment_config={
'ExperimentName': 'my-project', 'TrialName': 'my-commit-hash',
'TrialComponentDisplayName': "Training",
},
)

              Describe alternatives you've considered
              
An alternative would be to attach these trial components and tags after the fact, but this would require an additional call.
              Additional context
              
This would provide feature parity with AWS DataScience Step functions SDK.
              https://aws-step-functions-data-science-sdk.readthedocs.io/en/stable/sagemaker.html
              Activity
              Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
              Metadata
              Metadata
              Assignees
              Labels
              component: experimentsRelates to the SageMaker Experiements Platformcontributions welcometype: feature request
              Type
              No type
              Projects
              No projects
                Milestone
                No milestone
                Relationships
                None yet
                Development
                No branches or pull requests
                Issue actions
                
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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                feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes #2017
                Closed
                Closed
                feat: Add experiment_config and tags arguments to TrainingStep and ProcessingStep classes#2017
                Assignees
                mollyheamazon
                Labels
                component: experimentsRelates to the SageMaker Experiements Platformcontributions welcometype: feature request
                Description
                @brightsparc
                brightsparc
                opened on Dec 8, 2020
                Describe the feature you'd like
                
Extend the constructors for the TrainingStep and ProcessingStep classes to include an optional experiment_config dictionary which is passed down when creating the job args.
                eg: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/workflow/steps.py#L136
                How would this feature be used? Please describe.
                
This feature would allow the caller to pass down an experiment and trial name to a step that is part of a pipeline.
                from sagemaker.inputs import TrainingInput
from sagemaker.workflow.steps import TrainingStep
step_train = TrainingStep(
name="AbaloneTrain",
estimator=xgb_train,
inputs={
"train": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"train"
].S3Output.S3Uri,
content_type="text/csv"
),
"validation": TrainingInput(
s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
"validation"
].S3Output.S3Uri,
content_type="text/csv"
)
},
experiment_config={
'ExperimentName': 'my-project', 'TrialName': 'my-commit-hash',
'TrialComponentDisplayName': "Training",
},
)

                Describe alternatives you've considered
                
An alternative would be to attach these trial components and tags after the fact, but this would require an additional call.
                Additional context
                
This would provide feature parity with AWS DataScience Step functions SDK.
                https://aws-step-functions-data-science-sdk.readthedocs.io/en/stable/sagemaker.html
                Activity
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