change: Enhance model builder selection logic to include model size - #4429

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knikure merged 20 commits into
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samruds:2PR
Mar 6, 2024
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change: Enhance model builder selection logic to include model size#4429
knikure merged 20 commits into
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samruds:2PR

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@samruds

@samrudssamruds commented Feb 15, 2024•
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Issue #, if available:
https://docs.google.com/document/d/1OTXyZ-YpovntrzzBVBOBCnVl4Ijdl858s35UJFKf3dI/edit

Description of changes:
Use the model size and given instance to check if we can deploy.

Flow:

  1. If metadata tag is “text generation” go to TGI (exists today)

  2. Detect and Calculate Model size (M1) from Hugging Face Hub

  3. Calculate : Total memory inference (M2) = 1.2 * Model size (20% buffer for inference)

  4. Detect container type (CT1) - GPU or CPU using metadata tags

  5. If Model needs to be run on GPU :

    1. Query instance types and get number of GPUs, and memory size using Pricing API
      1. If total memory inference (M2) <= total GPU memory size go to HF DLC

  6. If number of GPUs > 1 check for DJL as supported architecture

  7. Fail Gracefully :

    1. Check if it is still possible to query image using HuggingFace Metadata
    2. If nothing works - throw an error - Model Server is not supported with current container type (%CT1) with size (%M1)

Testing done:

  1. Unit tests
  2. Integ tests

Merge Checklist

Put an x in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your pull request.

General

  • I have read the CONTRIBUTING doc
  • I certify that the changes I am introducing will be backward compatible, and I have discussed concerns about this, if any, with the Python SDK team
  • I used the commit message format described in CONTRIBUTING
  • I have passed the region in to all S3 and STS clients that I've initialized as part of this change.
  • I have updated any necessary documentation, including READMEs and API docs (if appropriate)

Tests

  • I have added tests that prove my fix is effective or that my feature works (if appropriate)
  • I have added unit and/or integration tests as appropriate to ensure backward compatibility of the changes
  • I have checked that my tests are not configured for a specific region or account (if appropriate)
  • I have used unique_name_from_base to create resource names in integ tests (if appropriate)

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

@samruds
samruds requested a review from a team as a code ownerFebruary 15, 2024 00:36
@samruds
samruds requested review from gwang111 and mufaddal-rohawala and removed request for a teamFebruary 15, 2024 00:36
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@haNa-meister

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Curious if there is any design doc for HF improvement in model builder?

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Comment threadrequirements/extras/huggingface_requirements.txt Outdated
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/bot run local-mode-tests

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@mufaddal-rohawalamufaddal-rohawalamufaddal-rohawala approved these changes
@gwang111gwang111gwang111 approved these changes
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change: Enhance model builder selection logic to include model size - #4429
Merged
knikure merged 20 commits into
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samruds:2PR
Mar 6, 2024
Merged
change: Enhance model builder selection logic to include model size#4429
knikure merged 20 commits into
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samruds:2PR
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@samruds
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Issue #, if available:
https://docs.google.com/document/d/1OTXyZ-YpovntrzzBVBOBCnVl4Ijdl858s35UJFKf3dI/edit
Description of changes:

Use the model size and given instance to check if we can deploy.
Flow:
  1. If metadata tag is “text generation” go to TGI (exists today)
  2. Detect and Calculate Model size (M1) from Hugging Face Hub
  3. Calculate : Total memory inference (M2) = 1.2 * Model size (20% buffer for inference)
  4. Detect container type (CT1) - GPU or CPU using metadata tags
  5. If Model needs to be run on GPU :
    1. Query instance types and get number of GPUs, and memory size using Pricing API

      1. If total memory inference (M2) <= total GPU memory size go to HF DLC
  6. If number of GPUs > 1 check for DJL as supported architecture
  7. Fail Gracefully :
    1. Check if it is still possible to query image using HuggingFace Metadata
    2. If nothing works - throw an error - Model Server is not supported with current container type (%CT1) with size (%M1)
Testing done:
  1. Unit tests
  2. Integ tests
Merge Checklist
Put an x in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your pull request.
General
  •  I have read the CONTRIBUTING doc
  •  I certify that the changes I am introducing will be backward compatible, and I have discussed concerns about this, if any, with the Python SDK team
  •  I used the commit message format described in CONTRIBUTING
  •  I have passed the region in to all S3 and STS clients that I've initialized as part of this change.
  •  I have updated any necessary documentation, including READMEs and API docs (if appropriate)
Tests
  •  I have added tests that prove my fix is effective or that my feature works (if appropriate)
  •  I have added unit and/or integration tests as appropriate to ensure backward compatibility of the changes
  •  I have checked that my tests are not configured for a specific region or account (if appropriate)
  •  I have used unique_name_from_base to create resource names in integ tests (if appropriate)
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
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Curious if there is any design doc for HF improvement in model builder?
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/bot run local-mode-tests
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@mufaddal-rohawalamufaddal-rohawalamufaddal-rohawala approved these changes
@gwang111gwang111gwang111 approved these changes

+4 more reviewers

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@samruds@mufaddal-rohawala@haNa-meister@mohanasudhan@gwang111@makungaj1@jiapinw@knikure

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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");
}
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change: Enhance model builder selection logic to include model size - #4429
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knikure merged 20 commits into
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samruds:2PR
Mar 6, 2024
Merged
change: Enhance model builder selection logic to include model size#4429
knikure merged 20 commits into
aws:masterfrom 
samruds:2PR
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@samruds
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Feb 15, 2024•

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Issue #, if available:
https://docs.google.com/document/d/1OTXyZ-YpovntrzzBVBOBCnVl4Ijdl858s35UJFKf3dI/edit
Description of changes:

Use the model size and given instance to check if we can deploy.
Flow:
  1. If metadata tag is “text generation” go to TGI (exists today)
  2. Detect and Calculate Model size (M1) from Hugging Face Hub
  3. Calculate : Total memory inference (M2) = 1.2 * Model size (20% buffer for inference)
  4. Detect container type (CT1) - GPU or CPU using metadata tags
  5. If Model needs to be run on GPU :
    1. Query instance types and get number of GPUs, and memory size using Pricing API

      1. If total memory inference (M2) <= total GPU memory size go to HF DLC
  6. If number of GPUs > 1 check for DJL as supported architecture
  7. Fail Gracefully :
    1. Check if it is still possible to query image using HuggingFace Metadata
    2. If nothing works - throw an error - Model Server is not supported with current container type (%CT1) with size (%M1)
Testing done:
  1. Unit tests
  2. Integ tests
Merge Checklist
Put an x in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your pull request.
General
  •  I have read the CONTRIBUTING doc
  •  I certify that the changes I am introducing will be backward compatible, and I have discussed concerns about this, if any, with the Python SDK team
  •  I used the commit message format described in CONTRIBUTING
  •  I have passed the region in to all S3 and STS clients that I've initialized as part of this change.
  •  I have updated any necessary documentation, including READMEs and API docs (if appropriate)
Tests
  •  I have added tests that prove my fix is effective or that my feature works (if appropriate)
  •  I have added unit and/or integration tests as appropriate to ensure backward compatibility of the changes
  •  I have checked that my tests are not configured for a specific region or account (if appropriate)
  •  I have used unique_name_from_base to create resource names in integ tests (if appropriate)
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
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as a code ownerFebruary 15, 2024 00:36
@samruds
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gwang111 and mufaddal-rohawala
and removed request for
a teamFebruary 15, 2024 00:36
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Curious if there is any design doc for HF improvement in model builder?
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/bot run local-mode-tests
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@mufaddal-rohawalamufaddal-rohawalamufaddal-rohawala approved these changes
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@samruds@mufaddal-rohawala@haNa-meister@mohanasudhan@gwang111@makungaj1@jiapinw@knikure

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change: Enhance model builder selection logic to include model size - #4429
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knikure merged 20 commits into
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samruds:2PR
Mar 6, 2024
Merged
change: Enhance model builder selection logic to include model size#4429
knikure merged 20 commits into
aws:masterfrom 
samruds:2PR
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@samruds
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Issue #, if available:
https://docs.google.com/document/d/1OTXyZ-YpovntrzzBVBOBCnVl4Ijdl858s35UJFKf3dI/edit
Description of changes:

Use the model size and given instance to check if we can deploy.
Flow:
  1. If metadata tag is “text generation” go to TGI (exists today)
  2. Detect and Calculate Model size (M1) from Hugging Face Hub
  3. Calculate : Total memory inference (M2) = 1.2 * Model size (20% buffer for inference)
  4. Detect container type (CT1) - GPU or CPU using metadata tags
  5. If Model needs to be run on GPU :
    1. Query instance types and get number of GPUs, and memory size using Pricing API

      1. If total memory inference (M2) <= total GPU memory size go to HF DLC
  6. If number of GPUs > 1 check for DJL as supported architecture
  7. Fail Gracefully :
    1. Check if it is still possible to query image using HuggingFace Metadata
    2. If nothing works - throw an error - Model Server is not supported with current container type (%CT1) with size (%M1)
Testing done:
  1. Unit tests
  2. Integ tests
Merge Checklist
Put an x in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your pull request.
General
  •  I have read the CONTRIBUTING doc
  •  I certify that the changes I am introducing will be backward compatible, and I have discussed concerns about this, if any, with the Python SDK team
  •  I used the commit message format described in CONTRIBUTING
  •  I have passed the region in to all S3 and STS clients that I've initialized as part of this change.
  •  I have updated any necessary documentation, including READMEs and API docs (if appropriate)
Tests
  •  I have added tests that prove my fix is effective or that my feature works (if appropriate)
  •  I have added unit and/or integration tests as appropriate to ensure backward compatibility of the changes
  •  I have checked that my tests are not configured for a specific region or account (if appropriate)
  •  I have used unique_name_from_base to create resource names in integ tests (if appropriate)
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
@samruds
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as a code ownerFebruary 15, 2024 00:36
@samruds
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Curious if there is any design doc for HF improvement in model builder?
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/bot run local-mode-tests
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@mufaddal-rohawalamufaddal-rohawalamufaddal-rohawala approved these changes
@gwang111gwang111gwang111 approved these changes

+4 more reviewers

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@samruds@mufaddal-rohawala@haNa-meister@mohanasudhan@gwang111@makungaj1@jiapinw@knikure

, '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" + '

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change: Enhance model builder selection logic to include model size - #4429
Merged
knikure merged 20 commits into
aws:masterfrom 
samruds:2PR
Mar 6, 2024
Merged
change: Enhance model builder selection logic to include model size#4429
knikure merged 20 commits into
aws:masterfrom 
samruds:2PR
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@samruds
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Feb 15, 2024•

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Issue #, if available:
https://docs.google.com/document/d/1OTXyZ-YpovntrzzBVBOBCnVl4Ijdl858s35UJFKf3dI/edit
Description of changes:

Use the model size and given instance to check if we can deploy.
Flow:
  1. If metadata tag is “text generation” go to TGI (exists today)
  2. Detect and Calculate Model size (M1) from Hugging Face Hub
  3. Calculate : Total memory inference (M2) = 1.2 * Model size (20% buffer for inference)
  4. Detect container type (CT1) - GPU or CPU using metadata tags
  5. If Model needs to be run on GPU :
    1. Query instance types and get number of GPUs, and memory size using Pricing API

      1. If total memory inference (M2) <= total GPU memory size go to HF DLC
  6. If number of GPUs > 1 check for DJL as supported architecture
  7. Fail Gracefully :
    1. Check if it is still possible to query image using HuggingFace Metadata
    2. If nothing works - throw an error - Model Server is not supported with current container type (%CT1) with size (%M1)
Testing done:
  1. Unit tests
  2. Integ tests
Merge Checklist
Put an x in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your pull request.
General
  •  I have read the CONTRIBUTING doc
  •  I certify that the changes I am introducing will be backward compatible, and I have discussed concerns about this, if any, with the Python SDK team
  •  I used the commit message format described in CONTRIBUTING
  •  I have passed the region in to all S3 and STS clients that I've initialized as part of this change.
  •  I have updated any necessary documentation, including READMEs and API docs (if appropriate)
Tests
  •  I have added tests that prove my fix is effective or that my feature works (if appropriate)
  •  I have added unit and/or integration tests as appropriate to ensure backward compatibility of the changes
  •  I have checked that my tests are not configured for a specific region or account (if appropriate)
  •  I have used unique_name_from_base to create resource names in integ tests (if appropriate)
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
@samruds
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as a code ownerFebruary 15, 2024 00:36
@samruds
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requested review from
gwang111 and mufaddal-rohawala
and removed request for
a teamFebruary 15, 2024 00:36
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Curious if there is any design doc for HF improvement in model builder?
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/bot run local-mode-tests
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@mufaddal-rohawalamufaddal-rohawalamufaddal-rohawala approved these changes
@gwang111gwang111gwang111 approved these changes

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@samruds@mufaddal-rohawala@haNa-meister@mohanasudhan@gwang111@makungaj1@jiapinw@knikure

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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");
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change: Enhance model builder selection logic to include model size - #4429
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knikure merged 20 commits into
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samruds:2PR
Mar 6, 2024
Merged
change: Enhance model builder selection logic to include model size#4429
knikure merged 20 commits into
aws:masterfrom 
samruds:2PR
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@samruds
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Feb 15, 2024•

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Issue #, if available:
https://docs.google.com/document/d/1OTXyZ-YpovntrzzBVBOBCnVl4Ijdl858s35UJFKf3dI/edit
Description of changes:

Use the model size and given instance to check if we can deploy.
Flow:
  1. If metadata tag is “text generation” go to TGI (exists today)
  2. Detect and Calculate Model size (M1) from Hugging Face Hub
  3. Calculate : Total memory inference (M2) = 1.2 * Model size (20% buffer for inference)
  4. Detect container type (CT1) - GPU or CPU using metadata tags
  5. If Model needs to be run on GPU :
    1. Query instance types and get number of GPUs, and memory size using Pricing API

      1. If total memory inference (M2) <= total GPU memory size go to HF DLC
  6. If number of GPUs > 1 check for DJL as supported architecture
  7. Fail Gracefully :
    1. Check if it is still possible to query image using HuggingFace Metadata
    2. If nothing works - throw an error - Model Server is not supported with current container type (%CT1) with size (%M1)
Testing done:
  1. Unit tests
  2. Integ tests
Merge Checklist
Put an x in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your pull request.
General
  •  I have read the CONTRIBUTING doc
  •  I certify that the changes I am introducing will be backward compatible, and I have discussed concerns about this, if any, with the Python SDK team
  •  I used the commit message format described in CONTRIBUTING
  •  I have passed the region in to all S3 and STS clients that I've initialized as part of this change.
  •  I have updated any necessary documentation, including READMEs and API docs (if appropriate)
Tests
  •  I have added tests that prove my fix is effective or that my feature works (if appropriate)
  •  I have added unit and/or integration tests as appropriate to ensure backward compatibility of the changes
  •  I have checked that my tests are not configured for a specific region or account (if appropriate)
  •  I have used unique_name_from_base to create resource names in integ tests (if appropriate)
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
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as a code ownerFebruary 15, 2024 00:36
@samruds
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gwang111 and mufaddal-rohawala
and removed request for
a teamFebruary 15, 2024 00:36
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Curious if there is any design doc for HF improvement in model builder?
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/bot run local-mode-tests
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@mufaddal-rohawalamufaddal-rohawalamufaddal-rohawala approved these changes
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@samruds@mufaddal-rohawala@haNa-meister@mohanasudhan@gwang111@makungaj1@jiapinw@knikure

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change: Enhance model builder selection logic to include model size - #4429
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knikure merged 20 commits into
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samruds:2PR
Mar 6, 2024
Merged
change: Enhance model builder selection logic to include model size#4429
knikure merged 20 commits into
aws:masterfrom 
samruds:2PR
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@samruds
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Issue #, if available:
https://docs.google.com/document/d/1OTXyZ-YpovntrzzBVBOBCnVl4Ijdl858s35UJFKf3dI/edit
Description of changes:

Use the model size and given instance to check if we can deploy.
Flow:
  1. If metadata tag is “text generation” go to TGI (exists today)
  2. Detect and Calculate Model size (M1) from Hugging Face Hub
  3. Calculate : Total memory inference (M2) = 1.2 * Model size (20% buffer for inference)
  4. Detect container type (CT1) - GPU or CPU using metadata tags
  5. If Model needs to be run on GPU :
    1. Query instance types and get number of GPUs, and memory size using Pricing API

      1. If total memory inference (M2) <= total GPU memory size go to HF DLC
  6. If number of GPUs > 1 check for DJL as supported architecture
  7. Fail Gracefully :
    1. Check if it is still possible to query image using HuggingFace Metadata
    2. If nothing works - throw an error - Model Server is not supported with current container type (%CT1) with size (%M1)
Testing done:
  1. Unit tests
  2. Integ tests
Merge Checklist
Put an x in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your pull request.
General
  •  I have read the CONTRIBUTING doc
  •  I certify that the changes I am introducing will be backward compatible, and I have discussed concerns about this, if any, with the Python SDK team
  •  I used the commit message format described in CONTRIBUTING
  •  I have passed the region in to all S3 and STS clients that I've initialized as part of this change.
  •  I have updated any necessary documentation, including READMEs and API docs (if appropriate)
Tests
  •  I have added tests that prove my fix is effective or that my feature works (if appropriate)
  •  I have added unit and/or integration tests as appropriate to ensure backward compatibility of the changes
  •  I have checked that my tests are not configured for a specific region or account (if appropriate)
  •  I have used unique_name_from_base to create resource names in integ tests (if appropriate)
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
@samruds
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as a code ownerFebruary 15, 2024 00:36
@samruds
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gwang111 and mufaddal-rohawala
and removed request for
a teamFebruary 15, 2024 00:36
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Curious if there is any design doc for HF improvement in model builder?
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/bot run local-mode-tests
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@mufaddal-rohawalamufaddal-rohawalamufaddal-rohawala approved these changes
@gwang111gwang111gwang111 approved these changes

+4 more reviewers

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@samruds@mufaddal-rohawala@haNa-meister@mohanasudhan@gwang111@makungaj1@jiapinw@knikure

, '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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change: Enhance model builder selection logic to include model size - #4429
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knikure merged 20 commits into
aws:masterfrom 
samruds:2PR
Mar 6, 2024
Merged
change: Enhance model builder selection logic to include model size#4429
knikure merged 20 commits into
aws:masterfrom 
samruds:2PR
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@samruds
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Feb 15, 2024•

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Issue #, if available:
https://docs.google.com/document/d/1OTXyZ-YpovntrzzBVBOBCnVl4Ijdl858s35UJFKf3dI/edit
Description of changes:

Use the model size and given instance to check if we can deploy.
Flow:
  1. If metadata tag is “text generation” go to TGI (exists today)
  2. Detect and Calculate Model size (M1) from Hugging Face Hub
  3. Calculate : Total memory inference (M2) = 1.2 * Model size (20% buffer for inference)
  4. Detect container type (CT1) - GPU or CPU using metadata tags
  5. If Model needs to be run on GPU :
    1. Query instance types and get number of GPUs, and memory size using Pricing API

      1. If total memory inference (M2) <= total GPU memory size go to HF DLC
  6. If number of GPUs > 1 check for DJL as supported architecture
  7. Fail Gracefully :
    1. Check if it is still possible to query image using HuggingFace Metadata
    2. If nothing works - throw an error - Model Server is not supported with current container type (%CT1) with size (%M1)
Testing done:
  1. Unit tests
  2. Integ tests
Merge Checklist
Put an x in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your pull request.
General
  •  I have read the CONTRIBUTING doc
  •  I certify that the changes I am introducing will be backward compatible, and I have discussed concerns about this, if any, with the Python SDK team
  •  I used the commit message format described in CONTRIBUTING
  •  I have passed the region in to all S3 and STS clients that I've initialized as part of this change.
  •  I have updated any necessary documentation, including READMEs and API docs (if appropriate)
Tests
  •  I have added tests that prove my fix is effective or that my feature works (if appropriate)
  •  I have added unit and/or integration tests as appropriate to ensure backward compatibility of the changes
  •  I have checked that my tests are not configured for a specific region or account (if appropriate)
  •  I have used unique_name_from_base to create resource names in integ tests (if appropriate)
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
@samruds
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as a code ownerFebruary 15, 2024 00:36
@samruds
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gwang111 and mufaddal-rohawala
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@mufaddal-rohawala
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Curious if there is any design doc for HF improvement in model builder?
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/bot run local-mode-tests
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@mufaddal-rohawalamufaddal-rohawalamufaddal-rohawala approved these changes
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