TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation #669

Closed
Closed
TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation#669
Labels
contributions welcometype: bug

Description

@mmeidl
mmeidl
opened on Feb 27, 2019

Opening this issue on behalf of a SageMaker customer. The customer has pre-compressed and uploaded their source_dir to S3, and wants to set requirements_file to a relative path contained in the source.

The generic Frameworks Estimator allows source_dir to be an S3 location. In this case it skips validation/upload.
Skipping source_dir validation:

sagemaker-python-sdk/src/sagemaker/estimator.py

Lines 830 to 833 in 8b33a30

# validate source dir will raise a ValueError if there is something wrong with the
# source directory. We are intentionally not handling it because this is a critical error.
ifself.source_dirandnotself.source_dir.lower().startswith('s3://'):
validate_source_dir(self.entry_point, self.source_dir)

Skipping source_dir upload:

sagemaker-python-sdk/src/sagemaker/fw_utils.py

Lines 143 to 144 in 8b33a30

If directory is an S3 URI, an UploadedCode object will be returned, but nothing will be
uploaded to S3 (this allow reuse of code already in S3).

However the Tensorflow Estimator runs a validation for requirements_file which fails if the location source_dir/requirements_file is not a valid path on the local os:

sagemaker-python-sdk/src/sagemaker/tensorflow/estimator.py

Lines 273 to 285 in 8b33a30

def_validate_requirements_file(self, requirements_file):
ifnotrequirements_file:
return
ifnotself.source_dir:
raiseValueError('Must specify source_dir along with a requirements file.')
ifos.path.isabs(requirements_file):
raiseValueError('Requirements file {} is not a path relative to source_dir.'.format(
requirements_file))
ifnotos.path.exists(os.path.join(self.source_dir, requirements_file)):
raiseValueError('Requirements file {} does not exist.'.format(requirements_file))

Seems like it would be easy to skip the local path validation if source_dir is an S3 location. Something like this could be added to _validate_requirements_file:

if source_dir.lower().startswith('s3://'):
return

I know that support for requirements.txt files is limited between the "legacy" TensorFlow container and the newer "script mode" version. But this is a small bug in the SDK which could easily be fixed.

Activity

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      , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
       blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} 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
      TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation #669
      Closed
      Closed
      TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation#669
      Labels
      contributions welcometype: bug
      Description
      @mmeidl
      mmeidl
      opened on Feb 27, 2019
      Opening this issue on behalf of a SageMaker customer. The customer has pre-compressed and uploaded their source_dir to S3, and wants to set requirements_file to a relative path contained in the source.
      The generic Frameworks Estimator allows source_dir to be an S3 location. In this case it skips validation/upload.
      
Skipping source_dir validation:
      sagemaker-python-sdk/src/sagemaker/estimator.py
      
Lines 830 to 833
in
8b33a30
      # validate source dir will raise a ValueError if there is something wrong with the
      # source directory. We are intentionally not handling it because this is a critical error.
      ifself.source_dirandnotself.source_dir.lower().startswith('s3://'): 
      validate_source_dir(self.entry_point, self.source_dir) 

      
Skipping source_dir upload:
      sagemaker-python-sdk/src/sagemaker/fw_utils.py
      
Lines 143 to 144
in
8b33a30
       If directory is an S3 URI, an UploadedCode object will be returned, but nothing will be
       uploaded to S3 (this allow reuse of code already in S3).
      However the Tensorflow Estimator runs a validation for requirements_file which fails if the location source_dir/requirements_file is not a valid path on the local os:
      sagemaker-python-sdk/src/sagemaker/tensorflow/estimator.py
      
Lines 273 to 285
in
8b33a30
      def_validate_requirements_file(self, requirements_file): 
      ifnotrequirements_file: 
      return
      ifnotself.source_dir: 
      raiseValueError('Must specify source_dir along with a requirements file.') 
      ifos.path.isabs(requirements_file): 
      raiseValueError('Requirements file {} is not a path relative to source_dir.'.format( 
      requirements_file)) 
      ifnotos.path.exists(os.path.join(self.source_dir, requirements_file)): 
      raiseValueError('Requirements file {} does not exist.'.format(requirements_file)) 
      Seems like it would be easy to skip the local path validation if source_dir is an S3 location. Something like this could be added to _validate_requirements_file:
      if source_dir.lower().startswith('s3://'):
return

      I know that support for requirements.txt files is limited between the "legacy" TensorFlow container and the newer "script mode" version. But this is a small bug in the SDK which could easily be fixed.
      Activity
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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
          TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation #669
          Closed
          Closed
          TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation#669
          Labels
          contributions welcometype: bug
          Description
          @mmeidl
          mmeidl
          opened on Feb 27, 2019
          Opening this issue on behalf of a SageMaker customer. The customer has pre-compressed and uploaded their source_dir to S3, and wants to set requirements_file to a relative path contained in the source.
          The generic Frameworks Estimator allows source_dir to be an S3 location. In this case it skips validation/upload.
          
Skipping source_dir validation:
          sagemaker-python-sdk/src/sagemaker/estimator.py
          
Lines 830 to 833
in
8b33a30
          # validate source dir will raise a ValueError if there is something wrong with the
          # source directory. We are intentionally not handling it because this is a critical error.
          ifself.source_dirandnotself.source_dir.lower().startswith('s3://'): 
          validate_source_dir(self.entry_point, self.source_dir) 

          
Skipping source_dir upload:
          sagemaker-python-sdk/src/sagemaker/fw_utils.py
          
Lines 143 to 144
in
8b33a30
           If directory is an S3 URI, an UploadedCode object will be returned, but nothing will be
           uploaded to S3 (this allow reuse of code already in S3).
          However the Tensorflow Estimator runs a validation for requirements_file which fails if the location source_dir/requirements_file is not a valid path on the local os:
          sagemaker-python-sdk/src/sagemaker/tensorflow/estimator.py
          
Lines 273 to 285
in
8b33a30
          def_validate_requirements_file(self, requirements_file): 
          ifnotrequirements_file: 
          return
          ifnotself.source_dir: 
          raiseValueError('Must specify source_dir along with a requirements file.') 
          ifos.path.isabs(requirements_file): 
          raiseValueError('Requirements file {} is not a path relative to source_dir.'.format( 
          requirements_file)) 
          ifnotos.path.exists(os.path.join(self.source_dir, requirements_file)): 
          raiseValueError('Requirements file {} does not exist.'.format(requirements_file)) 
          Seems like it would be easy to skip the local path validation if source_dir is an S3 location. Something like this could be added to _validate_requirements_file:
          if source_dir.lower().startswith('s3://'):
return

          I know that support for requirements.txt files is limited between the "legacy" TensorFlow container and the newer "script mode" version. But this is a small bug in the SDK which could easily be fixed.
          Activity
          Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
          Metadata
          Metadata
          Assignees
          No one assigned
            Labels
            contributions welcometype: bug
            Type
            No type
            Projects
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              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
              TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation #669
              Closed
              Closed
              TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation#669
              Labels
              contributions welcometype: bug
              Description
              @mmeidl
              mmeidl
              opened on Feb 27, 2019
              Opening this issue on behalf of a SageMaker customer. The customer has pre-compressed and uploaded their source_dir to S3, and wants to set requirements_file to a relative path contained in the source.
              The generic Frameworks Estimator allows source_dir to be an S3 location. In this case it skips validation/upload.
              
Skipping source_dir validation:
              sagemaker-python-sdk/src/sagemaker/estimator.py
              
Lines 830 to 833
in
8b33a30
              # validate source dir will raise a ValueError if there is something wrong with the
              # source directory. We are intentionally not handling it because this is a critical error.
              ifself.source_dirandnotself.source_dir.lower().startswith('s3://'): 
              validate_source_dir(self.entry_point, self.source_dir) 

              
Skipping source_dir upload:
              sagemaker-python-sdk/src/sagemaker/fw_utils.py
              
Lines 143 to 144
in
8b33a30
               If directory is an S3 URI, an UploadedCode object will be returned, but nothing will be
               uploaded to S3 (this allow reuse of code already in S3).
              However the Tensorflow Estimator runs a validation for requirements_file which fails if the location source_dir/requirements_file is not a valid path on the local os:
              sagemaker-python-sdk/src/sagemaker/tensorflow/estimator.py
              
Lines 273 to 285
in
8b33a30
              def_validate_requirements_file(self, requirements_file): 
              ifnotrequirements_file: 
              return
              ifnotself.source_dir: 
              raiseValueError('Must specify source_dir along with a requirements file.') 
              ifos.path.isabs(requirements_file): 
              raiseValueError('Requirements file {} is not a path relative to source_dir.'.format( 
              requirements_file)) 
              ifnotos.path.exists(os.path.join(self.source_dir, requirements_file)): 
              raiseValueError('Requirements file {} does not exist.'.format(requirements_file)) 
              Seems like it would be easy to skip the local path validation if source_dir is an S3 location. Something like this could be added to _validate_requirements_file:
              if source_dir.lower().startswith('s3://'):
return

              I know that support for requirements.txt files is limited between the "legacy" TensorFlow container and the newer "script mode" version. But this is a small bug in the SDK which could easily be fixed.
              Activity
              Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
              Metadata
              Metadata
              Assignees
              No one assigned
                Labels
                contributions welcometype: bug
                Type
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                  No milestone
                  Relationships
                  None yet
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                  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
                  TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation #669
                  Closed
                  Closed
                  TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation#669
                  Labels
                  contributions welcometype: bug
                  Description
                  @mmeidl
                  mmeidl
                  opened on Feb 27, 2019
                  Opening this issue on behalf of a SageMaker customer. The customer has pre-compressed and uploaded their source_dir to S3, and wants to set requirements_file to a relative path contained in the source.
                  The generic Frameworks Estimator allows source_dir to be an S3 location. In this case it skips validation/upload.
                  
Skipping source_dir validation:
                  sagemaker-python-sdk/src/sagemaker/estimator.py
                  
Lines 830 to 833
in
8b33a30
                  # validate source dir will raise a ValueError if there is something wrong with the
                  # source directory. We are intentionally not handling it because this is a critical error.
                  ifself.source_dirandnotself.source_dir.lower().startswith('s3://'): 
                  validate_source_dir(self.entry_point, self.source_dir) 

                  
Skipping source_dir upload:
                  sagemaker-python-sdk/src/sagemaker/fw_utils.py
                  
Lines 143 to 144
in
8b33a30
                   If directory is an S3 URI, an UploadedCode object will be returned, but nothing will be
                   uploaded to S3 (this allow reuse of code already in S3).
                  However the Tensorflow Estimator runs a validation for requirements_file which fails if the location source_dir/requirements_file is not a valid path on the local os:
                  sagemaker-python-sdk/src/sagemaker/tensorflow/estimator.py
                  
Lines 273 to 285
in
8b33a30
                  def_validate_requirements_file(self, requirements_file): 
                  ifnotrequirements_file: 
                  return
                  ifnotself.source_dir: 
                  raiseValueError('Must specify source_dir along with a requirements file.') 
                  ifos.path.isabs(requirements_file): 
                  raiseValueError('Requirements file {} is not a path relative to source_dir.'.format( 
                  requirements_file)) 
                  ifnotos.path.exists(os.path.join(self.source_dir, requirements_file)): 
                  raiseValueError('Requirements file {} does not exist.'.format(requirements_file)) 
                  Seems like it would be easy to skip the local path validation if source_dir is an S3 location. Something like this could be added to _validate_requirements_file:
                  if source_dir.lower().startswith('s3://'):
return

                  I know that support for requirements.txt files is limited between the "legacy" TensorFlow container and the newer "script mode" version. But this is a small bug in the SDK which could easily be fixed.
                  Activity
                  Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
                  Metadata
                  Metadata
                  Assignees
                  No one assigned
                    Labels
                    contributions welcometype: bug
                    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
                      TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation #669
                      Closed
                      Closed
                      TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation#669
                      Labels
                      contributions welcometype: bug
                      Description
                      @mmeidl
                      mmeidl
                      opened on Feb 27, 2019
                      Opening this issue on behalf of a SageMaker customer. The customer has pre-compressed and uploaded their source_dir to S3, and wants to set requirements_file to a relative path contained in the source.
                      The generic Frameworks Estimator allows source_dir to be an S3 location. In this case it skips validation/upload.
                      
Skipping source_dir validation:
                      sagemaker-python-sdk/src/sagemaker/estimator.py
                      
Lines 830 to 833
in
8b33a30
                      # validate source dir will raise a ValueError if there is something wrong with the
                      # source directory. We are intentionally not handling it because this is a critical error.
                      ifself.source_dirandnotself.source_dir.lower().startswith('s3://'): 
                      validate_source_dir(self.entry_point, self.source_dir) 

                      
Skipping source_dir upload:
                      sagemaker-python-sdk/src/sagemaker/fw_utils.py
                      
Lines 143 to 144
in
8b33a30
                       If directory is an S3 URI, an UploadedCode object will be returned, but nothing will be
                       uploaded to S3 (this allow reuse of code already in S3).
                      However the Tensorflow Estimator runs a validation for requirements_file which fails if the location source_dir/requirements_file is not a valid path on the local os:
                      sagemaker-python-sdk/src/sagemaker/tensorflow/estimator.py
                      
Lines 273 to 285
in
8b33a30
                      def_validate_requirements_file(self, requirements_file): 
                      ifnotrequirements_file: 
                      return
                      ifnotself.source_dir: 
                      raiseValueError('Must specify source_dir along with a requirements file.') 
                      ifos.path.isabs(requirements_file): 
                      raiseValueError('Requirements file {} is not a path relative to source_dir.'.format( 
                      requirements_file)) 
                      ifnotos.path.exists(os.path.join(self.source_dir, requirements_file)): 
                      raiseValueError('Requirements file {} does not exist.'.format(requirements_file)) 
                      Seems like it would be easy to skip the local path validation if source_dir is an S3 location. Something like this could be added to _validate_requirements_file:
                      if source_dir.lower().startswith('s3://'):
return

                      I know that support for requirements.txt files is limited between the "legacy" TensorFlow container and the newer "script mode" version. But this is a small bug in the SDK which could easily be fixed.
                      Activity
                      Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
                      Metadata
                      Metadata
                      Assignees
                      No one assigned
                        Labels
                        contributions welcometype: bug
                        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
                          TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation #669
                          Closed
                          Closed
                          TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation#669
                          Labels
                          contributions welcometype: bug
                          Description
                          @mmeidl
                          mmeidl
                          opened on Feb 27, 2019
                          Opening this issue on behalf of a SageMaker customer. The customer has pre-compressed and uploaded their source_dir to S3, and wants to set requirements_file to a relative path contained in the source.
                          The generic Frameworks Estimator allows source_dir to be an S3 location. In this case it skips validation/upload.
                          
Skipping source_dir validation:
                          sagemaker-python-sdk/src/sagemaker/estimator.py
                          
Lines 830 to 833
in
8b33a30
                          # validate source dir will raise a ValueError if there is something wrong with the
                          # source directory. We are intentionally not handling it because this is a critical error.
                          ifself.source_dirandnotself.source_dir.lower().startswith('s3://'): 
                          validate_source_dir(self.entry_point, self.source_dir) 

                          
Skipping source_dir upload:
                          sagemaker-python-sdk/src/sagemaker/fw_utils.py
                          
Lines 143 to 144
in
8b33a30
                           If directory is an S3 URI, an UploadedCode object will be returned, but nothing will be
                           uploaded to S3 (this allow reuse of code already in S3).
                          However the Tensorflow Estimator runs a validation for requirements_file which fails if the location source_dir/requirements_file is not a valid path on the local os:
                          sagemaker-python-sdk/src/sagemaker/tensorflow/estimator.py
                          
Lines 273 to 285
in
8b33a30
                          def_validate_requirements_file(self, requirements_file): 
                          ifnotrequirements_file: 
                          return
                          ifnotself.source_dir: 
                          raiseValueError('Must specify source_dir along with a requirements file.') 
                          ifos.path.isabs(requirements_file): 
                          raiseValueError('Requirements file {} is not a path relative to source_dir.'.format( 
                          requirements_file)) 
                          ifnotos.path.exists(os.path.join(self.source_dir, requirements_file)): 
                          raiseValueError('Requirements file {} does not exist.'.format(requirements_file)) 
                          Seems like it would be easy to skip the local path validation if source_dir is an S3 location. Something like this could be added to _validate_requirements_file:
                          if source_dir.lower().startswith('s3://'):
return

                          I know that support for requirements.txt files is limited between the "legacy" TensorFlow container and the newer "script mode" version. But this is a small bug in the SDK which could easily be fixed.
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, '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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                              TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation #669
                              Closed
                              Closed
                              TensorFlow Estimator allows S3 location for source_dir, but fails requirements_file validation#669
                              Labels
                              contributions welcometype: bug
                              Description
                              @mmeidl
                              mmeidl
                              opened on Feb 27, 2019
                              Opening this issue on behalf of a SageMaker customer. The customer has pre-compressed and uploaded their source_dir to S3, and wants to set requirements_file to a relative path contained in the source.
                              The generic Frameworks Estimator allows source_dir to be an S3 location. In this case it skips validation/upload.
                              
Skipping source_dir validation:
                              sagemaker-python-sdk/src/sagemaker/estimator.py
                              
Lines 830 to 833
in
8b33a30
                              # validate source dir will raise a ValueError if there is something wrong with the
                              # source directory. We are intentionally not handling it because this is a critical error.
                              ifself.source_dirandnotself.source_dir.lower().startswith('s3://'): 
                              validate_source_dir(self.entry_point, self.source_dir) 

                              
Skipping source_dir upload:
                              sagemaker-python-sdk/src/sagemaker/fw_utils.py
                              
Lines 143 to 144
in
8b33a30
                               If directory is an S3 URI, an UploadedCode object will be returned, but nothing will be
                               uploaded to S3 (this allow reuse of code already in S3).
                              However the Tensorflow Estimator runs a validation for requirements_file which fails if the location source_dir/requirements_file is not a valid path on the local os:
                              sagemaker-python-sdk/src/sagemaker/tensorflow/estimator.py
                              
Lines 273 to 285
in
8b33a30
                              def_validate_requirements_file(self, requirements_file): 
                              ifnotrequirements_file: 
                              return
                              ifnotself.source_dir: 
                              raiseValueError('Must specify source_dir along with a requirements file.') 
                              ifos.path.isabs(requirements_file): 
                              raiseValueError('Requirements file {} is not a path relative to source_dir.'.format( 
                              requirements_file)) 
                              ifnotos.path.exists(os.path.join(self.source_dir, requirements_file)): 
                              raiseValueError('Requirements file {} does not exist.'.format(requirements_file)) 
                              Seems like it would be easy to skip the local path validation if source_dir is an S3 location. Something like this could be added to _validate_requirements_file:
                              if source_dir.lower().startswith('s3://'):
return

                              I know that support for requirements.txt files is limited between the "legacy" TensorFlow container and the newer "script mode" version. But this is a small bug in the SDK which could easily be fixed.
                              Activity
                              Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
                              Metadata
                              Metadata
                              Assignees
                              No one assigned
                                Labels
                                contributions welcometype: bug
                                Type
                                No type
                                Projects
                                No projects
                                  Milestone
                                  No milestone
                                  Relationships
                                  None yet
                                  Development
                                  No branches or pull requests
                                  Issue actions