container_log_level does not work in TensorFlow estimator #1875

Closed
Closed
container_log_level does not work in TensorFlow estimator#1875
Labels
type: bug

Description

@tailaiw
tailaiw
opened on Sep 8, 2020

Describe the bug
Parameter container_log_level does not work in TensorFlow estimator

To reproduce
I have a TensorFlow estimator built roughly as follows

importloggingfromsagemaker.tensorflowimportTensorFlowestimator=TensorFlow(
sagemaker_session=sagemaker_session,
entry_point="my_entry_point.py",
source_dir=my_source_dir,
role=my_role,
instance_count=1,
instance_type="ml.p3.2xlarge", framework_version="2.1",
py_version="py3",
checkpoint_s3_uri=my_checkpoint_s3_uri,
container_log_level=logging.WARNING)

I expect now logs lower than WARNING will be included in the training job logs. However, a lot of INFO level logs are observed. Particularly, a lot of INFO logs related to frequent checkpoint uploading (from instance to S3) are observed, which makes the entire log super long. I tried logging.ERROR and no luck either.

Expected behavior
No log except WARNING and ERROR level ones should be observed.

Screenshots or logs
If applicable, add screenshots or logs to help explain your problem.
image

System information
A description of your system. Please provide:

  • SageMaker Python SDK version: 2.5.1
  • Framework name (eg. PyTorch) or algorithm (eg. KMeans): TensorFlow
  • Framework version: 2.1
  • Python version: 3.7
  • CPU or GPU: GPU
  • Custom Docker image (Y/N): N

Metadata

Metadata

Assignees

No one assigned

    Labels

    type: 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("// 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
      container_log_level does not work in TensorFlow estimator #1875
      Closed
      Closed
      container_log_level does not work in TensorFlow estimator#1875
      Labels
      type: bug
      Description
      @tailaiw
      tailaiw
      opened on Sep 8, 2020
      Describe the bug
      
Parameter container_log_level does not work in TensorFlow estimator
      To reproduce
      
I have a TensorFlow estimator built roughly as follows
      importloggingfromsagemaker.tensorflowimportTensorFlowestimator=TensorFlow(
sagemaker_session=sagemaker_session,
entry_point="my_entry_point.py",
source_dir=my_source_dir,
role=my_role,
instance_count=1,
instance_type="ml.p3.2xlarge", framework_version="2.1",
py_version="py3",
checkpoint_s3_uri=my_checkpoint_s3_uri,
container_log_level=logging.WARNING)
      I expect now logs lower than WARNING will be included in the training job logs. However, a lot of INFO level logs are observed. Particularly, a lot of INFO logs related to frequent checkpoint uploading (from instance to S3) are observed, which makes the entire log super long. I tried logging.ERROR and no luck either.
      Expected behavior
      
No log except WARNING and ERROR level ones should be observed.
      Screenshots or logs
      
If applicable, add screenshots or logs to help explain your problem.
      image
      System information
      
A description of your system. Please provide:
      • SageMaker Python SDK version: 2.5.1
      • Framework name (eg. PyTorch) or algorithm (eg. KMeans): TensorFlow
      • Framework version: 2.1
      • Python version: 3.7
      • CPU or GPU: GPU
      • Custom Docker image (Y/N): N
      Metadata
      Metadata
      Assignees
      No one assigned
        Labels
        type: 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("// 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
          container_log_level does not work in TensorFlow estimator #1875
          Closed
          Closed
          container_log_level does not work in TensorFlow estimator#1875
          Labels
          type: bug
          Description
          @tailaiw
          tailaiw
          opened on Sep 8, 2020
          Describe the bug
          
Parameter container_log_level does not work in TensorFlow estimator
          To reproduce
          
I have a TensorFlow estimator built roughly as follows
          importloggingfromsagemaker.tensorflowimportTensorFlowestimator=TensorFlow(
sagemaker_session=sagemaker_session,
entry_point="my_entry_point.py",
source_dir=my_source_dir,
role=my_role,
instance_count=1,
instance_type="ml.p3.2xlarge", framework_version="2.1",
py_version="py3",
checkpoint_s3_uri=my_checkpoint_s3_uri,
container_log_level=logging.WARNING)
          I expect now logs lower than WARNING will be included in the training job logs. However, a lot of INFO level logs are observed. Particularly, a lot of INFO logs related to frequent checkpoint uploading (from instance to S3) are observed, which makes the entire log super long. I tried logging.ERROR and no luck either.
          Expected behavior
          
No log except WARNING and ERROR level ones should be observed.
          Screenshots or logs
          
If applicable, add screenshots or logs to help explain your problem.
          image
          System information
          
A description of your system. Please provide:
          • SageMaker Python SDK version: 2.5.1
          • Framework name (eg. PyTorch) or algorithm (eg. KMeans): TensorFlow
          • Framework version: 2.1
          • Python version: 3.7
          • CPU or GPU: GPU
          • Custom Docker image (Y/N): N
          Metadata
          Metadata
          Assignees
          No one assigned
            Labels
            type: 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("// 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
              container_log_level does not work in TensorFlow estimator #1875
              Closed
              Closed
              container_log_level does not work in TensorFlow estimator#1875
              Labels
              type: bug
              Description
              @tailaiw
              tailaiw
              opened on Sep 8, 2020
              Describe the bug
              
Parameter container_log_level does not work in TensorFlow estimator
              To reproduce
              
I have a TensorFlow estimator built roughly as follows
              importloggingfromsagemaker.tensorflowimportTensorFlowestimator=TensorFlow(
sagemaker_session=sagemaker_session,
entry_point="my_entry_point.py",
source_dir=my_source_dir,
role=my_role,
instance_count=1,
instance_type="ml.p3.2xlarge", framework_version="2.1",
py_version="py3",
checkpoint_s3_uri=my_checkpoint_s3_uri,
container_log_level=logging.WARNING)
              I expect now logs lower than WARNING will be included in the training job logs. However, a lot of INFO level logs are observed. Particularly, a lot of INFO logs related to frequent checkpoint uploading (from instance to S3) are observed, which makes the entire log super long. I tried logging.ERROR and no luck either.
              Expected behavior
              
No log except WARNING and ERROR level ones should be observed.
              Screenshots or logs
              
If applicable, add screenshots or logs to help explain your problem.
              image
              System information
              
A description of your system. Please provide:
              • SageMaker Python SDK version: 2.5.1
              • Framework name (eg. PyTorch) or algorithm (eg. KMeans): TensorFlow
              • Framework version: 2.1
              • Python version: 3.7
              • CPU or GPU: GPU
              • Custom Docker image (Y/N): N
              Metadata
              Metadata
              Assignees
              No one assigned
                Labels
                type: 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("// 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
                  container_log_level does not work in TensorFlow estimator #1875
                  Closed
                  Closed
                  container_log_level does not work in TensorFlow estimator#1875
                  Labels
                  type: bug
                  Description
                  @tailaiw
                  tailaiw
                  opened on Sep 8, 2020
                  Describe the bug
                  
Parameter container_log_level does not work in TensorFlow estimator
                  To reproduce
                  
I have a TensorFlow estimator built roughly as follows
                  importloggingfromsagemaker.tensorflowimportTensorFlowestimator=TensorFlow(
sagemaker_session=sagemaker_session,
entry_point="my_entry_point.py",
source_dir=my_source_dir,
role=my_role,
instance_count=1,
instance_type="ml.p3.2xlarge", framework_version="2.1",
py_version="py3",
checkpoint_s3_uri=my_checkpoint_s3_uri,
container_log_level=logging.WARNING)
                  I expect now logs lower than WARNING will be included in the training job logs. However, a lot of INFO level logs are observed. Particularly, a lot of INFO logs related to frequent checkpoint uploading (from instance to S3) are observed, which makes the entire log super long. I tried logging.ERROR and no luck either.
                  Expected behavior
                  
No log except WARNING and ERROR level ones should be observed.
                  Screenshots or logs
                  
If applicable, add screenshots or logs to help explain your problem.
                  image
                  System information
                  
A description of your system. Please provide:
                  • SageMaker Python SDK version: 2.5.1
                  • Framework name (eg. PyTorch) or algorithm (eg. KMeans): TensorFlow
                  • Framework version: 2.1
                  • Python version: 3.7
                  • CPU or GPU: GPU
                  • Custom Docker image (Y/N): N
                  Metadata
                  Metadata
                  Assignees
                  No one assigned
                    Labels
                    type: 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
                      container_log_level does not work in TensorFlow estimator #1875
                      Closed
                      Closed
                      container_log_level does not work in TensorFlow estimator#1875
                      Labels
                      type: bug
                      Description
                      @tailaiw
                      tailaiw
                      opened on Sep 8, 2020
                      Describe the bug
                      
Parameter container_log_level does not work in TensorFlow estimator
                      To reproduce
                      
I have a TensorFlow estimator built roughly as follows
                      importloggingfromsagemaker.tensorflowimportTensorFlowestimator=TensorFlow(
sagemaker_session=sagemaker_session,
entry_point="my_entry_point.py",
source_dir=my_source_dir,
role=my_role,
instance_count=1,
instance_type="ml.p3.2xlarge", framework_version="2.1",
py_version="py3",
checkpoint_s3_uri=my_checkpoint_s3_uri,
container_log_level=logging.WARNING)
                      I expect now logs lower than WARNING will be included in the training job logs. However, a lot of INFO level logs are observed. Particularly, a lot of INFO logs related to frequent checkpoint uploading (from instance to S3) are observed, which makes the entire log super long. I tried logging.ERROR and no luck either.
                      Expected behavior
                      
No log except WARNING and ERROR level ones should be observed.
                      Screenshots or logs
                      
If applicable, add screenshots or logs to help explain your problem.
                      image
                      System information
                      
A description of your system. Please provide:
                      • SageMaker Python SDK version: 2.5.1
                      • Framework name (eg. PyTorch) or algorithm (eg. KMeans): TensorFlow
                      • Framework version: 2.1
                      • Python version: 3.7
                      • CPU or GPU: GPU
                      • Custom Docker image (Y/N): N
                      Metadata
                      Metadata
                      Assignees
                      No one assigned
                        Labels
                        type: 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
                          container_log_level does not work in TensorFlow estimator #1875
                          Closed
                          Closed
                          container_log_level does not work in TensorFlow estimator#1875
                          Labels
                          type: bug
                          Description
                          @tailaiw
                          tailaiw
                          opened on Sep 8, 2020
                          Describe the bug
                          
Parameter container_log_level does not work in TensorFlow estimator
                          To reproduce
                          
I have a TensorFlow estimator built roughly as follows
                          importloggingfromsagemaker.tensorflowimportTensorFlowestimator=TensorFlow(
sagemaker_session=sagemaker_session,
entry_point="my_entry_point.py",
source_dir=my_source_dir,
role=my_role,
instance_count=1,
instance_type="ml.p3.2xlarge", framework_version="2.1",
py_version="py3",
checkpoint_s3_uri=my_checkpoint_s3_uri,
container_log_level=logging.WARNING)
                          I expect now logs lower than WARNING will be included in the training job logs. However, a lot of INFO level logs are observed. Particularly, a lot of INFO logs related to frequent checkpoint uploading (from instance to S3) are observed, which makes the entire log super long. I tried logging.ERROR and no luck either.
                          Expected behavior
                          
No log except WARNING and ERROR level ones should be observed.
                          Screenshots or logs
                          
If applicable, add screenshots or logs to help explain your problem.
                          image
                          System information
                          
A description of your system. Please provide:
                          • SageMaker Python SDK version: 2.5.1
                          • Framework name (eg. PyTorch) or algorithm (eg. KMeans): TensorFlow
                          • Framework version: 2.1
                          • Python version: 3.7
                          • CPU or GPU: GPU
                          • Custom Docker image (Y/N): N
                          Metadata
                          Metadata
                          Assignees
                          No one assigned
                            Labels
                            type: 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("// 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); }
})();
})();

                              Skip to content
                              container_log_level does not work in TensorFlow estimator #1875
                              Closed
                              Closed
                              container_log_level does not work in TensorFlow estimator#1875
                              Labels
                              type: bug
                              Description
                              @tailaiw
                              tailaiw
                              opened on Sep 8, 2020
                              Describe the bug
                              
Parameter container_log_level does not work in TensorFlow estimator
                              To reproduce
                              
I have a TensorFlow estimator built roughly as follows
                              importloggingfromsagemaker.tensorflowimportTensorFlowestimator=TensorFlow(
sagemaker_session=sagemaker_session,
entry_point="my_entry_point.py",
source_dir=my_source_dir,
role=my_role,
instance_count=1,
instance_type="ml.p3.2xlarge", framework_version="2.1",
py_version="py3",
checkpoint_s3_uri=my_checkpoint_s3_uri,
container_log_level=logging.WARNING)
                              I expect now logs lower than WARNING will be included in the training job logs. However, a lot of INFO level logs are observed. Particularly, a lot of INFO logs related to frequent checkpoint uploading (from instance to S3) are observed, which makes the entire log super long. I tried logging.ERROR and no luck either.
                              Expected behavior
                              
No log except WARNING and ERROR level ones should be observed.
                              Screenshots or logs
                              
If applicable, add screenshots or logs to help explain your problem.
                              image
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                              • SageMaker Python SDK version: 2.5.1
                              • Framework name (eg. PyTorch) or algorithm (eg. KMeans): TensorFlow
                              • Framework version: 2.1
                              • Python version: 3.7
                              • CPU or GPU: GPU
                              • Custom Docker image (Y/N): N
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