[SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100 #1970

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Closed
[SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100#1970
Labels
PySparkcontributions welcometype: bug

Description

@andremoeller
andremoeller
opened on Oct 24, 2020

Describe the bug

Calling SparkProcessor.run more than once on the same SparkProcessor object fails since the ContainerEntrypoint value has the same arguments repeated over and over.

To reproduce
A clear, step-by-step set of instructions to reproduce the bug.

spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)
spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)

Expected behavior

Both spark_processor.run calls create a processing job successfully, with ContainerEntrypoint having value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar]

Screenshots or logs

The second run call fails with:

ClientError: An error occurred (ValidationException) when calling the CreateProcessingJob operation: 1 validation error detected: Value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, /opt/ml/processing/input/code/spark_script.py]' at 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100

System information
A description of your system. Please provide:

  • SageMaker Python SDK version: latest
  • Framework name (eg. PyTorch) or algorithm (eg. KMeans): SparkProcessor related -- see additional info
  • Framework version: N/A
  • Python version: 3
  • CPU or GPU: N/A
  • Custom Docker image (Y/N): N

Additional context

The SparkProcessor class inherits from ScriptProcessor, which has this self.command attribute: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/processing.py#L405

The problem happens since SparkProcessor appends to the self.command list with each run invocation:

https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L230-L235
https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L600-L601

Solution suggests are to reset self.command with each run call, or copy the command to another list with each run call rather than mutating the same self.command list.

Activity

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      , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

      Skip to content
      [SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100 #1970
      Closed
      Closed
      [SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100#1970
      Labels
      PySparkcontributions welcometype: bug
      Description
      @andremoeller
      andremoeller
      opened on Oct 24, 2020
      Describe the bug
      Calling SparkProcessor.run more than once on the same SparkProcessor object fails since the ContainerEntrypoint value has the same arguments repeated over and over.
      To reproduce
      
A clear, step-by-step set of instructions to reproduce the bug.
      spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)
spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)

      Expected behavior
      Both spark_processor.run calls create a processing job successfully, with ContainerEntrypoint having value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar]
      Screenshots or logs
      The second run call fails with:
      ClientError: An error occurred (ValidationException) when calling the CreateProcessingJob operation: 1 validation error detected: Value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, /opt/ml/processing/input/code/spark_script.py]' at 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100

      System information
      
A description of your system. Please provide:
      • SageMaker Python SDK version: latest
      • Framework name (eg. PyTorch) or algorithm (eg. KMeans): SparkProcessor related -- see additional info
      • Framework version: N/A
      • Python version: 3
      • CPU or GPU: N/A
      • Custom Docker image (Y/N): N
      Additional context
      The SparkProcessor class inherits from ScriptProcessor, which has this self.command attribute: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/processing.py#L405
      The problem happens since SparkProcessor appends to the self.command list with each run invocation:
      https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L230-L235
      https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L600-L601
      Solution suggests are to reset self.command with each run call, or copy the command to another list with each run call rather than mutating the same self.command list.
      Activity
      Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
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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
          [SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100 #1970
          Closed
          Closed
          [SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100#1970
          Labels
          PySparkcontributions welcometype: bug
          Description
          @andremoeller
          andremoeller
          opened on Oct 24, 2020
          Describe the bug
          Calling SparkProcessor.run more than once on the same SparkProcessor object fails since the ContainerEntrypoint value has the same arguments repeated over and over.
          To reproduce
          
A clear, step-by-step set of instructions to reproduce the bug.
          spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)
spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)

          Expected behavior
          Both spark_processor.run calls create a processing job successfully, with ContainerEntrypoint having value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar]
          Screenshots or logs
          The second run call fails with:
          ClientError: An error occurred (ValidationException) when calling the CreateProcessingJob operation: 1 validation error detected: Value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, /opt/ml/processing/input/code/spark_script.py]' at 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100

          System information
          
A description of your system. Please provide:
          • SageMaker Python SDK version: latest
          • Framework name (eg. PyTorch) or algorithm (eg. KMeans): SparkProcessor related -- see additional info
          • Framework version: N/A
          • Python version: 3
          • CPU or GPU: N/A
          • Custom Docker image (Y/N): N
          Additional context
          The SparkProcessor class inherits from ScriptProcessor, which has this self.command attribute: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/processing.py#L405
          The problem happens since SparkProcessor appends to the self.command list with each run invocation:
          https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L230-L235
          https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L600-L601
          Solution suggests are to reset self.command with each run call, or copy the command to another list with each run call rather than mutating the same self.command list.
          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
            PySparkcontributions 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("// 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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

              Skip to content
              [SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100 #1970
              Closed
              Closed
              [SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100#1970
              Labels
              PySparkcontributions welcometype: bug
              Description
              @andremoeller
              andremoeller
              opened on Oct 24, 2020
              Describe the bug
              Calling SparkProcessor.run more than once on the same SparkProcessor object fails since the ContainerEntrypoint value has the same arguments repeated over and over.
              To reproduce
              
A clear, step-by-step set of instructions to reproduce the bug.
              spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)
spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)

              Expected behavior
              Both spark_processor.run calls create a processing job successfully, with ContainerEntrypoint having value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar]
              Screenshots or logs
              The second run call fails with:
              ClientError: An error occurred (ValidationException) when calling the CreateProcessingJob operation: 1 validation error detected: Value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, /opt/ml/processing/input/code/spark_script.py]' at 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100

              System information
              
A description of your system. Please provide:
              • SageMaker Python SDK version: latest
              • Framework name (eg. PyTorch) or algorithm (eg. KMeans): SparkProcessor related -- see additional info
              • Framework version: N/A
              • Python version: 3
              • CPU or GPU: N/A
              • Custom Docker image (Y/N): N
              Additional context
              The SparkProcessor class inherits from ScriptProcessor, which has this self.command attribute: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/processing.py#L405
              The problem happens since SparkProcessor appends to the self.command list with each run invocation:
              https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L230-L235
              https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L600-L601
              Solution suggests are to reset self.command with each run call, or copy the command to another list with each run call rather than mutating the same self.command list.
              Activity
              Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
              Metadata
              Metadata
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              No one assigned
                Labels
                PySparkcontributions 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("// 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
                  [SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100 #1970
                  Closed
                  Closed
                  [SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100#1970
                  Labels
                  PySparkcontributions welcometype: bug
                  Description
                  @andremoeller
                  andremoeller
                  opened on Oct 24, 2020
                  Describe the bug
                  Calling SparkProcessor.run more than once on the same SparkProcessor object fails since the ContainerEntrypoint value has the same arguments repeated over and over.
                  To reproduce
                  
A clear, step-by-step set of instructions to reproduce the bug.
                  spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)
spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)

                  Expected behavior
                  Both spark_processor.run calls create a processing job successfully, with ContainerEntrypoint having value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar]
                  Screenshots or logs
                  The second run call fails with:
                  ClientError: An error occurred (ValidationException) when calling the CreateProcessingJob operation: 1 validation error detected: Value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, /opt/ml/processing/input/code/spark_script.py]' at 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100

                  System information
                  
A description of your system. Please provide:
                  • SageMaker Python SDK version: latest
                  • Framework name (eg. PyTorch) or algorithm (eg. KMeans): SparkProcessor related -- see additional info
                  • Framework version: N/A
                  • Python version: 3
                  • CPU or GPU: N/A
                  • Custom Docker image (Y/N): N
                  Additional context
                  The SparkProcessor class inherits from ScriptProcessor, which has this self.command attribute: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/processing.py#L405
                  The problem happens since SparkProcessor appends to the self.command list with each run invocation:
                  https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L230-L235
                  https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L600-L601
                  Solution suggests are to reset self.command with each run call, or copy the command to another list with each run call rather than mutating the same self.command list.
                  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
                    PySparkcontributions 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
                      [SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100 #1970
                      Closed
                      Closed
                      [SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100#1970
                      Labels
                      PySparkcontributions welcometype: bug
                      Description
                      @andremoeller
                      andremoeller
                      opened on Oct 24, 2020
                      Describe the bug
                      Calling SparkProcessor.run more than once on the same SparkProcessor object fails since the ContainerEntrypoint value has the same arguments repeated over and over.
                      To reproduce
                      
A clear, step-by-step set of instructions to reproduce the bug.
                      spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)
spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)

                      Expected behavior
                      Both spark_processor.run calls create a processing job successfully, with ContainerEntrypoint having value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar]
                      Screenshots or logs
                      The second run call fails with:
                      ClientError: An error occurred (ValidationException) when calling the CreateProcessingJob operation: 1 validation error detected: Value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, /opt/ml/processing/input/code/spark_script.py]' at 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100

                      System information
                      
A description of your system. Please provide:
                      • SageMaker Python SDK version: latest
                      • Framework name (eg. PyTorch) or algorithm (eg. KMeans): SparkProcessor related -- see additional info
                      • Framework version: N/A
                      • Python version: 3
                      • CPU or GPU: N/A
                      • Custom Docker image (Y/N): N
                      Additional context
                      The SparkProcessor class inherits from ScriptProcessor, which has this self.command attribute: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/processing.py#L405
                      The problem happens since SparkProcessor appends to the self.command list with each run invocation:
                      https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L230-L235
                      https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L600-L601
                      Solution suggests are to reset self.command with each run call, or copy the command to another list with each run call rather than mutating the same self.command list.
                      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
                        PySparkcontributions 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
                          [SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100 #1970
                          Closed
                          Closed
                          [SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100#1970
                          Labels
                          PySparkcontributions welcometype: bug
                          Description
                          @andremoeller
                          andremoeller
                          opened on Oct 24, 2020
                          Describe the bug
                          Calling SparkProcessor.run more than once on the same SparkProcessor object fails since the ContainerEntrypoint value has the same arguments repeated over and over.
                          To reproduce
                          
A clear, step-by-step set of instructions to reproduce the bug.
                          spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)
spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)

                          Expected behavior
                          Both spark_processor.run calls create a processing job successfully, with ContainerEntrypoint having value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar]
                          Screenshots or logs
                          The second run call fails with:
                          ClientError: An error occurred (ValidationException) when calling the CreateProcessingJob operation: 1 validation error detected: Value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, /opt/ml/processing/input/code/spark_script.py]' at 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100

                          System information
                          
A description of your system. Please provide:
                          • SageMaker Python SDK version: latest
                          • Framework name (eg. PyTorch) or algorithm (eg. KMeans): SparkProcessor related -- see additional info
                          • Framework version: N/A
                          • Python version: 3
                          • CPU or GPU: N/A
                          • Custom Docker image (Y/N): N
                          Additional context
                          The SparkProcessor class inherits from ScriptProcessor, which has this self.command attribute: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/processing.py#L405
                          The problem happens since SparkProcessor appends to the self.command list with each run invocation:
                          https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L230-L235
                          https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L600-L601
                          Solution suggests are to reset self.command with each run call, or copy the command to another list with each run call rather than mutating the same self.command list.
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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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                              [SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100 #1970
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                              [SparkProcessor]: calling run twice on the same SparkProcessor fails with 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100#1970
                              Labels
                              PySparkcontributions welcometype: bug
                              Description
                              @andremoeller
                              andremoeller
                              opened on Oct 24, 2020
                              Describe the bug
                              Calling SparkProcessor.run more than once on the same SparkProcessor object fails since the ContainerEntrypoint value has the same arguments repeated over and over.
                              To reproduce
                              
A clear, step-by-step set of instructions to reproduce the bug.
                              spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)
spark_processor.run(
submit_app="./spark_script.py",
arguments=["--s3_output_bucket", bucket,
"--s3_output_key_prefix", output_prefix],
submit_jars=['s3://bucket/myjar.jar'],
spark_event_logs_s3_uri="s3://{}/{}/spark_event_logs".format(bucket, prefix),
)

                              Expected behavior
                              Both spark_processor.run calls create a processing job successfully, with ContainerEntrypoint having value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar]
                              Screenshots or logs
                              The second run call fails with:
                              ClientError: An error occurred (ValidationException) when calling the CreateProcessingJob operation: 1 validation error detected: Value '[smspark-submit, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, --jars, s3://bucket/myjar.jar, --local-spark-event-logs-dir, /opt/ml/processing/spark-events/, /opt/ml/processing/input/code/spark_script.py]' at 'appSpecification.containerEntrypoint' failed to satisfy constraint: Member must have length less than or equal to 100

                              System information
                              
A description of your system. Please provide:
                              • SageMaker Python SDK version: latest
                              • Framework name (eg. PyTorch) or algorithm (eg. KMeans): SparkProcessor related -- see additional info
                              • Framework version: N/A
                              • Python version: 3
                              • CPU or GPU: N/A
                              • Custom Docker image (Y/N): N
                              Additional context
                              The SparkProcessor class inherits from ScriptProcessor, which has this self.command attribute: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/processing.py#L405
                              The problem happens since SparkProcessor appends to the self.command list with each run invocation:
                              https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L230-L235
                              https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/spark/processing.py#L600-L601
                              Solution suggests are to reset self.command with each run call, or copy the command to another list with each run call rather than mutating the same self.command list.
                              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
                                PySparkcontributions welcometype: bug
                                Type
                                No type
                                Projects
                                No projects
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                                  No milestone
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                                  Issue actions