Fix description of an argument of sagemaker.session.train#69

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Merged
laurenyu merged 4 commits into from
Feb 7, 2018
Merged

Fix description of an argument of sagemaker.session.train #69

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762650f
Fix description of an argument of sagemaker.session.train
Feb 2, 2018
3671efe
Add a link to the botocore docs
Feb 6, 2018
a7178c3
Use 'list' instead of 'array' in the description
Feb 7, 2018
7052518
Merge branch 'master' into fix-session-train-doc
laurenyu Feb 7, 2018
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14 changes: 3 additions & 11 deletions src/sagemaker/session.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -195,17 +195,9 @@ def train(self, image, input_mode, input_config, role, job_name, output_config,
a directory in the Docker container.
* 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe.

input_config (str or dict or sagemaker.session.s3_input): Information about the training data.
This can be one of three types:

* (str) - the S3 location where training data is saved.
* (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple channels for
training data, you can specify a dict mapping channel names
to strings or :func:`~sagemaker.session.s3_input` objects.
* (sagemaker.session.s3_input) - channel configuration for S3 data sources that can provide
additional information about the training dataset. See :func:`sagemaker.session.s3_input`
for full details.

input_config (list): A list of Channel objects. Each channel is a named input source. Please refer to
the format details described:
https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_training_job
role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs
that create Amazon SageMaker endpoints use this role to access training data and model artifacts.
You must grant sufficient permissions to this role.
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Fix description of an argument of sagemaker.session.train#69

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 Merged

laurenyu
merged 4 commits into

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Feb 7, 2018
 Merged


Fix description of an argument of sagemaker.session.train
#69
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4 commits


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762650f

Fix description of an argument of sagemaker.session.train

Feb 2, 2018
3671efe

Add a link to the botocore docs

Feb 6, 2018
a7178c3

Use 'list' instead of 'array' in the description

Feb 7, 2018
7052518

Merge branch 'master' into fix-session-train-doc


laurenyu Feb 7, 2018

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14 changes: 3 additions & 11 deletions
src/sagemaker/session.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -195,17 +195,9 @@ def train(self, image, input_mode, input_config, role, job_name, output_config,
 a directory in the Docker container.
 * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe.

 input_config (str or dict or sagemaker.session.s3_input): Information about the training data.
 This can be one of three types:

 * (str) - the S3 location where training data is saved.
 * (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple channels for
 training data, you can specify a dict mapping channel names
 to strings or :func:`~sagemaker.session.s3_input` objects.
 * (sagemaker.session.s3_input) - channel configuration for S3 data sources that can provide
 additional information about the training dataset. See :func:`sagemaker.session.s3_input`
 for full details.

 input_config (list): A list of Channel objects. Each channel is a named input source. Please refer to
 the format details described:
 https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_training_job
 role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs
 that create Amazon SageMaker endpoints use this role to access training data and model artifacts.
 You must grant sufficient permissions to this role.
Expand Down

, '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('^' + ".*" + '

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Fix description of an argument of sagemaker.session.train#69

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 Merged

laurenyu
merged 4 commits into

from

Feb 7, 2018
 Merged


Fix description of an argument of sagemaker.session.train
#69
 Changes from all commits
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4 commits


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762650f

Fix description of an argument of sagemaker.session.train

Feb 2, 2018
3671efe

Add a link to the botocore docs

Feb 6, 2018
a7178c3

Use 'list' instead of 'array' in the description

Feb 7, 2018
7052518

Merge branch 'master' into fix-session-train-doc


laurenyu Feb 7, 2018

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14 changes: 3 additions & 11 deletions
src/sagemaker/session.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -195,17 +195,9 @@ def train(self, image, input_mode, input_config, role, job_name, output_config,
 a directory in the Docker container.
 * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe.

 input_config (str or dict or sagemaker.session.s3_input): Information about the training data.
 This can be one of three types:

 * (str) - the S3 location where training data is saved.
 * (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple channels for
 training data, you can specify a dict mapping channel names
 to strings or :func:`~sagemaker.session.s3_input` objects.
 * (sagemaker.session.s3_input) - channel configuration for S3 data sources that can provide
 additional information about the training dataset. See :func:`sagemaker.session.s3_input`
 for full details.

 input_config (list): A list of Channel objects. Each channel is a named input source. Please refer to
 the format details described:
 https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_training_job
 role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs
 that create Amazon SageMaker endpoints use this role to access training data and model artifacts.
 You must grant sufficient permissions to this role.
Expand Down

, '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
Fix description of an argument of sagemaker.session.train#69

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 Merged

laurenyu
merged 4 commits into

from

Feb 7, 2018
 Merged


Fix description of an argument of sagemaker.session.train
#69
 Changes from all commits
Commits

Show all changes


4 commits


Select commit
Hold shift + click to select a range
762650f

Fix description of an argument of sagemaker.session.train

Feb 2, 2018
3671efe

Add a link to the botocore docs

Feb 6, 2018
a7178c3

Use 'list' instead of 'array' in the description

Feb 7, 2018
7052518

Merge branch 'master' into fix-session-train-doc


laurenyu Feb 7, 2018

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14 changes: 3 additions & 11 deletions
src/sagemaker/session.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -195,17 +195,9 @@ def train(self, image, input_mode, input_config, role, job_name, output_config,
 a directory in the Docker container.
 * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe.

 input_config (str or dict or sagemaker.session.s3_input): Information about the training data.
 This can be one of three types:

 * (str) - the S3 location where training data is saved.
 * (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple channels for
 training data, you can specify a dict mapping channel names
 to strings or :func:`~sagemaker.session.s3_input` objects.
 * (sagemaker.session.s3_input) - channel configuration for S3 data sources that can provide
 additional information about the training dataset. See :func:`sagemaker.session.s3_input`
 for full details.

 input_config (list): A list of Channel objects. Each channel is a named input source. Please refer to
 the format details described:
 https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_training_job
 role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs
 that create Amazon SageMaker endpoints use this role to access training data and model artifacts.
 You must grant sufficient permissions to this role.
Expand Down

, '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
Fix description of an argument of sagemaker.session.train#69

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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 Merged

laurenyu
merged 4 commits into

from

Feb 7, 2018
 Merged


Fix description of an argument of sagemaker.session.train
#69
 Changes from all commits
Commits

Show all changes


4 commits


Select commit
Hold shift + click to select a range
762650f

Fix description of an argument of sagemaker.session.train

Feb 2, 2018
3671efe

Add a link to the botocore docs

Feb 6, 2018
a7178c3

Use 'list' instead of 'array' in the description

Feb 7, 2018
7052518

Merge branch 'master' into fix-session-train-doc


laurenyu Feb 7, 2018

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14 changes: 3 additions & 11 deletions
src/sagemaker/session.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -195,17 +195,9 @@ def train(self, image, input_mode, input_config, role, job_name, output_config,
 a directory in the Docker container.
 * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe.

 input_config (str or dict or sagemaker.session.s3_input): Information about the training data.
 This can be one of three types:

 * (str) - the S3 location where training data is saved.
 * (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple channels for
 training data, you can specify a dict mapping channel names
 to strings or :func:`~sagemaker.session.s3_input` objects.
 * (sagemaker.session.s3_input) - channel configuration for S3 data sources that can provide
 additional information about the training dataset. See :func:`sagemaker.session.s3_input`
 for full details.

 input_config (list): A list of Channel objects. Each channel is a named input source. Please refer to
 the format details described:
 https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_training_job
 role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs
 that create Amazon SageMaker endpoints use this role to access training data and model artifacts.
 You must grant sufficient permissions to this role.
Expand Down

, '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
Fix description of an argument of sagemaker.session.train#69

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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 Merged

laurenyu
merged 4 commits into

from

Feb 7, 2018
 Merged


Fix description of an argument of sagemaker.session.train
#69
 Changes from all commits
Commits

Show all changes


4 commits


Select commit
Hold shift + click to select a range
762650f

Fix description of an argument of sagemaker.session.train

Feb 2, 2018
3671efe

Add a link to the botocore docs

Feb 6, 2018
a7178c3

Use 'list' instead of 'array' in the description

Feb 7, 2018
7052518

Merge branch 'master' into fix-session-train-doc


laurenyu Feb 7, 2018

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14 changes: 3 additions & 11 deletions
src/sagemaker/session.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -195,17 +195,9 @@ def train(self, image, input_mode, input_config, role, job_name, output_config,
 a directory in the Docker container.
 * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe.

 input_config (str or dict or sagemaker.session.s3_input): Information about the training data.
 This can be one of three types:

 * (str) - the S3 location where training data is saved.
 * (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple channels for
 training data, you can specify a dict mapping channel names
 to strings or :func:`~sagemaker.session.s3_input` objects.
 * (sagemaker.session.s3_input) - channel configuration for S3 data sources that can provide
 additional information about the training dataset. See :func:`sagemaker.session.s3_input`
 for full details.

 input_config (list): A list of Channel objects. Each channel is a named input source. Please refer to
 the format details described:
 https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_training_job
 role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs
 that create Amazon SageMaker endpoints use this role to access training data and model artifacts.
 You must grant sufficient permissions to this role.
Expand Down

, '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
Fix description of an argument of sagemaker.session.train#69

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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 Merged

laurenyu
merged 4 commits into

from

Feb 7, 2018
 Merged


Fix description of an argument of sagemaker.session.train
#69
 Changes from all commits
Commits

Show all changes


4 commits


Select commit
Hold shift + click to select a range
762650f

Fix description of an argument of sagemaker.session.train

Feb 2, 2018
3671efe

Add a link to the botocore docs

Feb 6, 2018
a7178c3

Use 'list' instead of 'array' in the description

Feb 7, 2018
7052518

Merge branch 'master' into fix-session-train-doc


laurenyu Feb 7, 2018

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14 changes: 3 additions & 11 deletions
src/sagemaker/session.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -195,17 +195,9 @@ def train(self, image, input_mode, input_config, role, job_name, output_config,
 a directory in the Docker container.
 * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe.

 input_config (str or dict or sagemaker.session.s3_input): Information about the training data.
 This can be one of three types:

 * (str) - the S3 location where training data is saved.
 * (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple channels for
 training data, you can specify a dict mapping channel names
 to strings or :func:`~sagemaker.session.s3_input` objects.
 * (sagemaker.session.s3_input) - channel configuration for S3 data sources that can provide
 additional information about the training dataset. See :func:`sagemaker.session.s3_input`
 for full details.

 input_config (list): A list of Channel objects. Each channel is a named input source. Please refer to
 the format details described:
 https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_training_job
 role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs
 that create Amazon SageMaker endpoints use this role to access training data and model artifacts.
 You must grant sufficient permissions to this role.
Expand Down

, '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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Fix description of an argument of sagemaker.session.train#69

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 Merged

laurenyu
merged 4 commits into

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Feb 7, 2018
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Fix description of an argument of sagemaker.session.train
#69
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762650f

Fix description of an argument of sagemaker.session.train

Feb 2, 2018
3671efe

Add a link to the botocore docs

Feb 6, 2018
a7178c3

Use 'list' instead of 'array' in the description

Feb 7, 2018
7052518

Merge branch 'master' into fix-session-train-doc


laurenyu Feb 7, 2018

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14 changes: 3 additions & 11 deletions
src/sagemaker/session.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -195,17 +195,9 @@ def train(self, image, input_mode, input_config, role, job_name, output_config,
 a directory in the Docker container.
 * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe.

 input_config (str or dict or sagemaker.session.s3_input): Information about the training data.
 This can be one of three types:

 * (str) - the S3 location where training data is saved.
 * (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple channels for
 training data, you can specify a dict mapping channel names
 to strings or :func:`~sagemaker.session.s3_input` objects.
 * (sagemaker.session.s3_input) - channel configuration for S3 data sources that can provide
 additional information about the training dataset. See :func:`sagemaker.session.s3_input`
 for full details.

 input_config (list): A list of Channel objects. Each channel is a named input source. Please refer to
 the format details described:
 https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_training_job
 role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs
 that create Amazon SageMaker endpoints use this role to access training data and model artifacts.
 You must grant sufficient permissions to this role.
Expand Down