_id stringlengths 5 9 | text stringlengths 5 385k | title stringclasses 1
value |
|---|---|---|
doc_29600 | See Migration guide for more details. tf.compat.v1.raw_ops.PrintV2
tf.raw_ops.PrintV2(
input, output_stream='stderr', end='\n', name=None
)
Prints a string scalar to the desired output_stream.
Args
input A Tensor of type string. The string scalar to print.
output_stream An optional string. Defaults to "stderr". A string specifying the output stream or logging level to print to.
end An optional string. Defaults to "\n".
name A name for the operation (optional).
Returns The created Operation. | |
doc_29601 |
Dataset wrapping tensors. Each sample will be retrieved by indexing tensors along the first dimension. Parameters
*tensors (Tensor) – tensors that have the same size of the first dimension. | |
doc_29602 | Returns the (real) processor name, e.g. 'amdk6'. An empty string is returned if the value cannot be determined. Note that many platforms do not provide this information or simply return the same value as for machine(). NetBSD does this. | |
doc_29603 | Convert the header set into an HTTP header string. | |
doc_29604 | A generator can be annotated by the generic type Generator[YieldType, SendType, ReturnType]. For example: def echo_round() -> Generator[int, float, str]:
sent = yield 0
while sent >= 0:
sent = yield round(sent)
return 'Done'
Note that unlike many other generics in the typing module, the SendType of Generator behaves contravariantly, not covariantly or invariantly. If your generator will only yield values, set the SendType and ReturnType to None: def infinite_stream(start: int) -> Generator[int, None, None]:
while True:
yield start
start += 1
Alternatively, annotate your generator as having a return type of either Iterable[YieldType] or Iterator[YieldType]: def infinite_stream(start: int) -> Iterator[int]:
while True:
yield start
start += 1
Deprecated since version 3.9: collections.abc.Generator now supports []. See PEP 585 and Generic Alias Type. | |
doc_29605 | Checks for an ASCII control character (in the range 0x00 to 0x1f or 0x7f). | |
doc_29606 |
Update offset of the children and return the extent of the box. Parameters
rendererRendererBase subclass
Returns
width
height
xdescent
ydescent
list of (xoffset, yoffset) pairs | |
doc_29607 | tf.compat.v1.losses.huber_loss(
labels, predictions, weights=1.0, delta=1.0, scope=None,
loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)
For each value x in error=labels-predictions, the following is calculated: 0.5 * x^2 if |x| <= d
0.5 * d^2 + d * (|x| - d) if |x| > d
where d is delta. weights acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weights is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the weights vector. If the shape of weights matches the shape of predictions, then the loss of each measurable element of predictions is scaled by the corresponding value of weights.
Args
labels The ground truth output tensor, same dimensions as 'predictions'.
predictions The predicted outputs.
weights Optional Tensor whose rank is either 0, or the same rank as labels, and must be broadcastable to labels (i.e., all dimensions must be either 1, or the same as the corresponding losses dimension).
delta float, the point where the huber loss function changes from a quadratic to linear.
scope The scope for the operations performed in computing the loss.
loss_collection collection to which the loss will be added.
reduction Type of reduction to apply to loss.
Returns Weighted loss float Tensor. If reduction is NONE, this has the same shape as labels; otherwise, it is scalar.
Raises
ValueError If the shape of predictions doesn't match that of labels or if the shape of weights is invalid. Also if labels or predictions is None. Eager Compatibility The loss_collection argument is ignored when executing eagerly. Consider holding on to the return value or collecting losses via a tf.keras.Model. | |
doc_29608 | This method should be called from handlers when an exception is encountered during an emit() call. If the module-level attribute raiseExceptions is False, exceptions get silently ignored. This is what is mostly wanted for a logging system - most users will not care about errors in the logging system, they are more interested in application errors. You could, however, replace this with a custom handler if you wish. The specified record is the one which was being processed when the exception occurred. (The default value of raiseExceptions is True, as that is more useful during development). | |
doc_29609 | See Migration guide for more details. tf.compat.v1.QueueBase, tf.compat.v1.io.QueueBase, tf.compat.v1.queue.QueueBase
tf.queue.QueueBase(
dtypes, shapes, names, queue_ref
)
A queue is a TensorFlow data structure that stores tensors across multiple steps, and exposes operations that enqueue and dequeue tensors. Each queue element is a tuple of one or more tensors, where each tuple component has a static dtype, and may have a static shape. The queue implementations support versions of enqueue and dequeue that handle single elements, versions that support enqueuing and dequeuing a batch of elements at once. See tf.queue.FIFOQueue and tf.queue.RandomShuffleQueue for concrete implementations of this class, and instructions on how to create them.
Args
dtypes A list of types. The length of dtypes must equal the number of tensors in each element.
shapes Constraints on the shapes of tensors in an element: A list of shape tuples or None. This list is the same length as dtypes. If the shape of any tensors in the element are constrained, all must be; shapes can be None if the shapes should not be constrained.
names Optional list of names. If provided, the enqueue() and dequeue() methods will use dictionaries with these names as keys. Must be None or a list or tuple of the same length as dtypes.
queue_ref The queue reference, i.e. the output of the queue op.
Raises
ValueError If one of the arguments is invalid.
Attributes
dtypes The list of dtypes for each component of a queue element.
name The name of the underlying queue.
names The list of names for each component of a queue element.
queue_ref The underlying queue reference.
shapes The list of shapes for each component of a queue element. Methods close View source
close(
cancel_pending_enqueues=False, name=None
)
Closes this queue. This operation signals that no more elements will be enqueued in the given queue. Subsequent enqueue and enqueue_many operations will fail. Subsequent dequeue and dequeue_many operations will continue to succeed if sufficient elements remain in the queue. Subsequently dequeue and dequeue_many operations that would otherwise block waiting for more elements (if close hadn't been called) will now fail immediately. If cancel_pending_enqueues is True, all pending requests will also be canceled.
Args
cancel_pending_enqueues (Optional.) A boolean, defaulting to False (described above).
name A name for the operation (optional).
Returns The operation that closes the queue.
dequeue View source
dequeue(
name=None
)
Dequeues one element from this queue. If the queue is empty when this operation executes, it will block until there is an element to dequeue. At runtime, this operation may raise an error if the queue is tf.QueueBase.close before or during its execution. If the queue is closed, the queue is empty, and there are no pending enqueue operations that can fulfill this request, tf.errors.OutOfRangeError will be raised. If the session is tf.Session.close, tf.errors.CancelledError will be raised.
Args
name A name for the operation (optional).
Returns The tuple of tensors that was dequeued.
dequeue_many View source
dequeue_many(
n, name=None
)
Dequeues and concatenates n elements from this queue. This operation concatenates queue-element component tensors along the 0th dimension to make a single component tensor. All of the components in the dequeued tuple will have size n in the 0th dimension. If the queue is closed and there are less than n elements left, then an OutOfRange exception is raised. At runtime, this operation may raise an error if the queue is tf.QueueBase.close before or during its execution. If the queue is closed, the queue contains fewer than n elements, and there are no pending enqueue operations that can fulfill this request, tf.errors.OutOfRangeError will be raised. If the session is tf.Session.close, tf.errors.CancelledError will be raised.
Args
n A scalar Tensor containing the number of elements to dequeue.
name A name for the operation (optional).
Returns The list of concatenated tensors that was dequeued.
dequeue_up_to View source
dequeue_up_to(
n, name=None
)
Dequeues and concatenates n elements from this queue.
Note: This operation is not supported by all queues. If a queue does not support DequeueUpTo, then a tf.errors.UnimplementedError is raised.
This operation concatenates queue-element component tensors along the 0th dimension to make a single component tensor. If the queue has not been closed, all of the components in the dequeued tuple will have size n in the 0th dimension. If the queue is closed and there are more than 0 but fewer than n elements remaining, then instead of raising a tf.errors.OutOfRangeError like tf.QueueBase.dequeue_many, less than n elements are returned immediately. If the queue is closed and there are 0 elements left in the queue, then a tf.errors.OutOfRangeError is raised just like in dequeue_many. Otherwise the behavior is identical to dequeue_many.
Args
n A scalar Tensor containing the number of elements to dequeue.
name A name for the operation (optional).
Returns The tuple of concatenated tensors that was dequeued.
enqueue View source
enqueue(
vals, name=None
)
Enqueues one element to this queue. If the queue is full when this operation executes, it will block until the element has been enqueued. At runtime, this operation may raise an error if the queue is tf.QueueBase.close before or during its execution. If the queue is closed before this operation runs, tf.errors.CancelledError will be raised. If this operation is blocked, and either (i) the queue is closed by a close operation with cancel_pending_enqueues=True, or (ii) the session is tf.Session.close, tf.errors.CancelledError will be raised.
Args
vals A tensor, a list or tuple of tensors, or a dictionary containing the values to enqueue.
name A name for the operation (optional).
Returns The operation that enqueues a new tuple of tensors to the queue.
enqueue_many View source
enqueue_many(
vals, name=None
)
Enqueues zero or more elements to this queue. This operation slices each component tensor along the 0th dimension to make multiple queue elements. All of the tensors in vals must have the same size in the 0th dimension. If the queue is full when this operation executes, it will block until all of the elements have been enqueued. At runtime, this operation may raise an error if the queue is tf.QueueBase.close before or during its execution. If the queue is closed before this operation runs, tf.errors.CancelledError will be raised. If this operation is blocked, and either (i) the queue is closed by a close operation with cancel_pending_enqueues=True, or (ii) the session is tf.Session.close, tf.errors.CancelledError will be raised.
Args
vals A tensor, a list or tuple of tensors, or a dictionary from which the queue elements are taken.
name A name for the operation (optional).
Returns The operation that enqueues a batch of tuples of tensors to the queue.
from_list View source
@staticmethod
from_list(
index, queues
)
Create a queue using the queue reference from queues[index].
Args
index An integer scalar tensor that determines the input that gets selected.
queues A list of QueueBase objects.
Returns A QueueBase object.
Raises
TypeError When queues is not a list of QueueBase objects, or when the data types of queues are not all the same. is_closed View source
is_closed(
name=None
)
Returns true if queue is closed. This operation returns true if the queue is closed and false if the queue is open.
Args
name A name for the operation (optional).
Returns True if the queue is closed and false if the queue is open.
size View source
size(
name=None
)
Compute the number of elements in this queue.
Args
name A name for the operation (optional).
Returns A scalar tensor containing the number of elements in this queue. | |
doc_29610 | See Migration guide for more details. tf.compat.v1.raw_ops.ArgMin
tf.raw_ops.ArgMin(
input, dimension, output_type=tf.dtypes.int64, name=None
)
Note that in case of ties the identity of the return value is not guaranteed. Usage: import tensorflow as tf
a = [1, 10, 26.9, 2.8, 166.32, 62.3]
b = tf.math.argmin(input = a)
c = tf.keras.backend.eval(b)
# c = 0
# here a[0] = 1 which is the smallest element of a across axis 0
Args
input A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64, bool.
dimension A Tensor. Must be one of the following types: int32, int64. int32 or int64, must be in the range [-rank(input), rank(input)). Describes which dimension of the input Tensor to reduce across. For vectors, use dimension = 0.
output_type An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int64.
name A name for the operation (optional).
Returns A Tensor of type output_type. | |
doc_29611 | Remove func from the list of functions to be run at interpreter shutdown. After calling unregister(), func is guaranteed not to be called when the interpreter shuts down, even if it was registered more than once. unregister() silently does nothing if func was not previously registered. | |
doc_29612 |
Returns the number of splitting iterations in the cross-validator Parameters
Xobject
Always ignored, exists for compatibility.
yobject
Always ignored, exists for compatibility.
groupsobject
Always ignored, exists for compatibility. Returns
n_splitsint
Returns the number of splitting iterations in the cross-validator. | |
doc_29613 | tf.experimental.numpy.round(
a, decimals=0
)
Unsupported arguments: out. See the NumPy documentation for numpy.round. | |
doc_29614 |
Alias for set_linewidth. | |
doc_29615 |
Transform a count matrix to a tf or tf-idf representation Parameters
Xsparse matrix of (n_samples, n_features)
a matrix of term/token counts
copybool, default=True
Whether to copy X and operate on the copy or perform in-place operations. Returns
vectorssparse matrix of shape (n_samples, n_features) | |
doc_29616 |
Return a list of AxesImages contained by the Axes. | |
doc_29617 | tf.compat.v1.report_uninitialized_variables(
var_list=None, name='report_uninitialized_variables'
)
When run, it returns a 1-D tensor containing the names of uninitialized variables if there are any, or an empty array if there are none.
Args
var_list List of Variable objects to check. Defaults to the value of global_variables() + local_variables()
name Optional name of the Operation.
Returns A 1-D tensor containing names of the uninitialized variables, or an empty 1-D tensor if there are no variables or no uninitialized variables.
Note: The output of this function should be used. If it is not, a warning will be logged or an error may be raised. To mark the output as used, call its .mark_used() method. | |
doc_29618 |
Return the path of this patch. | |
doc_29619 | The DocTest object that was being run when the example failed. | |
doc_29620 | See Migration guide for more details. tf.compat.v1.image.adjust_brightness
tf.image.adjust_brightness(
image, delta
)
This is a convenience method that converts RGB images to float representation, adjusts their brightness, and then converts them back to the original data type. If several adjustments are chained, it is advisable to minimize the number of redundant conversions. The value delta is added to all components of the tensor image. image is converted to float and scaled appropriately if it is in fixed-point representation, and delta is converted to the same data type. For regular images, delta should be in the range (-1,1), as it is added to the image in floating point representation, where pixel values are in the [0,1) range. Usage Example:
x = [[[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0]],
[[7.0, 8.0, 9.0],
[10.0, 11.0, 12.0]]]
tf.image.adjust_brightness(x, delta=0.1)
<tf.Tensor: shape=(2, 2, 3), dtype=float32, numpy=
array([[[ 1.1, 2.1, 3.1],
[ 4.1, 5.1, 6.1]],
[[ 7.1, 8.1, 9.1],
[10.1, 11.1, 12.1]]], dtype=float32)>
Args
image RGB image or images to adjust.
delta A scalar. Amount to add to the pixel values.
Returns A brightness-adjusted tensor of the same shape and type as image. | |
doc_29621 | See Migration guide for more details. tf.compat.v1.raw_ops.ReaderResetV2
tf.raw_ops.ReaderResetV2(
reader_handle, name=None
)
Args
reader_handle A Tensor of type resource. Handle to a Reader.
name A name for the operation (optional).
Returns The created Operation. | |
doc_29622 |
Definition of the core elements a generalized ufunc operates on. The signature determines how the dimensions of each input/output array are split into core and loop dimensions: Each dimension in the signature is matched to a dimension of the corresponding passed-in array, starting from the end of the shape tuple. Core dimensions assigned to the same label in the signature must have exactly matching sizes, no broadcasting is performed. The core dimensions are removed from all inputs and the remaining dimensions are broadcast together, defining the loop dimensions. Notes Generalized ufuncs are used internally in many linalg functions, and in the testing suite; the examples below are taken from these. For ufuncs that operate on scalars, the signature is None, which is equivalent to ‘()’ for every argument. Examples >>> np.core.umath_tests.matrix_multiply.signature
'(m,n),(n,p)->(m,p)'
>>> np.linalg._umath_linalg.det.signature
'(m,m)->()'
>>> np.add.signature is None
True # equivalent to '(),()->()' | |
doc_29623 | Return the message’s sub-content type. This is the subtype part of the string returned by get_content_type(). | |
doc_29624 |
Roll provided date backward to next offset only if not on offset. Returns
TimeStamp
Rolled timestamp if not on offset, otherwise unchanged timestamp. | |
doc_29625 | Similar to escape(), but also prepares data to be used as an attribute value. The return value is a quoted version of data with any additional required replacements. quoteattr() will select a quote character based on the content of data, attempting to avoid encoding any quote characters in the string. If both single- and double-quote characters are already in data, the double-quote characters will be encoded and data will be wrapped in double-quotes. The resulting string can be used directly as an attribute value: >>> print("<element attr=%s>" % quoteattr("ab ' cd \" ef"))
<element attr="ab ' cd " ef">
This function is useful when generating attribute values for HTML or any SGML using the reference concrete syntax. | |
doc_29626 | Close the connection. This is called automatically when the connection is garbage collected. | |
doc_29627 |
app = Flask(__name__)
@app.route("/")
def hello_world():
return "<p>Hello, World!</p>"
So what did that code do? First we imported the Flask class. An instance of this class will be our WSGI application. Next we create an instance of this class. The first argument is the name of the application’s module or package. __name__ is a convenient shortcut for this that is appropriate for most cases. This is needed so that Flask knows where to look for resources such as templates and static files. We then use the route() decorator to tell Flask what URL should trigger our function. The function returns the message we want to display in the user’s browser. The default content type is HTML, so HTML in the string will be rendered by the browser. Save it as hello.py or something similar. Make sure to not call your application flask.py because this would conflict with Flask itself. To run the application, use the flask command or python -m flask. Before you can do that you need to tell your terminal the application to work with by exporting the FLASK_APP environment variable:
BashCMDPowershell
$ export FLASK_APP=hello
$ flask run
* Running on http://127.0.0.1:5000/
> set FLASK_APP=hello
> flask run
* Running on http://127.0.0.1:5000/
> $env:FLASK_APP = "hello"
> flask run
* Running on http://127.0.0.1:5000/
Application Discovery Behavior As a shortcut, if the file is named app.py or wsgi.py, you don’t have to set the FLASK_APP environment variable. See Command Line Interface for more details. This launches a very simple builtin server, which is good enough for testing but probably not what you want to use in production. For deployment options see Deployment Options. Now head over to http://127.0.0.1:5000/, and you should see your hello world greeting. Externally Visible Server If you run the server you will notice that the server is only accessible from your own computer, not from any other in the network. This is the default because in debugging mode a user of the application can execute arbitrary Python code on your computer. If you have the debugger disabled or trust the users on your network, you can make the server publicly available simply by adding --host=0.0.0.0 to the command line: $ flask run --host=0.0.0.0
This tells your operating system to listen on all public IPs. What to do if the Server does not Start In case the python -m flask fails or flask does not exist, there are multiple reasons this might be the case. First of all you need to look at the error message. Old Version of Flask Versions of Flask older than 0.11 used to have different ways to start the application. In short, the flask command did not exist, and neither did python -m flask. In that case you have two options: either upgrade to newer Flask versions or have a look at Development Server to see the alternative method for running a server. Invalid Import Name The FLASK_APP environment variable is the name of the module to import at flask run. In case that module is incorrectly named you will get an import error upon start (or if debug is enabled when you navigate to the application). It will tell you what it tried to import and why it failed. The most common reason is a typo or because you did not actually create an app object. Debug Mode The flask run command can do more than just start the development server. By enabling debug mode, the server will automatically reload if code changes, and will show an interactive debugger in the browser if an error occurs during a request. Warning The debugger allows executing arbitrary Python code from the browser. It is protected by a pin, but still represents a major security risk. Do not run the development server or debugger in a production environment. To enable all development features, set the FLASK_ENV environment variable to development before calling flask run.
BashCMDPowershell
$ export FLASK_ENV=development
$ flask run
> set FLASK_ENV=development
> flask run
> $env:FLASK_ENV = "development"
> flask run
See also:
Development Server and Command Line Interface for information about running in development mode.
Debugging Application Errors for information about using the built-in debugger and other debuggers.
Logging and Handling Application Errors to log errors and display nice error pages. HTML Escaping When returning HTML (the default response type in Flask), any user-provided values rendered in the output must be escaped to protect from injection attacks. HTML templates rendered with Jinja, introduced later, will do this automatically. escape(), shown here, can be used manually. It is omitted in most examples for brevity, but you should always be aware of how you’re using untrusted data. from markupsafe import escape
@app.route("/<name>")
def hello(name):
return f"Hello, {escape(name)}!"
If a user managed to submit the name <script>alert("bad")</script>, escaping causes it to be rendered as text, rather than running the script in the user’s browser. <name> in the route captures a value from the URL and passes it to the view function. These variable rules are explained below. Routing Modern web applications use meaningful URLs to help users. Users are more likely to like a page and come back if the page uses a meaningful URL they can remember and use to directly visit a page. Use the route() decorator to bind a function to a URL. @app.route('/')
def index():
return 'Index Page'
@app.route('/hello')
def hello():
return 'Hello, World'
You can do more! You can make parts of the URL dynamic and attach multiple rules to a function. Variable Rules You can add variable sections to a URL by marking sections with <variable_name>. Your function then receives the <variable_name> as a keyword argument. Optionally, you can use a converter to specify the type of the argument like <converter:variable_name>. from markupsafe import escape
@app.route('/user/<username>')
def show_user_profile(username):
# show the user profile for that user
return f'User {username}'
@app.route('/post/<int:post_id>')
def show_post(post_id):
# show the post with the given id, the id is an integer
return f'Post {post_id}'
@app.route('/path/<path:subpath>')
def show_subpath(subpath):
# show the subpath after /path/
return f'Subpath {subpath}'
Converter types:
string (default) accepts any text without a slash
int accepts positive integers
float accepts positive floating point values
path like string but also accepts slashes
uuid accepts UUID strings Unique URLs / Redirection Behavior The following two rules differ in their use of a trailing slash. @app.route('/projects/')
def projects():
return 'The project page'
@app.route('/about')
def about():
return 'The about page'
The canonical URL for the projects endpoint has a trailing slash. It’s similar to a folder in a file system. If you access the URL without a trailing slash (/projects), Flask redirects you to the canonical URL with the trailing slash (/projects/). The canonical URL for the about endpoint does not have a trailing slash. It’s similar to the pathname of a file. Accessing the URL with a trailing slash (/about/) produces a 404 “Not Found” error. This helps keep URLs unique for these resources, which helps search engines avoid indexing the same page twice. URL Building To build a URL to a specific function, use the url_for() function. It accepts the name of the function as its first argument and any number of keyword arguments, each corresponding to a variable part of the URL rule. Unknown variable parts are appended to the URL as query parameters. Why would you want to build URLs using the URL reversing function url_for() instead of hard-coding them into your templates? Reversing is often more descriptive than hard-coding the URLs. You can change your URLs in one go instead of needing to remember to manually change hard-coded URLs. URL building handles escaping of special characters transparently. The generated paths are always absolute, avoiding unexpected behavior of relative paths in browsers. If your application is placed outside the URL root, for example, in /myapplication instead of /, url_for() properly handles that for you. For example, here we use the test_request_context() method to try out url_for(). test_request_context() tells Flask to behave as though it’s handling a request even while we use a Python shell. See Context Locals. from flask import url_for
@app.route('/')
def index():
return 'index'
@app.route('/login')
def login():
return 'login'
@app.route('/user/<username>')
def profile(username):
return f'{username}\'s profile'
with app.test_request_context():
print(url_for('index'))
print(url_for('login'))
print(url_for('login', next='/'))
print(url_for('profile', username='John Doe'))
/
/login
/login?next=/
/user/John%20Doe
HTTP Methods Web applications use different HTTP methods when accessing URLs. You should familiarize yourself with the HTTP methods as you work with Flask. By default, a route only answers to GET requests. You can use the methods argument of the route() decorator to handle different HTTP methods. from flask import request
@app.route('/login', methods=['GET', 'POST'])
def login():
if request.method == 'POST':
return do_the_login()
else:
return show_the_login_form()
If GET is present, Flask automatically adds support for the HEAD method and handles HEAD requests according to the HTTP RFC. Likewise, OPTIONS is automatically implemented for you. Static Files Dynamic web applications also need static files. That’s usually where the CSS and JavaScript files are coming from. Ideally your web server is configured to serve them for you, but during development Flask can do that as well. Just create a folder called static in your package or next to your module and it will be available at /static on the application. To generate URLs for static files, use the special 'static' endpoint name: url_for('static', filename='style.css')
The file has to be stored on the filesystem as static/style.css. Rendering Templates Generating HTML from within Python is not fun, and actually pretty cumbersome because you have to do the HTML escaping on your own to keep the application secure. Because of that Flask configures the Jinja2 template engine for you automatically. To render a template you can use the render_template() method. All you have to do is provide the name of the template and the variables you want to pass to the template engine as keyword arguments. Here’s a simple example of how to render a template: from flask import render_template
@app.route('/hello/')
@app.route('/hello/<name>')
def hello(name=None):
return render_template('hello.html', name=name)
Flask will look for templates in the templates folder. So if your application is a module, this folder is next to that module, if it’s a package it’s actually inside your package: Case 1: a module: /application.py
/templates
/hello.html
Case 2: a package: /application
/__init__.py
/templates
/hello.html
For templates you can use the full power of Jinja2 templates. Head over to the official Jinja2 Template Documentation for more information. Here is an example template: <!doctype html>
<title>Hello from Flask</title>
{% if name %}
<h1>Hello {{ name }}!</h1>
{% else %}
<h1>Hello, World!</h1>
{% endif %}
Inside templates you also have access to the request, session and g 1 objects as well as the get_flashed_messages() function. Templates are especially useful if inheritance is used. If you want to know how that works, see Template Inheritance. Basically template inheritance makes it possible to keep certain elements on each page (like header, navigation and footer). Automatic escaping is enabled, so if name contains HTML it will be escaped automatically. If you can trust a variable and you know that it will be safe HTML (for example because it came from a module that converts wiki markup to HTML) you can mark it as safe by using the Markup class or by using the |safe filter in the template. Head over to the Jinja 2 documentation for more examples. Here is a basic introduction to how the Markup class works: >>> from markupsafe import Markup
>>> Markup('<strong>Hello %s!</strong>') % '<blink>hacker</blink>'
Markup('<strong>Hello <blink>hacker</blink>!</strong>')
>>> Markup.escape('<blink>hacker</blink>')
Markup('<blink>hacker</blink>')
>>> Markup('<em>Marked up</em> » HTML').striptags()
'Marked up \xbb HTML'
Changelog Changed in version 0.5: Autoescaping is no longer enabled for all templates. The following extensions for templates trigger autoescaping: .html, .htm, .xml, .xhtml. Templates loaded from a string will have autoescaping disabled.
1
Unsure what that g object is? It’s something in which you can store information for your own needs. See the documentation for flask.g and Using SQLite 3 with Flask. Accessing Request Data For web applications it’s crucial to react to the data a client sends to the server. In Flask this information is provided by the global request object. If you have some experience with Python you might be wondering how that object can be global and how Flask manages to still be threadsafe. The answer is context locals: Context Locals Insider Information If you want to understand how that works and how you can implement tests with context locals, read this section, otherwise just skip it. Certain objects in Flask are global objects, but not of the usual kind. These objects are actually proxies to objects that are local to a specific context. What a mouthful. But that is actually quite easy to understand. Imagine the context being the handling thread. A request comes in and the web server decides to spawn a new thread (or something else, the underlying object is capable of dealing with concurrency systems other than threads). When Flask starts its internal request handling it figures out that the current thread is the active context and binds the current application and the WSGI environments to that context (thread). It does that in an intelligent way so that one application can invoke another application without breaking. So what does this mean to you? Basically you can completely ignore that this is the case unless you are doing something like unit testing. You will notice that code which depends on a request object will suddenly break because there is no request object. The solution is creating a request object yourself and binding it to the context. The easiest solution for unit testing is to use the test_request_context() context manager. In combination with the with statement it will bind a test request so that you can interact with it. Here is an example: from flask import request
with app.test_request_context('/hello', method='POST'):
# now you can do something with the request until the
# end of the with block, such as basic assertions:
assert request.path == '/hello'
assert request.method == 'POST'
The other possibility is passing a whole WSGI environment to the request_context() method: with app.request_context(environ):
assert request.method == 'POST'
The Request Object The request object is documented in the API section and we will not cover it here in detail (see Request). Here is a broad overview of some of the most common operations. First of all you have to import it from the flask module: from flask import request
The current request method is available by using the method attribute. To access form data (data transmitted in a POST or PUT request) you can use the form attribute. Here is a full example of the two attributes mentioned above: @app.route('/login', methods=['POST', 'GET'])
def login():
error = None
if request.method == 'POST':
if valid_login(request.form['username'],
request.form['password']):
return log_the_user_in(request.form['username'])
else:
error = 'Invalid username/password'
# the code below is executed if the request method
# was GET or the credentials were invalid
return render_template('login.html', error=error)
What happens if the key does not exist in the form attribute? In that case a special KeyError is raised. You can catch it like a standard KeyError but if you don’t do that, a HTTP 400 Bad Request error page is shown instead. So for many situations you don’t have to deal with that problem. To access parameters submitted in the URL (?key=value) you can use the args attribute: searchword = request.args.get('key', '')
We recommend accessing URL parameters with get or by catching the KeyError because users might change the URL and presenting them a 400 bad request page in that case is not user friendly. For a full list of methods and attributes of the request object, head over to the Request documentation. File Uploads You can handle uploaded files with Flask easily. Just make sure not to forget to set the enctype="multipart/form-data" attribute on your HTML form, otherwise the browser will not transmit your files at all. Uploaded files are stored in memory or at a temporary location on the filesystem. You can access those files by looking at the files attribute on the request object. Each uploaded file is stored in that dictionary. It behaves just like a standard Python file object, but it also has a save() method that allows you to store that file on the filesystem of the server. Here is a simple example showing how that works: from flask import request
@app.route('/upload', methods=['GET', 'POST'])
def upload_file():
if request.method == 'POST':
f = request.files['the_file']
f.save('/var/www/uploads/uploaded_file.txt')
...
If you want to know how the file was named on the client before it was uploaded to your application, you can access the filename attribute. However please keep in mind that this value can be forged so never ever trust that value. If you want to use the filename of the client to store the file on the server, pass it through the secure_filename() function that Werkzeug provides for you: from werkzeug.utils import secure_filename
@app.route('/upload', methods=['GET', 'POST'])
def upload_file():
if request.method == 'POST':
file = request.files['the_file']
file.save(f"/var/www/uploads/{secure_filename(f.filename)}")
...
For some better examples, see Uploading Files. Cookies To access cookies you can use the cookies attribute. To set cookies you can use the set_cookie method of response objects. The cookies attribute of request objects is a dictionary with all the cookies the client transmits. If you want to use sessions, do not use the cookies directly but instead use the Sessions in Flask that add some security on top of cookies for you. Reading cookies: from flask import request
@app.route('/')
def index():
username = request.cookies.get('username')
# use cookies.get(key) instead of cookies[key] to not get a
# KeyError if the cookie is missing.
Storing cookies: from flask import make_response
@app.route('/')
def index():
resp = make_response(render_template(...))
resp.set_cookie('username', 'the username')
return resp
Note that cookies are set on response objects. Since you normally just return strings from the view functions Flask will convert them into response objects for you. If you explicitly want to do that you can use the make_response() function and then modify it. Sometimes you might want to set a cookie at a point where the response object does not exist yet. This is possible by utilizing the Deferred Request Callbacks pattern. For this also see About Responses. Redirects and Errors To redirect a user to another endpoint, use the redirect() function; to abort a request early with an error code, use the abort() function: from flask import abort, redirect, url_for
@app.route('/')
def index():
return redirect(url_for('login'))
@app.route('/login')
def login():
abort(401)
this_is_never_executed()
This is a rather pointless example because a user will be redirected from the index to a page they cannot access (401 means access denied) but it shows how that works. By default a black and white error page is shown for each error code. If you want to customize the error page, you can use the errorhandler() decorator: from flask import render_template
@app.errorhandler(404)
def page_not_found(error):
return render_template('page_not_found.html'), 404
Note the 404 after the render_template() call. This tells Flask that the status code of that page should be 404 which means not found. By default 200 is assumed which translates to: all went well. See Handling Application Errors for more details. About Responses The return value from a view function is automatically converted into a response object for you. If the return value is a string it’s converted into a response object with the string as response body, a 200 OK status code and a text/html mimetype. If the return value is a dict, jsonify() is called to produce a response. The logic that Flask applies to converting return values into response objects is as follows: If a response object of the correct type is returned it’s directly returned from the view. If it’s a string, a response object is created with that data and the default parameters. If it’s a dict, a response object is created using jsonify. If a tuple is returned the items in the tuple can provide extra information. Such tuples have to be in the form (response, status), (response, headers), or (response, status, headers). The status value will override the status code and headers can be a list or dictionary of additional header values. If none of that works, Flask will assume the return value is a valid WSGI application and convert that into a response object. If you want to get hold of the resulting response object inside the view you can use the make_response() function. Imagine you have a view like this: from flask import render_template
@app.errorhandler(404)
def not_found(error):
return render_template('error.html'), 404
You just need to wrap the return expression with make_response() and get the response object to modify it, then return it: from flask import make_response
@app.errorhandler(404)
def not_found(error):
resp = make_response(render_template('error.html'), 404)
resp.headers['X-Something'] = 'A value'
return resp
APIs with JSON A common response format when writing an API is JSON. It’s easy to get started writing such an API with Flask. If you return a dict from a view, it will be converted to a JSON response. @app.route("/me")
def me_api():
user = get_current_user()
return {
"username": user.username,
"theme": user.theme,
"image": url_for("user_image", filename=user.image),
}
Depending on your API design, you may want to create JSON responses for types other than dict. In that case, use the jsonify() function, which will serialize any supported JSON data type. Or look into Flask community extensions that support more complex applications. from flask import jsonify
@app.route("/users")
def users_api():
users = get_all_users()
return jsonify([user.to_json() for user in users])
Sessions In addition to the request object there is also a second object called session which allows you to store information specific to a user from one request to the next. This is implemented on top of cookies for you and signs the cookies cryptographically. What this means is that the user could look at the contents of your cookie but not modify it, unless they know the secret key used for signing. In order to use sessions you have to set a secret key. Here is how sessions work: from flask import session
# Set the secret key to some random bytes. Keep this really secret!
app.secret_key = b'_5#y2L"F4Q8z\n\xec]/'
@app.route('/')
def index():
if 'username' in session:
return f'Logged in as {session["username"]}'
return 'You are not logged in'
@app.route('/login', methods=['GET', 'POST'])
def login():
if request.method == 'POST':
session['username'] = request.form['username']
return redirect(url_for('index'))
return '''
<form method="post">
<p><input type=text name=username>
<p><input type=submit value=Login>
</form>
'''
@app.route('/logout')
def logout():
# remove the username from the session if it's there
session.pop('username', None)
return redirect(url_for('index'))
How to generate good secret keys A secret key should be as random as possible. Your operating system has ways to generate pretty random data based on a cryptographic random generator. Use the following command to quickly generate a value for Flask.secret_key (or SECRET_KEY): $ python -c 'import os; print(os.urandom(16))'
b'_5#y2L"F4Q8z\n\xec]/'
A note on cookie-based sessions: Flask will take the values you put into the session object and serialize them into a cookie. If you are finding some values do not persist across requests, cookies are indeed enabled, and you are not getting a clear error message, check the size of the cookie in your page responses compared to the size supported by web browsers. Besides the default client-side based sessions, if you want to handle sessions on the server-side instead, there are several Flask extensions that support this. Message Flashing Good applications and user interfaces are all about feedback. If the user does not get enough feedback they will probably end up hating the application. Flask provides a really simple way to give feedback to a user with the flashing system. The flashing system basically makes it possible to record a message at the end of a request and access it on the next (and only the next) request. This is usually combined with a layout template to expose the message. To flash a message use the flash() method, to get hold of the messages you can use get_flashed_messages() which is also available in the templates. See Message Flashing for a full example. Logging Changelog New in version 0.3. Sometimes you might be in a situation where you deal with data that should be correct, but actually is not. For example you may have some client-side code that sends an HTTP request to the server but it’s obviously malformed. This might be caused by a user tampering with the data, or the client code failing. Most of the time it’s okay to reply with 400 Bad Request in that situation, but sometimes that won’t do and the code has to continue working. You may still want to log that something fishy happened. This is where loggers come in handy. As of Flask 0.3 a logger is preconfigured for you to use. Here are some example log calls: app.logger.debug('A value for debugging')
app.logger.warning('A warning occurred (%d apples)', 42)
app.logger.error('An error occurred')
The attached logger is a standard logging Logger, so head over to the official logging docs for more information. See Handling Application Errors. Hooking in WSGI Middleware To add WSGI middleware to your Flask application, wrap the application’s wsgi_app attribute. For example, to apply Werkzeug’s ProxyFix middleware for running behind Nginx: from werkzeug.middleware.proxy_fix import ProxyFix
app.wsgi_app = ProxyFix(app.wsgi_app)
Wrapping app.wsgi_app instead of app means that app still points at your Flask application, not at the middleware, so you can continue to use and configure app directly. Using Flask Extensions Extensions are packages that help you accomplish common tasks. For example, Flask-SQLAlchemy provides SQLAlchemy support that makes it simple and easy to use with Flask. For more on Flask extensions, see Extensions. Deploying to a Web Server Ready to deploy your new Flask app? See Deployment Options. | |
doc_29628 |
Print loops of cyclic references in the given objects. It is often useful to pass in gc.garbage to find the cycles that are preventing some objects from being garbage collected. Parameters
objects
A list of objects to find cycles in. outstream
The stream for output.
show_progressbool
If True, print the number of objects reached as they are found. | |
doc_29629 |
Compute the ‘inverse’ of an N-dimensional array. The result is an inverse for a relative to the tensordot operation tensordot(a, b, ind), i. e., up to floating-point accuracy, tensordot(tensorinv(a), a, ind) is the “identity” tensor for the tensordot operation. Parameters
aarray_like
Tensor to ‘invert’. Its shape must be ‘square’, i. e., prod(a.shape[:ind]) == prod(a.shape[ind:]).
indint, optional
Number of first indices that are involved in the inverse sum. Must be a positive integer, default is 2. Returns
bndarray
a’s tensordot inverse, shape a.shape[ind:] + a.shape[:ind]. Raises
LinAlgError
If a is singular or not ‘square’ (in the above sense). See also
numpy.tensordot, tensorsolve
Examples >>> a = np.eye(4*6)
>>> a.shape = (4, 6, 8, 3)
>>> ainv = np.linalg.tensorinv(a, ind=2)
>>> ainv.shape
(8, 3, 4, 6)
>>> b = np.random.randn(4, 6)
>>> np.allclose(np.tensordot(ainv, b), np.linalg.tensorsolve(a, b))
True
>>> a = np.eye(4*6)
>>> a.shape = (24, 8, 3)
>>> ainv = np.linalg.tensorinv(a, ind=1)
>>> ainv.shape
(8, 3, 24)
>>> b = np.random.randn(24)
>>> np.allclose(np.tensordot(ainv, b, 1), np.linalg.tensorsolve(a, b))
True | |
doc_29630 | This iterates over all the payloads in all the subparts of msg, returning the string payloads line-by-line. It skips over all the subpart headers, and it skips over any subpart with a payload that isn’t a Python string. This is somewhat equivalent to reading the flat text representation of the message from a file using readline(), skipping over all the intervening headers. Optional decode is passed through to Message.get_payload. | |
doc_29631 |
Return an period of which this timestamp is an observation. Examples
>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
>>> # Year end frequency
>>> ts.to_period(freq='Y')
Period('2020', 'A-DEC')
>>> # Month end frequency
>>> ts.to_period(freq='M')
Period('2020-03', 'M')
>>> # Weekly frequency
>>> ts.to_period(freq='W')
Period('2020-03-09/2020-03-15', 'W-SUN')
>>> # Quarter end frequency
>>> ts.to_period(freq='Q')
Period('2020Q1', 'Q-DEC') | |
doc_29632 |
Convert y using the unit type of the yaxis. If the artist is not in contained in an Axes or if the yaxis does not have units, y itself is returned. | |
doc_29633 | Should return True if adding an object is permitted, False otherwise. | |
doc_29634 |
Set the url for the artist. Parameters
urlstr | |
doc_29635 |
When this colormap exists on a scalar mappable and colorbar_extend is not False, colorbar creation will pick up colorbar_extend as the default value for the extend keyword in the matplotlib.colorbar.Colorbar constructor. | |
doc_29636 | gets a copy of an image with an interior area removed chop(Surface, rect) -> Surface Extracts a portion of an image. All vertical and horizontal pixels surrounding the given rectangle area are removed. The corner areas (diagonal to the rect) are then brought together. (The original image is not altered by this operation.) NOTE: If you want a "crop" that returns the part of an image within a rect, you can blit with a rect to a new surface or copy a subsurface. | |
doc_29637 |
Check if the object is a named tuple. Parameters
obj:The object to check
Returns
is_named_tuple:bool
Whether obj is a named tuple. Examples
>>> from collections import namedtuple
>>> Point = namedtuple("Point", ["x", "y"])
>>> p = Point(1, 2)
>>>
>>> is_named_tuple(p)
True
>>> is_named_tuple((1, 2))
False | |
doc_29638 | The delete_model method is given the HttpRequest and a model instance. Overriding this method allows doing pre- or post-delete operations. Call super().delete_model() to delete the object using Model.delete(). | |
doc_29639 | This method serves a similar purpose as __getnewargs_ex__(), but supports only positional arguments. It must return a tuple of arguments args which will be passed to the __new__() method upon unpickling. __getnewargs__() will not be called if __getnewargs_ex__() is defined. Changed in version 3.6: Before Python 3.6, __getnewargs__() was called instead of __getnewargs_ex__() in protocols 2 and 3. | |
doc_29640 | The base class of all multiprocessing exceptions. | |
doc_29641 |
Return a tuple width, height, xdescent, ydescent of the box. | |
doc_29642 | The name of the file including the relative path from MEDIA_ROOT. | |
doc_29643 | Close the watcher. This method has to be called to ensure that underlying resources are cleaned-up. | |
doc_29644 | tf.compat.v1.nn.sigmoid_cross_entropy_with_logits(
_sentinel=None, labels=None, logits=None, name=None
)
Measures the probability error in discrete classification tasks in which each class is independent and not mutually exclusive. For instance, one could perform multilabel classification where a picture can contain both an elephant and a dog at the same time. For brevity, let x = logits, z = labels. The logistic loss is z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
= z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
= z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
= z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
= (1 - z) * x + log(1 + exp(-x))
= x - x * z + log(1 + exp(-x))
For x < 0, to avoid overflow in exp(-x), we reformulate the above x - x * z + log(1 + exp(-x))
= log(exp(x)) - x * z + log(1 + exp(-x))
= - x * z + log(1 + exp(x))
Hence, to ensure stability and avoid overflow, the implementation uses this equivalent formulation max(x, 0) - x * z + log(1 + exp(-abs(x)))
logits and labels must have the same type and shape.
Args
_sentinel Used to prevent positional parameters. Internal, do not use.
labels A Tensor of the same type and shape as logits.
logits A Tensor of type float32 or float64.
name A name for the operation (optional).
Returns A Tensor of the same shape as logits with the componentwise logistic losses.
Raises
ValueError If logits and labels do not have the same shape. | |
doc_29645 |
Compute threshold value by Li’s iterative Minimum Cross Entropy method. Parameters
imagendarray
Input image.
tolerancefloat, optional
Finish the computation when the change in the threshold in an iteration is less than this value. By default, this is half the smallest difference between intensity values in image.
initial_guessfloat or Callable[[array[float]], float], optional
Li’s iterative method uses gradient descent to find the optimal threshold. If the image intensity histogram contains more than two modes (peaks), the gradient descent could get stuck in a local optimum. An initial guess for the iteration can help the algorithm find the globally-optimal threshold. A float value defines a specific start point, while a callable should take in an array of image intensities and return a float value. Example valid callables include numpy.mean (default), lambda arr: numpy.quantile(arr, 0.95), or even skimage.filters.threshold_otsu().
iter_callbackCallable[[float], Any], optional
A function that will be called on the threshold at every iteration of the algorithm. Returns
thresholdfloat
Upper threshold value. All pixels with an intensity higher than this value are assumed to be foreground. References
1
Li C.H. and Lee C.K. (1993) “Minimum Cross Entropy Thresholding” Pattern Recognition, 26(4): 617-625 DOI:10.1016/0031-3203(93)90115-D
2
Li C.H. and Tam P.K.S. (1998) “An Iterative Algorithm for Minimum Cross Entropy Thresholding” Pattern Recognition Letters, 18(8): 771-776 DOI:10.1016/S0167-8655(98)00057-9
3
Sezgin M. and Sankur B. (2004) “Survey over Image Thresholding Techniques and Quantitative Performance Evaluation” Journal of Electronic Imaging, 13(1): 146-165 DOI:10.1117/1.1631315
4
ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold Examples >>> from skimage.data import camera
>>> image = camera()
>>> thresh = threshold_li(image)
>>> binary = image > thresh | |
doc_29646 | class sklearn.feature_selection.SelectKBest(score_func=<function f_classif>, *, k=10) [source]
Select features according to the k highest scores. Read more in the User Guide. Parameters
score_funccallable, default=f_classif
Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues) or a single array with scores. Default is f_classif (see below “See Also”). The default function only works with classification tasks. New in version 0.18.
kint or “all”, default=10
Number of top features to select. The “all” option bypasses selection, for use in a parameter search. Attributes
scores_array-like of shape (n_features,)
Scores of features.
pvalues_array-like of shape (n_features,)
p-values of feature scores, None if score_func returned only scores. See also
f_classif
ANOVA F-value between label/feature for classification tasks.
mutual_info_classif
Mutual information for a discrete target.
chi2
Chi-squared stats of non-negative features for classification tasks.
f_regression
F-value between label/feature for regression tasks.
mutual_info_regression
Mutual information for a continuous target.
SelectPercentile
Select features based on percentile of the highest scores.
SelectFpr
Select features based on a false positive rate test.
SelectFdr
Select features based on an estimated false discovery rate.
SelectFwe
Select features based on family-wise error rate.
GenericUnivariateSelect
Univariate feature selector with configurable mode. Notes Ties between features with equal scores will be broken in an unspecified way. Examples >>> from sklearn.datasets import load_digits
>>> from sklearn.feature_selection import SelectKBest, chi2
>>> X, y = load_digits(return_X_y=True)
>>> X.shape
(1797, 64)
>>> X_new = SelectKBest(chi2, k=20).fit_transform(X, y)
>>> X_new.shape
(1797, 20)
Methods
fit(X, y) Run score function on (X, y) and get the appropriate features.
fit_transform(X[, y]) Fit to data, then transform it.
get_params([deep]) Get parameters for this estimator.
get_support([indices]) Get a mask, or integer index, of the features selected
inverse_transform(X) Reverse the transformation operation
set_params(**params) Set the parameters of this estimator.
transform(X) Reduce X to the selected features.
fit(X, y) [source]
Run score function on (X, y) and get the appropriate features. Parameters
Xarray-like of shape (n_samples, n_features)
The training input samples.
yarray-like of shape (n_samples,)
The target values (class labels in classification, real numbers in regression). Returns
selfobject
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
**fit_paramsdict
Additional fit parameters. Returns
X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values.
get_support(indices=False) [source]
Get a mask, or integer index, of the features selected Parameters
indicesbool, default=False
If True, the return value will be an array of integers, rather than a boolean mask. Returns
supportarray
An index that selects the retained features from a feature vector. If indices is False, this is a boolean array of shape [# input features], in which an element is True iff its corresponding feature is selected for retention. If indices is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.
inverse_transform(X) [source]
Reverse the transformation operation Parameters
Xarray of shape [n_samples, n_selected_features]
The input samples. Returns
X_rarray of shape [n_samples, n_original_features]
X with columns of zeros inserted where features would have been removed by transform.
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Estimator parameters. Returns
selfestimator instance
Estimator instance.
transform(X) [source]
Reduce X to the selected features. Parameters
Xarray of shape [n_samples, n_features]
The input samples. Returns
X_rarray of shape [n_samples, n_selected_features]
The input samples with only the selected features.
Examples using sklearn.feature_selection.SelectKBest
Pipeline Anova SVM
Univariate Feature Selection
Concatenating multiple feature extraction methods
Selecting dimensionality reduction with Pipeline and GridSearchCV
Classification of text documents using sparse features | |
doc_29647 |
Return the axis label as a Text instance.
Examples using matplotlib.axis.Axis.get_label
Parasite Simple | |
doc_29648 | Performs a batch matrix-matrix product of matrices stored in batch1 and batch2, with a reduced add step (all matrix multiplications get accumulated along the first dimension). input is added to the final result. batch1 and batch2 must be 3-D tensors each containing the same number of matrices. If batch1 is a (b×n×m)(b \times n \times m) tensor, batch2 is a (b×m×p)(b \times m \times p) tensor, input must be broadcastable with a (n×p)(n \times p) tensor and out will be a (n×p)(n \times p) tensor. out=β input+α(∑i=0b−1batch1i@batch2i)out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i)
If beta is 0, then input will be ignored, and nan and inf in it will not be propagated. For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha must be real numbers, otherwise they should be integers. This operator supports TensorFloat32. Parameters
batch1 (Tensor) – the first batch of matrices to be multiplied
batch2 (Tensor) – the second batch of matrices to be multiplied Keyword Arguments
beta (Number, optional) – multiplier for input (β\beta )
input (Tensor) – matrix to be added
alpha (Number, optional) – multiplier for batch1 @ batch2 (α\alpha )
out (Tensor, optional) – the output tensor. Example: >>> M = torch.randn(3, 5)
>>> batch1 = torch.randn(10, 3, 4)
>>> batch2 = torch.randn(10, 4, 5)
>>> torch.addbmm(M, batch1, batch2)
tensor([[ 6.6311, 0.0503, 6.9768, -12.0362, -2.1653],
[ -4.8185, -1.4255, -6.6760, 8.9453, 2.5743],
[ -3.8202, 4.3691, 1.0943, -1.1109, 5.4730]]) | |
doc_29649 | An abstract method for finding a loader for the specified module. Originally specified in PEP 302, this method was meant for use in sys.meta_path and in the path-based import subsystem. Changed in version 3.4: Returns None when called instead of raising NotImplementedError. | |
doc_29650 |
Number of elements in the array. Equal to np.prod(a.shape), i.e., the product of the array’s dimensions. Notes a.size returns a standard arbitrary precision Python integer. This may not be the case with other methods of obtaining the same value (like the suggested np.prod(a.shape), which returns an instance of np.int_), and may be relevant if the value is used further in calculations that may overflow a fixed size integer type. Examples >>> x = np.zeros((3, 5, 2), dtype=np.complex128)
>>> x.size
30
>>> np.prod(x.shape)
30 | |
doc_29651 | Decorator for the minimum version when running test on FreeBSD. If the FreeBSD version is less than the minimum, raise unittest.SkipTest. | |
doc_29652 | Default widget: TimeInput
Empty value: None
Normalizes to: A Python datetime.time object. Validates that the given value is either a datetime.time or string formatted in a particular time format. Error message keys: required, invalid
Takes one optional argument:
input_formats
A list of formats used to attempt to convert a string to a valid datetime.time object.
If no input_formats argument is provided, the default input formats are taken from TIME_INPUT_FORMATS if USE_L10N is False, or from the active locale format TIME_INPUT_FORMATS key if localization is enabled. See also format localization. | |
doc_29653 |
Update the location and the size of the bbox. This method should be used when the position and size of the bbox needs to be updated before actually drawing the bbox. | |
doc_29654 |
Bases: matplotlib.backends.backend_agg.FigureCanvasAgg, matplotlib.backends._backend_tk.FigureCanvasTk blit(bbox=None)[source]
Blit the canvas in bbox (default entire canvas).
draw()[source]
Render the Figure. It is important that this method actually walk the artist tree even if not output is produced because this will trigger deferred work (like computing limits auto-limits and tick values) that users may want access to before saving to disk. | |
doc_29655 |
Return the cached TexManager instance. | |
doc_29656 |
Roll provided date forward to next offset only if not on offset. Returns
TimeStamp
Rolled timestamp if not on offset, otherwise unchanged timestamp. | |
doc_29657 | See Migration guide for more details. tf.compat.v1.nn.max_pool_v2
tf.nn.max_pool(
input, ksize, strides, padding, data_format=None, name=None
)
Args
input Tensor of rank N+2, of shape [batch_size] + input_spatial_shape + [num_channels] if data_format does not start with "NC" (default), or [batch_size, num_channels] + input_spatial_shape if data_format starts with "NC". Pooling happens over the spatial dimensions only.
ksize An int or list of ints that has length 1, N or N+2. The size of the window for each dimension of the input tensor.
strides An int or list of ints that has length 1, N or N+2. The stride of the sliding window for each dimension of the input tensor.
padding Either the string"SAME"or"VALID"indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is"NHWC", this should be in the form[[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is"NCHW", this should be in the form[[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]]. When using explicit padding, the size of the paddings cannot be greater than the sliding window size. </td> </tr><tr> <td>data_format</td> <td> A string. Specifies the channel dimension. For N=1 it can be either "NWC" (default) or "NCW", for N=2 it can be either "NHWC" (default) or "NCHW" and for N=3 either "NDHWC" (default) or "NCDHW". </td> </tr><tr> <td>name` Optional name for the operation.
Returns A Tensor of format specified by data_format. The max pooled output tensor. | |
doc_29658 | specialized image doubler scale2x(Surface, DestSurface = None) -> Surface This will return a new image that is double the size of the original. It uses the AdvanceMAME Scale2X algorithm which does a 'jaggie-less' scale of bitmap graphics. This really only has an effect on simple images with solid colors. On photographic and antialiased images it will look like a regular unfiltered scale. An optional destination surface can be used, rather than have it create a new one. This is quicker if you want to repeatedly scale something. However the destination must be twice the size of the source surface passed in. Also the destination surface must be the same format. | |
doc_29659 | __enter__()
__exit__ View source
__exit__(
type_arg, value_arg, traceback_arg
) | |
doc_29660 | The WSGIRequest instance generated by the test handler that generated the response. | |
doc_29661 | class sklearn.linear_model.LassoLars(alpha=1.0, *, fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, positive=False, jitter=None, random_state=None) [source]
Lasso model fit with Least Angle Regression a.k.a. Lars It is a Linear Model trained with an L1 prior as regularizer. The optimization objective for Lasso is: (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
Read more in the User Guide. Parameters
alphafloat, default=1.0
Constant that multiplies the penalty term. Defaults to 1.0. alpha = 0 is equivalent to an ordinary least square, solved by LinearRegression. For numerical reasons, using alpha = 0 with the LassoLars object is not advised and you should prefer the LinearRegression object.
fit_interceptbool, default=True
whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).
verbosebool or int, default=False
Sets the verbosity amount.
normalizebool, default=True
This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use StandardScaler before calling fit on an estimator with normalize=False.
precomputebool, ‘auto’ or array-like, default=’auto’
Whether to use a precomputed Gram matrix to speed up calculations. If set to 'auto' let us decide. The Gram matrix can also be passed as argument.
max_iterint, default=500
Maximum number of iterations to perform.
epsfloat, default=np.finfo(float).eps
The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Unlike the tol parameter in some iterative optimization-based algorithms, this parameter does not control the tolerance of the optimization.
copy_Xbool, default=True
If True, X will be copied; else, it may be overwritten.
fit_pathbool, default=True
If True the full path is stored in the coef_path_ attribute. If you compute the solution for a large problem or many targets, setting fit_path to False will lead to a speedup, especially with a small alpha.
positivebool, default=False
Restrict coefficients to be >= 0. Be aware that you might want to remove fit_intercept which is set True by default. Under the positive restriction the model coefficients will not converge to the ordinary-least-squares solution for small values of alpha. Only coefficients up to the smallest alpha value (alphas_[alphas_ >
0.].min() when fit_path=True) reached by the stepwise Lars-Lasso algorithm are typically in congruence with the solution of the coordinate descent Lasso estimator.
jitterfloat, default=None
Upper bound on a uniform noise parameter to be added to the y values, to satisfy the model’s assumption of one-at-a-time computations. Might help with stability. New in version 0.23.
random_stateint, RandomState instance or None, default=None
Determines random number generation for jittering. Pass an int for reproducible output across multiple function calls. See Glossary. Ignored if jitter is None. New in version 0.23. Attributes
alphas_array-like of shape (n_alphas + 1,) or list of such arrays
Maximum of covariances (in absolute value) at each iteration. n_alphas is either max_iter, n_features or the number of nodes in the path with alpha >= alpha_min, whichever is smaller. If this is a list of array-like, the length of the outer list is n_targets.
active_list of length n_alphas or list of such lists
Indices of active variables at the end of the path. If this is a list of list, the length of the outer list is n_targets.
coef_path_array-like of shape (n_features, n_alphas + 1) or list of such arrays
If a list is passed it’s expected to be one of n_targets such arrays. The varying values of the coefficients along the path. It is not present if the fit_path parameter is False. If this is a list of array-like, the length of the outer list is n_targets.
coef_array-like of shape (n_features,) or (n_targets, n_features)
Parameter vector (w in the formulation formula).
intercept_float or array-like of shape (n_targets,)
Independent term in decision function.
n_iter_array-like or int
The number of iterations taken by lars_path to find the grid of alphas for each target. See also
lars_path
lasso_path
Lasso
LassoCV
LassoLarsCV
LassoLarsIC
sklearn.decomposition.sparse_encode
Examples >>> from sklearn import linear_model
>>> reg = linear_model.LassoLars(alpha=0.01)
>>> reg.fit([[-1, 1], [0, 0], [1, 1]], [-1, 0, -1])
LassoLars(alpha=0.01)
>>> print(reg.coef_)
[ 0. -0.963257...]
Methods
fit(X, y[, Xy]) Fit the model using X, y as training data.
get_params([deep]) Get parameters for this estimator.
predict(X) Predict using the linear model.
score(X, y[, sample_weight]) Return the coefficient of determination \(R^2\) of the prediction.
set_params(**params) Set the parameters of this estimator.
fit(X, y, Xy=None) [source]
Fit the model using X, y as training data. Parameters
Xarray-like of shape (n_samples, n_features)
Training data.
yarray-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
Xyarray-like of shape (n_samples,) or (n_samples, n_targets), default=None
Xy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomputed. Returns
selfobject
returns an instance of self.
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values.
predict(X) [source]
Predict using the linear model. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
Carray, shape (n_samples,)
Returns predicted values.
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0. Parameters
Xarray-like of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. Returns
scorefloat
\(R^2\) of self.predict(X) wrt. y. Notes The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Estimator parameters. Returns
selfestimator instance
Estimator instance. | |
doc_29662 | Accept: application/json
Might receive an error response indicating that the DELETE method is not allowed on that resource: HTTP/1.1 405 Method Not Allowed
Content-Type: application/json
Content-Length: 42
{"detail": "Method 'DELETE' not allowed."}
Validation errors are handled slightly differently, and will include the field names as the keys in the response. If the validation error was not specific to a particular field then it will use the "non_field_errors" key, or whatever string value has been set for the NON_FIELD_ERRORS_KEY setting. An example validation error might look like this: HTTP/1.1 400 Bad Request
Content-Type: application/json
Content-Length: 94
{"amount": ["A valid integer is required."], "description": ["This field may not be blank."]}
Custom exception handling You can implement custom exception handling by creating a handler function that converts exceptions raised in your API views into response objects. This allows you to control the style of error responses used by your API. The function must take a pair of arguments, the first is the exception to be handled, and the second is a dictionary containing any extra context such as the view currently being handled. The exception handler function should either return a Response object, or return None if the exception cannot be handled. If the handler returns None then the exception will be re-raised and Django will return a standard HTTP 500 'server error' response. For example, you might want to ensure that all error responses include the HTTP status code in the body of the response, like so: HTTP/1.1 405 Method Not Allowed
Content-Type: application/json
Content-Length: 62
{"status_code": 405, "detail": "Method 'DELETE' not allowed."}
In order to alter the style of the response, you could write the following custom exception handler: from rest_framework.views import exception_handler
def custom_exception_handler(exc, context):
# Call REST framework's default exception handler first,
# to get the standard error response.
response = exception_handler(exc, context)
# Now add the HTTP status code to the response.
if response is not None:
response.data['status_code'] = response.status_code
return response
The context argument is not used by the default handler, but can be useful if the exception handler needs further information such as the view currently being handled, which can be accessed as context['view']. The exception handler must also be configured in your settings, using the EXCEPTION_HANDLER setting key. For example: REST_FRAMEWORK = {
'EXCEPTION_HANDLER': 'my_project.my_app.utils.custom_exception_handler'
}
If not specified, the 'EXCEPTION_HANDLER' setting defaults to the standard exception handler provided by REST framework: REST_FRAMEWORK = {
'EXCEPTION_HANDLER': 'rest_framework.views.exception_handler'
}
Note that the exception handler will only be called for responses generated by raised exceptions. It will not be used for any responses returned directly by the view, such as the HTTP_400_BAD_REQUEST responses that are returned by the generic views when serializer validation fails. API Reference APIException Signature: APIException() The base class for all exceptions raised inside an APIView class or @api_view. To provide a custom exception, subclass APIException and set the .status_code, .default_detail, and default_code attributes on the class. For example, if your API relies on a third party service that may sometimes be unreachable, you might want to implement an exception for the "503 Service Unavailable" HTTP response code. You could do this like so: from rest_framework.exceptions import APIException
class ServiceUnavailable(APIException):
status_code = 503
default_detail = 'Service temporarily unavailable, try again later.'
default_code = 'service_unavailable'
Inspecting API exceptions There are a number of different properties available for inspecting the status of an API exception. You can use these to build custom exception handling for your project. The available attributes and methods are:
.detail - Return the textual description of the error.
.get_codes() - Return the code identifier of the error.
.get_full_details() - Return both the textual description and the code identifier. In most cases the error detail will be a simple item: >>> print(exc.detail)
You do not have permission to perform this action.
>>> print(exc.get_codes())
permission_denied
>>> print(exc.get_full_details())
{'message':'You do not have permission to perform this action.','code':'permission_denied'}
In the case of validation errors the error detail will be either a list or dictionary of items: >>> print(exc.detail)
{"name":"This field is required.","age":"A valid integer is required."}
>>> print(exc.get_codes())
{"name":"required","age":"invalid"}
>>> print(exc.get_full_details())
{"name":{"message":"This field is required.","code":"required"},"age":{"message":"A valid integer is required.","code":"invalid"}}
ParseError Signature: ParseError(detail=None, code=None) Raised if the request contains malformed data when accessing request.data. By default this exception results in a response with the HTTP status code "400 Bad Request". AuthenticationFailed Signature: AuthenticationFailed(detail=None, code=None) Raised when an incoming request includes incorrect authentication. By default this exception results in a response with the HTTP status code "401 Unauthenticated", but it may also result in a "403 Forbidden" response, depending on the authentication scheme in use. See the authentication documentation for more details. NotAuthenticated Signature: NotAuthenticated(detail=None, code=None) Raised when an unauthenticated request fails the permission checks. By default this exception results in a response with the HTTP status code "401 Unauthenticated", but it may also result in a "403 Forbidden" response, depending on the authentication scheme in use. See the authentication documentation for more details. PermissionDenied Signature: PermissionDenied(detail=None, code=None) Raised when an authenticated request fails the permission checks. By default this exception results in a response with the HTTP status code "403 Forbidden". NotFound Signature: NotFound(detail=None, code=None) Raised when a resource does not exists at the given URL. This exception is equivalent to the standard Http404 Django exception. By default this exception results in a response with the HTTP status code "404 Not Found". MethodNotAllowed Signature: MethodNotAllowed(method, detail=None, code=None) Raised when an incoming request occurs that does not map to a handler method on the view. By default this exception results in a response with the HTTP status code "405 Method Not Allowed". NotAcceptable Signature: NotAcceptable(detail=None, code=None) Raised when an incoming request occurs with an Accept header that cannot be satisfied by any of the available renderers. By default this exception results in a response with the HTTP status code "406 Not Acceptable". UnsupportedMediaType Signature: UnsupportedMediaType(media_type, detail=None, code=None) Raised if there are no parsers that can handle the content type of the request data when accessing request.data. By default this exception results in a response with the HTTP status code "415 Unsupported Media Type". Throttled Signature: Throttled(wait=None, detail=None, code=None) Raised when an incoming request fails the throttling checks. By default this exception results in a response with the HTTP status code "429 Too Many Requests". ValidationError Signature: ValidationError(detail, code=None) The ValidationError exception is slightly different from the other APIException classes: The detail argument is mandatory, not optional. The detail argument may be a list or dictionary of error details, and may also be a nested data structure. By using a dictionary, you can specify field-level errors while performing object-level validation in the validate() method of a serializer. For example. raise serializers.ValidationError({'name': 'Please enter a valid name.'})
By convention you should import the serializers module and use a fully qualified ValidationError style, in order to differentiate it from Django's built-in validation error. For example. raise serializers.ValidationError('This field must be an integer value.')
The ValidationError class should be used for serializer and field validation, and by validator classes. It is also raised when calling serializer.is_valid with the raise_exception keyword argument: serializer.is_valid(raise_exception=True)
The generic views use the raise_exception=True flag, which means that you can override the style of validation error responses globally in your API. To do so, use a custom exception handler, as described above. By default this exception results in a response with the HTTP status code "400 Bad Request". Generic Error Views Django REST Framework provides two error views suitable for providing generic JSON 500 Server Error and 400 Bad Request responses. (Django's default error views provide HTML responses, which may not be appropriate for an API-only application.) Use these as per Django's Customizing error views documentation. rest_framework.exceptions.server_error Returns a response with status code 500 and application/json content type. Set as handler500: handler500 = 'rest_framework.exceptions.server_error'
rest_framework.exceptions.bad_request Returns a response with status code 400 and application/json content type. Set as handler400: handler400 = 'rest_framework.exceptions.bad_request'
exceptions.py | |
doc_29663 | tf.compat.v1.initializers.he_normal(
seed=None
)
It draws samples from a truncated normal distribution centered on 0 with standard deviation (after truncation) given by stddev = sqrt(2 / fan_in) where fan_in is the number of input units in the weight tensor.
Arguments
seed A Python integer. Used to seed the random generator.
Returns An initializer.
References: He et al., 2015 (pdf) | |
doc_29664 | Specifies an email.message.Message-like class to parse HTTP headers. Typically, this is not overridden, and it defaults to http.client.HTTPMessage. | |
doc_29665 |
Generate missing values indicator for X. Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)
The input data to complete. Returns
Xt{ndarray or sparse matrix}, shape (n_samples, n_features) or (n_samples, n_features_with_missing)
The missing indicator for input data. The data type of Xt will be boolean. | |
doc_29666 | The first call to add for a given key creates a counter associated with key in the store, initialized to amount. Subsequent calls to add with the same key increment the counter by the specified amount. Calling add() with a key that has already been set in the store by set() will result in an exception. Parameters
key (str) – The key in the store whose counter will be incremented.
amount (int) – The quantity by which the counter will be incremented. Example::
>>> import torch.distributed as dist
>>> from datetime import timedelta
>>> # Using TCPStore as an example, other store types can also be used
>>> store = dist.TCPStore("127.0.0.1", 0, 1, True, timedelta(seconds=30))
>>> store.add("first_key", 1)
>>> store.add("first_key", 6)
>>> # Should return 7
>>> store.get("first_key") | |
doc_29667 | If this is set (not None), Python will write bytecode-cache .pyc files to (and read them from) a parallel directory tree rooted at this directory, rather than from __pycache__ directories in the source code tree. Any __pycache__ directories in the source code tree will be ignored and new .pyc files written within the pycache prefix. Thus if you use compileall as a pre-build step, you must ensure you run it with the same pycache prefix (if any) that you will use at runtime. A relative path is interpreted relative to the current working directory. This value is initially set based on the value of the -X pycache_prefix=PATH command-line option or the PYTHONPYCACHEPREFIX environment variable (command-line takes precedence). If neither are set, it is None. New in version 3.8. | |
doc_29668 | Uses django.utils.ipv6 to check the validity of an IPv6 address. | |
doc_29669 | Returns the median of the values in input. Note The median is not unique for input tensors with an even number of elements. In this case the lower of the two medians is returned. To compute the mean of both medians, use torch.quantile() with q=0.5 instead. Warning This function produces deterministic (sub)gradients unlike median(dim=0) Parameters
input (Tensor) – the input tensor. Example: >>> a = torch.randn(1, 3)
>>> a
tensor([[ 1.5219, -1.5212, 0.2202]])
>>> torch.median(a)
tensor(0.2202)
torch.median(input, dim=-1, keepdim=False, *, out=None) -> (Tensor, LongTensor)
Returns a namedtuple (values, indices) where values contains the median of each row of input in the dimension dim, and indices contains the index of the median values found in the dimension dim. By default, dim is the last dimension of the input tensor. If keepdim is True, the output tensors are of the same size as input except in the dimension dim where they are of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the outputs tensor having 1 fewer dimension than input. Note The median is not unique for input tensors with an even number of elements in the dimension dim. In this case the lower of the two medians is returned. To compute the mean of both medians in input, use torch.quantile() with q=0.5 instead. Warning indices does not necessarily contain the first occurrence of each median value found, unless it is unique. The exact implementation details are device-specific. Do not expect the same result when run on CPU and GPU in general. For the same reason do not expect the gradients to be deterministic. Parameters
input (Tensor) – the input tensor.
dim (int) – the dimension to reduce.
keepdim (bool) – whether the output tensor has dim retained or not. Keyword Arguments
out ((Tensor, Tensor), optional) – The first tensor will be populated with the median values and the second tensor, which must have dtype long, with their indices in the dimension dim of input. Example: >>> a = torch.randn(4, 5)
>>> a
tensor([[ 0.2505, -0.3982, -0.9948, 0.3518, -1.3131],
[ 0.3180, -0.6993, 1.0436, 0.0438, 0.2270],
[-0.2751, 0.7303, 0.2192, 0.3321, 0.2488],
[ 1.0778, -1.9510, 0.7048, 0.4742, -0.7125]])
>>> torch.median(a, 1)
torch.return_types.median(values=tensor([-0.3982, 0.2270, 0.2488, 0.4742]), indices=tensor([1, 4, 4, 3])) | |
doc_29670 | Stop the child. On POSIX OSs the method sends SIGTERM to the child. On Windows the Win32 API function TerminateProcess() is called to stop the child. | |
doc_29671 |
Find the shortest path through an n-d array from one side to another. Parameters
arrndarray of float64
reachint, optional
By default (reach = 1), the shortest path can only move one row up or down for every step it moves forward (i.e., the path gradient is limited to 1). reach defines the number of elements that can be skipped along each non-axis dimension at each step.
axisint, optional
The axis along which the path must always move forward (default -1)
output_indexlistbool, optional
See return value p for explanation. Returns
piterable of int
For each step along axis, the coordinate of the shortest path. If output_indexlist is True, then the path is returned as a list of n-d tuples that index into arr. If False, then the path is returned as an array listing the coordinates of the path along the non-axis dimensions for each step along the axis dimension. That is, p.shape == (arr.shape[axis], arr.ndim-1) except that p is squeezed before returning so if arr.ndim == 2, then p.shape == (arr.shape[axis],)
costfloat
Cost of path. This is the absolute sum of all the differences along the path. | |
doc_29672 | from flask import Flask
app = Flask(__name__)
...
asgi_app = WsgiToAsgi(app)
and then serving the asgi_app with the asgi server, e.g. using Hypercorn, $ hypercorn module:asgi_app | |
doc_29673 |
Set the antialiasing state for rendering. Parameters
aabool or list of bools | |
doc_29674 | Take two (non complex) numbers as arguments and return a pair of numbers consisting of their quotient and remainder when using integer division. With mixed operand types, the rules for binary arithmetic operators apply. For integers, the result is the same as (a // b, a % b). For floating point numbers the result is (q, a % b), where q is usually math.floor(a /
b) but may be 1 less than that. In any case q * b + a % b is very close to a, if a % b is non-zero it has the same sign as b, and 0
<= abs(a % b) < abs(b). | |
doc_29675 | BallTree for fast generalized N-point problems Read more in the User Guide. Parameters
Xarray-like of shape (n_samples, n_features)
n_samples is the number of points in the data set, and n_features is the dimension of the parameter space. Note: if X is a C-contiguous array of doubles then data will not be copied. Otherwise, an internal copy will be made.
leaf_sizepositive int, default=40
Number of points at which to switch to brute-force. Changing leaf_size will not affect the results of a query, but can significantly impact the speed of a query and the memory required to store the constructed tree. The amount of memory needed to store the tree scales as approximately n_samples / leaf_size. For a specified leaf_size, a leaf node is guaranteed to satisfy leaf_size <= n_points <= 2 * leaf_size, except in the case that n_samples < leaf_size.
metricstr or DistanceMetric object
the distance metric to use for the tree. Default=’minkowski’ with p=2 (that is, a euclidean metric). See the documentation of the DistanceMetric class for a list of available metrics. ball_tree.valid_metrics gives a list of the metrics which are valid for BallTree. Additional keywords are passed to the distance metric class.
Note: Callable functions in the metric parameter are NOT supported for KDTree
and Ball Tree. Function call overhead will result in very poor performance.
Attributes
datamemory view
The training data Examples Query for k-nearest neighbors >>> import numpy as np
>>> rng = np.random.RandomState(0)
>>> X = rng.random_sample((10, 3)) # 10 points in 3 dimensions
>>> tree = BallTree(X, leaf_size=2)
>>> dist, ind = tree.query(X[:1], k=3)
>>> print(ind) # indices of 3 closest neighbors
[0 3 1]
>>> print(dist) # distances to 3 closest neighbors
[ 0. 0.19662693 0.29473397]
Pickle and Unpickle a tree. Note that the state of the tree is saved in the pickle operation: the tree needs not be rebuilt upon unpickling. >>> import numpy as np
>>> import pickle
>>> rng = np.random.RandomState(0)
>>> X = rng.random_sample((10, 3)) # 10 points in 3 dimensions
>>> tree = BallTree(X, leaf_size=2)
>>> s = pickle.dumps(tree)
>>> tree_copy = pickle.loads(s)
>>> dist, ind = tree_copy.query(X[:1], k=3)
>>> print(ind) # indices of 3 closest neighbors
[0 3 1]
>>> print(dist) # distances to 3 closest neighbors
[ 0. 0.19662693 0.29473397]
Query for neighbors within a given radius >>> import numpy as np
>>> rng = np.random.RandomState(0)
>>> X = rng.random_sample((10, 3)) # 10 points in 3 dimensions
>>> tree = BallTree(X, leaf_size=2)
>>> print(tree.query_radius(X[:1], r=0.3, count_only=True))
3
>>> ind = tree.query_radius(X[:1], r=0.3)
>>> print(ind) # indices of neighbors within distance 0.3
[3 0 1]
Compute a gaussian kernel density estimate: >>> import numpy as np
>>> rng = np.random.RandomState(42)
>>> X = rng.random_sample((100, 3))
>>> tree = BallTree(X)
>>> tree.kernel_density(X[:3], h=0.1, kernel='gaussian')
array([ 6.94114649, 7.83281226, 7.2071716 ])
Compute a two-point auto-correlation function >>> import numpy as np
>>> rng = np.random.RandomState(0)
>>> X = rng.random_sample((30, 3))
>>> r = np.linspace(0, 1, 5)
>>> tree = BallTree(X)
>>> tree.two_point_correlation(X, r)
array([ 30, 62, 278, 580, 820])
Methods
get_arrays(self) Get data and node arrays.
get_n_calls(self) Get number of calls.
get_tree_stats(self) Get tree status.
kernel_density(self, X, h[, kernel, atol, …]) Compute the kernel density estimate at points X with the given kernel, using the distance metric specified at tree creation.
query(X[, k, return_distance, dualtree, …]) query the tree for the k nearest neighbors
query_radius(X, r[, return_distance, …]) query the tree for neighbors within a radius r
reset_n_calls(self) Reset number of calls to 0.
two_point_correlation(X, r[, dualtree]) Compute the two-point correlation function
get_arrays(self)
Get data and node arrays. Returns
arrays: tuple of array
Arrays for storing tree data, index, node data and node bounds.
get_n_calls(self)
Get number of calls. Returns
n_calls: int
number of distance computation calls
get_tree_stats(self)
Get tree status. Returns
tree_stats: tuple of int
(number of trims, number of leaves, number of splits)
kernel_density(self, X, h, kernel='gaussian', atol=0, rtol=1e-08, breadth_first=True, return_log=False)
Compute the kernel density estimate at points X with the given kernel, using the distance metric specified at tree creation. Parameters
Xarray-like of shape (n_samples, n_features)
An array of points to query. Last dimension should match dimension of training data.
hfloat
the bandwidth of the kernel
kernelstr, default=”gaussian”
specify the kernel to use. Options are - ‘gaussian’ - ‘tophat’ - ‘epanechnikov’ - ‘exponential’ - ‘linear’ - ‘cosine’ Default is kernel = ‘gaussian’
atol, rtolfloat, default=0, 1e-8
Specify the desired relative and absolute tolerance of the result. If the true result is K_true, then the returned result K_ret satisfies abs(K_true - K_ret) < atol + rtol * K_ret The default is zero (i.e. machine precision) for both.
breadth_firstbool, default=False
If True, use a breadth-first search. If False (default) use a depth-first search. Breadth-first is generally faster for compact kernels and/or high tolerances.
return_logbool, default=False
Return the logarithm of the result. This can be more accurate than returning the result itself for narrow kernels. Returns
densityndarray of shape X.shape[:-1]
The array of (log)-density evaluations
query(X, k=1, return_distance=True, dualtree=False, breadth_first=False)
query the tree for the k nearest neighbors Parameters
Xarray-like of shape (n_samples, n_features)
An array of points to query
kint, default=1
The number of nearest neighbors to return
return_distancebool, default=True
if True, return a tuple (d, i) of distances and indices if False, return array i
dualtreebool, default=False
if True, use the dual tree formalism for the query: a tree is built for the query points, and the pair of trees is used to efficiently search this space. This can lead to better performance as the number of points grows large.
breadth_firstbool, default=False
if True, then query the nodes in a breadth-first manner. Otherwise, query the nodes in a depth-first manner.
sort_resultsbool, default=True
if True, then distances and indices of each point are sorted on return, so that the first column contains the closest points. Otherwise, neighbors are returned in an arbitrary order. Returns
iif return_distance == False
(d,i)if return_distance == True
dndarray of shape X.shape[:-1] + (k,), dtype=double
Each entry gives the list of distances to the neighbors of the corresponding point.
indarray of shape X.shape[:-1] + (k,), dtype=int
Each entry gives the list of indices of neighbors of the corresponding point.
query_radius(X, r, return_distance=False, count_only=False, sort_results=False)
query the tree for neighbors within a radius r Parameters
Xarray-like of shape (n_samples, n_features)
An array of points to query
rdistance within which neighbors are returned
r can be a single value, or an array of values of shape x.shape[:-1] if different radii are desired for each point.
return_distancebool, default=False
if True, return distances to neighbors of each point if False, return only neighbors Note that unlike the query() method, setting return_distance=True here adds to the computation time. Not all distances need to be calculated explicitly for return_distance=False. Results are not sorted by default: see sort_results keyword.
count_onlybool, default=False
if True, return only the count of points within distance r if False, return the indices of all points within distance r If return_distance==True, setting count_only=True will result in an error.
sort_resultsbool, default=False
if True, the distances and indices will be sorted before being returned. If False, the results will not be sorted. If return_distance == False, setting sort_results = True will result in an error. Returns
countif count_only == True
indif count_only == False and return_distance == False
(ind, dist)if count_only == False and return_distance == True
countndarray of shape X.shape[:-1], dtype=int
Each entry gives the number of neighbors within a distance r of the corresponding point.
indndarray of shape X.shape[:-1], dtype=object
Each element is a numpy integer array listing the indices of neighbors of the corresponding point. Note that unlike the results of a k-neighbors query, the returned neighbors are not sorted by distance by default.
distndarray of shape X.shape[:-1], dtype=object
Each element is a numpy double array listing the distances corresponding to indices in i.
reset_n_calls(self)
Reset number of calls to 0.
two_point_correlation(X, r, dualtree=False)
Compute the two-point correlation function Parameters
Xarray-like of shape (n_samples, n_features)
An array of points to query. Last dimension should match dimension of training data.
rarray-like
A one-dimensional array of distances
dualtreebool, default=False
If True, use a dualtree algorithm. Otherwise, use a single-tree algorithm. Dual tree algorithms can have better scaling for large N. Returns
countsndarray
counts[i] contains the number of pairs of points with distance less than or equal to r[i] | |
doc_29676 |
Bases: matplotlib.transforms.Affine2DBase A mutable 2D affine transformation. Initialize an Affine transform from a 3x3 numpy float array: a c e
b d f
0 0 1
If matrix is None, initialize with the identity transform. __init__(matrix=None, **kwargs)[source]
Initialize an Affine transform from a 3x3 numpy float array: a c e
b d f
0 0 1
If matrix is None, initialize with the identity transform.
__module__='matplotlib.transforms'
__str__()[source]
Return str(self).
clear()[source]
Reset the underlying matrix to the identity transform.
staticfrom_values(a, b, c, d, e, f)[source]
Create a new Affine2D instance from the given values: a c e
b d f
0 0 1
.
get_matrix()[source]
Get the underlying transformation matrix as a 3x3 numpy array: a c e
b d f
0 0 1
.
staticidentity()[source]
Return a new Affine2D object that is the identity transform. Unless this transform will be mutated later on, consider using the faster IdentityTransform class instead.
rotate(theta)[source]
Add a rotation (in radians) to this transform in place. Returns self, so this method can easily be chained with more calls to rotate(), rotate_deg(), translate() and scale().
rotate_around(x, y, theta)[source]
Add a rotation (in radians) around the point (x, y) in place. Returns self, so this method can easily be chained with more calls to rotate(), rotate_deg(), translate() and scale().
rotate_deg(degrees)[source]
Add a rotation (in degrees) to this transform in place. Returns self, so this method can easily be chained with more calls to rotate(), rotate_deg(), translate() and scale().
rotate_deg_around(x, y, degrees)[source]
Add a rotation (in degrees) around the point (x, y) in place. Returns self, so this method can easily be chained with more calls to rotate(), rotate_deg(), translate() and scale().
scale(sx, sy=None)[source]
Add a scale in place. If sy is None, the same scale is applied in both the x- and y-directions. Returns self, so this method can easily be chained with more calls to rotate(), rotate_deg(), translate() and scale().
set(other)[source]
Set this transformation from the frozen copy of another Affine2DBase object.
set_matrix(mtx)[source]
Set the underlying transformation matrix from a 3x3 numpy array: a c e
b d f
0 0 1
.
skew(xShear, yShear)[source]
Add a skew in place. xShear and yShear are the shear angles along the x- and y-axes, respectively, in radians. Returns self, so this method can easily be chained with more calls to rotate(), rotate_deg(), translate() and scale().
skew_deg(xShear, yShear)[source]
Add a skew in place. xShear and yShear are the shear angles along the x- and y-axes, respectively, in degrees. Returns self, so this method can easily be chained with more calls to rotate(), rotate_deg(), translate() and scale().
translate(tx, ty)[source]
Add a translation in place. Returns self, so this method can easily be chained with more calls to rotate(), rotate_deg(), translate() and scale(). | |
doc_29677 | Immediately unwinds the callback stack, invoking callbacks in the reverse order of registration. For any context managers and exit callbacks registered, the arguments passed in will indicate that no exception occurred. | |
doc_29678 | Return the password database entry for the given user name. | |
doc_29679 | 'blogs.blog': lambda o: "/blogs/%s/" % o.slug,
'news.story': lambda o: "/stories/%s/%s/" % (o.pub_year, o.slug),
}
The model name used in this setting should be all lowercase, regardless of the case of the actual model class name. ADMINS Default: [] (Empty list) A list of all the people who get code error notifications. When DEBUG=False and AdminEmailHandler is configured in LOGGING (done by default), Django emails these people the details of exceptions raised in the request/response cycle. Each item in the list should be a tuple of (Full name, email address). Example: [('John', 'john@example.com'), ('Mary', 'mary@example.com')]
ALLOWED_HOSTS Default: [] (Empty list) A list of strings representing the host/domain names that this Django site can serve. This is a security measure to prevent HTTP Host header attacks, which are possible even under many seemingly-safe web server configurations. Values in this list can be fully qualified names (e.g. 'www.example.com'), in which case they will be matched against the request’s Host header exactly (case-insensitive, not including port). A value beginning with a period can be used as a subdomain wildcard: '.example.com' will match example.com, www.example.com, and any other subdomain of example.com. A value of '*' will match anything; in this case you are responsible to provide your own validation of the Host header (perhaps in a middleware; if so this middleware must be listed first in MIDDLEWARE). Django also allows the fully qualified domain name (FQDN) of any entries. Some browsers include a trailing dot in the Host header which Django strips when performing host validation. If the Host header (or X-Forwarded-Host if USE_X_FORWARDED_HOST is enabled) does not match any value in this list, the django.http.HttpRequest.get_host() method will raise SuspiciousOperation. When DEBUG is True and ALLOWED_HOSTS is empty, the host is validated against ['.localhost', '127.0.0.1', '[::1]']. ALLOWED_HOSTS is also checked when running tests. This validation only applies via get_host(); if your code accesses the Host header directly from request.META you are bypassing this security protection. APPEND_SLASH Default: True When set to True, if the request URL does not match any of the patterns in the URLconf and it doesn’t end in a slash, an HTTP redirect is issued to the same URL with a slash appended. Note that the redirect may cause any data submitted in a POST request to be lost. The APPEND_SLASH setting is only used if CommonMiddleware is installed (see Middleware). See also PREPEND_WWW. CACHES Default: {
'default': {
'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',
}
}
A dictionary containing the settings for all caches to be used with Django. It is a nested dictionary whose contents maps cache aliases to a dictionary containing the options for an individual cache. The CACHES setting must configure a default cache; any number of additional caches may also be specified. If you are using a cache backend other than the local memory cache, or you need to define multiple caches, other options will be required. The following cache options are available. BACKEND Default: '' (Empty string) The cache backend to use. The built-in cache backends are: 'django.core.cache.backends.db.DatabaseCache' 'django.core.cache.backends.dummy.DummyCache' 'django.core.cache.backends.filebased.FileBasedCache' 'django.core.cache.backends.locmem.LocMemCache' 'django.core.cache.backends.memcached.PyMemcacheCache' 'django.core.cache.backends.memcached.PyLibMCCache' 'django.core.cache.backends.redis.RedisCache' You can use a cache backend that doesn’t ship with Django by setting BACKEND to a fully-qualified path of a cache backend class (i.e. mypackage.backends.whatever.WhateverCache). Changed in Django 3.2: The PyMemcacheCache backend was added. Changed in Django 4.0: The RedisCache backend was added. KEY_FUNCTION A string containing a dotted path to a function (or any callable) that defines how to compose a prefix, version and key into a final cache key. The default implementation is equivalent to the function: def make_key(key, key_prefix, version):
return ':'.join([key_prefix, str(version), key])
You may use any key function you want, as long as it has the same argument signature. See the cache documentation for more information. KEY_PREFIX Default: '' (Empty string) A string that will be automatically included (prepended by default) to all cache keys used by the Django server. See the cache documentation for more information. LOCATION Default: '' (Empty string) The location of the cache to use. This might be the directory for a file system cache, a host and port for a memcache server, or an identifying name for a local memory cache. e.g.: CACHES = {
'default': {
'BACKEND': 'django.core.cache.backends.filebased.FileBasedCache',
'LOCATION': '/var/tmp/django_cache',
}
}
OPTIONS Default: None Extra parameters to pass to the cache backend. Available parameters vary depending on your cache backend. Some information on available parameters can be found in the cache arguments documentation. For more information, consult your backend module’s own documentation. TIMEOUT Default: 300 The number of seconds before a cache entry is considered stale. If the value of this setting is None, cache entries will not expire. A value of 0 causes keys to immediately expire (effectively “don’t cache”). VERSION Default: 1 The default version number for cache keys generated by the Django server. See the cache documentation for more information. CACHE_MIDDLEWARE_ALIAS Default: 'default' The cache connection to use for the cache middleware. CACHE_MIDDLEWARE_KEY_PREFIX Default: '' (Empty string) A string which will be prefixed to the cache keys generated by the cache middleware. This prefix is combined with the KEY_PREFIX setting; it does not replace it. See Django’s cache framework. CACHE_MIDDLEWARE_SECONDS Default: 600 The default number of seconds to cache a page for the cache middleware. See Django’s cache framework. CSRF_COOKIE_AGE Default: 31449600 (approximately 1 year, in seconds) The age of CSRF cookies, in seconds. The reason for setting a long-lived expiration time is to avoid problems in the case of a user closing a browser or bookmarking a page and then loading that page from a browser cache. Without persistent cookies, the form submission would fail in this case. Some browsers (specifically Internet Explorer) can disallow the use of persistent cookies or can have the indexes to the cookie jar corrupted on disk, thereby causing CSRF protection checks to (sometimes intermittently) fail. Change this setting to None to use session-based CSRF cookies, which keep the cookies in-memory instead of on persistent storage. CSRF_COOKIE_DOMAIN Default: None The domain to be used when setting the CSRF cookie. This can be useful for easily allowing cross-subdomain requests to be excluded from the normal cross site request forgery protection. It should be set to a string such as ".example.com" to allow a POST request from a form on one subdomain to be accepted by a view served from another subdomain. Please note that the presence of this setting does not imply that Django’s CSRF protection is safe from cross-subdomain attacks by default - please see the CSRF limitations section. CSRF_COOKIE_HTTPONLY Default: False Whether to use HttpOnly flag on the CSRF cookie. If this is set to True, client-side JavaScript will not be able to access the CSRF cookie. Designating the CSRF cookie as HttpOnly doesn’t offer any practical protection because CSRF is only to protect against cross-domain attacks. If an attacker can read the cookie via JavaScript, they’re already on the same domain as far as the browser knows, so they can do anything they like anyway. (XSS is a much bigger hole than CSRF.) Although the setting offers little practical benefit, it’s sometimes required by security auditors. If you enable this and need to send the value of the CSRF token with an AJAX request, your JavaScript must pull the value from a hidden CSRF token form input instead of from the cookie. See SESSION_COOKIE_HTTPONLY for details on HttpOnly. CSRF_COOKIE_NAME Default: 'csrftoken' The name of the cookie to use for the CSRF authentication token. This can be whatever you want (as long as it’s different from the other cookie names in your application). See Cross Site Request Forgery protection. CSRF_COOKIE_PATH Default: '/' The path set on the CSRF cookie. This should either match the URL path of your Django installation or be a parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths, and each instance will only see its own CSRF cookie. CSRF_COOKIE_SAMESITE Default: 'Lax' The value of the SameSite flag on the CSRF cookie. This flag prevents the cookie from being sent in cross-site requests. See SESSION_COOKIE_SAMESITE for details about SameSite. CSRF_COOKIE_SECURE Default: False Whether to use a secure cookie for the CSRF cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent with an HTTPS connection. CSRF_USE_SESSIONS Default: False Whether to store the CSRF token in the user’s session instead of in a cookie. It requires the use of django.contrib.sessions. Storing the CSRF token in a cookie (Django’s default) is safe, but storing it in the session is common practice in other web frameworks and therefore sometimes demanded by security auditors. Since the default error views require the CSRF token, SessionMiddleware must appear in MIDDLEWARE before any middleware that may raise an exception to trigger an error view (such as PermissionDenied) if you’re using CSRF_USE_SESSIONS. See Middleware ordering. CSRF_FAILURE_VIEW Default: 'django.views.csrf.csrf_failure' A dotted path to the view function to be used when an incoming request is rejected by the CSRF protection. The function should have this signature: def csrf_failure(request, reason=""):
...
where reason is a short message (intended for developers or logging, not for end users) indicating the reason the request was rejected. It should return an HttpResponseForbidden. django.views.csrf.csrf_failure() accepts an additional template_name parameter that defaults to '403_csrf.html'. If a template with that name exists, it will be used to render the page. CSRF_HEADER_NAME Default: 'HTTP_X_CSRFTOKEN' The name of the request header used for CSRF authentication. As with other HTTP headers in request.META, the header name received from the server is normalized by converting all characters to uppercase, replacing any hyphens with underscores, and adding an 'HTTP_' prefix to the name. For example, if your client sends a 'X-XSRF-TOKEN' header, the setting should be 'HTTP_X_XSRF_TOKEN'. CSRF_TRUSTED_ORIGINS Default: [] (Empty list) A list of trusted origins for unsafe requests (e.g. POST). For requests that include the Origin header, Django’s CSRF protection requires that header match the origin present in the Host header. For a secure unsafe request that doesn’t include the Origin header, the request must have a Referer header that matches the origin present in the Host header. These checks prevent, for example, a POST request from subdomain.example.com from succeeding against api.example.com. If you need cross-origin unsafe requests, continuing the example, add 'https://subdomain.example.com' to this list (and/or http://... if requests originate from an insecure page). The setting also supports subdomains, so you could add 'https://*.example.com', for example, to allow access from all subdomains of example.com. Changed in Django 4.0: The values in older versions must only include the hostname (possibly with a leading dot) and not the scheme or an asterisk. Also, Origin header checking isn’t performed in older versions. DATABASES Default: {} (Empty dictionary) A dictionary containing the settings for all databases to be used with Django. It is a nested dictionary whose contents map a database alias to a dictionary containing the options for an individual database. The DATABASES setting must configure a default database; any number of additional databases may also be specified. The simplest possible settings file is for a single-database setup using SQLite. This can be configured using the following: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': 'mydatabase',
}
}
When connecting to other database backends, such as MariaDB, MySQL, Oracle, or PostgreSQL, additional connection parameters will be required. See the ENGINE setting below on how to specify other database types. This example is for PostgreSQL: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': 'mydatabase',
'USER': 'mydatabaseuser',
'PASSWORD': 'mypassword',
'HOST': '127.0.0.1',
'PORT': '5432',
}
}
The following inner options that may be required for more complex configurations are available: ATOMIC_REQUESTS Default: False Set this to True to wrap each view in a transaction on this database. See Tying transactions to HTTP requests. AUTOCOMMIT Default: True Set this to False if you want to disable Django’s transaction management and implement your own. ENGINE Default: '' (Empty string) The database backend to use. The built-in database backends are: 'django.db.backends.postgresql' 'django.db.backends.mysql' 'django.db.backends.sqlite3' 'django.db.backends.oracle' You can use a database backend that doesn’t ship with Django by setting ENGINE to a fully-qualified path (i.e. mypackage.backends.whatever). HOST Default: '' (Empty string) Which host to use when connecting to the database. An empty string means localhost. Not used with SQLite. If this value starts with a forward slash ('/') and you’re using MySQL, MySQL will connect via a Unix socket to the specified socket. For example: "HOST": '/var/run/mysql'
If you’re using MySQL and this value doesn’t start with a forward slash, then this value is assumed to be the host. If you’re using PostgreSQL, by default (empty HOST), the connection to the database is done through UNIX domain sockets (‘local’ lines in pg_hba.conf). If your UNIX domain socket is not in the standard location, use the same value of unix_socket_directory from postgresql.conf. If you want to connect through TCP sockets, set HOST to ‘localhost’ or ‘127.0.0.1’ (‘host’ lines in pg_hba.conf). On Windows, you should always define HOST, as UNIX domain sockets are not available. NAME Default: '' (Empty string) The name of the database to use. For SQLite, it’s the full path to the database file. When specifying the path, always use forward slashes, even on Windows (e.g. C:/homes/user/mysite/sqlite3.db). CONN_MAX_AGE Default: 0 The lifetime of a database connection, as an integer of seconds. Use 0 to close database connections at the end of each request — Django’s historical behavior — and None for unlimited persistent connections. OPTIONS Default: {} (Empty dictionary) Extra parameters to use when connecting to the database. Available parameters vary depending on your database backend. Some information on available parameters can be found in the Database Backends documentation. For more information, consult your backend module’s own documentation. PASSWORD Default: '' (Empty string) The password to use when connecting to the database. Not used with SQLite. PORT Default: '' (Empty string) The port to use when connecting to the database. An empty string means the default port. Not used with SQLite. TIME_ZONE Default: None A string representing the time zone for this database connection or None. This inner option of the DATABASES setting accepts the same values as the general TIME_ZONE setting. When USE_TZ is True and this option is set, reading datetimes from the database returns aware datetimes in this time zone instead of UTC. When USE_TZ is False, it is an error to set this option.
If the database backend doesn’t support time zones (e.g. SQLite, MySQL, Oracle), Django reads and writes datetimes in local time according to this option if it is set and in UTC if it isn’t. Changing the connection time zone changes how datetimes are read from and written to the database. If Django manages the database and you don’t have a strong reason to do otherwise, you should leave this option unset. It’s best to store datetimes in UTC because it avoids ambiguous or nonexistent datetimes during daylight saving time changes. Also, receiving datetimes in UTC keeps datetime arithmetic simple — there’s no need to consider potential offset changes over a DST transition. If you’re connecting to a third-party database that stores datetimes in a local time rather than UTC, then you must set this option to the appropriate time zone. Likewise, if Django manages the database but third-party systems connect to the same database and expect to find datetimes in local time, then you must set this option.
If the database backend supports time zones (e.g. PostgreSQL), the TIME_ZONE option is very rarely needed. It can be changed at any time; the database takes care of converting datetimes to the desired time zone. Setting the time zone of the database connection may be useful for running raw SQL queries involving date/time functions provided by the database, such as date_trunc, because their results depend on the time zone. However, this has a downside: receiving all datetimes in local time makes datetime arithmetic more tricky — you must account for possible offset changes over DST transitions. Consider converting to local time explicitly with AT TIME ZONE in raw SQL queries instead of setting the TIME_ZONE option. DISABLE_SERVER_SIDE_CURSORS Default: False Set this to True if you want to disable the use of server-side cursors with QuerySet.iterator(). Transaction pooling and server-side cursors describes the use case. This is a PostgreSQL-specific setting. USER Default: '' (Empty string) The username to use when connecting to the database. Not used with SQLite. TEST Default: {} (Empty dictionary) A dictionary of settings for test databases; for more details about the creation and use of test databases, see The test database. Here’s an example with a test database configuration: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'USER': 'mydatabaseuser',
'NAME': 'mydatabase',
'TEST': {
'NAME': 'mytestdatabase',
},
},
}
The following keys in the TEST dictionary are available: CHARSET Default: None The character set encoding used to create the test database. The value of this string is passed directly through to the database, so its format is backend-specific. Supported by the PostgreSQL (postgresql) and MySQL (mysql) backends. COLLATION Default: None The collation order to use when creating the test database. This value is passed directly to the backend, so its format is backend-specific. Only supported for the mysql backend (see the MySQL manual for details). DEPENDENCIES Default: ['default'], for all databases other than default, which has no dependencies. The creation-order dependencies of the database. See the documentation on controlling the creation order of test databases for details. MIGRATE Default: True When set to False, migrations won’t run when creating the test database. This is similar to setting None as a value in MIGRATION_MODULES, but for all apps. MIRROR Default: None The alias of the database that this database should mirror during testing. This setting exists to allow for testing of primary/replica (referred to as master/slave by some databases) configurations of multiple databases. See the documentation on testing primary/replica configurations for details. NAME Default: None The name of database to use when running the test suite. If the default value (None) is used with the SQLite database engine, the tests will use a memory resident database. For all other database engines the test database will use the name 'test_' + DATABASE_NAME. See The test database. SERIALIZE Boolean value to control whether or not the default test runner serializes the database into an in-memory JSON string before running tests (used to restore the database state between tests if you don’t have transactions). You can set this to False to speed up creation time if you don’t have any test classes with serialized_rollback=True. Deprecated since version 4.0: This setting is deprecated as it can be inferred from the databases with the serialized_rollback option enabled. TEMPLATE This is a PostgreSQL-specific setting. The name of a template (e.g. 'template0') from which to create the test database. CREATE_DB Default: True This is an Oracle-specific setting. If it is set to False, the test tablespaces won’t be automatically created at the beginning of the tests or dropped at the end. CREATE_USER Default: True This is an Oracle-specific setting. If it is set to False, the test user won’t be automatically created at the beginning of the tests and dropped at the end. USER Default: None This is an Oracle-specific setting. The username to use when connecting to the Oracle database that will be used when running tests. If not provided, Django will use 'test_' + USER. PASSWORD Default: None This is an Oracle-specific setting. The password to use when connecting to the Oracle database that will be used when running tests. If not provided, Django will generate a random password. ORACLE_MANAGED_FILES Default: False This is an Oracle-specific setting. If set to True, Oracle Managed Files (OMF) tablespaces will be used. DATAFILE and DATAFILE_TMP will be ignored. TBLSPACE Default: None This is an Oracle-specific setting. The name of the tablespace that will be used when running tests. If not provided, Django will use 'test_' + USER. TBLSPACE_TMP Default: None This is an Oracle-specific setting. The name of the temporary tablespace that will be used when running tests. If not provided, Django will use 'test_' + USER + '_temp'. DATAFILE Default: None This is an Oracle-specific setting. The name of the datafile to use for the TBLSPACE. If not provided, Django will use TBLSPACE + '.dbf'. DATAFILE_TMP Default: None This is an Oracle-specific setting. The name of the datafile to use for the TBLSPACE_TMP. If not provided, Django will use TBLSPACE_TMP + '.dbf'. DATAFILE_MAXSIZE Default: '500M' This is an Oracle-specific setting. The maximum size that the DATAFILE is allowed to grow to. DATAFILE_TMP_MAXSIZE Default: '500M' This is an Oracle-specific setting. The maximum size that the DATAFILE_TMP is allowed to grow to. DATAFILE_SIZE Default: '50M' This is an Oracle-specific setting. The initial size of the DATAFILE. DATAFILE_TMP_SIZE Default: '50M' This is an Oracle-specific setting. The initial size of the DATAFILE_TMP. DATAFILE_EXTSIZE Default: '25M' This is an Oracle-specific setting. The amount by which the DATAFILE is extended when more space is required. DATAFILE_TMP_EXTSIZE Default: '25M' This is an Oracle-specific setting. The amount by which the DATAFILE_TMP is extended when more space is required. DATA_UPLOAD_MAX_MEMORY_SIZE Default: 2621440 (i.e. 2.5 MB). The maximum size in bytes that a request body may be before a SuspiciousOperation (RequestDataTooBig) is raised. The check is done when accessing request.body or request.POST and is calculated against the total request size excluding any file upload data. You can set this to None to disable the check. Applications that are expected to receive unusually large form posts should tune this setting. The amount of request data is correlated to the amount of memory needed to process the request and populate the GET and POST dictionaries. Large requests could be used as a denial-of-service attack vector if left unchecked. Since web servers don’t typically perform deep request inspection, it’s not possible to perform a similar check at that level. See also FILE_UPLOAD_MAX_MEMORY_SIZE. DATA_UPLOAD_MAX_NUMBER_FIELDS Default: 1000 The maximum number of parameters that may be received via GET or POST before a SuspiciousOperation (TooManyFields) is raised. You can set this to None to disable the check. Applications that are expected to receive an unusually large number of form fields should tune this setting. The number of request parameters is correlated to the amount of time needed to process the request and populate the GET and POST dictionaries. Large requests could be used as a denial-of-service attack vector if left unchecked. Since web servers don’t typically perform deep request inspection, it’s not possible to perform a similar check at that level. DATABASE_ROUTERS Default: [] (Empty list) The list of routers that will be used to determine which database to use when performing a database query. See the documentation on automatic database routing in multi database configurations. DATE_FORMAT Default: 'N j, Y' (e.g. Feb. 4, 2003) The default formatting to use for displaying date fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATETIME_FORMAT, TIME_FORMAT and SHORT_DATE_FORMAT. DATE_INPUT_FORMATS Default: [
'%Y-%m-%d', '%m/%d/%Y', '%m/%d/%y', # '2006-10-25', '10/25/2006', '10/25/06'
'%b %d %Y', '%b %d, %Y', # 'Oct 25 2006', 'Oct 25, 2006'
'%d %b %Y', '%d %b, %Y', # '25 Oct 2006', '25 Oct, 2006'
'%B %d %Y', '%B %d, %Y', # 'October 25 2006', 'October 25, 2006'
'%d %B %Y', '%d %B, %Y', # '25 October 2006', '25 October, 2006'
]
A list of formats that will be accepted when inputting data on a date field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATETIME_INPUT_FORMATS and TIME_INPUT_FORMATS. DATETIME_FORMAT Default: 'N j, Y, P' (e.g. Feb. 4, 2003, 4 p.m.) The default formatting to use for displaying datetime fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATE_FORMAT, TIME_FORMAT and SHORT_DATETIME_FORMAT. DATETIME_INPUT_FORMATS Default: [
'%Y-%m-%d %H:%M:%S', # '2006-10-25 14:30:59'
'%Y-%m-%d %H:%M:%S.%f', # '2006-10-25 14:30:59.000200'
'%Y-%m-%d %H:%M', # '2006-10-25 14:30'
'%m/%d/%Y %H:%M:%S', # '10/25/2006 14:30:59'
'%m/%d/%Y %H:%M:%S.%f', # '10/25/2006 14:30:59.000200'
'%m/%d/%Y %H:%M', # '10/25/2006 14:30'
'%m/%d/%y %H:%M:%S', # '10/25/06 14:30:59'
'%m/%d/%y %H:%M:%S.%f', # '10/25/06 14:30:59.000200'
'%m/%d/%y %H:%M', # '10/25/06 14:30'
]
A list of formats that will be accepted when inputting data on a datetime field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. Date-only formats are not included as datetime fields will automatically try DATE_INPUT_FORMATS in last resort. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATE_INPUT_FORMATS and TIME_INPUT_FORMATS. DEBUG Default: False A boolean that turns on/off debug mode. Never deploy a site into production with DEBUG turned on. One of the main features of debug mode is the display of detailed error pages. If your app raises an exception when DEBUG is True, Django will display a detailed traceback, including a lot of metadata about your environment, such as all the currently defined Django settings (from settings.py). As a security measure, Django will not include settings that might be sensitive, such as SECRET_KEY. Specifically, it will exclude any setting whose name includes any of the following: 'API' 'KEY' 'PASS' 'SECRET' 'SIGNATURE' 'TOKEN' Note that these are partial matches. 'PASS' will also match PASSWORD, just as 'TOKEN' will also match TOKENIZED and so on. Still, note that there are always going to be sections of your debug output that are inappropriate for public consumption. File paths, configuration options and the like all give attackers extra information about your server. It is also important to remember that when running with DEBUG turned on, Django will remember every SQL query it executes. This is useful when you’re debugging, but it’ll rapidly consume memory on a production server. Finally, if DEBUG is False, you also need to properly set the ALLOWED_HOSTS setting. Failing to do so will result in all requests being returned as “Bad Request (400)”. Note The default settings.py file created by django-admin
startproject sets DEBUG = True for convenience. DEBUG_PROPAGATE_EXCEPTIONS Default: False If set to True, Django’s exception handling of view functions (handler500, or the debug view if DEBUG is True) and logging of 500 responses (django.request) is skipped and exceptions propagate upward. This can be useful for some test setups. It shouldn’t be used on a live site unless you want your web server (instead of Django) to generate “Internal Server Error” responses. In that case, make sure your server doesn’t show the stack trace or other sensitive information in the response. DECIMAL_SEPARATOR Default: '.' (Dot) Default decimal separator used when formatting decimal numbers. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also NUMBER_GROUPING, THOUSAND_SEPARATOR and USE_THOUSAND_SEPARATOR. DEFAULT_AUTO_FIELD New in Django 3.2. Default: 'django.db.models.AutoField' Default primary key field type to use for models that don’t have a field with primary_key=True. Migrating auto-created through tables The value of DEFAULT_AUTO_FIELD will be respected when creating new auto-created through tables for many-to-many relationships. Unfortunately, the primary keys of existing auto-created through tables cannot currently be updated by the migrations framework. This means that if you switch the value of DEFAULT_AUTO_FIELD and then generate migrations, the primary keys of the related models will be updated, as will the foreign keys from the through table, but the primary key of the auto-created through table will not be migrated. In order to address this, you should add a RunSQL operation to your migrations to perform the required ALTER TABLE step. You can check the existing table name through sqlmigrate, dbshell, or with the field’s remote_field.through._meta.db_table property. Explicitly defined through models are already handled by the migrations system. Allowing automatic migrations for the primary key of existing auto-created through tables may be implemented at a later date. DEFAULT_CHARSET Default: 'utf-8' Default charset to use for all HttpResponse objects, if a MIME type isn’t manually specified. Used when constructing the Content-Type header. DEFAULT_EXCEPTION_REPORTER Default: 'django.views.debug.ExceptionReporter' Default exception reporter class to be used if none has been assigned to the HttpRequest instance yet. See Custom error reports. DEFAULT_EXCEPTION_REPORTER_FILTER Default: 'django.views.debug.SafeExceptionReporterFilter' Default exception reporter filter class to be used if none has been assigned to the HttpRequest instance yet. See Filtering error reports. DEFAULT_FILE_STORAGE Default: 'django.core.files.storage.FileSystemStorage' Default file storage class to be used for any file-related operations that don’t specify a particular storage system. See Managing files. DEFAULT_FROM_EMAIL Default: 'webmaster@localhost' Default email address to use for various automated correspondence from the site manager(s). This doesn’t include error messages sent to ADMINS and MANAGERS; for that, see SERVER_EMAIL. DEFAULT_INDEX_TABLESPACE Default: '' (Empty string) Default tablespace to use for indexes on fields that don’t specify one, if the backend supports it (see Tablespaces). DEFAULT_TABLESPACE Default: '' (Empty string) Default tablespace to use for models that don’t specify one, if the backend supports it (see Tablespaces). DISALLOWED_USER_AGENTS Default: [] (Empty list) List of compiled regular expression objects representing User-Agent strings that are not allowed to visit any page, systemwide. Use this for bots/crawlers. This is only used if CommonMiddleware is installed (see Middleware). EMAIL_BACKEND Default: 'django.core.mail.backends.smtp.EmailBackend' The backend to use for sending emails. For the list of available backends see Sending email. EMAIL_FILE_PATH Default: Not defined The directory used by the file email backend to store output files. EMAIL_HOST Default: 'localhost' The host to use for sending email. See also EMAIL_PORT. EMAIL_HOST_PASSWORD Default: '' (Empty string) Password to use for the SMTP server defined in EMAIL_HOST. This setting is used in conjunction with EMAIL_HOST_USER when authenticating to the SMTP server. If either of these settings is empty, Django won’t attempt authentication. See also EMAIL_HOST_USER. EMAIL_HOST_USER Default: '' (Empty string) Username to use for the SMTP server defined in EMAIL_HOST. If empty, Django won’t attempt authentication. See also EMAIL_HOST_PASSWORD. EMAIL_PORT Default: 25 Port to use for the SMTP server defined in EMAIL_HOST. EMAIL_SUBJECT_PREFIX Default: '[Django] ' Subject-line prefix for email messages sent with django.core.mail.mail_admins or django.core.mail.mail_managers. You’ll probably want to include the trailing space. EMAIL_USE_LOCALTIME Default: False Whether to send the SMTP Date header of email messages in the local time zone (True) or in UTC (False). EMAIL_USE_TLS Default: False Whether to use a TLS (secure) connection when talking to the SMTP server. This is used for explicit TLS connections, generally on port 587. If you are experiencing hanging connections, see the implicit TLS setting EMAIL_USE_SSL. EMAIL_USE_SSL Default: False Whether to use an implicit TLS (secure) connection when talking to the SMTP server. In most email documentation this type of TLS connection is referred to as SSL. It is generally used on port 465. If you are experiencing problems, see the explicit TLS setting EMAIL_USE_TLS. Note that EMAIL_USE_TLS/EMAIL_USE_SSL are mutually exclusive, so only set one of those settings to True. EMAIL_SSL_CERTFILE Default: None If EMAIL_USE_SSL or EMAIL_USE_TLS is True, you can optionally specify the path to a PEM-formatted certificate chain file to use for the SSL connection. EMAIL_SSL_KEYFILE Default: None If EMAIL_USE_SSL or EMAIL_USE_TLS is True, you can optionally specify the path to a PEM-formatted private key file to use for the SSL connection. Note that setting EMAIL_SSL_CERTFILE and EMAIL_SSL_KEYFILE doesn’t result in any certificate checking. They’re passed to the underlying SSL connection. Please refer to the documentation of Python’s ssl.wrap_socket() function for details on how the certificate chain file and private key file are handled. EMAIL_TIMEOUT Default: None Specifies a timeout in seconds for blocking operations like the connection attempt. FILE_UPLOAD_HANDLERS Default: [
'django.core.files.uploadhandler.MemoryFileUploadHandler',
'django.core.files.uploadhandler.TemporaryFileUploadHandler',
]
A list of handlers to use for uploading. Changing this setting allows complete customization – even replacement – of Django’s upload process. See Managing files for details. FILE_UPLOAD_MAX_MEMORY_SIZE Default: 2621440 (i.e. 2.5 MB). The maximum size (in bytes) that an upload will be before it gets streamed to the file system. See Managing files for details. See also DATA_UPLOAD_MAX_MEMORY_SIZE. FILE_UPLOAD_DIRECTORY_PERMISSIONS Default: None The numeric mode to apply to directories created in the process of uploading files. This setting also determines the default permissions for collected static directories when using the collectstatic management command. See collectstatic for details on overriding it. This value mirrors the functionality and caveats of the FILE_UPLOAD_PERMISSIONS setting. FILE_UPLOAD_PERMISSIONS Default: 0o644 The numeric mode (i.e. 0o644) to set newly uploaded files to. For more information about what these modes mean, see the documentation for os.chmod(). If None, you’ll get operating-system dependent behavior. On most platforms, temporary files will have a mode of 0o600, and files saved from memory will be saved using the system’s standard umask. For security reasons, these permissions aren’t applied to the temporary files that are stored in FILE_UPLOAD_TEMP_DIR. This setting also determines the default permissions for collected static files when using the collectstatic management command. See collectstatic for details on overriding it. Warning Always prefix the mode with 0o . If you’re not familiar with file modes, please note that the 0o prefix is very important: it indicates an octal number, which is the way that modes must be specified. If you try to use 644, you’ll get totally incorrect behavior. FILE_UPLOAD_TEMP_DIR Default: None The directory to store data to (typically files larger than FILE_UPLOAD_MAX_MEMORY_SIZE) temporarily while uploading files. If None, Django will use the standard temporary directory for the operating system. For example, this will default to /tmp on *nix-style operating systems. See Managing files for details. FIRST_DAY_OF_WEEK Default: 0 (Sunday) A number representing the first day of the week. This is especially useful when displaying a calendar. This value is only used when not using format internationalization, or when a format cannot be found for the current locale. The value must be an integer from 0 to 6, where 0 means Sunday, 1 means Monday and so on. FIXTURE_DIRS Default: [] (Empty list) List of directories searched for fixture files, in addition to the fixtures directory of each application, in search order. Note that these paths should use Unix-style forward slashes, even on Windows. See Providing data with fixtures and Fixture loading. FORCE_SCRIPT_NAME Default: None If not None, this will be used as the value of the SCRIPT_NAME environment variable in any HTTP request. This setting can be used to override the server-provided value of SCRIPT_NAME, which may be a rewritten version of the preferred value or not supplied at all. It is also used by django.setup() to set the URL resolver script prefix outside of the request/response cycle (e.g. in management commands and standalone scripts) to generate correct URLs when SCRIPT_NAME is not /. FORM_RENDERER Default: 'django.forms.renderers.DjangoTemplates' The class that renders forms and form widgets. It must implement the low-level render API. Included form renderers are:
'django.forms.renderers.DjangoTemplates'
'django.forms.renderers.Jinja2'
FORMAT_MODULE_PATH Default: None A full Python path to a Python package that contains custom format definitions for project locales. If not None, Django will check for a formats.py file, under the directory named as the current locale, and will use the formats defined in this file. For example, if FORMAT_MODULE_PATH is set to mysite.formats, and current language is en (English), Django will expect a directory tree like: mysite/
formats/
__init__.py
en/
__init__.py
formats.py
You can also set this setting to a list of Python paths, for example: FORMAT_MODULE_PATH = [
'mysite.formats',
'some_app.formats',
]
When Django searches for a certain format, it will go through all given Python paths until it finds a module that actually defines the given format. This means that formats defined in packages farther up in the list will take precedence over the same formats in packages farther down. Available formats are: DATE_FORMAT DATE_INPUT_FORMATS
DATETIME_FORMAT, DATETIME_INPUT_FORMATS DECIMAL_SEPARATOR FIRST_DAY_OF_WEEK MONTH_DAY_FORMAT NUMBER_GROUPING SHORT_DATE_FORMAT SHORT_DATETIME_FORMAT THOUSAND_SEPARATOR TIME_FORMAT TIME_INPUT_FORMATS YEAR_MONTH_FORMAT IGNORABLE_404_URLS Default: [] (Empty list) List of compiled regular expression objects describing URLs that should be ignored when reporting HTTP 404 errors via email (see How to manage error reporting). Regular expressions are matched against request's full paths (including query string, if any). Use this if your site does not provide a commonly requested file such as favicon.ico or robots.txt. This is only used if BrokenLinkEmailsMiddleware is enabled (see Middleware). INSTALLED_APPS Default: [] (Empty list) A list of strings designating all applications that are enabled in this Django installation. Each string should be a dotted Python path to: an application configuration class (preferred), or a package containing an application. Learn more about application configurations. Use the application registry for introspection Your code should never access INSTALLED_APPS directly. Use django.apps.apps instead. Application names and labels must be unique in INSTALLED_APPS Application names — the dotted Python path to the application package — must be unique. There is no way to include the same application twice, short of duplicating its code under another name. Application labels — by default the final part of the name — must be unique too. For example, you can’t include both django.contrib.auth and myproject.auth. However, you can relabel an application with a custom configuration that defines a different label. These rules apply regardless of whether INSTALLED_APPS references application configuration classes or application packages. When several applications provide different versions of the same resource (template, static file, management command, translation), the application listed first in INSTALLED_APPS has precedence. INTERNAL_IPS Default: [] (Empty list) A list of IP addresses, as strings, that: Allow the debug() context processor to add some variables to the template context. Can use the admindocs bookmarklets even if not logged in as a staff user. Are marked as “internal” (as opposed to “EXTERNAL”) in AdminEmailHandler emails. LANGUAGE_CODE Default: 'en-us' A string representing the language code for this installation. This should be in standard language ID format. For example, U.S. English is "en-us". See also the list of language identifiers and Internationalization and localization. USE_I18N must be active for this setting to have any effect. It serves two purposes: If the locale middleware isn’t in use, it decides which translation is served to all users. If the locale middleware is active, it provides a fallback language in case the user’s preferred language can’t be determined or is not supported by the website. It also provides the fallback translation when a translation for a given literal doesn’t exist for the user’s preferred language. See How Django discovers language preference for more details. LANGUAGE_COOKIE_AGE Default: None (expires at browser close) The age of the language cookie, in seconds. LANGUAGE_COOKIE_DOMAIN Default: None The domain to use for the language cookie. Set this to a string such as "example.com" for cross-domain cookies, or use None for a standard domain cookie. Be cautious when updating this setting on a production site. If you update this setting to enable cross-domain cookies on a site that previously used standard domain cookies, existing user cookies that have the old domain will not be updated. This will result in site users being unable to switch the language as long as these cookies persist. The only safe and reliable option to perform the switch is to change the language cookie name permanently (via the LANGUAGE_COOKIE_NAME setting) and to add a middleware that copies the value from the old cookie to a new one and then deletes the old one. LANGUAGE_COOKIE_HTTPONLY Default: False Whether to use HttpOnly flag on the language cookie. If this is set to True, client-side JavaScript will not be able to access the language cookie. See SESSION_COOKIE_HTTPONLY for details on HttpOnly. LANGUAGE_COOKIE_NAME Default: 'django_language' The name of the cookie to use for the language cookie. This can be whatever you want (as long as it’s different from the other cookie names in your application). See Internationalization and localization. LANGUAGE_COOKIE_PATH Default: '/' The path set on the language cookie. This should either match the URL path of your Django installation or be a parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths and each instance will only see its own language cookie. Be cautious when updating this setting on a production site. If you update this setting to use a deeper path than it previously used, existing user cookies that have the old path will not be updated. This will result in site users being unable to switch the language as long as these cookies persist. The only safe and reliable option to perform the switch is to change the language cookie name permanently (via the LANGUAGE_COOKIE_NAME setting), and to add a middleware that copies the value from the old cookie to a new one and then deletes the one. LANGUAGE_COOKIE_SAMESITE Default: None The value of the SameSite flag on the language cookie. This flag prevents the cookie from being sent in cross-site requests. See SESSION_COOKIE_SAMESITE for details about SameSite. LANGUAGE_COOKIE_SECURE Default: False Whether to use a secure cookie for the language cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent under an HTTPS connection. LANGUAGES Default: A list of all available languages. This list is continually growing and including a copy here would inevitably become rapidly out of date. You can see the current list of translated languages by looking in django/conf/global_settings.py. The list is a list of two-tuples in the format (language code, language name) – for example, ('ja', 'Japanese'). This specifies which languages are available for language selection. See Internationalization and localization. Generally, the default value should suffice. Only set this setting if you want to restrict language selection to a subset of the Django-provided languages. If you define a custom LANGUAGES setting, you can mark the language names as translation strings using the gettext_lazy() function. Here’s a sample settings file: from django.utils.translation import gettext_lazy as _
LANGUAGES = [
('de', _('German')),
('en', _('English')),
]
LANGUAGES_BIDI Default: A list of all language codes that are written right-to-left. You can see the current list of these languages by looking in django/conf/global_settings.py. The list contains language codes for languages that are written right-to-left. Generally, the default value should suffice. Only set this setting if you want to restrict language selection to a subset of the Django-provided languages. If you define a custom LANGUAGES setting, the list of bidirectional languages may contain language codes which are not enabled on a given site. LOCALE_PATHS Default: [] (Empty list) A list of directories where Django looks for translation files. See How Django discovers translations. Example: LOCALE_PATHS = [
'/home/www/project/common_files/locale',
'/var/local/translations/locale',
]
Django will look within each of these paths for the <locale_code>/LC_MESSAGES directories containing the actual translation files. LOGGING Default: A logging configuration dictionary. A data structure containing configuration information. The contents of this data structure will be passed as the argument to the configuration method described in LOGGING_CONFIG. Among other things, the default logging configuration passes HTTP 500 server errors to an email log handler when DEBUG is False. See also Configuring logging. You can see the default logging configuration by looking in django/utils/log.py. LOGGING_CONFIG Default: 'logging.config.dictConfig' A path to a callable that will be used to configure logging in the Django project. Points at an instance of Python’s dictConfig configuration method by default. If you set LOGGING_CONFIG to None, the logging configuration process will be skipped. MANAGERS Default: [] (Empty list) A list in the same format as ADMINS that specifies who should get broken link notifications when BrokenLinkEmailsMiddleware is enabled. MEDIA_ROOT Default: '' (Empty string) Absolute filesystem path to the directory that will hold user-uploaded files. Example: "/var/www/example.com/media/" See also MEDIA_URL. Warning MEDIA_ROOT and STATIC_ROOT must have different values. Before STATIC_ROOT was introduced, it was common to rely or fallback on MEDIA_ROOT to also serve static files; however, since this can have serious security implications, there is a validation check to prevent it. MEDIA_URL Default: '' (Empty string) URL that handles the media served from MEDIA_ROOT, used for managing stored files. It must end in a slash if set to a non-empty value. You will need to configure these files to be served in both development and production environments. If you want to use {{ MEDIA_URL }} in your templates, add 'django.template.context_processors.media' in the 'context_processors' option of TEMPLATES. Example: "http://media.example.com/" Warning There are security risks if you are accepting uploaded content from untrusted users! See the security guide’s topic on User-uploaded content for mitigation details. Warning MEDIA_URL and STATIC_URL must have different values. See MEDIA_ROOT for more details. Note If MEDIA_URL is a relative path, then it will be prefixed by the server-provided value of SCRIPT_NAME (or / if not set). This makes it easier to serve a Django application in a subpath without adding an extra configuration to the settings. MIDDLEWARE Default: None A list of middleware to use. See Middleware. MIGRATION_MODULES Default: {} (Empty dictionary) A dictionary specifying the package where migration modules can be found on a per-app basis. The default value of this setting is an empty dictionary, but the default package name for migration modules is migrations. Example: {'blog': 'blog.db_migrations'}
In this case, migrations pertaining to the blog app will be contained in the blog.db_migrations package. If you provide the app_label argument, makemigrations will automatically create the package if it doesn’t already exist. When you supply None as a value for an app, Django will consider the app as an app without migrations regardless of an existing migrations submodule. This can be used, for example, in a test settings file to skip migrations while testing (tables will still be created for the apps’ models). To disable migrations for all apps during tests, you can set the MIGRATE to False instead. If MIGRATION_MODULES is used in your general project settings, remember to use the migrate --run-syncdb option if you want to create tables for the app. MONTH_DAY_FORMAT Default: 'F j' The default formatting to use for date fields on Django admin change-list pages – and, possibly, by other parts of the system – in cases when only the month and day are displayed. For example, when a Django admin change-list page is being filtered by a date drilldown, the header for a given day displays the day and month. Different locales have different formats. For example, U.S. English would say “January 1,” whereas Spanish might say “1 Enero.” Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT, DATETIME_FORMAT, TIME_FORMAT and YEAR_MONTH_FORMAT. NUMBER_GROUPING Default: 0 Number of digits grouped together on the integer part of a number. Common use is to display a thousand separator. If this setting is 0, then no grouping will be applied to the number. If this setting is greater than 0, then THOUSAND_SEPARATOR will be used as the separator between those groups. Some locales use non-uniform digit grouping, e.g. 10,00,00,000 in en_IN. For this case, you can provide a sequence with the number of digit group sizes to be applied. The first number defines the size of the group preceding the decimal delimiter, and each number that follows defines the size of preceding groups. If the sequence is terminated with -1, no further grouping is performed. If the sequence terminates with a 0, the last group size is used for the remainder of the number. Example tuple for en_IN: NUMBER_GROUPING = (3, 2, 0)
Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also DECIMAL_SEPARATOR, THOUSAND_SEPARATOR and USE_THOUSAND_SEPARATOR. PREPEND_WWW Default: False Whether to prepend the “www.” subdomain to URLs that don’t have it. This is only used if CommonMiddleware is installed (see Middleware). See also APPEND_SLASH. ROOT_URLCONF Default: Not defined A string representing the full Python import path to your root URLconf, for example "mydjangoapps.urls". Can be overridden on a per-request basis by setting the attribute urlconf on the incoming HttpRequest object. See How Django processes a request for details. SECRET_KEY Default: '' (Empty string) A secret key for a particular Django installation. This is used to provide cryptographic signing, and should be set to a unique, unpredictable value. django-admin startproject automatically adds a randomly-generated SECRET_KEY to each new project. Uses of the key shouldn’t assume that it’s text or bytes. Every use should go through force_str() or force_bytes() to convert it to the desired type. Django will refuse to start if SECRET_KEY is not set. Warning Keep this value secret. Running Django with a known SECRET_KEY defeats many of Django’s security protections, and can lead to privilege escalation and remote code execution vulnerabilities. The secret key is used for: All sessions if you are using any other session backend than django.contrib.sessions.backends.cache, or are using the default get_session_auth_hash(). All messages if you are using CookieStorage or FallbackStorage. All PasswordResetView tokens. Any usage of cryptographic signing, unless a different key is provided. If you rotate your secret key, all of the above will be invalidated. Secret keys are not used for passwords of users and key rotation will not affect them. Note The default settings.py file created by django-admin
startproject creates a unique SECRET_KEY for convenience. SECURE_CONTENT_TYPE_NOSNIFF Default: True If True, the SecurityMiddleware sets the X-Content-Type-Options: nosniff header on all responses that do not already have it. SECURE_CROSS_ORIGIN_OPENER_POLICY New in Django 4.0. Default: 'same-origin' Unless set to None, the SecurityMiddleware sets the Cross-Origin Opener Policy header on all responses that do not already have it to the value provided. SECURE_HSTS_INCLUDE_SUBDOMAINS Default: False If True, the SecurityMiddleware adds the includeSubDomains directive to the HTTP Strict Transport Security header. It has no effect unless SECURE_HSTS_SECONDS is set to a non-zero value. Warning Setting this incorrectly can irreversibly (for the value of SECURE_HSTS_SECONDS) break your site. Read the HTTP Strict Transport Security documentation first. SECURE_HSTS_PRELOAD Default: False If True, the SecurityMiddleware adds the preload directive to the HTTP Strict Transport Security header. It has no effect unless SECURE_HSTS_SECONDS is set to a non-zero value. SECURE_HSTS_SECONDS Default: 0 If set to a non-zero integer value, the SecurityMiddleware sets the HTTP Strict Transport Security header on all responses that do not already have it. Warning Setting this incorrectly can irreversibly (for some time) break your site. Read the HTTP Strict Transport Security documentation first. SECURE_PROXY_SSL_HEADER Default: None A tuple representing an HTTP header/value combination that signifies a request is secure. This controls the behavior of the request object’s is_secure() method. By default, is_secure() determines if a request is secure by confirming that a requested URL uses https://. This method is important for Django’s CSRF protection, and it may be used by your own code or third-party apps. If your Django app is behind a proxy, though, the proxy may be “swallowing” whether the original request uses HTTPS or not. If there is a non-HTTPS connection between the proxy and Django then is_secure() would always return False – even for requests that were made via HTTPS by the end user. In contrast, if there is an HTTPS connection between the proxy and Django then is_secure() would always return True – even for requests that were made originally via HTTP. In this situation, configure your proxy to set a custom HTTP header that tells Django whether the request came in via HTTPS, and set SECURE_PROXY_SSL_HEADER so that Django knows what header to look for. Set a tuple with two elements – the name of the header to look for and the required value. For example: SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')
This tells Django to trust the X-Forwarded-Proto header that comes from our proxy, and any time its value is 'https', then the request is guaranteed to be secure (i.e., it originally came in via HTTPS). You should only set this setting if you control your proxy or have some other guarantee that it sets/strips this header appropriately. Note that the header needs to be in the format as used by request.META – all caps and likely starting with HTTP_. (Remember, Django automatically adds 'HTTP_' to the start of x-header names before making the header available in request.META.) Warning Modifying this setting can compromise your site’s security. Ensure you fully understand your setup before changing it. Make sure ALL of the following are true before setting this (assuming the values from the example above): Your Django app is behind a proxy. Your proxy strips the X-Forwarded-Proto header from all incoming requests. In other words, if end users include that header in their requests, the proxy will discard it. Your proxy sets the X-Forwarded-Proto header and sends it to Django, but only for requests that originally come in via HTTPS. If any of those are not true, you should keep this setting set to None and find another way of determining HTTPS, perhaps via custom middleware. SECURE_REDIRECT_EXEMPT Default: [] (Empty list) If a URL path matches a regular expression in this list, the request will not be redirected to HTTPS. The SecurityMiddleware strips leading slashes from URL paths, so patterns shouldn’t include them, e.g. SECURE_REDIRECT_EXEMPT = [r'^no-ssl/$', …]. If SECURE_SSL_REDIRECT is False, this setting has no effect. SECURE_REFERRER_POLICY Default: 'same-origin' If configured, the SecurityMiddleware sets the Referrer Policy header on all responses that do not already have it to the value provided. SECURE_SSL_HOST Default: None If a string (e.g. secure.example.com), all SSL redirects will be directed to this host rather than the originally-requested host (e.g. www.example.com). If SECURE_SSL_REDIRECT is False, this setting has no effect. SECURE_SSL_REDIRECT Default: False If True, the SecurityMiddleware redirects all non-HTTPS requests to HTTPS (except for those URLs matching a regular expression listed in SECURE_REDIRECT_EXEMPT). Note If turning this to True causes infinite redirects, it probably means your site is running behind a proxy and can’t tell which requests are secure and which are not. Your proxy likely sets a header to indicate secure requests; you can correct the problem by finding out what that header is and configuring the SECURE_PROXY_SSL_HEADER setting accordingly. SERIALIZATION_MODULES Default: Not defined A dictionary of modules containing serializer definitions (provided as strings), keyed by a string identifier for that serialization type. For example, to define a YAML serializer, use: SERIALIZATION_MODULES = {'yaml': 'path.to.yaml_serializer'}
SERVER_EMAIL Default: 'root@localhost' The email address that error messages come from, such as those sent to ADMINS and MANAGERS. Why are my emails sent from a different address? This address is used only for error messages. It is not the address that regular email messages sent with send_mail() come from; for that, see DEFAULT_FROM_EMAIL. SHORT_DATE_FORMAT Default: 'm/d/Y' (e.g. 12/31/2003) An available formatting that can be used for displaying date fields on templates. Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT and SHORT_DATETIME_FORMAT. SHORT_DATETIME_FORMAT Default: 'm/d/Y P' (e.g. 12/31/2003 4 p.m.) An available formatting that can be used for displaying datetime fields on templates. Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT and SHORT_DATE_FORMAT. SIGNING_BACKEND Default: 'django.core.signing.TimestampSigner' The backend used for signing cookies and other data. See also the Cryptographic signing documentation. SILENCED_SYSTEM_CHECKS Default: [] (Empty list) A list of identifiers of messages generated by the system check framework (i.e. ["models.W001"]) that you wish to permanently acknowledge and ignore. Silenced checks will not be output to the console. See also the System check framework documentation. TEMPLATES Default: [] (Empty list) A list containing the settings for all template engines to be used with Django. Each item of the list is a dictionary containing the options for an individual engine. Here’s a setup that tells the Django template engine to load templates from the templates subdirectory inside each installed application: TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'APP_DIRS': True,
},
]
The following options are available for all backends. BACKEND Default: Not defined The template backend to use. The built-in template backends are: 'django.template.backends.django.DjangoTemplates' 'django.template.backends.jinja2.Jinja2' You can use a template backend that doesn’t ship with Django by setting BACKEND to a fully-qualified path (i.e. 'mypackage.whatever.Backend'). NAME Default: see below The alias for this particular template engine. It’s an identifier that allows selecting an engine for rendering. Aliases must be unique across all configured template engines. It defaults to the name of the module defining the engine class, i.e. the next to last piece of BACKEND, when it isn’t provided. For example if the backend is 'mypackage.whatever.Backend' then its default name is 'whatever'. DIRS Default: [] (Empty list) Directories where the engine should look for template source files, in search order. APP_DIRS Default: False Whether the engine should look for template source files inside installed applications. Note The default settings.py file created by django-admin
startproject sets 'APP_DIRS': True. OPTIONS Default: {} (Empty dict) Extra parameters to pass to the template backend. Available parameters vary depending on the template backend. See DjangoTemplates and Jinja2 for the options of the built-in backends. TEST_RUNNER Default: 'django.test.runner.DiscoverRunner' The name of the class to use for starting the test suite. See Using different testing frameworks. TEST_NON_SERIALIZED_APPS Default: [] (Empty list) In order to restore the database state between tests for TransactionTestCases and database backends without transactions, Django will serialize the contents of all apps when it starts the test run so it can then reload from that copy before running tests that need it. This slows down the startup time of the test runner; if you have apps that you know don’t need this feature, you can add their full names in here (e.g. 'django.contrib.contenttypes') to exclude them from this serialization process. THOUSAND_SEPARATOR Default: ',' (Comma) Default thousand separator used when formatting numbers. This setting is used only when USE_THOUSAND_SEPARATOR is True and NUMBER_GROUPING is greater than 0. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also NUMBER_GROUPING, DECIMAL_SEPARATOR and USE_THOUSAND_SEPARATOR. TIME_FORMAT Default: 'P' (e.g. 4 p.m.) The default formatting to use for displaying time fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATE_FORMAT and DATETIME_FORMAT. TIME_INPUT_FORMATS Default: [
'%H:%M:%S', # '14:30:59'
'%H:%M:%S.%f', # '14:30:59.000200'
'%H:%M', # '14:30'
]
A list of formats that will be accepted when inputting data on a time field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATE_INPUT_FORMATS and DATETIME_INPUT_FORMATS. TIME_ZONE Default: 'America/Chicago' A string representing the time zone for this installation. See the list of time zones. Note Since Django was first released with the TIME_ZONE set to 'America/Chicago', the global setting (used if nothing is defined in your project’s settings.py) remains 'America/Chicago' for backwards compatibility. New project templates default to 'UTC'. Note that this isn’t necessarily the time zone of the server. For example, one server may serve multiple Django-powered sites, each with a separate time zone setting. When USE_TZ is False, this is the time zone in which Django will store all datetimes. When USE_TZ is True, this is the default time zone that Django will use to display datetimes in templates and to interpret datetimes entered in forms. On Unix environments (where time.tzset() is implemented), Django sets the os.environ['TZ'] variable to the time zone you specify in the TIME_ZONE setting. Thus, all your views and models will automatically operate in this time zone. However, Django won’t set the TZ environment variable if you’re using the manual configuration option as described in manually configuring settings. If Django doesn’t set the TZ environment variable, it’s up to you to ensure your processes are running in the correct environment. Note Django cannot reliably use alternate time zones in a Windows environment. If you’re running Django on Windows, TIME_ZONE must be set to match the system time zone. USE_DEPRECATED_PYTZ New in Django 4.0. Default: False A boolean that specifies whether to use pytz, rather than zoneinfo, as the default time zone implementation. Deprecated since version 4.0: This transitional setting is deprecated. Support for using pytz will be removed in Django 5.0. USE_I18N Default: True A boolean that specifies whether Django’s translation system should be enabled. This provides a way to turn it off, for performance. If this is set to False, Django will make some optimizations so as not to load the translation machinery. See also LANGUAGE_CODE, USE_L10N and USE_TZ. Note The default settings.py file created by django-admin
startproject includes USE_I18N = True for convenience. USE_L10N Default: True A boolean that specifies if localized formatting of data will be enabled by default or not. If this is set to True, e.g. Django will display numbers and dates using the format of the current locale. See also LANGUAGE_CODE, USE_I18N and USE_TZ. Changed in Django 4.0: In older versions, the default value is False. Deprecated since version 4.0: This setting is deprecated. Starting with Django 5.0, localized formatting of data will always be enabled. For example Django will display numbers and dates using the format of the current locale. USE_THOUSAND_SEPARATOR Default: False A boolean that specifies whether to display numbers using a thousand separator. When set to True and USE_L10N is also True, Django will format numbers using the NUMBER_GROUPING and THOUSAND_SEPARATOR settings. These settings may also be dictated by the locale, which takes precedence. See also DECIMAL_SEPARATOR, NUMBER_GROUPING and THOUSAND_SEPARATOR. USE_TZ Default: False Note In Django 5.0, the default value will change from False to True. A boolean that specifies if datetimes will be timezone-aware by default or not. If this is set to True, Django will use timezone-aware datetimes internally. When USE_TZ is False, Django will use naive datetimes in local time, except when parsing ISO 8601 formatted strings, where timezone information will always be retained if present. See also TIME_ZONE, USE_I18N and USE_L10N. Note The default settings.py file created by django-admin startproject includes USE_TZ = True for convenience. USE_X_FORWARDED_HOST Default: False A boolean that specifies whether to use the X-Forwarded-Host header in preference to the Host header. This should only be enabled if a proxy which sets this header is in use. This setting takes priority over USE_X_FORWARDED_PORT. Per RFC 7239#section-5.3, the X-Forwarded-Host header can include the port number, in which case you shouldn’t use USE_X_FORWARDED_PORT. USE_X_FORWARDED_PORT Default: False A boolean that specifies whether to use the X-Forwarded-Port header in preference to the SERVER_PORT META variable. This should only be enabled if a proxy which sets this header is in use. USE_X_FORWARDED_HOST takes priority over this setting. WSGI_APPLICATION Default: None The full Python path of the WSGI application object that Django’s built-in servers (e.g. runserver) will use. The django-admin
startproject management command will create a standard wsgi.py file with an application callable in it, and point this setting to that application. If not set, the return value of django.core.wsgi.get_wsgi_application() will be used. In this case, the behavior of runserver will be identical to previous Django versions. YEAR_MONTH_FORMAT Default: 'F Y' The default formatting to use for date fields on Django admin change-list pages – and, possibly, by other parts of the system – in cases when only the year and month are displayed. For example, when a Django admin change-list page is being filtered by a date drilldown, the header for a given month displays the month and the year. Different locales have different formats. For example, U.S. English would say “January 2006,” whereas another locale might say “2006/January.” Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT, DATETIME_FORMAT, TIME_FORMAT and MONTH_DAY_FORMAT. X_FRAME_OPTIONS Default: 'DENY' The default value for the X-Frame-Options header used by XFrameOptionsMiddleware. See the clickjacking protection documentation. Auth Settings for django.contrib.auth. AUTHENTICATION_BACKENDS Default: ['django.contrib.auth.backends.ModelBackend'] A list of authentication backend classes (as strings) to use when attempting to authenticate a user. See the authentication backends documentation for details. AUTH_USER_MODEL Default: 'auth.User' The model to use to represent a User. See Substituting a custom User model. Warning You cannot change the AUTH_USER_MODEL setting during the lifetime of a project (i.e. once you have made and migrated models that depend on it) without serious effort. It is intended to be set at the project start, and the model it refers to must be available in the first migration of the app that it lives in. See Substituting a custom User model for more details. LOGIN_REDIRECT_URL Default: '/accounts/profile/' The URL or named URL pattern where requests are redirected after login when the LoginView doesn’t get a next GET parameter. LOGIN_URL Default: '/accounts/login/' The URL or named URL pattern where requests are redirected for login when using the login_required() decorator, LoginRequiredMixin, or AccessMixin. LOGOUT_REDIRECT_URL Default: None The URL or named URL pattern where requests are redirected after logout if LogoutView doesn’t have a next_page attribute. If None, no redirect will be performed and the logout view will be rendered. PASSWORD_RESET_TIMEOUT Default: 259200 (3 days, in seconds) The number of seconds a password reset link is valid for. Used by the PasswordResetConfirmView. Note Reducing the value of this timeout doesn’t make any difference to the ability of an attacker to brute-force a password reset token. Tokens are designed to be safe from brute-forcing without any timeout. This timeout exists to protect against some unlikely attack scenarios, such as someone gaining access to email archives that may contain old, unused password reset tokens. PASSWORD_HASHERS See How Django stores passwords. Default: [
'django.contrib.auth.hashers.PBKDF2PasswordHasher',
'django.contrib.auth.hashers.PBKDF2SHA1PasswordHasher',
'django.contrib.auth.hashers.Argon2PasswordHasher',
'django.contrib.auth.hashers.BCryptSHA256PasswordHasher',
]
AUTH_PASSWORD_VALIDATORS Default: [] (Empty list) The list of validators that are used to check the strength of user’s passwords. See Password validation for more details. By default, no validation is performed and all passwords are accepted. Messages Settings for django.contrib.messages. MESSAGE_LEVEL Default: messages.INFO Sets the minimum message level that will be recorded by the messages framework. See message levels for more details. Important If you override MESSAGE_LEVEL in your settings file and rely on any of the built-in constants, you must import the constants module directly to avoid the potential for circular imports, e.g.: from django.contrib.messages import constants as message_constants
MESSAGE_LEVEL = message_constants.DEBUG
If desired, you may specify the numeric values for the constants directly according to the values in the above constants table. MESSAGE_STORAGE Default: 'django.contrib.messages.storage.fallback.FallbackStorage' Controls where Django stores message data. Valid values are: 'django.contrib.messages.storage.fallback.FallbackStorage' 'django.contrib.messages.storage.session.SessionStorage' 'django.contrib.messages.storage.cookie.CookieStorage' See message storage backends for more details. The backends that use cookies – CookieStorage and FallbackStorage – use the value of SESSION_COOKIE_DOMAIN, SESSION_COOKIE_SECURE and SESSION_COOKIE_HTTPONLY when setting their cookies. MESSAGE_TAGS Default: {
messages.DEBUG: 'debug',
messages.INFO: 'info',
messages.SUCCESS: 'success',
messages.WARNING: 'warning',
messages.ERROR: 'error',
}
This sets the mapping of message level to message tag, which is typically rendered as a CSS class in HTML. If you specify a value, it will extend the default. This means you only have to specify those values which you need to override. See Displaying messages above for more details. Important If you override MESSAGE_TAGS in your settings file and rely on any of the built-in constants, you must import the constants module directly to avoid the potential for circular imports, e.g.: from django.contrib.messages import constants as message_constants
MESSAGE_TAGS = {message_constants.INFO: ''}
If desired, you may specify the numeric values for the constants directly according to the values in the above constants table. Sessions Settings for django.contrib.sessions. SESSION_CACHE_ALIAS Default: 'default' If you’re using cache-based session storage, this selects the cache to use. SESSION_COOKIE_AGE Default: 1209600 (2 weeks, in seconds) The age of session cookies, in seconds. SESSION_COOKIE_DOMAIN Default: None The domain to use for session cookies. Set this to a string such as "example.com" for cross-domain cookies, or use None for a standard domain cookie. To use cross-domain cookies with CSRF_USE_SESSIONS, you must include a leading dot (e.g. ".example.com") to accommodate the CSRF middleware’s referer checking. Be cautious when updating this setting on a production site. If you update this setting to enable cross-domain cookies on a site that previously used standard domain cookies, existing user cookies will be set to the old domain. This may result in them being unable to log in as long as these cookies persist. This setting also affects cookies set by django.contrib.messages. SESSION_COOKIE_HTTPONLY Default: True Whether to use HttpOnly flag on the session cookie. If this is set to True, client-side JavaScript will not be able to access the session cookie. HttpOnly is a flag included in a Set-Cookie HTTP response header. It’s part of the RFC 6265#section-4.1.2.6 standard for cookies and can be a useful way to mitigate the risk of a client-side script accessing the protected cookie data. This makes it less trivial for an attacker to escalate a cross-site scripting vulnerability into full hijacking of a user’s session. There aren’t many good reasons for turning this off. Your code shouldn’t read session cookies from JavaScript. SESSION_COOKIE_NAME Default: 'sessionid' The name of the cookie to use for sessions. This can be whatever you want (as long as it’s different from the other cookie names in your application). SESSION_COOKIE_PATH Default: '/' The path set on the session cookie. This should either match the URL path of your Django installation or be parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths, and each instance will only see its own session cookie. SESSION_COOKIE_SAMESITE Default: 'Lax' The value of the SameSite flag on the session cookie. This flag prevents the cookie from being sent in cross-site requests thus preventing CSRF attacks and making some methods of stealing session cookie impossible. Possible values for the setting are:
'Strict': prevents the cookie from being sent by the browser to the target site in all cross-site browsing context, even when following a regular link. For example, for a GitHub-like website this would mean that if a logged-in user follows a link to a private GitHub project posted on a corporate discussion forum or email, GitHub will not receive the session cookie and the user won’t be able to access the project. A bank website, however, most likely doesn’t want to allow any transactional pages to be linked from external sites so the 'Strict' flag would be appropriate.
'Lax' (default): provides a balance between security and usability for websites that want to maintain user’s logged-in session after the user arrives from an external link. In the GitHub scenario, the session cookie would be allowed when following a regular link from an external website and be blocked in CSRF-prone request methods (e.g. POST).
'None' (string): the session cookie will be sent with all same-site and cross-site requests.
False: disables the flag. Note Modern browsers provide a more secure default policy for the SameSite flag and will assume Lax for cookies without an explicit value set. SESSION_COOKIE_SECURE Default: False Whether to use a secure cookie for the session cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent under an HTTPS connection. Leaving this setting off isn’t a good idea because an attacker could capture an unencrypted session cookie with a packet sniffer and use the cookie to hijack the user’s session. SESSION_ENGINE Default: 'django.contrib.sessions.backends.db' Controls where Django stores session data. Included engines are: 'django.contrib.sessions.backends.db' 'django.contrib.sessions.backends.file' 'django.contrib.sessions.backends.cache' 'django.contrib.sessions.backends.cached_db' 'django.contrib.sessions.backends.signed_cookies' See Configuring the session engine for more details. SESSION_EXPIRE_AT_BROWSER_CLOSE Default: False Whether to expire the session when the user closes their browser. See Browser-length sessions vs. persistent sessions. SESSION_FILE_PATH Default: None If you’re using file-based session storage, this sets the directory in which Django will store session data. When the default value (None) is used, Django will use the standard temporary directory for the system. SESSION_SAVE_EVERY_REQUEST Default: False Whether to save the session data on every request. If this is False (default), then the session data will only be saved if it has been modified – that is, if any of its dictionary values have been assigned or deleted. Empty sessions won’t be created, even if this setting is active. SESSION_SERIALIZER Default: 'django.contrib.sessions.serializers.JSONSerializer' Full import path of a serializer class to use for serializing session data. Included serializers are: 'django.contrib.sessions.serializers.PickleSerializer' 'django.contrib.sessions.serializers.JSONSerializer' See Session serialization for details, including a warning regarding possible remote code execution when using PickleSerializer. Sites Settings for django.contrib.sites. SITE_ID Default: Not defined The ID, as an integer, of the current site in the django_site database table. This is used so that application data can hook into specific sites and a single database can manage content for multiple sites. Static Files Settings for django.contrib.staticfiles. STATIC_ROOT Default: None The absolute path to the directory where collectstatic will collect static files for deployment. Example: "/var/www/example.com/static/" If the staticfiles contrib app is enabled (as in the default project template), the collectstatic management command will collect static files into this directory. See the how-to on managing static files for more details about usage. Warning This should be an initially empty destination directory for collecting your static files from their permanent locations into one directory for ease of deployment; it is not a place to store your static files permanently. You should do that in directories that will be found by staticfiles’s finders, which by default, are 'static/' app sub-directories and any directories you include in STATICFILES_DIRS). STATIC_URL Default: None URL to use when referring to static files located in STATIC_ROOT. Example: "static/" or "http://static.example.com/" If not None, this will be used as the base path for asset definitions (the Media class) and the staticfiles app. It must end in a slash if set to a non-empty value. You may need to configure these files to be served in development and will definitely need to do so in production. Note If STATIC_URL is a relative path, then it will be prefixed by the server-provided value of SCRIPT_NAME (or / if not set). This makes it easier to serve a Django application in a subpath without adding an extra configuration to the settings. STATICFILES_DIRS Default: [] (Empty list) This setting defines the additional locations the staticfiles app will traverse if the FileSystemFinder finder is enabled, e.g. if you use the collectstatic or findstatic management command or use the static file serving view. This should be set to a list of strings that contain full paths to your additional files directory(ies) e.g.: STATICFILES_DIRS = [
"/home/special.polls.com/polls/static",
"/home/polls.com/polls/static",
"/opt/webfiles/common",
]
Note that these paths should use Unix-style forward slashes, even on Windows (e.g. "C:/Users/user/mysite/extra_static_content"). Prefixes (optional) In case you want to refer to files in one of the locations with an additional namespace, you can optionally provide a prefix as (prefix, path) tuples, e.g.: STATICFILES_DIRS = [
# ...
("downloads", "/opt/webfiles/stats"),
]
For example, assuming you have STATIC_URL set to 'static/', the collectstatic management command would collect the “stats” files in a 'downloads' subdirectory of STATIC_ROOT. This would allow you to refer to the local file '/opt/webfiles/stats/polls_20101022.tar.gz' with '/static/downloads/polls_20101022.tar.gz' in your templates, e.g.: <a href="{% static 'downloads/polls_20101022.tar.gz' %}">
STATICFILES_STORAGE Default: 'django.contrib.staticfiles.storage.StaticFilesStorage' The file storage engine to use when collecting static files with the collectstatic management command. A ready-to-use instance of the storage backend defined in this setting can be found at django.contrib.staticfiles.storage.staticfiles_storage. For an example, see Serving static files from a cloud service or CDN. STATICFILES_FINDERS Default: [
'django.contrib.staticfiles.finders.FileSystemFinder',
'django.contrib.staticfiles.finders.AppDirectoriesFinder',
]
The list of finder backends that know how to find static files in various locations. The default will find files stored in the STATICFILES_DIRS setting (using django.contrib.staticfiles.finders.FileSystemFinder) and in a static subdirectory of each app (using django.contrib.staticfiles.finders.AppDirectoriesFinder). If multiple files with the same name are present, the first file that is found will be used. One finder is disabled by default: django.contrib.staticfiles.finders.DefaultStorageFinder. If added to your STATICFILES_FINDERS setting, it will look for static files in the default file storage as defined by the DEFAULT_FILE_STORAGE setting. Note When using the AppDirectoriesFinder finder, make sure your apps can be found by staticfiles by adding the app to the INSTALLED_APPS setting of your site. Static file finders are currently considered a private interface, and this interface is thus undocumented. Core Settings Topical Index Cache CACHES CACHE_MIDDLEWARE_ALIAS CACHE_MIDDLEWARE_KEY_PREFIX CACHE_MIDDLEWARE_SECONDS Database DATABASES DATABASE_ROUTERS DEFAULT_INDEX_TABLESPACE DEFAULT_TABLESPACE Debugging DEBUG DEBUG_PROPAGATE_EXCEPTIONS Email ADMINS DEFAULT_CHARSET DEFAULT_FROM_EMAIL EMAIL_BACKEND EMAIL_FILE_PATH EMAIL_HOST EMAIL_HOST_PASSWORD EMAIL_HOST_USER EMAIL_PORT EMAIL_SSL_CERTFILE EMAIL_SSL_KEYFILE EMAIL_SUBJECT_PREFIX EMAIL_TIMEOUT EMAIL_USE_LOCALTIME EMAIL_USE_TLS MANAGERS SERVER_EMAIL Error reporting DEFAULT_EXCEPTION_REPORTER DEFAULT_EXCEPTION_REPORTER_FILTER IGNORABLE_404_URLS MANAGERS SILENCED_SYSTEM_CHECKS File uploads DEFAULT_FILE_STORAGE FILE_UPLOAD_HANDLERS FILE_UPLOAD_MAX_MEMORY_SIZE FILE_UPLOAD_PERMISSIONS FILE_UPLOAD_TEMP_DIR MEDIA_ROOT MEDIA_URL Forms FORM_RENDERER Globalization (i18n/l10n) DATE_FORMAT DATE_INPUT_FORMATS DATETIME_FORMAT DATETIME_INPUT_FORMATS DECIMAL_SEPARATOR FIRST_DAY_OF_WEEK FORMAT_MODULE_PATH LANGUAGE_CODE LANGUAGE_COOKIE_AGE LANGUAGE_COOKIE_DOMAIN LANGUAGE_COOKIE_HTTPONLY LANGUAGE_COOKIE_NAME LANGUAGE_COOKIE_PATH LANGUAGE_COOKIE_SAMESITE LANGUAGE_COOKIE_SECURE LANGUAGES LANGUAGES_BIDI LOCALE_PATHS MONTH_DAY_FORMAT NUMBER_GROUPING SHORT_DATE_FORMAT SHORT_DATETIME_FORMAT THOUSAND_SEPARATOR TIME_FORMAT TIME_INPUT_FORMATS TIME_ZONE USE_I18N USE_L10N USE_THOUSAND_SEPARATOR USE_TZ YEAR_MONTH_FORMAT HTTP DATA_UPLOAD_MAX_MEMORY_SIZE DATA_UPLOAD_MAX_NUMBER_FIELDS DEFAULT_CHARSET DISALLOWED_USER_AGENTS FORCE_SCRIPT_NAME INTERNAL_IPS MIDDLEWARE Security SECURE_CONTENT_TYPE_NOSNIFF SECURE_CROSS_ORIGIN_OPENER_POLICY SECURE_HSTS_INCLUDE_SUBDOMAINS SECURE_HSTS_PRELOAD SECURE_HSTS_SECONDS SECURE_PROXY_SSL_HEADER SECURE_REDIRECT_EXEMPT SECURE_REFERRER_POLICY SECURE_SSL_HOST SECURE_SSL_REDIRECT SIGNING_BACKEND USE_X_FORWARDED_HOST USE_X_FORWARDED_PORT WSGI_APPLICATION Logging LOGGING LOGGING_CONFIG Models ABSOLUTE_URL_OVERRIDES FIXTURE_DIRS INSTALLED_APPS Security Cross Site Request Forgery Protection CSRF_COOKIE_DOMAIN CSRF_COOKIE_NAME CSRF_COOKIE_PATH CSRF_COOKIE_SAMESITE CSRF_COOKIE_SECURE CSRF_FAILURE_VIEW CSRF_HEADER_NAME CSRF_TRUSTED_ORIGINS CSRF_USE_SESSIONS SECRET_KEY X_FRAME_OPTIONS Serialization DEFAULT_CHARSET SERIALIZATION_MODULES Templates TEMPLATES Testing Database: TEST
TEST_NON_SERIALIZED_APPS TEST_RUNNER URLs APPEND_SLASH PREPEND_WWW ROOT_URLCONF | |
doc_29680 | Returns a datetime that represents the current point in time. Exactly what’s returned depends on the value of USE_TZ: If USE_TZ is False, this will be a naive datetime (i.e. a datetime without an associated timezone) that represents the current time in the system’s local timezone. If USE_TZ is True, this will be an aware datetime representing the current time in UTC. Note that now() will always return times in UTC regardless of the value of TIME_ZONE; you can use localtime() to get the time in the current time zone. | |
doc_29681 |
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Estimator parameters. Returns
selfestimator instance
Estimator instance. | |
doc_29682 |
Return the values (min, max) that are mapped to the colormap limits. | |
doc_29683 | Copy pixels into a 2d array array2d(Surface) -> array Copy the mapped (raw) pixels from a Surface into a 2D array. The bit depth of the surface will control the size of the integer values, and will work for any type of pixel format. This function will temporarily lock the Surface as pixels are copied (see the pygame.Surface.lock() - lock the Surface memory for pixel access method). | |
doc_29684 | tf.keras.constraints.min_max_norm Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.constraints.MinMaxNorm, tf.compat.v1.keras.constraints.min_max_norm
tf.keras.constraints.MinMaxNorm(
min_value=0.0, max_value=1.0, rate=1.0, axis=0
)
Constrains the weights incident to each hidden unit to have the norm between a lower bound and an upper bound. Also available via the shortcut function tf.keras.constraints.min_max_norm.
Arguments
min_value the minimum norm for the incoming weights.
max_value the maximum norm for the incoming weights.
rate rate for enforcing the constraint: weights will be rescaled to yield (1 - rate) * norm + rate * norm.clip(min_value, max_value). Effectively, this means that rate=1.0 stands for strict enforcement of the constraint, while rate<1.0 means that weights will be rescaled at each step to slowly move towards a value inside the desired interval.
axis integer, axis along which to calculate weight norms. For instance, in a Dense layer the weight matrix has shape (input_dim, output_dim), set axis to 0 to constrain each weight vector of length (input_dim,). In a Conv2D layer with data_format="channels_last", the weight tensor has shape (rows, cols, input_depth, output_depth), set axis to [0, 1, 2] to constrain the weights of each filter tensor of size (rows, cols, input_depth). | |
doc_29685 |
Get the epoch used by dates. Returns
epochstr
String for the epoch (parsable by numpy.datetime64). | |
doc_29686 | Process and return data already in the queues (lazy). Raise EOFError if connection closed and no data available. Return b'' if no cooked data available otherwise. Do not block unless in the midst of an IAC sequence. | |
doc_29687 |
Return the unit for input to the transform used by __call__. | |
doc_29688 |
Alias for field number 0 | |
doc_29689 | See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedInstanceNorm
tf.raw_ops.QuantizedInstanceNorm(
x, x_min, x_max, output_range_given=False, given_y_min=0, given_y_max=0,
variance_epsilon=1e-05, min_separation=0.001, name=None
)
Args
x A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. A 4D input Tensor.
x_min A Tensor of type float32. The value represented by the lowest quantized input.
x_max A Tensor of type float32. The value represented by the highest quantized input.
output_range_given An optional bool. Defaults to False. If True, given_y_min and given_y_min and given_y_max are used as the output range. Otherwise, the implementation computes the output range.
given_y_min An optional float. Defaults to 0. Output in y_min if output_range_given is True.
given_y_max An optional float. Defaults to 0. Output in y_max if output_range_given is True.
variance_epsilon An optional float. Defaults to 1e-05. A small float number to avoid dividing by 0.
min_separation An optional float. Defaults to 0.001. Minimum value of y_max - y_min
name A name for the operation (optional).
Returns A tuple of Tensor objects (y, y_min, y_max). y A Tensor. Has the same type as x.
y_min A Tensor of type float32.
y_max A Tensor of type float32. | |
doc_29690 |
Implements distributed data parallelism that is based on torch.distributed package at the module level. This container parallelizes the application of the given module by splitting the input across the specified devices by chunking in the batch dimension. The module is replicated on each machine and each device, and each such replica handles a portion of the input. During the backwards pass, gradients from each node are averaged. The batch size should be larger than the number of GPUs used locally. See also: Basics and Use nn.parallel.DistributedDataParallel instead of multiprocessing or nn.DataParallel. The same constraints on input as in torch.nn.DataParallel apply. Creation of this class requires that torch.distributed to be already initialized, by calling torch.distributed.init_process_group(). DistributedDataParallel is proven to be significantly faster than torch.nn.DataParallel for single-node multi-GPU data parallel training. To use DistributedDataParallel on a host with N GPUs, you should spawn up N processes, ensuring that each process exclusively works on a single GPU from 0 to N-1. This can be done by either setting CUDA_VISIBLE_DEVICES for every process or by calling: >>> torch.cuda.set_device(i)
where i is from 0 to N-1. In each process, you should refer the following to construct this module: >>> torch.distributed.init_process_group(
>>> backend='nccl', world_size=N, init_method='...'
>>> )
>>> model = DistributedDataParallel(model, device_ids=[i], output_device=i)
In order to spawn up multiple processes per node, you can use either torch.distributed.launch or torch.multiprocessing.spawn. Note Please refer to PyTorch Distributed Overview for a brief introduction to all features related to distributed training. Note nccl backend is currently the fastest and highly recommended backend when using GPUs. This applies to both single-node and multi-node distributed training. Note This module also supports mixed-precision distributed training. This means that your model can have different types of parameters such as mixed types of fp16 and fp32, the gradient reduction on these mixed types of parameters will just work fine. Note If you use torch.save on one process to checkpoint the module, and torch.load on some other processes to recover it, make sure that map_location is configured properly for every process. Without map_location, torch.load would recover the module to devices where the module was saved from. Note When a model is trained on M nodes with batch=N, the gradient will be M times smaller when compared to the same model trained on a single node with batch=M*N if the loss is summed (NOT averaged as usual) across instances in a batch (because the gradients between different nodes are averaged). You should take this into consideration when you want to obtain a mathematically equivalent training process compared to the local training counterpart. But in most cases, you can just treat a DistributedDataParallel wrapped model, a DataParallel wrapped model and an ordinary model on a single GPU as the same (E.g. using the same learning rate for equivalent batch size). Note Parameters are never broadcast between processes. The module performs an all-reduce step on gradients and assumes that they will be modified by the optimizer in all processes in the same way. Buffers (e.g. BatchNorm stats) are broadcast from the module in process of rank 0, to all other replicas in the system in every iteration. Note If you are using DistributedDataParallel in conjunction with the Distributed RPC Framework, you should always use torch.distributed.autograd.backward() to compute gradients and torch.distributed.optim.DistributedOptimizer for optimizing parameters. Example: >>> import torch.distributed.autograd as dist_autograd
>>> from torch.nn.parallel import DistributedDataParallel as DDP
>>> from torch import optim
>>> from torch.distributed.optim import DistributedOptimizer
>>> from torch.distributed.rpc import RRef
>>>
>>> t1 = torch.rand((3, 3), requires_grad=True)
>>> t2 = torch.rand((3, 3), requires_grad=True)
>>> rref = rpc.remote("worker1", torch.add, args=(t1, t2))
>>> ddp_model = DDP(my_model)
>>>
>>> # Setup optimizer
>>> optimizer_params = [rref]
>>> for param in ddp_model.parameters():
>>> optimizer_params.append(RRef(param))
>>>
>>> dist_optim = DistributedOptimizer(
>>> optim.SGD,
>>> optimizer_params,
>>> lr=0.05,
>>> )
>>>
>>> with dist_autograd.context() as context_id:
>>> pred = ddp_model(rref.to_here())
>>> loss = loss_func(pred, loss)
>>> dist_autograd.backward(context_id, loss)
>>> dist_optim.step()
Warning Constructor, forward method, and differentiation of the output (or a function of the output of this module) are distributed synchronization points. Take that into account in case different processes might be executing different code. Warning This module assumes all parameters are registered in the model by the time it is created. No parameters should be added nor removed later. Same applies to buffers. Warning This module assumes all parameters are registered in the model of each distributed processes are in the same order. The module itself will conduct gradient allreduce following the reverse order of the registered parameters of the model. In other words, it is users’ responsibility to ensure that each distributed process has the exact same model and thus the exact same parameter registration order. Warning This module allows parameters with non-rowmajor-contiguous strides. For example, your model may contain some parameters whose torch.memory_format is torch.contiguous_format and others whose format is torch.channels_last. However, corresponding parameters in different processes must have the same strides. Warning This module doesn’t work with torch.autograd.grad() (i.e. it will only work if gradients are to be accumulated in .grad attributes of parameters). Warning If you plan on using this module with a nccl backend or a gloo backend (that uses Infiniband), together with a DataLoader that uses multiple workers, please change the multiprocessing start method to forkserver (Python 3 only) or spawn. Unfortunately Gloo (that uses Infiniband) and NCCL2 are not fork safe, and you will likely experience deadlocks if you don’t change this setting. Warning Forward and backward hooks defined on module and its submodules won’t be invoked anymore, unless the hooks are initialized in the forward() method. Warning You should never try to change your model’s parameters after wrapping up your model with DistributedDataParallel. Because, when wrapping up your model with DistributedDataParallel, the constructor of DistributedDataParallel will register the additional gradient reduction functions on all the parameters of the model itself at the time of construction. If you change the model’s parameters afterwards, gradient redunction functions no longer match the correct set of parameters. Warning Using DistributedDataParallel in conjunction with the Distributed RPC Framework is experimental and subject to change. Warning The gradient_as_bucket_view mode does not yet work with Automatic Mixed Precision (AMP). AMP maintains stashed gradients that are used for unscaling gradients. With gradient_as_bucket_view=True, these stashed gradients will point to communication buckets in the first iteration. In the next iteration, the communication buckets are mutated and thus these stashed gradients will be unexpectedly mutated as well, which might lead to wrong results. Parameters
module (Module) – module to be parallelized
device_ids (list of python:int or torch.device) – CUDA devices. This should only be provided when the input module resides on a single CUDA device. For single-device modules, the i’th module replica is placed on device_ids[i]. For multi-device modules and CPU modules, device_ids must be None or an empty list, and input data for the forward pass must be placed on the correct device. (default: all visible devices for single-device modules)
output_device (int or torch.device) – Device location of output for single-device CUDA modules. For multi-device modules and CPU modules, it must be None, and the module itself dictates the output location. (default: device_ids[0] for single-device modules)
broadcast_buffers (bool) – Flag that enables syncing (broadcasting) buffers of the module at beginning of the forward function. (default: True)
process_group – The process group to be used for distributed data all-reduction. If None, the default process group, which is created by torch.distributed.init_process_group(), will be used. (default: None)
bucket_cap_mb – DistributedDataParallel will bucket parameters into multiple buckets so that gradient reduction of each bucket can potentially overlap with backward computation. bucket_cap_mb controls the bucket size in MegaBytes (MB). (default: 25)
find_unused_parameters (bool) – Traverse the autograd graph from all tensors contained in the return value of the wrapped module’s forward function. Parameters that don’t receive gradients as part of this graph are preemptively marked as being ready to be reduced. Note that all forward outputs that are derived from module parameters must participate in calculating loss and later the gradient computation. If they don’t, this wrapper will hang waiting for autograd to produce gradients for those parameters. Any outputs derived from module parameters that are otherwise unused can be detached from the autograd graph using torch.Tensor.detach. (default: False)
check_reduction – This argument is deprecated.
gradient_as_bucket_view (bool) – This is a prototype feature and subject to changes. When set to True, gradients will be views pointing to different offsets of allreduce communication buckets. This can reduce peak memory usage, where the saved memory size will be equal to the total gradients size. Moreover, it avoids the overhead of copying between gradients and allreduce communication buckets. When gradients are views, detach_() cannot be called on the gradients. If hitting such errors, please fix it by referring to the zero_grad() function in torch/optim/optimizer.py as a solution. Variables
~DistributedDataParallel.module (Module) – the module to be parallelized. Example: >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...')
>>> net = torch.nn.parallel.DistributedDataParallel(model, pg)
join(divide_by_initial_world_size=True, enable=True) [source]
A context manager to be used in conjunction with an instance of torch.nn.parallel.DistributedDataParallel to be able to train with uneven inputs across participating processes. This context manager will keep track of already-joined DDP processes, and “shadow” the forward and backward passes by inserting collective communication operations to match with the ones created by non-joined DDP processes. This will ensure each collective call has a corresponding call by already-joined DDP processes, preventing hangs or errors that would otherwise happen when training with uneven inputs across processes. Once all DDP processes have joined, the context manager will broadcast the model corresponding to the last joined process to all processes to ensure the model is the same across all processes (which is guaranteed by DDP). To use this to enable training with uneven inputs across processes, simply wrap this context manager around your training loop. No further modifications to the model or data loading is required. Warning This module works only with the multi-process, single-device usage of torch.nn.parallel.DistributedDataParallel, which means that a single process works on a single GPU. Warning This module currently does not support custom distributed collective operations in the forward pass, such as SyncBatchNorm or other custom defined collectives in the model’s forward pass. Parameters
divide_by_initial_world_size (bool) – If True, will divide gradients by the initial world_size DDP training was launched with. If False, will compute the effective world size (number of ranks that have not depleted their inputs yet) and divide gradients by that during allreduce. Set divide_by_initial_world_size=True to ensure every input sample including the uneven inputs have equal weight in terms of how much they contribute to the global gradient. This is achieved by always dividing the gradient by the initial world_size even when we encounter uneven inputs. If you set this to False, we divide the gradient by the remaining number of nodes. This ensures parity with training on a smaller world_size although it also means the uneven inputs would contribute more towards the global gradient. Typically, you would want to set this to True for cases where the last few inputs of your training job are uneven. In extreme cases, where there is a large discrepancy in the number of inputs, setting this to False might provide better results.
enable (bool) – Whether to enable uneven input detection or not. Pass in enable=False to disable in cases where you know that inputs are even across participating processes. Default is True. Example: >>> import torch
>>> import torch.distributed as dist
>>> import os
>>> import torch.multiprocessing as mp
>>> import torch.nn as nn
>>> # On each spawned worker
>>> def worker(rank):
>>> dist.init_process_group("nccl", rank=rank, world_size=2)
>>> torch.cuda.set_device(rank)
>>> model = nn.Linear(1, 1, bias=False).to(rank)
>>> model = torch.nn.parallel.DistributedDataParallel(
>>> model, device_ids=[rank], output_device=rank
>>> )
>>> # Rank 1 gets one more input than rank 0.
>>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)]
>>> with model.join():
>>> for _ in range(5):
>>> for inp in inputs:
>>> loss = model(inp).sum()
>>> loss.backward()
>>> # Without the join() API, the below synchronization will hang
>>> # blocking for rank 1's allreduce to complete.
>>> torch.cuda.synchronize(device=rank)
no_sync() [source]
A context manager to disable gradient synchronizations across DDP processes. Within this context, gradients will be accumulated on module variables, which will later be synchronized in the first forward-backward pass exiting the context. Example: >>> ddp = torch.nn.parallel.DistributedDataParallel(model, pg)
>>> with ddp.no_sync():
>>> for input in inputs:
>>> ddp(input).backward() # no synchronization, accumulate grads
>>> ddp(another_input).backward() # synchronize grads
register_comm_hook(state, hook) [source]
Registers a communication hook which is an enhancement that provides a flexible hook to users where they can specify how DDP aggregates gradients across multiple workers. This hook would be very useful for researchers to try out new ideas. For example, this hook can be used to implement several algorithms like GossipGrad and gradient compression which involve different communication strategies for parameter syncs while running Distributed DataParallel training. Parameters
state (object) –
Passed to the hook to maintain any state information during the training process. Examples include error feedback in gradient compression, peers to communicate with next in GossipGrad, etc. It is locally stored by each worker and shared by all the gradient tensors on the worker.
hook (callable) –
Averages gradient tensors across workers and defined as: hook(state: object, bucket: dist._GradBucket) -> torch.futures.Future: This function is called once the bucket is ready. The hook can perform whatever processing is needed and return a Future indicating completion of any async work (ex: allreduce). If the hook doesn’t perform any communication, it can also just return a completed Future. The Future should hold the new value of grad bucket’s tensors. Once a bucket is ready, c10d reducer would call this hook and use the tensors returned by the Future and copy grads to individual parameters. We also provide an API called get_future to retrieve a Future associated with the completion of c10d.ProcessGroup.work. Warning Grad bucket’s tensors will not be predivided by world_size. User is responsible to divide by the world_size in case of operations like allreduce. Warning DDP communication hook can only be registered once and should be registered before calling backward. Warning The Future object that hook returns should contain a result that has the same shape with the tensors inside grad bucket. Warning DDP communication hook does not support single-process multiple-device mode. Gradbucket tensors should consist of only a single tensor. Warning get_future API supports only NCCL backend and will return a torch._C.Future which is an internal type and should be used with caution. It can still be used by register_comm_hook API, but it is subject to some subtle differences compared to torch.futures.Future. Warning DDP communication hook is experimental and subject to change. Example::
Below is an example of a noop hook that returns the same tensors. >>> def noop(state: object, bucket: dist._GradBucket): -> torch.futures.Future
>>> fut = torch.futures.Future()
>>> fut.set_result(bucket.get_tensors())
>>> return fut
>>> ddp.register_comm_hook(state = None, hook = noop)
Example::
Below is an example of a Parallel SGD algorithm where gradients are encoded before allreduce, and then decoded after allreduce. >>> def encode_and_decode(state: object, bucket: dist._GradBucket): -> torch.futures.Future
>>> tensors = [t / process_group.world_size for t in bucket.get_tensors()]
>>> encoded_tensors = encode(tensors) # encode gradients
>>> fut = process_group.allreduce(encoded_tensors).get_future()
>>> # Define the then callback to decode.
>>> def decode(fut):
>>> decoded_tensors = decode(fut.value()) # decode gradients
>>> return decoded_tensors
>>> return fut.then(decode)
>>> ddp.register_comm_hook(state = None, hook = encode_and_decode) | |
doc_29691 |
Divide a path into two segments at the point where inside(x, y) becomes False. | |
doc_29692 | See Migration guide for more details. tf.compat.v1.train.BytesList
Attributes
value repeated bytes value | |
doc_29693 | Sets the current object instance (self.object). | |
doc_29694 |
Draw samples from a Hypergeometric distribution. Samples are drawn from a hypergeometric distribution with specified parameters, ngood (ways to make a good selection), nbad (ways to make a bad selection), and nsample (number of items sampled, which is less than or equal to the sum ngood + nbad). Note New code should use the hypergeometric method of a default_rng() instance instead; please see the Quick Start. Parameters
ngoodint or array_like of ints
Number of ways to make a good selection. Must be nonnegative.
nbadint or array_like of ints
Number of ways to make a bad selection. Must be nonnegative.
nsampleint or array_like of ints
Number of items sampled. Must be at least 1 and at most ngood + nbad.
sizeint or tuple of ints, optional
Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a single value is returned if ngood, nbad, and nsample are all scalars. Otherwise, np.broadcast(ngood, nbad, nsample).size samples are drawn. Returns
outndarray or scalar
Drawn samples from the parameterized hypergeometric distribution. Each sample is the number of good items within a randomly selected subset of size nsample taken from a set of ngood good items and nbad bad items. See also scipy.stats.hypergeom
probability density function, distribution or cumulative density function, etc. Generator.hypergeometric
which should be used for new code. Notes The probability density for the Hypergeometric distribution is \[P(x) = \frac{\binom{g}{x}\binom{b}{n-x}}{\binom{g+b}{n}},\] where \(0 \le x \le n\) and \(n-b \le x \le g\) for P(x) the probability of x good results in the drawn sample, g = ngood, b = nbad, and n = nsample. Consider an urn with black and white marbles in it, ngood of them are black and nbad are white. If you draw nsample balls without replacement, then the hypergeometric distribution describes the distribution of black balls in the drawn sample. Note that this distribution is very similar to the binomial distribution, except that in this case, samples are drawn without replacement, whereas in the Binomial case samples are drawn with replacement (or the sample space is infinite). As the sample space becomes large, this distribution approaches the binomial. References 1
Lentner, Marvin, “Elementary Applied Statistics”, Bogden and Quigley, 1972. 2
Weisstein, Eric W. “Hypergeometric Distribution.” From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/HypergeometricDistribution.html 3
Wikipedia, “Hypergeometric distribution”, https://en.wikipedia.org/wiki/Hypergeometric_distribution Examples Draw samples from the distribution: >>> ngood, nbad, nsamp = 100, 2, 10
# number of good, number of bad, and number of samples
>>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)
>>> from matplotlib.pyplot import hist
>>> hist(s)
# note that it is very unlikely to grab both bad items
Suppose you have an urn with 15 white and 15 black marbles. If you pull 15 marbles at random, how likely is it that 12 or more of them are one color? >>> s = np.random.hypergeometric(15, 15, 15, 100000)
>>> sum(s>=12)/100000. + sum(s<=3)/100000.
# answer = 0.003 ... pretty unlikely! | |
doc_29695 | Returns the system’s release version, e.g. '#3 on degas'. An empty string is returned if the value cannot be determined. | |
doc_29696 |
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Estimator parameters. Returns
selfestimator instance
Estimator instance. | |
doc_29697 |
lookup_name = 'hour' | |
doc_29698 |
Alias for set_edgecolor. | |
doc_29699 | sklearn.cluster.spectral_clustering(affinity, *, n_clusters=8, n_components=None, eigen_solver=None, random_state=None, n_init=10, eigen_tol=0.0, assign_labels='kmeans', verbose=False) [source]
Apply clustering to a projection of the normalized Laplacian. In practice Spectral Clustering is very useful when the structure of the individual clusters is highly non-convex or more generally when a measure of the center and spread of the cluster is not a suitable description of the complete cluster. For instance, when clusters are nested circles on the 2D plane. If affinity is the adjacency matrix of a graph, this method can be used to find normalized graph cuts. Read more in the User Guide. Parameters
affinity{array-like, sparse matrix} of shape (n_samples, n_samples)
The affinity matrix describing the relationship of the samples to embed. Must be symmetric. Possible examples:
adjacency matrix of a graph, heat kernel of the pairwise distance matrix of the samples, symmetric k-nearest neighbours connectivity matrix of the samples.
n_clustersint, default=None
Number of clusters to extract.
n_componentsint, default=n_clusters
Number of eigen vectors to use for the spectral embedding
eigen_solver{None, ‘arpack’, ‘lobpcg’, or ‘amg’}
The eigenvalue decomposition strategy to use. AMG requires pyamg to be installed. It can be faster on very large, sparse problems, but may also lead to instabilities. If None, then 'arpack' is used.
random_stateint, RandomState instance, default=None
A pseudo random number generator used for the initialization of the lobpcg eigen vectors decomposition when eigen_solver == ‘amg’ and by the K-Means initialization. Use an int to make the randomness deterministic. See Glossary.
n_initint, default=10
Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.
eigen_tolfloat, default=0.0
Stopping criterion for eigendecomposition of the Laplacian matrix when using arpack eigen_solver.
assign_labels{‘kmeans’, ‘discretize’}, default=’kmeans’
The strategy to use to assign labels in the embedding space. There are two ways to assign labels after the laplacian embedding. k-means can be applied and is a popular choice. But it can also be sensitive to initialization. Discretization is another approach which is less sensitive to random initialization. See the ‘Multiclass spectral clustering’ paper referenced below for more details on the discretization approach.
verbosebool, default=False
Verbosity mode. New in version 0.24. Returns
labelsarray of integers, shape: n_samples
The labels of the clusters. Notes The graph should contain only one connect component, elsewhere the results make little sense. This algorithm solves the normalized cut for k=2: it is a normalized spectral clustering. References Normalized cuts and image segmentation, 2000 Jianbo Shi, Jitendra Malik http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324
A Tutorial on Spectral Clustering, 2007 Ulrike von Luxburg http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323
Multiclass spectral clustering, 2003 Stella X. Yu, Jianbo Shi https://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.pdf
Examples using sklearn.cluster.spectral_clustering
Segmenting the picture of greek coins in regions
Spectral clustering for image segmentation |
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