Graph
A TensorFlow computation, represented as a dataflow graph.
tf.compat.v1.Graph
tf.compat.v2.Graph
A Graph
contains a set of
tf.Operation
objects,
which represent units of computation; and
tf.Tensor
objects, which represent
the units of data that flow between operations.
A default Graph
is always registered, and accessible by calling
tf.compat.v1.get_default_graph
.
To add an operation to the default graph, simply call one of the functions
that defines a new Operation
:
c = tf.constant(4.0) assert c.graph is tf.compat.v1.get_default_graph()
Another typical usage involves the
tf.Graph.as_default
context manager, which overrides the current default graph for the
lifetime of the context:
g = tf.Graph() with g.as_default(): # Define operations and tensors in `g`. c = tf.constant(30.0) assert c.graph is g
Important note: This class is not thread-safe for graph construction. All operations should be created from a single thread, or external synchronization must be provided. Unless otherwise specified, all methods are not thread-safe.
A Graph
instance supports an arbitrary number of "collections"
that are identified by name. For convenience when building a large
graph, collections can store groups of related objects: for
example, the tf.Variable
uses a collection (named
tf.GraphKeys.GLOBAL_VARIABLES
) for
all variables that are created during the construction of a graph. The caller
may define additional collections by specifying a new name.
__init__
__init__()
Creates a new, empty Graph.
building_function
Returns True iff this graph represents a function.
collections
Returns the names of the collections known to this graph.
finalized
True if this graph has been finalized.
graph_def_versions
The GraphDef version information of this graph.
For details on the meaning of each version, see
GraphDef
.
A VersionDef
.
seed
The graph-level random seed of this graph.
version
Returns a version number that increases as ops are added to the graph.
Note that this is unrelated to the
tf.Graph.graph_def_versions
.
An integer version that increases as ops are added to the graph.
add_to_collection
add_to_collection( name, value )
Stores value
in the collection with the given name
.
Note that collections are not sets, so it is possible to add a value to a collection several times.
name
: The key for the collection. The GraphKeys
class contains many
standard names for collections.value
: The value to add to the collection.add_to_collections
add_to_collections( names, value )
Stores value
in the collections given by names
.
Note that collections are not sets, so it is possible to add a value to
a collection several times. This function makes sure that duplicates in
names
are ignored, but it will not check for pre-existing membership of
value
in any of the collections in names
.
names
can be any iterable, but if names
is a string, it is treated as a
single collection name.
names
: The keys for the collections to add to. The GraphKeys
class
contains many standard names for collections.value
: The value to add to the collections.as_default
as_default()
Returns a context manager that makes this Graph
the default graph.
This method should be used if you want to create multiple graphs in the same process. For convenience, a global default graph is provided, and all ops will be added to this graph if you do not create a new graph explicitly.
Use this method with the with
keyword to specify that ops created within
the scope of a block should be added to this graph. In this case, once
the scope of the with
is exited, the previous default graph is set again
as default. There is a stack, so it's ok to have multiple nested levels
of as_default
calls.
The default graph is a property of the current thread. If you
create a new thread, and wish to use the default graph in that
thread, you must explicitly add a with g.as_default():
in that
thread's function.
The following code examples are equivalent:
# 1. Using Graph.as_default(): g = tf.Graph() with g.as_default(): c = tf.constant(5.0) assert c.graph is g # 2. Constructing and making default: with tf.Graph().as_default() as g: c = tf.constant(5.0) assert c.graph is g
If eager execution is enabled ops created under this context manager will be added to the graph instead of executed eagerly.
A context manager for using this graph as the default graph.
as_graph_def
as_graph_def( from_version=None, add_shapes=False )
Returns a serialized GraphDef
representation of this graph.
The serialized GraphDef
can be imported into another Graph
(using tf.import_graph_def
) or used with the
C++ Session API.
This method is thread-safe.
from_version
: Optional. If this is set, returns a GraphDef
containing
only the nodes that were added to this graph since its version
property had the given value.add_shapes
: If true, adds an "_output_shapes" list attr to each node with
the inferred shapes of each of its outputs.A
GraphDef
protocol buffer.
ValueError
: If the graph_def
would be too large.as_graph_element
as_graph_element( obj, allow_tensor=True, allow_operation=True )
Returns the object referred to by obj
, as an Operation
or Tensor
.
This function validates that obj
represents an element of this
graph, and gives an informative error message if it is not.
This function is the canonical way to get/validate an object of one of the allowed types from an external argument reference in the Session API.
This method may be called concurrently from multiple threads.
obj
: A Tensor
, an Operation
, or the name of a tensor or operation. Can
also be any object with an _as_graph_element()
method that returns a
value of one of these types. Note: _as_graph_element
will be called
inside the graph's lock and so may not modify the graph.allow_tensor
: If true, obj
may refer to a Tensor
.allow_operation
: If true, obj
may refer to an Operation
.The Tensor
or Operation
in the Graph corresponding to obj
.
TypeError
: If obj
is not a type we support attempting to convert
to types.ValueError
: If obj
is of an appropriate type but invalid. For
example, an invalid string.KeyError
: If obj
is not an object in the graph.clear_collection
clear_collection(name)
Clears all values in a collection.
name
: The key for the collection. The GraphKeys
class contains many
standard names for collections.colocate_with
colocate_with( op, ignore_existing=False )
Returns a context manager that specifies an op to colocate with.
Note: this function is not for public use, only for internal libraries.
a = tf.Variable([1.0]) with g.colocate_with(a): b = tf.constant(1.0) c = tf.add(a, b)
b
and c
will always be colocated with a
, no matter where a
is eventually placed.
NOTE Using a colocation scope resets any existing device constraints.
If op
is None
then ignore_existing
must be True
and the new
scope resets all colocation and device constraints.
op
: The op to colocate all created ops with, or None
.ignore_existing
: If true, only applies colocation of this op within the
context, rather than applying all colocation properties on the stack.
If op
is None
, this value must be True
.ValueError
: if op is None but ignore_existing is False.A context manager that specifies the op with which to colocate newly created ops.
container
container(container_name)
Returns a context manager that specifies the resource container to use.
Stateful operations, such as variables and queues, can maintain their
states on devices so that they can be shared by multiple processes.
A resource container is a string name under which these stateful
operations are tracked. These resources can be released or cleared
with tf.Session.reset()
.
with g.container('experiment0'): # All stateful Operations constructed in this context will be placed # in resource container "experiment0". v1 = tf.Variable([1.0]) v2 = tf.Variable([2.0]) with g.container("experiment1"): # All stateful Operations constructed in this context will be # placed in resource container "experiment1". v3 = tf.Variable([3.0]) q1 = tf.queue.FIFOQueue(10, tf.float32) # All stateful Operations constructed in this context will be # be created in the "experiment0". v4 = tf.Variable([4.0]) q1 = tf.queue.FIFOQueue(20, tf.float32) with g.container(""): # All stateful Operations constructed in this context will be # be placed in the default resource container. v5 = tf.Variable([5.0]) q3 = tf.queue.FIFOQueue(30, tf.float32) # Resets container "experiment0", after which the state of v1, v2, v4, q1 # will become undefined (such as uninitialized). tf.Session.reset(target, ["experiment0"])
container_name
: container name string.A context manager for defining resource containers for stateful ops, yields the container name.
control_dependencies
control_dependencies(control_inputs)
Returns a context manager that specifies control dependencies.
Use with the with
keyword to specify that all operations constructed
within the context should have control dependencies on
control_inputs
. For example:
with g.control_dependencies([a, b, c]): # `d` and `e` will only run after `a`, `b`, and `c` have executed. d = ... e = ...
Multiple calls to control_dependencies()
can be nested, and in
that case a new Operation
will have control dependencies on the union
of control_inputs
from all active contexts.
with g.control_dependencies([a, b]): # Ops constructed here run after `a` and `b`. with g.control_dependencies([c, d]): # Ops constructed here run after `a`, `b`, `c`, and `d`.
You can pass None to clear the control dependencies:
with g.control_dependencies([a, b]): # Ops constructed here run after `a` and `b`. with g.control_dependencies(None): # Ops constructed here run normally, not waiting for either `a` or `b`. with g.control_dependencies([c, d]): # Ops constructed here run after `c` and `d`, also not waiting # for either `a` or `b`.
N.B. The control dependencies context applies only to ops that are constructed within the context. Merely using an op or tensor in the context does not add a control dependency. The following example illustrates this point:
# WRONG def my_func(pred, tensor): t = tf.matmul(tensor, tensor) with tf.control_dependencies([pred]): # The matmul op is created outside the context, so no control # dependency will be added. return t # RIGHT def my_func(pred, tensor): with tf.control_dependencies([pred]): # The matmul op is created in the context, so a control dependency # will be added. return tf.matmul(tensor, tensor)
Also note that though execution of ops created under this scope will trigger execution of the dependencies, the ops created under this scope might still be pruned from a normal tensorflow graph. For example, in the following snippet of code the dependencies are never executed:
loss = model.loss() with tf.control_dependencies(dependencies): loss = loss + tf.constant(1) # note: dependencies ignored in the # backward pass return tf.gradients(loss, model.variables)
This is because evaluating the gradient graph does not require evaluating the constant(1) op created in the forward pass.
control_inputs
: A list of Operation
or Tensor
objects which must be
executed or computed before running the operations defined in the
context. Can also be None
to clear the control dependencies.A context manager that specifies control dependencies for all operations constructed within the context.
TypeError
: If control_inputs
is not a list of Operation
or
Tensor
objects.create_op
create_op( op_type, inputs, dtypes=None, input_types=None, name=None, attrs=None, op_def=None, compute_shapes=True, compute_device=True )
Creates an Operation
in this graph. (deprecated arguments)
Warning: SOME ARGUMENTS ARE DEPRECATED: (compute_shapes)
. They will be removed in a future version.
Instructions for updating:
Shapes are always computed; don't use the compute_shapes as it has no effect.
This is a low-level interface for creating an Operation
. Most
programs will not call this method directly, and instead use the
Python op constructors, such as tf.constant()
, which add ops to
the default graph.
op_type
: The Operation
type to create. This corresponds to the
OpDef.name
field for the proto that defines the operation.inputs
: A list of Tensor
objects that will be inputs to the Operation
.dtypes
: (Optional) A list of DType
objects that will be the types of the
tensors that the operation produces.input_types
: (Optional.) A list of DType
s that will be the types of the
tensors that the operation consumes. By default, uses the base DType
of each input in inputs
. Operations that expect reference-typed inputs
must specify input_types
explicitly.name
: (Optional.) A string name for the operation. If not specified, a
name is generated based on op_type
.attrs
: (Optional.) A dictionary where the key is the attribute name (a
string) and the value is the respective attr
attribute of the
NodeDef
proto that will represent the operation (an AttrValue
proto).op_def
: (Optional.) The OpDef
proto that describes the op_type
that
the operation will have.compute_shapes
: (Optional.) Deprecated. Has no effect (shapes are always
computed).compute_device
: (Optional.) If True, device functions will be executed to
compute the device property of the Operation.TypeError
: if any of the inputs is not a Tensor
.ValueError
: if colocation conflicts with existing device assignment.An Operation
object.
device
device(device_name_or_function)
Returns a context manager that specifies the default device to use.
The device_name_or_function
argument may either be a device name
string, a device function, or None:
device()
context.device()
invocations from the enclosing context
will be ignored.For information about the valid syntax of device name strings, see
the documentation in
DeviceNameUtils
.
with g.device('/device:GPU:0'): # All operations constructed in this context will be placed # on GPU 0. with g.device(None): # All operations constructed in this context will have no # assigned device. # Defines a function from `Operation` to device string. def matmul_on_gpu(n): if n.type == "MatMul": return "/device:GPU:0" else: return "/cpu:0" with g.device(matmul_on_gpu): # All operations of type "MatMul" constructed in this context # will be placed on GPU 0; all other operations will be placed # on CPU 0.
N.B. The device scope may be overridden by op wrappers or
other library code. For example, a variable assignment op
v.assign()
must be colocated with the tf.Variable
v
, and
incompatible device scopes will be ignored.
device_name_or_function
: The device name or function to use in the
context.A context manager that specifies the default device to use for newly created ops.
RuntimeError
: If device scopes are not properly nested.finalize
finalize()
Finalizes this graph, making it read-only.
After calling g.finalize()
, no new operations can be added to
g
. This method is used to ensure that no operations are added
to a graph when it is shared between multiple threads, for example
when using a tf.compat.v1.train.QueueRunner
.
get_all_collection_keys
get_all_collection_keys()
Returns a list of collections used in this graph.
get_collection
get_collection( name, scope=None )
Returns a list of values in the collection with the given name
.
This is different from get_collection_ref()
which always returns the
actual collection list if it exists in that it returns a new list each time
it is called.
name
: The key for the collection. For example, the GraphKeys
class
contains many standard names for collections.scope
: (Optional.) A string. If supplied, the resulting list is filtered
to include only items whose name
attribute matches scope
using
re.match
. Items without a name
attribute are never returned if a
scope is supplied. The choice of re.match
means that a scope
without
special tokens filters by prefix.The list of values in the collection with the given name
, or
an empty list if no value has been added to that collection. The
list contains the values in the order under which they were
collected.
get_collection_ref
get_collection_ref(name)
Returns a list of values in the collection with the given name
.
If the collection exists, this returns the list itself, which can be modified in place to change the collection. If the collection does not exist, it is created as an empty list and the list is returned.
This is different from get_collection()
which always returns a copy of
the collection list if it exists and never creates an empty collection.
name
: The key for the collection. For example, the GraphKeys
class
contains many standard names for collections.The list of values in the collection with the given name
, or an empty
list if no value has been added to that collection.
get_name_scope
get_name_scope()
Returns the current name scope.
with tf.name_scope('scope1'): with tf.name_scope('scope2'): print(tf.compat.v1.get_default_graph().get_name_scope())
would print the string scope1/scope2
.
A string representing the current name scope.
get_operation_by_name
get_operation_by_name(name)
Returns the Operation
with the given name
.
This method may be called concurrently from multiple threads.
name
: The name of the Operation
to return.The Operation
with the given name
.
TypeError
: If name
is not a string.KeyError
: If name
does not correspond to an operation in this graph.get_operations
get_operations()
Return the list of operations in the graph.
You can modify the operations in place, but modifications to the list such as inserts/delete have no effect on the list of operations known to the graph.
This method may be called concurrently from multiple threads.
A list of Operations.
get_tensor_by_name
get_tensor_by_name(name)
Returns the Tensor
with the given name
.
This method may be called concurrently from multiple threads.
name
: The name of the Tensor
to return.The Tensor
with the given name
.
TypeError
: If name
is not a string.KeyError
: If name
does not correspond to a tensor in this graph.gradient_override_map
gradient_override_map(op_type_map)
EXPERIMENTAL: A context manager for overriding gradient functions.
This context manager can be used to override the gradient function that will be used for ops within the scope of the context.
@tf.RegisterGradient("CustomSquare") def _custom_square_grad(op, grad): # ... with tf.Graph().as_default() as g: c = tf.constant(5.0) s_1 = tf.square(c) # Uses the default gradient for tf.square. with g.gradient_override_map({"Square": "CustomSquare"}): s_2 = tf.square(s_2) # Uses _custom_square_grad to compute the # gradient of s_2.
op_type_map
: A dictionary mapping op type strings to alternative op type
strings.A context manager that sets the alternative op type to be used for one or more ops created in that context.
TypeError
: If op_type_map
is not a dictionary mapping strings to
strings.is_feedable
is_feedable(tensor)
Returns True
if and only if tensor
is feedable.
is_fetchable
is_fetchable(tensor_or_op)
Returns True
if and only if tensor_or_op
is fetchable.
name_scope
name_scope(name)
Returns a context manager that creates hierarchical names for operations.
A graph maintains a stack of name scopes. A with name_scope(...):
statement pushes a new name onto the stack for the lifetime of the context.
The name
argument will be interpreted as follows:
name
is appended to the prefix of all operations created in the
context. If name
has been used before, it will be made unique by
calling self.unique_name(name)
.with g.name_scope(...) as
scope:
statement will be treated as an "absolute" name scope, which
makes it possible to re-enter existing scopes.None
or the empty string will reset the current name scope
to the top-level (empty) name scope.with tf.Graph().as_default() as g: c = tf.constant(5.0, name="c") assert c.op.name == "c" c_1 = tf.constant(6.0, name="c") assert c_1.op.name == "c_1" # Creates a scope called "nested" with g.name_scope("nested") as scope: nested_c = tf.constant(10.0, name="c") assert nested_c.op.name == "nested/c" # Creates a nested scope called "inner". with g.name_scope("inner"): nested_inner_c = tf.constant(20.0, name="c") assert nested_inner_c.op.name == "nested/inner/c" # Create a nested scope called "inner_1". with g.name_scope("inner"): nested_inner_1_c = tf.constant(30.0, name="c") assert nested_inner_1_c.op.name == "nested/inner_1/c" # Treats `scope` as an absolute name scope, and # switches to the "nested/" scope. with g.name_scope(scope): nested_d = tf.constant(40.0, name="d") assert nested_d.op.name == "nested/d" with g.name_scope(""): e = tf.constant(50.0, name="e") assert e.op.name == "e"
The name of the scope itself can be captured by with
g.name_scope(...) as scope:
, which stores the name of the scope
in the variable scope
. This value can be used to name an
operation that represents the overall result of executing the ops
in a scope. For example:
inputs = tf.constant(...) with g.name_scope('my_layer') as scope: weights = tf.Variable(..., name="weights") biases = tf.Variable(..., name="biases") affine = tf.matmul(inputs, weights) + biases output = tf.nn.relu(affine, name=scope)
NOTE: This constructor validates the given name
. Valid scope
names match one of the following regular expressions:
[A-Za-z0-9.][A-Za-z0-9_.\-/]* (for scopes at the root) [A-Za-z0-9_.\-/]* (for other scopes)
name
: A name for the scope.A context manager that installs name
as a new name scope.
ValueError
: If name
is not a valid scope name, according to the rules
above.prevent_feeding
prevent_feeding(tensor)
Marks the given tensor
as unfeedable in this graph.
prevent_fetching
prevent_fetching(op)
Marks the given op
as unfetchable in this graph.
switch_to_thread_local
switch_to_thread_local()
Make device, colocation and dependencies stacks thread-local.
Device, colocation and dependencies stacks are not thread-local be default. If multiple threads access them, then the state is shared. This means that one thread may affect the behavior of another thread.
After this method is called, the stacks become thread-local. If multiple threads access them, then the state is not shared. Each thread uses its own value; a thread doesn't affect other threads by mutating such a stack.
The initial value for every thread's stack is set to the current value
of the stack when switch_to_thread_local()
was first called.
unique_name
unique_name( name, mark_as_used=True )
Return a unique operation name for name
.
Note: You rarely need to call unique_name()
directly. Most of
the time you just need to create with g.name_scope()
blocks to
generate structured names.
unique_name
is used to generate structured names, separated by
"/"
, to help identify operations when debugging a graph.
Operation names are displayed in error messages reported by the
TensorFlow runtime, and in various visualization tools such as
TensorBoard.
If mark_as_used
is set to True
, which is the default, a new
unique name is created and marked as in use. If it's set to False
,
the unique name is returned without actually being marked as used.
This is useful when the caller simply wants to know what the name
to be created will be.
name
: The name for an operation.mark_as_used
: Whether to mark this name as being used.A string to be passed to create_op()
that will be used
to name the operation being created.


Welcome to AMLVM
This is ambersun1234's web machine learning virtual machine
Here are the features you can use
-
Image Recognition
- Use Imagenet weight as recognize backend, able to detect over 1000 category
- Each Recognition time on a six-core processor needs to take at least 3 minutes
-
K-means Clustering
- Simple K-means calculation based on random generated input
- Provide custom range of input


Like most people, I am many different things depending on the situation, most germane to this conversation - I am a guy who loves code. Though new to the subject (about a year in now), it has become increasingly clear to me this where I want to spend most of my time.
As to why you should consider me; I think I have a combination of skills that would make me a great candidate for the position. For the past ten years I have been working as an educator throughout South East Asia, which gives me a unique skill set: the patience of a teacher, the understanding that to help someone (be it student or customer) you have to meet them where they are, and the ability to adapt to novel situations quickly and successfully.
I also have a strong background in customer service and sales from my career before deciding to vagabond around the world.
More importantly than any of the above; you should consider me because I love solving problems in code (check out my CodeWars profile above to see how much) and I love working with people.
Back in the bad old days of the Dot.com boom in San Francisco, I worked for SquareTrade, which at that time was doing dispute resolution for eBay buyers and sellers.
My job mainly consisted of saying no - a lot. Mostly to sellers who wanted negative feedback removed from their profiles. At that time there was no procedure to have feedback taken off, usually all we could do was put a blurb on the seller's profile saying that they had tried to resolve the issue.
To say that the majority of the people I worked with were upset would be putting it mildly. For the most part, they were honest people who worked very hard to create their business only to have it hurt by what they saw as an unjustified attack.
Unfortunately, there was little I could do for most of them, except listen. But turns out that listening helps, it really does. Commiserating, feeling like you're being heard and not just sending emails off into a void...it all helps. That job really taught me that you deal with people, not problems. Sometimes you can't resolve a problem, but if you deal with the people decently, it may be enough.
No.
Is the customer satisfied? Are you able to satisfy the customer? I think those are better questions. A lot of customer service is managing customer expectations.
As long as you are accurately representing yourself, the organization and the product I think you are doing the best that can be expected. Not all products are right for every customer. Some may be better served by a different product or service. There is no point in trying to sell hotdogs to a vegan or mushroom burgers to an avowed carnivore.
Pre-Sales
Fire Alarm
Step-By-Step
Pre-Sales: While the Product Customizer does not have that exact feature, with the release of version 2.0, it is now an embedded app in Shopify - meaning depending on your normal workflow that might not be an issue.
The power of the app is its ability to meet the needs of individual sellers; if you let me know a bit more how you are thinking of using it (ie what kinds of issues you are running into currently, what type of customization you would like to be able to do), I would be able to give you an idea of how it could help you maximize your efforts and really make your brand stand out.
Thanks
Matt
Fire Alarm: The first thing I would do is a quick assessment of the nature of the problems the customers are facing, looking for patterns, ie does it look like server problems, account login problems, is it specific to a particular product... then I would contact the proper person (depending on the protocol) to report the issue and find out if it is a known one.
Depending on their response, I would then do a number of things. If there was anything I could do to start resolving the issue myself, I would start taking action. If the solution depended on another's actions, I would start contacting the customers letting them know that we are working on the issue and that I will keep them updated with information as it became available. I would also start any bug reporting processes, as needed.
Step-By-Step
How to Purchase a GH4 Digital Camera:
Step 1: Spend months thinking about getting a new camera.
Step 2: Spend another few months researching cameras online.
 He recommends the aforementioned GH4.
Step 4: Spend another couple of months comparing the GH4 to every other camera in the world, calling your friend to debate every feature until he finally stops taking your calls.
Step 5: Buy the GH4 and love it.
Step 6: Take your friend to dinner for being a pain.


Have you at anytime witnessed a relative of yours or a close friend searching frantically for a blood donor, when blood banks say out of stock, the donors in mind are out of reach and the time keeps ticking? Have you witnessed loss of life for the only reason that a donor was not available at the most needed hour? Is it something that we as a society can do nothing to prevent? This thought laid our foundation.
"Innovo" is an organization that brings voluntary blood donors and those in need of blood on to a common platform. Through this website, we seek donors who are willing to donate blood, as well as provide the timeliest support to those in frantic need of it.
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Our mission is to fulfill every blood request in the country with a promising web portal and motivated individuals who are willing to donate blood.
Our vision is to be “The hope of every Indian in search of a voluntary blood donor”
Our motto - “Donate blood to save the most precious human life”
(c)Arun


public interface NavuCdbConfigDiffIterate extends NavuCdbDiffIterate
For each entry in the change set one invocation per entry is done
on the iterate
method supplying the same instance of
NavuCdbSubscriptionConfigContext
for the entire change set.
Before the iterate
method ends the implementation should
call either NavuCdbSubscriptionContext.iterContinue()
or
NavuCdbSubscriptionContext.iterRecurse()
. Note however the
iterContinue method invocation is the same as calling
"return".
A instance of the implementation needs to be
registered within a specific NavuCdbConfigSubscriber
through
NavuCdbSubscriber.register(NavuCdbDiffIterate,ConfPath)
before
the subscriber is started.
NavuCdbSubscriber x = NavuCdbSubscribers.configSubscriber("localhost",port,name); x.register(new NavuCdbConfigDiffIterate() { public void iterate (NavuCdbSubsciptionConfigContext ctx) { //react on a change entry ctx.iterRecurse(); //continues with all children to the node } }); x.subscriberStart();
| Modifier and Type | Method and Description |
|---|---|
void |
iterate(NavuCdbSubscriptionConfigContext ctx)
This method will be called for each change entry in the
change set
|
void iterate(NavuCdbSubscriptionConfigContext ctx)
The implementation should call any of
NavuCdbSubscriptionContext.iterContinue()
,
NavuCdbSubscriptionContext.iterRecurse()
at the end of
each iteration or NavuCdbSubscriptionContext.iterStop()
to stop the current iteration.
Note however the
iterContinue method invocation is the same as calling
"return".
The same instance of ctx
is supplied in each
invocation of the this method.
ctx
- the subscription context for the change set


Challenge 03: write4
Contents
Challenge 03: write4#
I am not sure if I should be concerned that the instructions list this as the “first foray into propery gadget use”. I would have told you we’ve been there with the last two challenges.
After going through the normal steps, I confirmed that the initial attack
surface is the same… the pwnme()
function allocates a buffer of length 32
,
does a memset on it (all zeros), and then accepts in a string of up to 512
characters. This length gives us a little hint as to the complexity of the chain
we are going to have to stitch together.
Solution#
OK… I’ve solved it (4 hours later). Lots of interesting learning here for me
today. The first thing is minor yet helpful… that is the pwn tools fit()
command. This is a helper function for building a payload. Rather than having to
build a string by hand, it accepts a dictionary with the information you must
have in the payload and some description of what you need, and it handles
formatting and building it out. The solution file for the 64 bit version shows
the use, but essentially it is a dictionary where the key is the index where a
value should start and then the value to be placed there. You also indicate the
length and it will fill in the rest with random junk.
The instructions suggest that there are multiple ways to solve this challenge. I
ended up using the /bin/cat flag.txt
option. To accomplish this, I needed to
do the following:
store the command string -
/bin/cat flag.txt
in some location within memoryplace the location (memory address) of that string in the
RDI
registercall
system()
Conceptually, there isn’t much to this. I used the helpful
CyberChef website to easily convert my command string
to the hex representation. The resulting string was 17 bytes long, meaning it
would take 3 qwords (3 stack entries) to get it into memory. Using ropper
, I
found a couple of helpful gadgets. The first is pop r14; pop r15; ret
which,
when paired with the second: mov qword ptr [r14], r15; ret;
makes a pretty
handy tool. You can call the first to place an address in r14 and a value in
r15. Calling the second then moves the value in r15 to the location specified by
r14. Calling this pair repeatedly allows you to build up a long string, 8
bytes/characters at a time.
The next question, as suggested by the challenge instructions, is to figure out
where to put it. Running readelf -S <exe>
provides insight into the file
sections and their permissions. Evaluating those with a W
indicating write
access gives you only a few that are long enough. I tried writing to the data
section with success, but it is only 16 bytes long and cannot hold the command
string. I also experimented with the dynamic section but this failed (I may have
done something wrong). I ended up overwriting part of the GOT - ensuring to
not touch the part of the GOT that contained the resoution for the system()
call I was planning on using.
Having written the command string, I simply put the address into RDI via a pop rdi; ret;
gadget followed by the address to a call system
location within the
program.
NOTE: I lost alot of time attempting to bind directly to system within the GOT. My understanding is that this should work, but it clearly doesn’t (at least, not as I’m calling it). I need to dig into this more and understand.
The solution is available in exploit_03.py.
32-bit solution#
The 32 bit solution is similar to the 64 bit version, but some of the utilized
gadgets are different. We started with a mov dword ptr [edi], ebp; ret;
and a
handy pop edi; pop ebp; ret;
With these primitives, the process is fairly straight-forward. I ended up using
the .bss
section to store the data as the other sections weren’t quite big
enough due to the nature of 32 bit structures vs. 64 bit structures. Similarly,
it took 5 “loops” through the “load into registers, move from register to point”
process to get the entire command string in place. Once this was done, calling
it was easy.
The solution is available in exploit_0332.py
What Did I Learn?#
Increased comfort with
pwntools
- specifically thefit()
function for crafting payloadsThe idea that you can stuff things (a small, limited amount) in memory already owned by the file and, so long as you are careful, it may not blow up
Increased understanding of the different file sections -
readelf()
and how they work. Had some of this knowlege from Ben’s ‘server’ challenge, but this reinforced it


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Interface for a Spi Master with resource management, which allows to synchronize multiple drivers requesting hardware access.
More...
#include <xpcc/architecture/interface/spi_master.hpp>
|
| template<class SystemClock , uint32_t baudrate, uint16_t tolerance = Tolerance::FivePercent> |
| static void | initialize () |
| | Initializes the hardware and sets the baudrate. More...
|
| |
|
static void | setDataMode (DataMode mode) |
| | Sets a new data mode.
|
| |
|
static void | setDataOrder (DataOrder order) |
| | Sets a new data order.
|
| |
| static uint8_t | acquire (void *ctx, ConfigurationHandler handler=nullptr) |
| | Request access to the spi master within a context. More...
|
| |
| static uint8_t | release (void *ctx) |
| | Release access to the spi master within a context. More...
|
| |
| static uint8_t | transferBlocking (uint8_t data) |
| | Swap a single byte and wait for completion. More...
|
| |
| static void | transferBlocking (uint8_t *tx, uint8_t *rx, std::size_t length) |
| | Set the data buffers and length with options and starts a transfer. More...
|
| |
| static xpcc::ResumableResult< uint8_t > | transfer (uint8_t data) |
| | Swap a single byte and wait for completion non-blocking!. More...
|
| |
| static xpcc::ResumableResult< void > | transfer (uint8_t *tx, uint8_t *rx, std::size_t length) |
| | Set the data buffers and length with options and starts a non-blocking transfer. More...
|
| |
| template<uint32_t available, uint32_t requested, uint16_t tolerance> |
| static void | assertBaudrateInTolerance () |
| | Since baudrates are usually generated by prescaling a system clock, only several distinct values can be generated. More...
|
| |
◆ initialize()
template<class SystemClock , uint32_t baudrate, uint16_t tolerance = Tolerance::FivePercent>
| static void xpcc::SpiMaster::initialize |
( |
| ) |
|
|
static |
- Template Parameters
-
| SystemClock | the currently active system clock |
| baudrate | the desired baudrate in Hz |
| tolerance | the allowed relative tolerance for the resulting baudrate |
◆ acquire()
You may acquire the spi master multiple times within the same context.
The configuration handler will only be called when aquiring the spi master for the first time (if it is not a nullptr
).
- Warning
- Aquires must be balanced with releases of the same context!
-
Aquires are persistent even after calling
initialize()
!
- Returns
0
if another context is using the spi master, otherwise >0
as the number of times this context acquired the master.
◆ release()
| static uint8_t xpcc::SpiMaster::release |
( |
void * |
ctx | ) |
|
|
static |
- Warning
- Releases must be balanced with acquires of the same context!
-
Releases are persistent even after calling
initialize()
!
- Returns
0
if nothing can be released anymore (for any context) >0
as the number of times this context can still release the master.
◆ transferBlocking() [1/2]
| static uint8_t xpcc::SpiMaster::transferBlocking |
( |
uint8_t |
data | ) |
|
|
static |
- Parameters
-
- Returns
- received data
◆ transferBlocking() [2/2]
| static void xpcc::SpiMaster::transferBlocking |
( |
uint8_t * |
tx, |
|
|
uint8_t * |
rx, |
|
|
std::size_t |
length |
|
) |
| |
|
static |
This may be hardware accelerated (DMA or Interrupt), but not guaranteed.
- Parameters
-
| [in] | tx | pointer to transmit buffer, set to nullptr to send dummy bytes |
| [out] | rx | pointer to receive buffer, set to nullptr to discard received bytes |
| length | number of bytes to be shifted out |
◆ transfer() [1/2]
You must call this inside a Protothread or Resumable using PT_CALL
or RF_CALL
respectively.
- Warning
- These methods differ from Resumables by lacking context protection! You must ensure that only one driver is accessing this resumable function by using
acquire(ctx)
and release(ctx)
.
- Parameters
-
- Returns
- received data
◆ transfer() [2/2]
| static xpcc::ResumableResult<void> xpcc::SpiMaster::transfer |
( |
uint8_t * |
tx, |
|
|
uint8_t * |
rx, |
|
|
std::size_t |
length |
|
) |
| |
|
static |
This may be hardware accelerated (DMA or Interrupt), but not guaranteed.
You must call this inside a Protothread or Resumable using PT_CALL
or RF_CALL
respectively.
- Warning
- These methods differ from Resumables by lacking context protection! You must ensure that only one driver is accessing this resumable function by using
acquire(ctx)
and release(ctx)
.
- Parameters
-
| [in] | tx | pointer to transmit buffer, set to nullptr to send dummy bytes |
| [out] | rx | pointer to receive buffer, set to nullptr to discard received bytes |
| length | number of bytes to be shifted out |
The documentation for this class was generated from the following file:
- src/xpcc/architecture/interface/spi_master.hpp


Prerequisites
This is a manual for the R tools hosted at our GitHub repository. A RStudio tutorial is beyond the scope of this readme, but there are great resources to get you started with installing R and RStudio.
How to use GitHub
- Create an account.
- Work through chapters 6-12 here if you need to install git, and connect it all with RStudio:
- Follow the instructions (at least through 5) here under “How to do this using RStudio and GitHub?”
- you don’t need to enter the backticks in the shell
- this example is a bit misleading because it doesn’t include the .git, copy the link to the clipboard like before
- RESTART RSTUDIO BEFORE MOVING ONTO STEP 6 IN THIS TUTORIAL
- If you want to pull updates from here to your copy, see chapter 31.


{% extends "core/base.html" %} {% block title %} Last games {% endblock %} {% block script %} {% include "js/Paging.js" with url="" page=page max_page=max_page only %} {% endblock %} {% block main %}
| ID | Date | Rating change | Player name | Score | Player name | Rating change |
|---|


Preview: Sharks vs. Nashville Predators
October 28th, 2015
The Puck Times is a concisely curated e-mail newsletter for Sharks fans. We scan the news and synthesize it for you, every morning before the puck drops. Find past issues at pucktimes.com/archives.
NEED TO KNOW
Game details: Sharks vs Nashville Predators, puck drops 730pm.
- Through eight games, the Sharks are off to a solid 5-3 start. After winning their first four games, they dropped three in a row before rebounding last game with a 5-2 win over Carolina (more on that below).
- Finally some positive news on the injury report - last game saw the return of defenseman Paul Martin after he missed the previous three games (maybe not so coincidentally all three losses). Additionally, Joonas Donskoi, Ben Smith and Melker Karlsson are all seemingly close to being available again for the big club.
- With a number of teams off to great starts like the Montreal Canadiens (9-0-0), Dallas Stars (7-2-0) and the New York Islanders (6-2-1), the Nashville Predators aren't getting the attention they deserve. They are also out to a fantastic start at (6-1-1), and are tied for second in the league in goals against with just 16.
- Historically, San Jose has had good success against the Predators, and last year they took the season series with a 2-1-0 record. Both wins were 2-0 shutouts on home ice.
- Both the Sharks and the Predators last played Saturday night, so all legs should be fresh for what ought to be an exciting hockey game between two top caliber teams.
IN CASE YOU MISSED IT: Game Recap: Sharks Beat Carolina 5-2
Who's Hot & Who's Cold?
- The Reggae man Joel Ward now has five goals in his last four games and leads the team in points with eight.
- Joe Pavelski's goal last game gives him seven points in eight games to start the season.
- After scoring three goals in his first four games, Patrick Marleau has gone cold with no goals and just a single assist in his last four games, while accumulating a minus-6 rating in that span.
- Former Pittsburgh Penguins star James Neal already has six goals and nine points to lead the Predators in scoring.
- In seven games, Nashville's stud goaltender Pekka Rinne is sporting a .932 save percentage. Over the last two games he has stopped 50 of 53 shots for a .943 save percentage.
Nashville Projected Lineup:
Goaltender: Pekka Rinne #35
Defense:
| Shea Weber #6 |
Roman Josi #59 |
| Mattias Ekholm #26 |
Ryan Ellis #4 |
| Barret Jackman #5 |
Seth Jones #3 |
Offense:
| LW |
C |
RW |
| Filip Forsberg #9 |
Mike Fisher #12 |
James Neal #18 |
| Colin Wilson #33 |
Mike Ribeiro #63 |
Craig Smith #15 |
| Gabriel Bourque #57 |
Cody Hodgson #11 |
Calle Jarnkrok #19 |
| Eric Nystrom #24 |
Paul Gaustad #28 |
Austin Watson #51 |
Sharks Projected Lineup:
Goaltender: Martin Jones #31
Defense:
| Marc-Edouard Vlasic #44 |
Justin Braun #61 |
| Paul Martin #7 |
Brent Burns #88 |
| Brenden Dillon #4 |
Matt Tennyson #80 |
Offense:
| LW |
C |
RW |
| Joonas Donskoi #27 |
Joe Thornton #19 |
Joe Pavelski #8 |
| Patrick Marleau #12 |
Tomas Hertl #48 |
Joel Ward #42 |
| Matthew Nieto #83 |
Chris Tierney #50 |
Tommy Wingels #57 |
| Barclay Goodrow #89 |
Ben Smith #21 |
Mike Brown #18 |
FURTHER READING FROM AROUND THE WEB
In Closing - Memorable Moments
In the 2006 playoffs against the Predators, Patrick Marleau finished off his hat trick with this goal off the 2-on-1 rush.
SUBSCRIBE NOW
Enjoy reading today's issue of The Puck Times? If so, please subscribe here. You can expect to receive one email for every Sharks game-day, and can unsubscribe at any time.


H.B. Woodrose
A few weeks ago, my room-mate showed me some Hawaiian Baby Woodrose seeds he had purchased over the internet. I knew little about them besides the fact that they were similar to LSD with fewer visuals, which I thought meant less powerful. We soaked them overnight, then peeled of the coating, ate them, and drank the liquid.
Me and my friend took the LSA at around 9 - 9:30. I start to feel a bit sick almost immediately. We had just eaten a hearty supper earlier, something we had been advised against. I could have booted then, but was curious as to what I had in store. I decided to check online again, and noticed that some people had very bad experiences. I started to get worried, and a bit sick, and a half hour in decided to abort. I ran to the toilet and threw up.
A thing to note about this stuff is that it kept coming in waves. Good waves and bad waves. Me and the OP were usually not quite in sync throughout the night. I seemed to have gone through the trip faster than him, hitting the various 'stages' before he did. And boy, were the stages different.
At this point I was feeling pretty wierd.  We listened to his 'Loveline' archives. We were laughing loud. Eventually we decided we were ready for a bowl. Not too much had happened so far, but I felt very drunk. We smoked, and I left the room to play 'Shadow of the Colossus'. I was at a point just before the final boss. I picked up the controller and the most amazing thing happened. I entered the videogame.
I bullsh*t thee not. Me and Wander became one and the same. His world felt like the world around me. Like I was in a dream. Like I was there. And when I saw that final collossus, A hundred story fire-eyed black stone devil, I was floored. I can't believe I was able to play as well as I was, but I wasn't doing too well. 'There are awesome things in this world' I thought to myself as I dodged fireballs, but soon realized there was no way I could beat him in the state I was in. I got up from the game and left my room.
Things were now very, very amiss. I was now travelling through time, through my own memories. Like rapidly changing channels I relived random parts of my past, as though I was there. I was walking and dreaming at the same time. The walls cycled textures, reality was on the fritz. I stumbled into the hallway, thinking 'I'm having a dream.' I saw myself stumble over. At this point, I knew I was insane. A lunatic. My thoughts flew at random. I felt good- great. Amazing. In fact, I was experiencing what heaven must be like. And therefore, I knew heaven existed, because I was there.  Would I be heading there as well that night?
It wouldn't be long. I had to get out of the hallway before I got in trouble. I thought: 'There's no place like home' as I tried to force myself back into reality. I returned to my bed to lie down, and contemplated the rapidly exploding universe. Then a thought hit me: My friend was in the next room and he was probably tripping even harder than me! I quickly went to check on him and found him battling a thought loop. Him: “…We’re fighiting to keep our memories. But we aren’t going to remember this. Every thought is fighting to be remembered. But we’re stuck here forever! We have to record this…”
I assured him I would remember everything, but he was in a loop and I wasn’t getting in. Our minds were as schizophrenics now, flipping from topic to topic, flying from past to present like our brains were spinning. Our style of banter was not unlike the dialog in Fear and Loathing. He said something about levels. Everything was levels. I noted that Paul McCartney said the same thing the first time he tripped.
We were walking into walls. Lucidity came and went. I went to lie down in my bathtub. And this where my hellish, introspective nightmare began. I have no idea how long I lay in that tub. The neurons in my brain did things they never did before, and I wasn’t liking it. I felt sick, drunk, and scared. “What is existence?” “Am I just a malfunctioning brain in bathtub?”
By 4 AM, I was finally ready to pass out. My friend had commandeered my bed due to the presence of an air conditioner, so we traded beds for a while, and I contemplated his giant wall mural. We eventually traded back and I fell asleep, while he continued to trip.
I was sober in the morning, but somehow he was still in deep brain-sh*t. He was having trouble breathing, and we decided he should get checked out at the hospital. Fortunately he recovered.   But this isn’t Acid Lite. It was the most powerful thing I’ve ever tried and made 2C-E (which I’ve tried before) feel like near-beer. And as the my friend could tell you, it can screw you sideways.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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Leaked footage allegedly shows Russian spy bribing an unidentified Serbian official – video
Serbian authorities are investigating a video that purportedly shows a Russian spy handing over a bag of cash to an unnamed Serbian official. The video, uploaded anonymously to YouTube on Wednesday, later shows the Serbian man counting money from inside the bag while sitting in his car. Serbian officials confirmed the video was genuine and identified the agent as Georgy Kleban.


If you have archives from earlier than 1994, send them on.
Globillum is a mailing list started back in 1989. It's now essentially gone, so don't try to subscribe. This page was created because of this tweet by Pete Shirley. I knew this question about specular and whatnot terminology had been discussed on the globillum mailing list, but I didn't have access to the archive. I figured someone had these emails somewhere, so contacted Holly Rushmeier, who pointed me at George Drettakis as the current guardian of globillum. I contacted George to see if he had a copy, who pulled Nicolas Holzschuch in to help.
It turned out the archives of the globillum mailing list were lost during a change of e-mail servers. Nicolas kindly contacted past subscribers and asked for any emails they had saved from this list. The most complete were Greg Ward's and Werner Purgathofer's, though I won't swear either has everything. In fact, I'm betting they don't, as between the two of them there are globillum emails in one but not the other, both ways. If someone wants to merge these two files into one, go for it and I'll host it. It looks like an afternoon Python or Perl project, but I have cookies to bake.
Thanks to all who sent on their archives (in alphabetical order):
Werner's was the most complete, saving me writing a merge script. So, that's what you are downloading.


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