ops | Deployment and server management utilities | Continuous Deployment library
kandi X-RAY | ops Summary
kandi X-RAY | ops Summary
buedafab — a collection of Fabric utilities used at Bueda.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Upload and restart celery
- Execute a single command
- Change file permissions
- Wrapper for sudo
- Create a new release
- Make the head commit string
- Make a git release
- Deploy the given release
- Deploy to a given git branch
- Default deploy
- Symlink to symlinks
- Install development
- Conditionally conditional an s3 bucket
- Stores the deployed version
- Setup production directory
- Install production
- Install crontab
- Install JCC
- Bootstrap release folders
- Install requirements
- Load data
- Roll back the repository
- Start maintenance mode
- Migrate database
- Returns the alternative release path
- Update the database
ops Key Features
ops Examples and Code Snippets
Lines of Code : 107License : Non-SPDX (Apache License 2.0)
Copydef split_inputs_and_generate_enqueue_ops(self,
inputs,
device_assignment=None,
placement_function=None,
def _get_logged_ops(graph, run_meta=None, add_trace=True,
add_trainable_var=True):
"""Extract trainable model parameters and FLOPs for ops from a Graph.
Args:
graph: tf.Graph.
run_meta: RunMetadata proto used to compl def configure_collective_ops(
self,
collective_leader="",
scoped_allocator_enabled_ops=("CollectiveReduce",),
use_nccl_communication=False,
device_filters=None):
"""Configure collective ops.
Collective group l Community Discussions
Trending Discussions on ops
QUESTION
What's the XLA class XlaBuilder for? The docs describe its interface but don't provide a motivation.
The presentation in the docs, and indeed the comment above XlaBuilder in the source code
ANSWER
Answered 2021-Dec-15 at 01:32XlaBuilder is the C++ API for building up XLA computations -- conceptually this is like building up a function, full of various operations, that you could execute over and over again on different input data.
Some background, XLA serves as an abstraction layer for creating executable blobs that run on various target accelerators (CPU, GPU, TPU, IPU, ...), conceptually kind of an "accelerator virtual machine" with conceptual similarities to earlier systems like PeakStream or the line of work that led to ArBB.
The XlaBuilder is a way to enqueue operations into a "computation" (similar to a function) that you want to run against the various set of accelerators that XLA can target. The operations at this level are often referred to as "High Level Operations" (HLOs).
The returned XlaOp represents the result of the operation you've just enqueued. (Aside/nerdery: this is a classic technique used in "builder" APIs that represent the program in "Static Single Assignment" form under the hood, the operation itself and the result of the operation can be unified as one concept!)
XLA computations are very similar to functions, so you can think of what you're doing with an XlaBuilder like building up a function. (Aside: they're called "computations" because they do a little bit more than a straightforward function -- conceptually they are coroutines that can talk to an external "host" world and also talk to each other via networking facilities.)
So the fact XlaOps can't be used across XlaBuilders may make more sense with that context -- in the same way that when building up a function you can't grab intermediate results in the internals of other functions, you have to compose them with function calls / parameters. In XlaBuilder you can Call another built computation, which is a reason you might use multiple builders.
As you note, you can choose to inline everything into one "mega builder", but often programs are structured as functions that get composed together, and ultimately get called from a few different "entry points". XLA currently aggressively specializes for the entry points it sees API users using, but this is a design artifact similar to inlining decisions, XLA can conceptually reuse computations built up / invoked from multiple callers if it thought that was the right thing to do. Usually it's most natural to enqueue things into XLA however is convenient for your description from the "outside world", and allow XLA to inline and aggressively specialize the "entry point" computations you've built up as you execute them, in Just-in-Time compilation fashion.
QUESTION
I have a little library where I can define integer types. These are intended for type-safe indexing into arrays and strings in the kind of algorithms I often write. For example, I can use it to define an offset type, Offset and an index type, Idx such that you can get an Offset by subtracting two Idx, you can get Idx by adding or subtracting Offset, but you cannot for example multiple or add Idx.
ANSWER
Answered 2022-Feb-10 at 05:54No, you can't.
By definition of the orphan rules:
Given
impl Trait for T0, animplis valid only if at least one of the following is true:
- Trait is a local trait
- All of
- At least one of the types
T0..=Tnmust be a local type. LetTibe the first such type.- No uncovered type parameters
P1..=Pnmay appear inT0..Ti(excludingTi)Only the appearance of uncovered type parameters is restricted. Note that for the purposes of coherence, fundamental types are special. The T in Box is not considered covered, and Box is considered local.
Local traitA
traitwhich was defined in the current crate. A trait definition is local or not independent of applied type arguments. Giventrait Foo,Foois always local, regardless of the types substituted forTandU.
Local typeA
struct,enum, orunionwhich was defined in the current crate. This is not affected by applied type arguments.struct Foois considered local, butVecis not.LocalTypeis local. Type aliases do not affect locality.
As neither Index nor Range nor Vec are local, and Range is not a fundamental type, you cannot impl Index<...>> for Vec, no matter what you put in the place of the ....
The reason for these rules is that nothing prevents Range or Vec from implementing impl Index> for Vec. Such impl does not exist, and probably never will, but the rules are the same among all types, and in the general case this definitely can happen.
You cannot overload the range operator either - it always creates a Range (or RangeInclusive, RangeFull, etc.).
The only solution I can think about is to create a newtype wrapper for Vec, as suggested in the comments.
If you want your vector to return a wrapped slice, you can use a bit of unsafe code:
QUESTION
I am trying to use a BTreeSet<(String, String, String)> as a way to create a simple in-memory 'triple store'.
To be precise:
...ANSWER
Answered 2022-Jan-17 at 09:59I'd like to be able to build range-queries for all of the following:
QUESTION
The JLS states, that for arrays, "The enhanced for statement is equivalent to a basic for statement of the form". However if I check the generated bytecode for JDK8, for both variants different bytecode is generated, and if I try to measure the performance, surprisingly, the enhanced one seems to be giving better results(on jdk8)... Can someone advise why it's that? I'd guess it's because of incorrect jmh testing, so if it's that, please suggest how to fix that. (I know that JMH states not to test using loops, but I don't think this applies here, as I'm actually trying to measure the loops here)
My JMH testing was rather simple (probably too simple), but I cannot explain the results. Testing JMH code is below, typical results are:
...ANSWER
Answered 2022-Jan-05 at 19:41TL;DR: You are observing what happens when JIT compiler cannot trust that values are not changing inside the loop. Additionally, in the tiny benchmark like this, Blackhole.consume costs dominate, obscuring the results.
Simplifying the test:
QUESTION
Since I am working with TensorFlow, I would like to know how to map my rows from a tensor C to the index of its corresponding row in matrix B.
Here is the code I wrote:
...ANSWER
Answered 2022-Jan-03 at 18:53You do not have to use tf.map_fn. Maybe try something like this:
QUESTION
use std::ops::Deref;
use std::sync::{Arc, Mutex, MutexGuard};
struct Var {}
fn multithreading() -> Var {
let shared_var = Arc::new(Mutex::new(Var {}));
/*
multithreading job
*/
return *(shared_var.lock().unwrap().deref());
}
ANSWER
Answered 2021-Dec-13 at 11:40The problem here is that if you remove your Var from the shared variable, what would be left there? What happens if any other copy of your Arc is left somewhere and it tries to access the now removed object?
There are several possible answers to that question:
1. I'm positively sure there is no other strong reference, this is the lastArc. If not, let it panic.
If that is the case, you can use Arc::try_unwrap() to get to the inner mutex. Then another into_inner() to get the real value.
QUESTION
I have to do a large number of operations (additions) on relatively small integers, and I started considering which datatype would give the best performance on a 64 bit machine.
I was convinced that adding together 4 uint16 would take the same time as one uint64, since the ALU could make 4 uint16 additions using only 1 uint64 adder. (Carry propagation means this doesn't work that easily for a single 64-bit adder, but this is how integer SIMD instructions work.)
Apparently this is not the case:
...ANSWER
Answered 2021-Nov-29 at 00:22TL;DR: I made an experimental analysis on Numpy 1.21.1. Experimental results show that np.sum does NOT (really) make use of SIMD instructions: no SIMD instruction are used for integers, and scalar SIMD instructions are used for floating-point numbers! Moreover, Numpy converts the integers to 64-bits values for smaller integer types by default so to avoid overflows!
Note that this may not reflect all Numpy versions since there is an ongoing work to provide SIMD support for commonly used functions (the version Numpy 1.22.0rc1 not yet released continue this long-standing work). Moreover, the compiler or the processor used may significantly impact the results. The following experiments have been done using a Numpy retrieved from pip on a Debian Linux with a i5-9600KF processor.
Under the hood ofnp.sum
For floating-point numbers, Numpy uses a pairwise algorithm which is known to be quite numerically stable while being relatively fast. This can be seen in the code, but also simply using a profiler: TYPE_pairwise_sum is the C function called to compute the sum at runtime (where TYPE is DOUBLE or FLOAT).
For integers, Numpy use a classical naive reduction. The C function called is ULONG_add_avx2 on AVX2-compatible machines. It also surprisingly convert items to 64-bit ones if the type is not np.int64.
Here is the hot part of the assembly code executed by the DOUBLE_pairwise_sum function
QUESTION
Task: Keras captcha ocr model training.
Problem: I am trying to print CAPTCHAS from my validation set, but doing so is causing the following error
...ANSWER
Answered 2021-Nov-24 at 13:26Here is a complete running example based on your dataset running in Google Colab:
QUESTION
I recently started learning JavaScript and faced a task that I can't complete in any way, every time I get the wrong data that I need There is an object that contains data on banking transactions, I need to make a selection and form a new object using filter, map or reduce:
We assume that the initial balance on the card = 0.
- Output the TOP 3 months with the largest number of operations by month.
Formalize it as a task_1(arr) function, where arr is the source array with data for all months.
Output format:
...ANSWER
Answered 2021-Oct-27 at 10:48Task 1
QUESTION
I am working with OSM data to create vector street maps. For the roads, I use line geometry provided by OSM and add a buffer to convert the line to geometry that looks like a road.
My question is related to geometry, not OSM, so I will use basic lines for simplicity.
...ANSWER
Answered 2021-Oct-16 at 14:36You can buffer the lines and then negative buffer that result:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install ops
You can use ops like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
