pyinstrument | 🚴 Call stack profiler for Python Shows you why your code is slow! | Monitoring library
kandi X-RAY | pyinstrument Summary
kandi X-RAY | pyinstrument Summary
🚴 Call stack profiler for Python. Shows you why your code is slow!
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Top functions reviewed by kandi - BETA
- Decorates a function into a function
- Create a function from a source template
- Create a function definition
- Update the attributes of this function
- Say something
- Print the message to file
- Starts profiling
- Bump the latest version
- Appends a to the list of ancestors
- Print to file
- Return the text representation of the message
- Mark a function as deprecated
- Convert a function into a decorator
- Render the profile
- Render the application
- Removes the first pyinstrument frame processor
- Remove this node from the parent
- Saves the report to a temp storage
- Render the pipeline
- Output the message as text
- Render a Speedscope frame
- Start profiling
- Sample a frame
- Record the received call stack
- Generate a decorator function
- Open the document in a browser
- Start the sampler session
- Profile a frame
- Create a renderer instance
- Resolve module
pyinstrument Key Features
pyinstrument Examples and Code Snippets
├─ 4.703 backward torch/tensor.py:74
│ [10 frames hidden] torch
│ 4.702 backward torch/autograd/__init__.py:38
│ ├─ 2.604 AddmmBackward (addmm:1) ../:0
│ │ └─ 2.499 mm:0 ../:0
│ ├─ 1.019 AddmmBa
from datasette import hookimpl
from functools import wraps
@hookimpl
def asgi_wrapper(datasette):
def wrap_with_databases_header(app):
@wraps(app)
async def add_x_databases_header(
scope, receive, send
):
import os
BASE_DIR = os.path.dirname(os.path.dirname(__file__))
DATABASES = {
"default": {
"ENGINE": "django.db.backends.sqlite3",
"NAME": os.path.join(BASE_DIR, "db.sqlite3"),
}
}
DEBUG = True
TEMPLATE_DEBUG = True
SECRET
import os
from optparse import OptionParser
try:
import django
except ImportError:
print("This example requires Django.")
print("Install using `pip install Django`.")
exit(1)
import django.conf
import django.template.loader
def ma
import time
from pyinstrument import Profiler
try:
from flask import Flask, g, make_response, request
except ImportError:
print("This example requires Flask.")
print("Install using `pip install flask`.")
exit(1)
app = Flask(__name_
# Uses pseudo-namespacing to avoid collisions.
_EXT_SUFFIX = "___"
_NEXT_EXT_INDEX = 0
def is_ext_var(element) -> bool:
return element.endswith(_EXT_SUFFIX)
def ext_var() -> str:
global _NEXT_EXT_INDEX
ext_index = _NE
conda install cryptography
pip install keepalive
def get_all_genes_uri(endpoint, the_offset):
sparql = SPARQLWrapper(endpoint)
sparql.setUseKeepAlive() # <--- Added this line
sparql.setQuery("""
#My_query
""")
Community Discussions
Trending Discussions on pyinstrument
QUESTION
Question in short
To have a proper input for pycosat, is there a way to speed up calculation from dnf to cnf, or to circumvent it altogether?
Question in detail
I have been watching this video from Raymond Hettinger about modern solvers. I downloaded the code, and implemented a solver for the game Towers in it. Below I share the code to do so.
Example Tower puzzle (solved):
...ANSWER
Answered 2022-Mar-19 at 22:23First, it's good to note the difference between equivalence and equisatisfiability. In general, converting an arbitrary boolean formula (say, something in DNF) to CNF can result in a exponential blow-up in size.
This blow-up is the issue with your from_dnf
approach: whenever you handle another product term, each of the literals in that product demands a new copy of the current cnf clause set (to which it will add itself in every clause). If you have n product terms of size k, the growth is O(k^n)
.
In your case n
is actually a function of k!
. What's kept as a product term is filtered to those satisfying the view constraint, but overall the runtime of your program is roughly in the region of O(k^f(k!))
. Even if f grows logarithmically, this is still O(k^(k lg k))
and not quite ideal!
Because you're asking "is this satisfiable?", you don't need an equivalent formula but merely an equisatisfiable one. This is some new formula that is satisfiable if and only if the original is, but which might not be satisfied by the same assignments.
For example, (a ∨ b)
and (a ∨ c) ∧ (¬b)
are each obviously satisfiable, so they are equisatisfiable. But setting b
true satisfies the first and falsifies the second, so they are not equivalent. Furthermore the first doesn't even have c
as a variable, again making it not equivalent to the second.
This relaxation is enough to replace this exponential blow-up with a linear-sized translation instead.
The critical idea is the use of extension variables. These are fresh variables (i.e., not already present in the formula) that allow us to abbreviate expressions, so we don't end up making multiple copies of them in the translation. Since the new variable is not present in the original, we'll no longer have an equivalent formula; but because the variable will be true if and only if the expression is, it will be equisatisfiable.
If we wanted to use x
as an abbreviation of y
, we'd state x ≡ y
. This is the same as x → y
and y → x
, which is the same as (¬x ∨ y) ∧ (¬y ∨ x)
, which is already in CNF.
Consider the abbreviation for a product term: x ≡ (a ∧ b)
. This is x → (a ∧ b)
and (a ∧ b) → x
, which works out to be three clauses: (¬x ∨ a) ∧ (¬x ∨ b) ∧ (¬a ∨ ¬b ∨ x)
. In general, abbreviating a product term of k literals with x
will produce k binary clauses expressing that x
implies each of them, and one (k+1)
-clause expressing that all together they imply x
. This is linear in k
.
To really see why this helps, try converting (a ∧ b ∧ c) ∨ (d ∧ e ∧ f) ∨ (g ∧ h ∧ i)
to an equivalent CNF with and without an extension variable for the first product term. Of course, we won't just stop with one term: if we abbreviate each term then the result is precisely a single CNF clause: (x ∨ y ∨ z)
where these each abbreviate a single product term. This is a lot smaller!
This approach can be used to turn any circuit into an equisatisfiable formula, linear in size and in CNF. This is called a Tseitin transformation. Your DNF formula is simply a circuit composed of a bunch of arbitrary fan-in AND gates, all feeding into a single arbitrary fan-in OR gate.
Best of all, although this formula is not equivalent due to additional variables, we can recover an assignment for the original formula by simply dropping the extension variables. It is sort of a 'best case' equisatisfiable formula, being a strict superset of the original.
To patch this into your code, I added:
QUESTION
I am trying to profile a python program with pyinstrument but it keeps throwing this error when trying to render the profile in html.
...ANSWER
Answered 2021-Aug-03 at 04:08After doing a bit of searching, I found a github issue for pyinstrument
with the exact same error, and it seems like they fixed it in an update (4.0.2
). Try updating your pyinstrument version and let me know if it works!
Note: If you do end up getting an error after upgrading to 4.0.2
, in another github issue, the maintainer is working on a fix at the moment, so unfortunately either you'll have to find an earlier version that does work, or you'll have to wait for the developer to fix it.
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