kandi X-RAY | devdocs Summary
kandi X-RAY | devdocs Summary
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Top functions reviewed by kandi - BETA
- Runs the image .
- Build a page
- Takes an absolute path and returns the url that matches the url .
- Builds the navigation links
- Sort the number of strings
- Fetch the url for the user
- Returns a hash of options .
- Returns the relative path relative to the destination directory
- Normalize a path .
- Return the diff of the given object
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I have the following code that uses tensorflow to calculate a custom average loss when the image is consistently rotated:...
ANSWERAnswered 2022-Apr-01 at 08:58
The error might be coming from using
TF tensors. As stated in the docs you linked regarding
Performs a random rotation of a Numpy image tensor.
Meaning you cannot use
TF tensors with this operation. If you are in eager execution mode you can use
Using the Azure Devops task with current setup :...
ANSWERAnswered 2022-Mar-21 at 16:40
Maybe you will have to use python 3.9 and the latest ubuntu agent in the pipeline
I installed python 3.8.0, numpy 1.22.3, pytorch 1.11.0 .
and I tried this code:
But I'm getting this error:
ANSWERAnswered 2022-Mar-21 at 14:44
** From the PyCharm Python Console you can digit ctrl+alt+S to access at the Project:pythonProject > Python Interpreter. Down in the page, you should see a + button to access to all available packages. Please, here search for torch and install it. Now try:**
When I try to run the (simplified/illustrative) Spark/Python script shown below in the Mac Terminal (Bash), errors occur if imports are used for
pyspark.ml. The sample Python code shown here runs well when using the 'Section 1' imports listed below (when they include
from pyspark.sql import SparkSession), but fails when any of the 'Section 2' imports are used. The full error message is shown below; part of it reads:
'..._multiarray_umath.cpython-310-darwin.so' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64'). Apparently, there was a problem importing NumPy 'c-extensions' to some of the computing nodes. Is there a way to resolve the error so a variety of
pyspark.ml and other imports will function normally? [Spoiler alert: It turns out there is! See the solution below!]
The problem could stem from one or more potential causes, I believe: (1) improper setting of the environment variables (e.g.,
PATH), (2) an incorrect
SparkSession setting in the code, (3) an omitted but necessary Python module import, (4) improper integration of related downloads (in this case, Spark 3.2.1 (spark-3.2.1-bin-hadoop2.7), Scala (2.12.15), Java (1.8.0_321), sbt (1.6.2), Python 3.10.1, and NumPy 1.22.2) in the local development environment (a 2021 MacBook Pro (Apple M1 Max) running macOS Monterey version 12.2.1), or (5) perhaps a hardware/software incompatibility.
Please note that the existing combination of code (in more complex forms), plus software and hardware runs fine to import and process data and display Spark dataframes, etc., using Terminal--as long as the imports are restricted to basic versions of
pyspark.sql. Other imports seem to cause problems, and probably shouldn't.
The sample code (a simple but working program only intended to illustrate the problem):...
ANSWERAnswered 2022-Mar-12 at 22:10
Solved it. The errors experienced while trying to import numpy c-extensions involved the challenge of ensuring each computing node had the environment it needed to execute the target script (
test.py). It turns out this can be accomplished by zipping the necessary modules (in this case, only
numpy) into a tarball (.tar.gz) for use in a 'spark-submit' command to execute the Python script. The approach I used involved leveraging conda-forge/miniforge to 'pack' the required dependencies into a file. (It felt like a hack, but it worked.)
The following websites were helpful for developing a solution:
- Hyukjin Kwon's blog, "How to Manage Python Dependencies in PySpark" https://databricks.com/blog/2020/12/22/how-to-manage-python-dependencies-in-pyspark.html
- "Python Package Management: Using Conda": https://spark.apache.org/docs/latest/api/python/user_guide/python_packaging.html
- Alex Ziskind's video "python environment setup on Apple Silicon | M1, M1 Pro/Max with Conda-forge": https://www.youtube.com/watch?v=2Acht_5_HTo
- conda-forge/miniforge on GitHub: https://github.com/conda-forge/miniforge (for Apple chips, use the
Miniforge3-MacOSX-arm64download for OS X (arm64, Apple Silicon).
Steps for implementing a solution:
- Install conda-forge/miniforge on your computer (in my case, a MacBook Pro with Apple silicon), following Alex's recommendations. You do not yet need to activate any conda environment on your computer. During installation, I recommend these settings:
Runtime python 3.7 w/ compatible runtime 3.7
I keep getting Import error when trying to test API in lambda function...
ANSWERAnswered 2022-Feb-12 at 23:12
Based on the comments.
The solution was to use Numpy layer provided by AWS.
I'm unable to import pandas with
import pandas as pd on replit.
I've already installed the package with
pip install pandas and it can be seen in packages. I've successfully imported it to other projects on replit. Every time I try importing it into my code on this project, it gives me the following error:
ANSWERAnswered 2022-Feb-10 at 03:15
You don't need to use
pip to install packages on repl.it -- and in fact, you shouldn't! Using Nix derivations not only works better (as you're using their OS distro the way it's designed), but also keeps their storage costs low, by allowing packages to be used from a read-only, hash-addressed, shared store.
Binaries built for other distributions might assume that there will be libraries in
/usr/lib, or the like, but that's not how NixOS works: Libraries will be in a path like
/nix/store/--/lib, and those paths get embedded into the executables that use those libraries.
The easiest thing to do here is to create a new bash repl, but to add a Python interpreter to it. (I suggest this instead of using a Python repl because the way they have their Python REPLs set up adds a bunch of extra tools that need to be reconfigured; a bash repl keeps it simple).
- Create a new bash repl.
- Click on the three-dots menu.
- Select "Show Hidden Files".
- Open the file named
- Edit the file by adding a Python interpreter with pandas, as follows:
I am making a docker image that needs pandas and numpy but the installation via pip takes around 20 mins which is too long for my use case. I then opt to install pandas and numpy from alpine package repo but it seems to fail to import numpy correctly.
Here is my Dockerfile:...
ANSWERAnswered 2021-Sep-28 at 11:25
I know it's been a while since this was asked, and you might've found a solution, or moved on from Alpine to a different distro. But I ran into the same issue, and this was the first thing that popped up on my search. So, after spending a couple of hours and finding a solution, I think it's worthwhile to document it here.
The issue is (obviously) with
pandas packages. I used pre-built wheels from the community repo and ran into the same issue as you. So, evidently, the build process itself is introducing the issue. Specifically, if you look, e.g., under
numpy/core at the install location (
/usr/lib/python3.9/site-packages), you'll find that all the C-extensions have
.cpython-39-x86_64-linux-musl in their name. So, for instance, the module you're having trouble with,
numpy.core._multiarray_umath, is named
_multiarray_umath.cpython-39-x86_64-linux-musl.so, and not just
_multiarray_umath.so. Dropping the
.cpython-39-x86_64-linux-musl from those filenames fixed the issue (edit: see addendum for details).
The following line can be added to your
Dockerfile after installing
py3-numpy to fix it:
I am learning how to write a Maximum Likelihood implementation in
Julia and currently, I am following this material (highly recommended btw!).
So the thing is I do not fully understand what a closure is in Julia nor when should I actually use it. Even after reading the official documentation the concept still remain a bit obscure to me.
For instance, in the tutorial, I mentioned the author defines the log-likelihood function as:...
ANSWERAnswered 2022-Feb-03 at 18:34
In the context you ask about you can think that closure is a function that references to some variables that are defined in its outer scope (for other cases see the answer by @phipsgabler). Here is a minimal example:
ANSWERAnswered 2022-Jan-22 at 03:48
Ah, so to be a bit technical this is really more accurately a question about how type annotations, dispatch, optional arguments, and keyword arguments work in Julia; the
methods function just gives you some insight into that process, but it's not the
methods function that makes those decisions. To answer your individual questions
It is not quite clear to me why there is no method
f(::Int64, ::Float64)(hence the error).
There is no method for this because you you can only omit optional normal (non-keyword) arguments contiguously from the last normal (non-keyword) argument. Consider the following case:
Update (1): The same problem can be seen with some compiled stdlib modules. This is not related to numpy (I'm removing the numpy tag and numpy from the title)
I'm writing a shared object (that is a plugin for a software) that contains an embedded python interpreter. The shared object launches an interpreter and the interpreter imports a python module to be executed. If the imported module includes numpy, I get an undefined symbol error. The actual undefined symbol error changes in function of the python version or numpy version, but it is always a struct of the
I've simplified the issue to this mimimum example (it comprises actually two files):...
ANSWERAnswered 2021-Dec-17 at 09:08
I've found a solution. Knowing that it was not tied to numpy halped quite a lot to switch the focus on the real problem: symbol missing. Taking the suggestion from this answer and in particular this point:
Solve a problem. Load the library found in step 1 by dlopen first (use RTLD_GLOBAL there as well).
I've modified the minimum example as follows:
No vulnerabilities reported
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