devdocs | API Documentation Browser | Frontend Framework library

Maybe you will have to use python 3.9 and the latest ubuntu agent in the pipeline

https://github.com/Azure/azure-functions-python-worker/issues/904

** 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:**

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:

1. 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
2. "Python Package Management: Using Conda": https://spark.apache.org/docs/latest/api/python/user_guide/python_packaging.html
3. 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
4. conda-forge/miniforge on GitHub: https://github.com/conda-forge/miniforge (for Apple chips, use the Miniforge3-MacOSX-arm64 download for OS X (arm64, Apple Silicon).

Steps for implementing a solution:

1. 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:

Based on the comments.

The solution was to use Numpy layer provided by AWS.

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 /lib, /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 replit.nix
• Edit the file by adding a Python interpreter with pandas, as follows:

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 numpy and 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-pandas and py3-numpy to fix it:

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:

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:

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: