msgpack-python | MessagePack serializer implementation for Python msgpack | Serialization library

Channel pypi means that the package was installed with pip. You may need to upgrade it with pip as well

Figured it out.

What works:

I want to configure the state to perform a certain action if curl returns an error.

There is a Salt state called http which can query a URL and return the status. Using this (instead of curl) we can check for the status code(s) (200, 201, etc.), as well as matching text. Then we can use requisites to run subsequent states depending on the success/failure of the http.query.

Example:

I have added a check for status code of 200, you can omit - status: 200 if you don't care about the status code.

The requisites that we mention, such as require, watch, etc. should be references to other states that are "scheduled" to run on the targets.

About watch

Even though the path is created by installing the "realvnc" package, there is no Salt state to handle the path. That is why it is showing the error.

Quoting from documentation for watch:

A watch requisite is used to add additional behavior when there are changes in other states.

So if you want to watch on a file, then there should be a state that "handles" the file using the file module.

About require

Also, while specifying require you are referring to pkg module, so Saltstack would look for pkg.installed for realvnc. Whereas you are using chocolatey.installed.

So with below modifications it should work fine.

I just did the following and wasn't able to reproduce your error:

1. make a new compute instance
2. open it up using JupyterLab
3. open a new terminal
4. conda activate azureml_py36
5. conda install seaborn -y
6. open a new notebook and run import seaborn as sns

1. Are you using the kernel, Python 3.6 - AzureML (i.e. the azureml_py36 conda env)?
2. Have you tried restarting the kernel and/or creating a new compute instance?

I've got some experience with rasterio, but I am not nearly a master with it. If I remember correctly, rasterio requires you to have installed the program GDAL(both binaries and python utilities), and some other dependencies listed on the PyPi page. I don't use conda at the moment, I like to use the regular python 3.8 installer with pip. Given what I'm seeing with your installation, I would uninstall rasterio and follow a different installation procedure.

I follow the instructions listed here: https://rasterio.readthedocs.io/en/latest/installation.html
This page also has separate instructions for those using Anaconda.

The GDAL installation is by far the most annoying but once it's done, the hard part is over. The python utilities for both rasterio and gdal can be found here:
https://www.lfd.uci.edu/~gohlke/pythonlibs/#gdal
The second link is also provided on the PyPi page but I like to keep it bookmarked because there's a lot of good resources there!

The documentation gives what you want : pip download

pip download does the same resolution and downloading as pip install, but instead of installing the dependencies, it collects the downloaded distributions into the directory provided

source

So you may try these option with pip download :

It looks like this question has an answer in the comments. I'm going to copy a response from Nick Becker

Dask's distributed scheduler is single threaded (CPU and GPU), and Dask-CUDA uses a one worker per GPU model. This means that each task assigned to a given GPU will run serially, but that the task itself will use the GPU for parallelized computation. You may want to look at the Dask documentation and explore Dask.Array (which also supports GPU arrays).

The answer to your misunderstanding is a Unix concept, softlinks which we could say that in Windows are similar to shortcuts. Let's explain this.

When you spacy download en, spaCy tries to find the best small model that matches your spaCy distribution. The small model that I am talking about defaults to en_core_web_sm which can be found in different variations which correspond to the different spaCy versions (for example spacy, spacy-nightly have en_core_web_sm of different sizes).

When spaCy finds the best model for you, it downloads it and then links the name en to the package it downloaded, e.g. en_core_web_sm. That basically means that whenever you refer to en you will be referring to en_core_web_sm. In other words, en after linking is not a "real" package, is just a name for en_core_web_sm.

However, it doesn't work the other way. You can't refer directly to en_core_web_sm because your system doesn't know you have it installed. When you did spacy download en you basically did a pip install. So pip knows that you have a package named en installed for your python distribution, but knows nothing about the package en_core_web_sm. This package is just replacing package en when you import it, which means that package en is just a softlink to en_core_web_sm.

Of course, you can directly download en_core_web_sm, using the command: python -m spacy download en_core_web_sm, or you can even link the name en to other models as well. For example, you could do python -m spacy download en_core_web_lg and then python -m spacy link en_core_web_lg en. That would make en a name for en_core_web_lg, which is a large spaCy model for the English language.

Hope it is clear now :)