system-install | system install scripts , to run after a clean ubuntu install

sudo pip3 uninstall pip will only uninstall pip, it won't uninstall python. You can just reinstall pip.

To reinstall pip:

I was able to generate the logstash service I was missing some options in the command.

step 1: check where your logstash is with command whereis logstash > for me :

The Dockerfile with which we are setting up ruby & bundler is similar to this one.

The machines where we deploy the bundled gems are RHEL machines and install ruby from the software collections repositories.

It seems like the deployment machines ruby is built with the --enable-shared=yes flag.

We changed our Dockerfile to configure the ruby build the same way, ./configure --enable-shared=yes. That solved our issue.

1. If you want to execute some script - you can use shell or command. Otherwise you can generate systemd service yourself

2. You don't need to use when to do that, you can use creates keyword in shell task.

Assuming your unit file gets created in /etc/systemd/system/logstash.service

You should write your first task as

The exception Expected one of #, input, filter, output at line 1, column 1 (byte 1) after usually occurs when:

• The config file is in an incorrect format - format logstash.conf file to UTF-8
• When the config file contains illegal characters between input, filter or output blocks - for example remove all whitespaces and new lines at the beginning of the file
• An invalid character has been used in comments - remove comments or format the file to UTF-8
• An incorrect separator has been used between the key and the value - check if every key and value is separated by =>
• Logstash tries to load different config file - make sure that the config file is either in Logstash bin folder or when running Logstash provide the full path -f PATH_TO/logstash.conf

This looks like unavoidable numeric imprecision. As explained here, NumPy uses a highly-optimized, carefully-tuned BLAS method for matrix multiplication. This means that probably the sequence of operations (sum and products) followed to multiply 2 matrices, changes when the size of the matrix changes.

Trying to be clearer, we know that, mathematically, each element of the resulting matrix can be calculated as the dot product of two vectors (equal-length sequences of numbers). But this is not how NumPy calculates an element of the resulting matrix. Infact there are more efficient but complex algorithms, like the Strassen algorithm, that obtain the same result without computing directly the row-column dot product .

When using such algorithms, even if the element C ij of a resulting matrix C = A B is mathematically defined as the dot product of the i-th row of A with the j-th column of B, if you multiply a matrix A2 having the same i-th row as A with a matrix B2 having the same j-th column as B, the element C2 ij will be actually computed following a different sequence of operations (that depends on the whole A2 and B2 matrices), possibly leading to different numerical errors.

That's why, even if mathematically C ij = C2 ij (like in your CASE 1), the different sequence of operations followed by the algorithm in the calculations (due to change in matrix size) leads to different numerical errors. The numerical error explains also the slightly different results depending on the environment and the fact that, in some cases, for some environments, the numerical error might be absent.

I think the answer may be found here.

My .gemrc was using --no-ri and --no-rdoc and while I read that these were deprecated, they may no longer be supported at all. Using --no-document instead seems to have solved the problem.

Generally, mixing PyPI package installs with Conda is a bad idea, especially if one plans to update any of the packages in the env. The Anaconda team has a nice overview of the various pitfalls that one might encounter in the blog post, "Using Pip in a Conda Environment".

In line with their recommendations, and what personally has worked well in my experience, is to be liberal about creating new envs and, once a project's specs have been hardened, to limit new package installs and updates to only what is necessary (e.g., needed bug fixes, API updates, etc.). I realize you explicitly said you'd prefer not to do this, but in my opinion it's the best option.

Although this didn't pan out in your case, checking alternative channels, such as Conda Forge, could be useful (i.e., -c conda-forge). At the same time, I'd caution against simply searching Anaconda Cloud for an arbitrary channel that happens to have what you want. Only install from channels that you trust.

Another option is to try pinning the packages you definitely don't want to change (e.g., python=3.7.4). This would force the dependency solver to figure out how to accomplish exactly what is requested in the question, and if it doesn't work, it be rather strong evidence that such a configuration is impossible with Conda packages alone.

The version of Vim you're running is the built-in Apple version, which you can see in the second line ("Compiled by root@apple.com"). You can install either the vim or macvim packages with Homebrew and they will have Python 3 built in, since, as ParthS007 mentioned, the default Python is Python 3.

However, your PATH environment variable will need to have /usr/local/bin before /usr/bin if you want to use the Homebrew version over the default system version (which you almost certainly do). You can edit your shell configuration to change the setting and then restart your terminal session.

If your PATH environment variable is set correctly, it's possible you may also have to run brew link vim (or brew link macvim) to create the appropriate symlinks. In general, you'll want command -v vim to show /usr/local/bin/vim. Running /usr/local/bin/vim by hand should show the correct Vim installed.