The instructions at https://docs.yugabyte.com/latest/quick-start/docker/install/ state that Docker for Windows is supported, however the yb-docker-ctl utility in the step that follows seems to be a *nix app and does not run on Windows 10 Pro. How do I install a 3-node local YugaByte cluster on Docker for Windows? (by the way StackOverflow would not let me add a YugaByte tag to the question, I could only add Docker)
The yb-docker-ctl utility is actually a Python2 script that will run on Windows 10 Pro if you have Python2 installed. I prefer to use Chocolately (https://chocolatey.org) to manage my package installations, so you could install python2 (not python -- as that will default to python3) using choco install python2 from PowerShell or CMD. You can also install wget in the same manner.
You will then need to a couple of changes to yb-docker-ctl. The script utilizes os.path.join which will utilize the Windows default of \\ for path separator. Add the line import posixpath after line 10 of yb-docker-ctl and substitute posixpath.join for os.path.join at lines 227 and 377.
After you have made those modifications, you can run python yb-docker-ctl create to create your 3 node cluster.
Related
I am trying to set up Docker rootless mode on Ubuntu 18.04, using the dockerd-rootless-setuptool.sh script. But I'm struggling to get the script to find the slirp4netns binary I downloaded (slirp4netns provides user-mode networking for unprivileged network namespaces). On Ubuntu 20 this is installable as a package using apt, but for lower Ubuntu versions a pre-built binary is provided here.
I am running the script (as non-root) with:
dockerd-rootless-setuptool.sh install
But it complains:
Either slirp4netns (>= v0.4.0) or vpnkit needs to be installed
I have downloaded the latest stable binary for slirp4netns (v1.1.12) as per the instructions:
curl -o slirp4netns --fail -L https://github.com/rootless-containers/slirp4netns/releases/download/v1.1.12/slirp4netns-$(uname -m)
chmod +x slirp4netns
I moved the binary (as root) to /usr/local/lib. What am I doing wrong? Do I need to update an environment variable? I don't really want to upgrade my entire OS just to get a single library.
Thanks.
It was my own stupidity. As the person says in the comment to my question, all I needed to do was to move the binary to /usr/local/bin. I had assumed /usr/local/lib was on PATH, but it wasn't.
I am trying to build Pyodide from source on Windows. In their documentation they recommend using Docker. From the documentation:
1 Install Docker
2 From a git checkout of Pyodide, run ./run_docker or ./run_docker --pre-built
3 Run make to build.
I don't understand how to run ./run_docker?
I don't even know exactly what the file is. Is it a shell script?
Combining your question, "How do I run a script file in Windows?", with the information provided (you want to run a file called run_docker from the Pyodide project) you should get started by installing the Windows Subsystem for Linux version 2 (WSL). After you install WSL, you will need to open a command prompt, run bash to enter the Ubuntu linux distribution. From here you should follow the steps for building on Linux. When you run into a problem you can search the internet for solutions related to "Linux" or "Ubuntu".
The background is, I'm responsible for maintaining a fancy Docker image that is used by our team for analytics. It uses a Jupyter notebook image as the base, and then adds various customisations, extra packages, etc.
One of the team members recently wanted to run Tensorflow. No problem, I'll just run mamba install and add it to the image. However, this created an issue: Tensorflow 2.4.3 (the latest version) is somehow incompatible with R 4.1.1 (also the latest version) or something else in the ecosystem, causing R to to be downgraded to 3.6.3. So I created a new environment and installed TF into that:
FROM hongooi/jupytermodelrisk:1.0.0
RUN mamba create -n tensorflow --clone base
# Make RUN commands use the new environment
RUN echo "conda activate tensorflow" >> ~/.bashrc
SHELL ["/bin/bash", "--login", "-c"]
RUN mamba install -y 'tensorflow=2.4.3'
But when I rebuilt the image, I found that while the tensorflow env had been created, the Tensorflow package had been installed into the base env, not the tensorflow env. Has anyone else encountered this? I can verify, if I login to the container, that the tensorflow env has been created: it just doesn't contain the Tensorflow package.
I don't get this problem if run the create, activate and install commands from inside the container. It's only when I try to do it in the Dockerfile.
I use mamba instead of conda because the latter takes forever to run, given the number of packages installed. In fact, trying to run conda install tensorflow crashes after ~5 hours.
Not an expert on dockerfiles, but in general you could just use the -n flag to the install command to specify the target environment for the installation like so:
mamba install -n tensorflow -y tensorflow=2.4.3
Is there a way to install VS Code extensions in a Dockerfile?
Apparently, while most browser-based VS Code forks (including openvscode-server) do not permit headless installation of VS Code extensions (as seen from my other answer), it is possible to do such automated installs using docker build in one of them: Code Server (code-server), like in this sample Dockerfile:
FROM ubuntu:22.04
RUN apt update && apt install -y curl
# install VS Code (code-server)
RUN curl -fsSL https://code-server.dev/install.sh | sh
# install VS Code extensions
RUN code-server --install-extension redhat.vscode-yaml \
--install-extension ms-python.python
Relevant fragment of the docker build log:
[..]
---> Running in 59eea050a2db
[2022-11-13T10:13:58.762Z] info Wrote default config file to ~/.config/code-server/config.yaml
Installing extensions...
Installing extension 'redhat.vscode-yaml'...
Installing extension 'ms-python.python'...
Extension 'redhat.vscode-yaml' v1.10.1 was successfully installed.
Extension 'ms-python.python' v2022.16.1 was successfully installed.
[..]
Per the VS Code Documentation, the extensions property is specific to VS Code and can only be configured using .devcontainer.
The best you can do is if the extension has a CLI, you can install that. For example,
RUN npm install prettier -D --save-exact
Then use npx:
npx prettier --check .
This is sadly disallowed by design, as confirmed by this error message you will see in your docker build log when you attempt to run code --install-extension or openvscode-server --install-extension:
Command is only available in WSL or inside a Visual Studio Code terminal.
This is also confirmed (and tagged) as being as designed by one of VS Code Remote devs in this GitHub issue:
This is correct the 'vs code server CLI' is only available from the integrated terminal.
So VS Code Remote or even openvscode-server make it impossible to automate installs of first- or third-party extensions for this popular Microsoft IDE, unless you run their custom terminal inside their closed-source IDE, which normally entails buying a license for their GUI-based closed-source operating system as well;)
Background: I am new to the Python world and am using Plotly for creating basic graphs in Python. I am using AWS Sagemaker's JupyterLab for creating the python scripts.
Issue: I have been trying to run the basic codes mentioned on Plotly's website however even those are returning blank graphs.
Issue Resolution Tried by myself:
pip installed plotly version 4.6.0
Steps mentioned on https://plotly.com/python/getting-started/ for JupyterLab support have already been executed
Code Example:
import plotly.graph_objects as go
fig = go.Figure(data=go.Bar(y=[2, 3, 1]))
fig.show()
I recently had the same issue. Simple change suggested here helped me. I know this is a temporary workaround until a proper fix is found.
// fig = go.Figure()
fig = go.FigureWidget() // replace with this
// fig.show()
fig // remove .show()
Sagemaker notebook instances are using (As of Jan 2022), for some reason, jupyterlab==1.2.21. You can verify that by running pip freeze | grep lab from the terminal or !pip freeze | grep lab from a notebook.
According to the documentation, you'll need to install the following jupyterlab extensions (which are not needed if sagemaker was running jupyterlab 3):
jupyterlab-plotly
jupyter-widgets/jupyterlab-manager
You can install those on a up-and-running instance by running
jupyter labextension install jupyterlab-plotly#5.5.0 #jupyter-widgets/jupyterlab-manager in the terminal or notebook (using ! if you are running on the notebook ofcourse). Notice that the jupyterlab-plotly extension version (here 5.5.0) should match the plotly version you are installing. Mismatches my cause issues. In this case by plotly version is 5.5.0 and thus that's also the jupyterlab-plotly version I've installed.
If you need, like I did, to have it ready upon spinning up a notebook instance, you'll need to:
Create a lifecycle script
To it, add:
PATH=$PATH:/home/ec2-user/anaconda3/envs/JupyterSystemEnv/bin - To ensure nodejs path which is needed for the extension installation
pip install plotly==5.5.0 - To ensure a specific version
jupyter labextension install jupyterlab-plotly#5.5.0 #jupyter-widgets/jupyterlab-manager - To ensure same version
of coures, you can change the version according to the most up to date.
I think that documentation is not on par. You now need to install jupyterlab-plotly extension.
jupyter labextension install jupyterlab-plotly
UPDATE
I followed a mix of instructions here and here.
First Enable Extention manager from jupyter-lab
then from terminal
conda install -c conda-forge "nbformat" "ipywidgets>=7.5" -y
jupyter labextension install jupyterlab-plotly
jupyter labextension install #jupyter-widgets/jupyterlab-manager plotlywidget
And within your environment
conda install nbformat