semvec/pypi-packages huggingface dataset:
richqueue 1.11.2
RichQueue
Requires Python: >=3.10

# RichQueue

> 💰 RichQueue: A colourful and pythonic SLURM queue viewer

Installation from a modern python environment (>3.10) should be as simple as:

```shell

pip install --upgrade richqueue
```

To see your live-updating SLURM queue:

```shell
rq
```

![queue_example](https://github.com/user-attachments/assets/d99ca5e9-7675-4853-ab28-2d7b4da855f2)

## Other `rq` options

To see more detail:

```rq --long```

To see someone else's queue:

```rq --user USER```

To see the last `X` weeks history:

```rq --hist X```

To see history for a given time period, e.g.:

```rq --hist '3 days'```

To list available nodes on the cluster:

```rq --idle```

To show a static view:

```rq --no-loop```

## Monitoring log files

If you keep your SLURM log files in a specific directory exported as the `LOGS` variable, you can use RichQueue to monitor results as they come in with

```
res <JOB_ID>
```

<img width="569" height="737" alt="Screenshot 2025-11-20 at 09 05 48" src="https://github.com/user-attachments/assets/1b0f6457-d769-4a1a-b331-bb7f121b5864" />

## Changing to the working directory of a job

To change to the submission/working directory of a job:

```
cd $(res --dir <JOB_ID>)
```

You might find it convenient to add a shortcut to this to your login profile, e.g. `.bashrc_user` or `.bash_profile`:

```
res --install-jd >> ~/.bashrc_user
source ~/.bashrc_user
```

This will make the `jd` executable available. Change to the directory of the single active or most recently submitted job:

```
jd
```

Change to a specific job's directory:

```
jd <JOB_ID>
```

## Submitting SLURM jobs

RichQueue also provides a convenient wrapper to the `sbatch` command which makes sure your log files end up in the directory specified by the `LOGS` variable, and prints a pretty summary of the submitted job. Just replace `sbatch` with `sb`. E.g.:

```
sb --job-name test script.sh --script-arg1
```

Would run this under the hood:

```
sbatch --output=$LOGS/%j.log --error=$LOGS/%j.log --job-name test script.sh --script-arg1
```

and display the following:

<img width="474" height="255" alt="Screenshot 2025-11-20 at 10 37 19" src="https://github.com/user-attachments/assets/4464f813-841a-45bd-864e-00dd3dabf924" />
beyondtrust-bips-cli 0.12.1
PS CLI is a CLI tool designed to allow the execution of BeyondInsight and Password Safe API calls
Requires Python: >=3.11

[![Scanned by Frogbot](https://raw.github.com/jfrog/frogbot/master/images/frogbot-badge.svg)](https://docs.jfrog-applications.jfrog.io/jfrog-applications/frogbot)

# ps-cli
The Password Safe CLI Application is an efficient Command Line Interface (CLI) tool specifically crafted to interface with the Beyond Insight and Password Safe APIs (version 24.3). This application allows users to access various API resources, including safes, folders, secrets, managed accounts, and more. It offers a user-friendly interface that simplifies command parsing, ensures input validation, and delivers detailed output.


# Available environment variables

## Required:
- **PSCLI_API_URL**: Beyond Insight and Password Safe API URL. This can also be set in the settings file.
- **PSCLI_CLIENT_ID**: Client ID to use when requesting data from the API. This can also be set in the settings file.
- **PSCLI_CLIENT_SECRET**: Client secret to use when requesting data from the API. This can also be set in the settings file.

## Optional:
- **PSCLI_SETTINGS_PATH**: Custom settings path to use for `ps-cli`. By default, the settings file is created in the user's home directory (~).
- **PSCLI_AUTH_RETRIES**: The number of times `ps-cli` should attempt to authenticate in case of an error.
- **PSCLI_TIMEOUT_CONNECTION**: How long to wait for the server to connect and send data before giving up. Integer value defined in seconds, by default 30 seconds.
- **PSCLI_TIMEOUT_REQUEST**: How long to wait for each request made to the API. Defined in seconds, by default 30 seconds.
- **PSCLI_VERIFY_CA**: Set to `false` to disable SSL certificate verification (useful for self-signed certificates). By default, SSL verification is enabled for secure connections.


# Prerequisites

- Python 3.12+
- Password Safe version 24.3


# Getting started

- Install `ps-cli` (package name: `beyondtrust-bips-cli`)

```sh
pip install beyondtrust-bips-cli
```

- Check that `ps-cli` is properly installed:

```sh
ps-cli -h
# Output usage instructions:
usage: ps-cli [-h] [-v] [-l {DEBUG,INFO,WARNING,ERROR,CRITICAL}] [--format {json,csv,tsv}] [--delimiter {,,;,   ,|, }]
              [-av {3.0,3.1}]
              {safes,folders,secrets,settings} ...
```


- Create the settings file using:

By default, the settings file is created in the user's home directory (~). If you would like to specify a custom path for the settings, you can achieve this by setting the **PSCLI_SETTINGS_PATH** environment variable. If you're using the custom settings path (PSCLI_SETTINGS_PATH), set it before running the settings initialization command:

```sh
ps-cli settings initialize-settings
```

After creating the settings file, proceed to edit it and configure the **api_url**, **client_id**, and **client_secret**, in case you did not define this configuration using the available environment variables (PSCLI_API_URL, PSCLI_CLIENT_ID and PSCLI_CLIENT_SECRET).


You can find more details on `ps-cli` official [documentation](https://docs.beyondtrust.com/).
FlashcardsXBlock 1.0.2rc2
A Flashcards XBlock for Open edX.
Requires Python: >=3.11
Keywords: Python, edx

# FlashcardsXBlock

An Open edX platform XBlock to create and display flashcards.

## Installation

In a Tutor deployment, you can add it to `OPENEDX_EXTRA_PIP_REQUIREMENTS`, installing from git:

```yaml
OPENEDX_EXTRA_PIP_REQUIREMENTS:
- git+https://github.com/open-craft/FlashcardsXBlock.git@main
```

Or from a [Pypi release](https://pypi.org/project/FlashcardsXBlock/):

```yaml
OPENEDX_EXTRA_PIP_REQUIREMENTS:
- FlashcardsXBlock==1.0.1
```

## Usage

Once the Flashcards XBlock is installed in the Open edX environment,
you can navigate to the "Advanced settings" of the course in Studio or Authoring,
then add "flashcards" to the "Advanced module list".

Then navigate to the unit in the course where you want to add a Flashcards component,
and select "Flashcards" from the "Advanced" component list.

## Screenshots

Student view:

![student view: starting screen](./screenshots/view-1.png)

![student view: a flashcard](./screenshots/view-2.png)

Editing modal in Authoring:

![edit view: edit screen one, where you can select theming](./screenshots/edit-1.png)

![edit view: edit screen two, where you can enter flashcard content](./screenshots/edit-2.png)

## Development notes

- There is a Github workflow that automatically creates Pypi releases when you create a release on Github.

### Frontend

The frontend is built using Typescript and React; it must be built before it can be used.
See ./mise.toml and ./frontend/package.json for available tasks.
An example development workflow could look something like this:

```sh
# install dependencies
mise run frontend-install

# run a development server with hot-reloading
mise run frontend-dev

# edit the frontend files

# when ready, kill the dev server, and build the frontend.
# If you have this XBlock installed on your Open edX devstack, you can then refresh the page to see updates in live components in a course on the devstack.
mise run frontend-build

# check the unit tests
mise run frontend-test
```

## Want to contribute?

If you have a suggestion, question or found a bug, please [open an issue](https://github.com/open-craft/FlashcardsXBlock/issues/new).

If you would like to contribute code, please open a pull request with details about the changes that you propose.
onnxoptimizer 0.4.2
ONNX Optimizer
Requires Python: >=3.10
Keywords: deep-learning, ONNX

<!--
SPDX-FileCopyrightText: ONNX Project Contributors

SPDX-License-Identifier: Apache-2.0
-->

# ONNX Optimizer

[![PyPI version](https://img.shields.io/pypi/v/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/)
[![PyPI license](https://img.shields.io/pypi/l/onnxoptimizer.svg)](https://pypi.python.org/pypi/onnxoptimizer/)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/onnx/optimizer/pulls)

## Introduction

ONNX provides a C++ library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes.

The primary motivation is to share work between the many ONNX backend implementations. Not all possible optimizations can be directly implemented on ONNX graphs - some will need additional backend-specific information - but many can, and our aim is to provide all such passes along with ONNX so that they can be re-used with a single function call.

You may be interested in invoking the provided passes, or in implementing new ones (or both).

## Installation

You can install onnxoptimizer from PyPI:

```bash
pip3 install onnxoptimizer
```

Note that you may need to upgrade your pip first if you have trouble:

```bash
pip3 install -U pip
```

If you want to build from source:

```bash
git clone --recursive https://github.com/onnx/optimizer onnxoptimizer
cd onnxoptimizer
pip3 install -e .
```

Note that you need to install protobuf before building from source.


## Command-line API
Now you can use command-line api in terminal instead of  python script.

```
python -m onnxoptimizer input_model.onnx output_model.onnx
```

Arguments list is following:
```
# python3 -m onnxoptimizer -h                                 
usage: python -m onnxoptimizer input_model.onnx output_model.onnx 

onnxoptimizer command-line api

optional arguments:
  -h, --help            show this help message and exit
  --print_all_passes    print all available passes
  --print_fuse_elimination_passes
                        print all fuse and elimination passes
  -p [PASSES ...], --passes [PASSES ...]
                        list of optimization passes name, if no set, fuse_and_elimination_passes will be used
  --fixed_point         fixed point
```
## Roadmap

* More built-in pass
* Separate graph rewriting and constant folding (or a pure graph rewriting mode, see [issue #9](https://github.com/onnx/optimizer/issues/9) for the details)

## Relevant tools

* [onnx-simplifier](https://github.com/daquexian/onnx-simplifier): A handy and popular tool based on onnxoptimizer

## Code of Conduct

[ONNX Open Source Code of Conduct](https://onnx.ai/codeofconduct.html)