waldorf | Waldorf is an efficient , parallel task execution framework | Job Scheduling library
kandi X-RAY | waldorf Summary
kandi X-RAY | waldorf Summary
Waldorf is an efficient, parallel task execution framework written in Python. It was developed for research into reinforcement learning algorithms at our startup company in Beijing, China. Waldorf is based on the Celery distributed task queue, and takes its name from Waldorf salad, which also has celery as an ingredient. It can speed up algorithms such as Monte Carlo Tree Search (MCTS) by spreading concurrent sub-tasks, written as Python functions, across multiple machines and automating the collection of outputs. Waldorf can also be used to implement MapReduce-style work-flows. Although Waldorf can be deployed on cloud servers, our emphasis at the moment is on utilizing the spare CPU capacity of a commodity PC cluster (e.g. normal office workstations). Support for GPUs may be included in a future release.
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Top functions reviewed by kandi - BETA
- Respond to a get_info request
- Update the cores
- Add a slave
- Update the list of cores
- Main loop
- Handle retry
- Format log record
- Respond to a GET request
- Parse an environment
- Handle a GET request response
- Called when the worker is received
- Parse command line arguments
- Called when a clean response is received
- Respond to an update table
- Register a new task
- Get environment variables
- Deal with a git credential
- Called when a connection is received
- Called when a GET command is received
- Setup celery
- Receive a client info response
- Get system information
- Clone a git repository
- Start the celery worker
- Called when a response is received
- Parse the response of get_env
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waldorf Examples and Code Snippets
Community Discussions
Trending Discussions on waldorf
QUESTION
Attempt
After reading a large json file and capturing only the 'text'
column, I would like to add a column to dataframe and set all rows to a specific value:
ANSWER
Answered 2021-Feb-19 at 04:23The problem is that your read_json(....).text
line returns a series, not a dataframe.
Adding a .to_frame()
and referencing the column in the following line should fix it:
QUESTION
I have a (large) table like this:
...ANSWER
Answered 2020-Sep-01 at 12:12One way to achieve this is to select all the distinct city/date combinations in a subquery and then count the occurrence of each city:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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