Skip to main content

mcy-dist-ai

This is a package used in Mercury Protocol's vulkan repository for training AI models distributed.

Usage:

Each instance has a role: WATCHER or LEADER. Watchers do the batch training and the leader does the gradient aggregation.

The user who wants to train an AI model has to write the script in a file called user_script.py and also a user_requirements.txt where the dependencies of the user_script.py are specified. This file is used by this component to perform the training. To see how it should be written check docs/user_script_requirements.md and docs/user_script_template.py.

mcy-split-data:

mcy-split-data is command which can be used by the user to split the data into a specified number of partitions and save them as tensors.

args:
split_into: the data will be split into this many partitions
data_path: the path of the directory which contains the data
output_dir_path: the path of the directory where the split tensors will be saved
user_script_path: the path of the user_script.py file where the create_data_loader function is specified by the user

example usage:

mcy-split-data 2 path/to/data/dir path/to/output/data/dir path/to/user_script.py

Metadata

Release files for mcy-dist-ai 1.0.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mcy-dist-ai 1.0.0
File Size Uploaded
mcy_dist_ai-1.0.0.tar.gz 8.8 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for mcy-dist-ai 1.0.0
File Interpreter ABI Platform
mcy_dist_ai-1.0.0-py3-none-any.whl Python 3 none any Details

Total release size: 20.1 kB

Release files / mcy_dist_ai-1.0.0.tar.gz

Download URL mcy_dist_ai-1.0.0.tar.gz
Size 8.8 kB
Tags Source
SHA-256 checksum
How to use checksums
090b3683bb8cfa00f1afa44096a10ff5d5b7fbaad4f7641b51a13acad03794a7
BLAKE2b-256 checksum
How to use checksums
448585bc56fce3daecf082b2260fd482929188eda9349eebcd9d37e7a746c79f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.1 CPython/3.11.9

Release files / mcy_dist_ai-1.0.0-py3-none-any.whl

Download URL mcy_dist_ai-1.0.0-py3-none-any.whl
Size 11.3 kB
Tags Python 3
SHA-256 checksum
How to use checksums
7a2404d9e6116530305b93e6f4b5e6dc6a0c97d800f658e2d65e95bd0d8422f0
BLAKE2b-256 checksum
How to use checksums
9b4339f3ad7bff681c811b2c0435e25421454791dd1ce38ad6973027d4bb8ad2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.1 CPython/3.11.9
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page