MLComp

MLComp is a distributed DAG (Directed acyclic graph) framework for machine learning with UI.

The goal of MLComp is to provide tools for training, inferencing, creating complex pipelines (especially for computer vision) in a rapid, well manageable way.

MLComp is compatible with: Python 3.6+, Unix operation system.

Features

Amazing UI
Catalyst support
Distributed training
Supervisor that controls computational resources
Synchronization of both code and data
Resource monitoring
Full functionality of the pause and continue on UI
Auto control of the requirements
Code dumping (with syntax highlight on UI)
Kaggle integration
Hierarchical logging
Grid search
Experiments comparison
Customizing layouts

Screenshots

Dags

dags

Computers

computers

Reports

reports

Code

code

Graph

graph

Installation

Install MLComp package
```
pip install mlcomp
mlcomp init
```
Setup your environment. Please consider Environment variables section
Run db, redis, mlcomp-server, mlcomp-workers:

Variant 1: minimal (if you have 1 computer)

Run all necessary (mlcomp-server, mlcomp-workers, redis-server), it uses SQLITE:
```
mlcomp-server start
```
Variant 2: full

a. Change your Environment variables to use PostgreSql

b. Install rsync on each work computer
```
sudo apt-get install rsync
```
Ensure that every computer is available by SSH protocol with IP/PORT you specified
in the Environment variables file.

rsync will perform the following commands:

to upload
```
rsync -vhru -e "ssh -p {target.port} -o StrictHostKeyChecking=no" \
{folder}/ {target.user}@{target.ip}:{folder}/ --perms  --chmod=777
```
to download
```
rsync -vhru -e "ssh -p {source.port} -o StrictHostKeyChecking=no" \
{source.user}@{source.ip}:{folder}/ {folder}/ --perms  --chmod=777
```
c. Install apex for distributed learning

d. To Run postgresql, redis-server, mlcomp-server, execute on your server-computer:
```
cd ~/mlcomp/configs/
docker-compose -f server-compose.yml up -d
```
e. Run on each worker-computer:
```
mlcomp-worker start
```

UI

Web site is available at http://{WEB_HOST}:{WEB_PORT}

By default, it is http://localhost:4201

The front is built with AngularJS.

In case you desire to change it, please consider front's Readme page

Usage

Run

mlcomp dag PATH_TO_CONFIG.yml

This command copies files of the directory to the database.

Then, the server schedules the DAG considering free resources.

For more information, please consider Docs

Docs and examples

API documentation and an overview of the library can be
found here

You can find advanced tutorials and MLComp best practices in the examples folder of the repository.

Environment variables

The single file to setup your computer environment is located at ~/mlcomp/configs/.env

ROOT_FOLDER - folder to save MLComp files: configs, db, tasks, etc.
TOKEN - site security token. Please change it to any string
DB_TYPE. Either SQLITE or POSTGRESQL
POSTGRES_DB. PostgreSql db name
POSTGRES_USER. PostgreSql user
POSTGRES_PASSWORD. PostgreSql password
POSTGRES_HOST. PostgreSql host
PGDATA. PostgreSql db files location
REDIS_HOST. Redis host
REDIS_PORT. Redis port
REDIS_PASSWORD. Redis password
WEB_HOST. MLComp site host. 0.0.0.0 means it is available from everywhere
WEB_PORT. MLComp site port
CONSOLE_LOG_LEVEL. log level for output to the console
DB_LOG_LEVEL. log level for output to the database
IP. Ip of a work computer. The work computer must be accessible from other work computers by these IP/PORT
PORT. Port of a work computer. The work computer must be accessible from other work computers by these IP/PORT (SSH protocol)
MASTER_PORT_RANGE. distributed port range for a work computer. 29500-29510 means that if
this work computer is a master in a distributed learning, it will use the first free port
from this range. Ranges of different work computers must not overlap.
NCCL_SOCKET_IFNAME. NCCL network interface.
You can see your network interfaces with ifconfig command.
Please consider nvidia doc