Metadata-Version: 2.1
Name: labml-remote
Version: 0.1.0
Summary: Run python code on remote servers
Home-page: https://github.com/lab-ml/remote
Author: Varuna Jayasiri
Author-email: vpjayasiri@gmail.com
License: UNKNOWN
Project-URL: Documentation, https://lab-ml.com/
Description: [![PyPI - Python Version](https://badge.fury.io/py/labml-remote.svg)](https://badge.fury.io/py/labml-remote)
        [![PyPI Status](https://pepy.tech/badge/labml_remote)](https://pepy.tech/project/labml_remote)
        [![PyPI Status](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/labforml/shared_invite/zt-egj9zvq9-Dl3hhZqobexgT7aVKnD14g/)
        
        ![labml_remote job-list](https://github.com/lab-ml/remote/raw/master/notes/ddp-job-list.png)
        
        `labml_remote` is a very simple tool that lets you setup python and run python on remote computers.
        It's mainly intended for deep learning training.
        It doesn't use layers and technologies such as docker, terraform or slurm.
        It simply SSH's into the remote computers and run commands, and jobs with `nohup`,
         and synchronises using rsync.
         
        `labml_remote` comes with a easy-to-use **Commandline Interface**.
        You can also use the API to launch 
        customized distributed training sessions.
        [Here is a sample](https://github.com/lab-ml/remote/blob/master/sample/api_sample.py).
        
        ## Install from PIP
        
        ```bash
        pip install labml_remote
        ```
        
        ## Initialization
        
        Go to your project folder.
        
        ```bash
        cd [PATH TO YOUR PROJECT FOLDER]
        ```
        
        Initialize for remote execution
        ```bash
        labml_remote init
        ```
        
        
        ## Configurations
        
        `labml_remote init` asks for your SSH credentials and creates two files `.remote/configs.yaml`
        and `.remote/exclude.txt`.
        `.remote/configs.yaml` keeps the remote configurations for the project.
        
        Here's a sample `.remote/configs.yaml`:
        
        ```yaml
        name: sample
        servers:
          primary:
            hostname: 3.19.32.53
            private_key: ./.remote/private_key
            username: ubuntu
          secondary:
            hostname: ec2-3-20-234-50.us-east-2.compute.amazonaws.com
            private_key: ./.remote/private_key
        ```
        
        `.remote/exclude.txt` is like `.gitignore` - it specifies the files and folders that you dont need
        to sync up with the remote server. The excludes generated by `labml_remote --init` excludes
        things like `.git`, `.remote`, `logs` and `__pycache__`.
        You should edit this if you have things that you don't want to be synced with your remote computer.
        
        ## CLI
        
        Get the command line interface help with,
        
        ```bash
        labml_remote --help
        ```
        
        Use the flag `--help` with any command to get the help for that command.
        
        ### Prepare the servers
        
        ```bash
        labml_remote prepare
        ```
        
        This will install Conda on the servers, rsync your project content and install the pip packages,
        based on your `requirements.txt` or `Pipfile`.
        
        ### Run a command
        
        ```bash
        labml_remote run --cmd 'python my_script.py'
        ```
        
        This will execute the command on the server and show you the outputs of it.
        
        ### Start a job
        
        ```bash
        labml_remote job-run --cmd 'python my_script.py' --tag my-job
        ```
        
        ### List jobs
        
        ```bash
        labml_remote job-list --rsync
        ```
        
        `--rysnc` flag will sync up the job information from server to your local computer before
        listing.
        
        ### Tail a job output
        
        ```bash
        labml_remote job-tail --tag my-job
        ```
        
        This will keep on tailing the output of the job.
        
        ### Kill jobs
        
        ```bash
        labml_remote job-kill --tag my-job
        ```
        
        ### Launch a PyTorch distributed training session
        
        ```bash
        labml_remote helper-torch-launch --cmd 'train.py' --nproc-per-node 2 --env GLOO_SOCKET_IFNAME enp1s0
        ```
        Here `train.py` is the training script. We are using computers  with 2 GPUs, we want two processes per computer
        so `--nproc-per-node` is 2. `--env GLOO_SOCKET_IFNAME enp1s0` sets environment variable `GLOO_SOCKET_IFNAME` to
        `enp1s0`. You can specify multiple environment variables with `--env`.
        
        ## How it works
        
        It sets up *miniconda* if it is not already installed and create a new environment for the project.
        Then it creates a folder by the name of the project inside home folder and synchronises the contents
        of your local folder with the remote computer.
        It syncs using *rsync* so subsequent synchronisations only need to send the changes.
        Then it installs packages from `requirements.txt` or with *pipenv* if a `Pipfile` is found.
        It will use *pipenv* to run your commands if a `Pipfile` is present.
        The outputs of commands are streamed backed to the local computer and the outputs of jobs redirected to
        files on the server which are synchronized back to the local computer using *rsync*.
        
        ## What it doesn't do
        
        This won't install things like drivers or CUDA. So if you need them you should pick an
        image that comes with those for your instance. For example, on AWS pick a deep learning
        AMI if you want to use an instance with GPUs.
        
Keywords: machine learning
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
