periflow-cli 0.1.23

Description:

periflowcli 0.1.23

⛔️ IMPORTANT
This repository has been deprecated and is no longer maintained. Please use periflow-client to continue receiving updates and support. You can install the package with pip install periflow-client.
PeriFlow CLI



Welcome to PeriFlow ☁︎
PeriFlow is a reliable, speedy, and efficient service for training and serving your own large-scale AI model on any data of your choice. PeriFlow makes use of the cloud infrastructure without your need to invest in on-premise supercomputers. With one click, PeriFlow loads your data and runs optimized massive-scale AI training, handling any headaches that may arise plus visualizing your training progress.
Please visit docs.periflow.ai for the detailed guide:

CLI documentation
Python SDK documentation
PeriFlow tutorials

Installation
You can simply install the package using pip. Python version >= 3.8 is required.
pip install periflow-cli

Basic Commands
PeriFlow CLI commands start with the app name prefix pf.
pf [OPTIONS] COMMAND [ARGS]...

You can see the detail of each command using one of the following:
pf COMMAND
pf COMMAND -h
pf COMMAND --help

Workflow
You may go through the following steps when training AI models in PeriFlow.

Sign in.
Create/upload a dataset.
Run a job and monitor the job status.
Download trained model checkpoints to your local computer or deploy it!

Step 1. Sign In
You can login to PeriFlow with the following command.
pf login

Step 2. Manage Datasets
PeriFlow manages multiple datasets for your jobs. Once you create a dataset, the dataset can be easily used in any job you want. You can create datasets in two different ways:

Upload the dataset files from your local file system.
Link(register) the dataset files from your cloud storage (e.g., AWS S3, Azure Blob Storage, Google Cloud Storage).

# Option 1)
pf dataset create [OPTIONS]

# Option 2)
pf dataset upload [OPTIONS]

For the second option, you need to create a credential to access your cloud storage with pf credential create [OPTIONS] command in advance. You can also list, view, edit and delete the datasets using the CLI. Please use pf dataset --help for more details.
Step 3. Manage Job
You can easily configure jobs using a YAML file or CLI options and run them in cloud virtual machines with as many as GPUs you want. These are the options that you can configure for a job:

Cloud virtual machine type
The number of GPUs
Docker image and command
Workspace directory
Dataset
Model checkpoint
Slack Notification
Weights & Biases
Distributed training

Job Type: "Custom" & "Predefined"
There are two types of jobs in PeriFlow: "custom" and "predefined".

Custom Job: Jobs running with your own source codes are categorized as "custom" jobs. You can freely customize a job with your own source code and own docker image.
Predefined Job: PeriFlow provides a predefined job training service for some popular AI models like GPT-3. You can easily configure the predefined jobs without your effort to find hyper-parameters and distributed training configurations. Instead, you can use pre-configured hyper-parameters and train the models in our highly optimized training engine.

Job Configuraion File
You can describe the job setting in configuration YAML files. By using pf job template create [OPTIONS] command, you can get a job configuration template file generated with the required fields that you need to fill in. For example, suppose that you want to run a custom job with the following requirements:

Use private docker image.
Use a training dataset you've created.
The training will be resumed from a specific model checkpoint file.
Get Slack notification to get notified of the job status.

To make the configuration file for this kind of job, you may select the following options in the interactive prompt from pf job template create [OPTIONS] command.
$ pf job template create -s /path/to/config.yaml # Interactive prompt will be started.
What kind of job do you want?
(predefined, custom)
>> custom
Will you use your private docker image? (You should provide credential). [y/N]
>> y
Do you want to run the job with the scripts in your local directory? [y/N]
>> y
Will you use a dataset for the job? [y/N]
>> y
Will you use an input checkpoint for the job? [y/N]
>> y
Does your job generate model checkpoint files? [y/N]
>> y
Will you run distributed training job? [y/N]
>>
Will you use W&B monitoring for the job? [y/N]
>>
Do you want to get a Slack notification for the job? [y/N]
>> y
Do you want to open an editor to configure the job YAML file? (default editor: nvim) [y/N]
>>

Then, the configuration template file will be saved at /path/to/config.yaml with the following content.
# The name of job
name:

# The name of vm type
vm:

# The number of GPU devices
num_devices:

# Configure your job!
job_setting:
type: custom

# Docker config
docker:
# Docker image you want to use in the job
image:
# Bash shell command to run the job.
#
# NOTE: PeriFlow automatically sets the following environment variables for PyTorch DDP.
# - MASTER_ADDR: Address of rank 0 node.
# - WORLD_SIZE: The total number of GPUs participating in the task.
# - RANK: Rank of the current process.
# - LOCAL_RANK: Local rank of the current process in the node.
# - NODE_RANK: Index of the current node.
command:
credential_id:
# Path to mount your workspace volume. If not specified, '/workspace' will be used by default.
workspace:
mount_path:

# Configure dataset
data:
# The name of dataset
name:
# Path to mount your dataset volume
mount_path:

# Checkpoint config
checkpoint:
input:
# UUID of input checkpoint
id:
# Input checkpoint mount path
mount_path:
# Path to output checkpoint
output_checkpoint_dir:

# Additional plugin for job monitoring and push notification
plugin:
slack:
credential_id:
channel:

When you finished filling in the fields in the configuration file, you can use it to run jobs with pf job run -f /path/to/config.yaml.
Run & Monitor Jobs
You can run jobs and monitor the status using the CLI.

pf job run [OPTIONS]: Run a job.
pf job list [OPTIONS]: List up jobs.
pf job view JOB_ID: View the detail of a specific job.
pf job log [OPTIONS] JOB_ID: View the console output (i.e., stdout, stderr) of a specific job.

For more detail, please use -h or --help option.
Step 4. Manage Model Checkpoints
You can manage model checkpoint files generated from your jobs using the CLI.

📝 NOTE: pf job view JOB_ID will show a list of checkpoints generated from the job.


pf checkpoint list [OPTIONS]: List up checkpoints.
pf checkpoint view CHECKPOINT_ID: View the detail of a specific checkpoint.
pf checkpoint download [OPTIONS] CHECKPOINT_ID: Download checkpoint files to your local file system.

Getting Help

Contact us via [email protected] to get trial access to PeriFlow.
If you have any issues or questions, please visit PeriFlow Discussion Forum and get support.