68 lines
1.6 KiB
Markdown
68 lines
1.6 KiB
Markdown
# Examples
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This folder has 3 sections:
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## Basic Examples
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Use these examples to test how lightning works.
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#### Test on CPU
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```bash
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python cpu_template.py
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```
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---
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#### Train on a single GPU
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```bash
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python gpu_template.py --gpus 1
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```
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---
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#### DataParallel (dp)
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Train on multiple GPUs using DataParallel.
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```bash
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python gpu_template.py --gpus 2 --distributed_backend dp
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```
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---
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#### DistributedDataParallel (ddp)
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Train on multiple GPUs using DistributedDataParallel
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```bash
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python gpu_template.py --gpus 2 --distributed_backend ddp
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```
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---
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#### DistributedDataParallel+DP (ddp2)
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Train on multiple GPUs using DistributedDataParallel + dataparallel.
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On a single node, uses all GPUs for 1 model. Then shares gradient information
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across nodes.
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```bash
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python gpu_template.py --gpus 2 --distributed_backend ddp2
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```
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## Multi-node example
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This demo launches a job using 2 GPUs on 2 different nodes (4 GPUs total).
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To run this demo do the following:
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1. Log into the jumphost node of your SLURM-managed cluster.
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2. Create a conda environment with Lightning and a GPU PyTorch version.
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3. Choose a script to submit
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### DDP
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Submit this job to run with DistributedDataParallel (2 nodes, 2 gpus each)
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```bash
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sbatch ddp_job_submit.sh YourEnv
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```
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### DDP2
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Submit this job to run with a different implementation of DistributedDataParallel.
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In this version, each node acts like DataParallel but syncs across nodes like DDP.
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```bash
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sbatch ddp2_job_submit.sh YourEnv
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```
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## Domain templates
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These are templates to show common approaches such as GANs and RL.
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