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Updated readmes according to restructured config (#266)
* Updated readmes according to restructured config * Update proper format * Refactor readmes using merging templates * Modify Formatting * Update typo * Modify commands to be on one line * Merge branch 'main' into 'update-readmes' - Added optional attributes - train_epoch_length and eval_epoch_length have been set as optional according to engine definition * Added new line to replace, replaces the text and new line - Avoids new lines and adheres to formatting
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‎src/store.js

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Original file line numberDiff line numberDiff line change
@@ -62,7 +62,7 @@ export function saveConfig(key, value) {
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// merges the code from the common and specific files using ejs
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function mergeCode(specificFileText, commonFileText) {
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const replaced = specificFileText.replace(
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/#::= from_template_common ::#/g,
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/#::= from_template_common ::#\n/g,
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commonFileText
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)
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return replaced

‎src/templates/template-common/README.md

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@@ -1,3 +1,11 @@
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## Getting Started
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Install the dependencies with `pip`:
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```sh
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pip install -r requirements.txt --progress-bar off -U
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```
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### Code structure
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#::: if (it.include_test) { :::#
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|- models.py : helper module with functions to create a model or multiple models
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|- trainers.py : helper module with functions to create trainer and evaluator
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|- utils.py : module with various helper functions
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|
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#::: if (it.template === 'template-vision-segmentation') { :::#
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|- vis.py : helper module for data visualizations
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#::: } :::#
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|- requirements.txt : dependencies to install with pip
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|
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|- config.yaml : global configuration YAML file
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|- models.py : helper module with functions to create a model or multiple models
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|- trainers.py : helper module with functions to create trainer and evaluator
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|- utils.py : module with various helper functions
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|
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#::: if (it.template === 'template-vision-segmentation') { :::#
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|- vis.py : helper module for data visualizations
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#::: } :::#
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|- requirements.txt : dependencies to install with pip
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|
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|- config.yaml : global configuration YAML file
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```
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#::: } :::#
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## Training
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#::: if (it.use_dist) { :::#
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#::: if (it.dist === 'torchrun') { :::#
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#::: if (it.nproc_per_node) { :::#
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--node_rank 0 \
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--master_addr #:::= it.master_addr :::# \
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--master_port #:::= it.master_port :::# \
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main.py \
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--backend #:::= it.backend :::#
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main.py config.yaml --backend #:::= it.backend :::#
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```
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- Execute on worker nodes
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--node_rank <node_rank> \
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--master_addr #:::= it.master_addr :::# \
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--master_port #:::= it.master_port :::# \
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main.py \
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--backend #:::= it.backend :::#
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main.py config.yaml --backend #:::= it.backend :::#
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```
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#::: } else { :::#
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```sh
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torchrun \
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--nproc_per_node #:::= it.nproc_per_node :::# \
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main.py \
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--backend #:::= it.backend :::#
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main.py config.yaml --backend #:::= it.backend :::#
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```
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#::: } :::#
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- Execute on master node
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```yaml
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# config.yaml
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nproc_per_node: #:::= it.nproc_per_node :::#
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nnodes: #:::= it.nnodes :::#
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node_rank: 0
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master_addr: #:::= it.master_addr :::#
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master_port: #:::= it.master_port :::#
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```
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```sh
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python main.py \
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--nproc_per_node #:::= it.nproc_per_node :::# \
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--nnodes #:::= it.nnodes :::# \
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--node_rank 0 \
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--master_addr #:::= it.master_addr :::# \
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--master_port #:::= it.master_port :::# \
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--backend #:::= it.backend :::#
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python main.py config.yaml --backend #:::= it.backend :::#
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```
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- Execute on worker nodes
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```yaml
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# config.yaml
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nproc_per_node: #:::= it.nproc_per_node :::#
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nnodes: #:::= it.nnodes :::#
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node_rank: <node_rank>
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master_addr: #:::= it.master_addr :::#
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master_port: #:::= it.master_port :::#
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```
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```sh
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python main.py \
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--nproc_per_node #:::= it.nproc_per_node :::# \
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--nnodes #:::= it.nnodes :::# \
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--node_rank <node_rank> \
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--master_addr #:::= it.master_addr :::# \
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--master_port #:::= it.master_port :::# \
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--backend #:::= it.backend :::#
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python main.py config.yaml --backend #:::= it.backend :::#
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```
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#::: } else { :::#
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### Multi GPU Training (`torch.multiprocessing.spawn`)
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```yaml
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# config.yaml
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nproc_per_node: #:::= it.nproc_per_node :::#
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```
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```sh
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python main.py \
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--nproc_per_node #:::= it.nproc_per_node :::# \
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--backend #:::= it.backend :::#
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python main.py config.yaml --backend #:::= it.backend :::#
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```
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#::: } :::#
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### 1 GPU Training
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```sh
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python main.py
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python main.py config.yaml
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```
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#::: } :::#

‎src/templates/template-text-classification/README.md

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This is the text classification template by Code-Generator using `bert-base-uncased` model from HuggingFace Transformers and `imdb` dataset from HuggingFace datasets and training is powered by PyTorch and PyTorch-Ignite.
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## Getting Started
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Install the dependencies with `pip`:
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```sh
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pip install -r requirements.txt --progress-bar off -U
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```
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### Code structure
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#::: if (it.include_test) { :::#
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```
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|
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|- README.md
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|
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|- main.py : main script to run
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|- data.py : helper module with functions to setup input datasets and create dataloaders
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|- models.py : helper module with functions to create a model or multiple models
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|- trainers.py : helper module with functions to create trainer and evaluator
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|- utils.py : module with various helper functions
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|
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|- requirements.txt : dependencies to install with pip
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|
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|- config.yaml : global configuration YAML file
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|
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|- test_all.py : test file with few basic sanity checks
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```
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#::: } else { :::#
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```
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|
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|- README.md
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|
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|- main.py : main script to run
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|- data.py : helper module with functions to setup input datasets and create dataloaders
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|- models.py : helper module with functions to create a model or multiple models
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|- trainers.py : helper module with functions to create trainer and evaluator
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|- utils.py : module with various helper functions
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|
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|- requirements.txt : dependencies to install with pip
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|
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|- config.yaml : global configuration YAML file
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```
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#::: } :::#
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## Training
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#::: if (it.use_dist) { :::#
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#::: if (it.dist === 'torchrun') { :::#
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#::: if (it.nproc_per_node) { :::#
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#::: if (it.nnodes > 1 && it.master_addr && it.master_port) { :::#
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### Multi Node, Multi GPU Training (`torchrun`) (recommended)
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- Execute on master node
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```sh
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torchrun \
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--nproc_per_node #:::= it.nproc_per_node :::# \
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--nnodes #:::= it.nnodes :::# \
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--node_rank 0 \
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--master_addr #:::= it.master_addr :::# \
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--master_port #:::= it.master_port :::# \
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main.py \
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--backend #:::= it.backend :::#
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```
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- Execute on worker nodes
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```sh
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torchrun \
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--nproc_per_node #:::= it.nproc_per_node :::# \
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--nnodes #:::= it.nnodes :::# \
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--node_rank <node_rank> \
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--master_addr #:::= it.master_addr :::# \
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--master_port #:::= it.master_port :::# \
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main.py \
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--backend #:::= it.backend :::#
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```
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#::: } else { :::#
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### Multi GPU Training (`torchrun`) (recommended)
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```sh
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torchrun \
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--nproc_per_node #:::= it.nproc_per_node :::# \
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main.py \
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--backend #:::= it.backend :::#
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```
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#::: } :::#
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#::: } :::#
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#::: } :::#
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#::: if (it.dist === 'spawn') { :::#
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#::: if (it.nproc_per_node) { :::#
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#::: if (it.nnodes > 1 && it.master_addr && it.master_port) { :::#
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### Multi Node, Multi GPU Training (`torch.multiprocessing.spawn`)
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- Execute on master node
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```sh
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python main.py \
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--nproc_per_node #:::= it.nproc_per_node :::# \
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--nnodes #:::= it.nnodes :::# \
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--node_rank 0 \
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--master_addr #:::= it.master_addr :::# \
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--master_port #:::= it.master_port :::# \
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--backend #:::= it.backend :::#
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```
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- Execute on worker nodes
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```sh
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python main.py \
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--nproc_per_node #:::= it.nproc_per_node :::# \
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--nnodes #:::= it.nnodes :::# \
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--node_rank <node_rank> \
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--master_addr #:::= it.master_addr :::# \
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--master_port #:::= it.master_port :::# \
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--backend #:::= it.backend :::#
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```
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#::: } else { :::#
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### Multi GPU Training (`torch.multiprocessing.spawn`)
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```sh
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python main.py \
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--nproc_per_node #:::= it.nproc_per_node :::# \
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--backend #:::= it.backend :::#
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```
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#::: } :::#
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#::: } :::#
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#::: } :::#
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#::: } else { :::#
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### 1 GPU Training
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```sh
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python main.py
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```
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#::: } :::#
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#::= from_template_common ::#

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