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Adjust Readme for consistency of src/train.py #624

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Adjust Readme for consistency of src/train.py #624

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14 changes: 8 additions & 6 deletions README.md
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Expand Up @@ -145,7 +145,7 @@ git clone https://github.com/ashleve/lightning-hydra-template
cd lightning-hydra-template

# [OPTIONAL] create conda environment
conda create -n myenv python=3.9
conda create -n myenv python=3.10
conda activate myenv

# install pytorch according to instructions
Expand All @@ -166,6 +166,8 @@ When running `python src/train.py` you should see something like this:

## ⚡  Your Superpowers

Disclaimer: Run the following commands after navigating to the `src/` folder.

<details>
<summary><b>Override any config parameter from command line</b></summary>

Expand Down Expand Up @@ -680,7 +682,7 @@ _Say you want to execute many runs to plot how accuracy changes in respect to ba
1. Execute the runs with some config parameter that allows you to identify them easily, like tags:

```bash
python train.py -m logger=csv data.batch_size=16,32,64,128 tags=["batch_size_exp"]
python src/train.py -m logger=csv data.batch_size=16,32,64,128 tags=["batch_size_exp"]
```

2. Write a script or notebook that searches over the `logs/` folder and retrieves csv logs from runs containing given tags in config. Plot the results.
Expand Down Expand Up @@ -729,7 +731,7 @@ PyTorch Lightning supports many popular logging frameworks: [Weights&Biases](htt
These tools help you keep track of hyperparameters and output metrics and allow you to compare and visualize results. To use one of them simply complete its configuration in [configs/logger](configs/logger) and run:

```bash
python train.py logger=logger_name
python src/train.py logger=logger_name
```

You can use many of them at once (see [configs/logger/many_loggers.yaml](configs/logger/many_loggers.yaml) for example).
Expand Down Expand Up @@ -816,7 +818,7 @@ hydra:

</details>

Next, execute it with: `python train.py -m hparams_search=mnist_optuna`
Next, execute it with: `python src/train.py -m hparams_search=mnist_optuna`

Using this approach doesn't require adding any boilerplate to code, everything is defined in a single config file. The only necessary thing is to return the optimized metric value from the launch file.

Expand Down Expand Up @@ -845,7 +847,7 @@ Lightning supports multiple ways of doing distributed training. The most common
You can run DDP on mnist example with 4 GPUs like this:

```bash
python train.py trainer=ddp
python src/train.py trainer=ddp
```

> **Note**: When using DDP you have to be careful how you write your models - read the [docs](https://lightning.ai/docs/pytorch/latest/advanced/speed.html).
Expand Down Expand Up @@ -1248,7 +1250,7 @@ git clone https://github.com/YourGithubName/your-repo-name
cd your-repo-name

# [OPTIONAL] create conda environment
conda create -n myenv python=3.9
conda create -n myenv python=3.10
conda activate myenv

# install pytorch according to instructions
Expand Down
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