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Update the url of outdated links #5

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12 changes: 6 additions & 6 deletions pytorch/README.md
Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,7 @@ docker pull nvcr.io/nvidia/${NAME_NGC}

__ImageNet (For ResNet only)__

You can use synthetic data or real data to benchmark ResNet. To run benchmark with synthetic data, simply add `--data-backend syntetic` to the [config file](https://github.com/lambdal/deeplearning-benchmark/blob/master/scripts/config/config_pytorch_2xA100_p4.sh#L38) (right, there is a typo in NVidia's code).
You can use synthetic data or real data to benchmark ResNet. To run benchmark with synthetic data, simply add `--data-backend syntetic` to the [config file](https://github.com/lambdal/deeplearning-benchmark/blob/master/pytorch/scripts/config/config_pytorch_2xA100_p4.sh#L38) (right, there is a typo in NVidia's code).

If you want to benchmark ResNet with real data, here are the steps assuming `ILSVRC2012_img_train.tar` and `ILSVRC2012_img_val.tar` have already been downloaded to your home directory.

Expand Down Expand Up @@ -78,7 +78,7 @@ docker run --gpus all --rm --shm-size=64g \

#### Prepare configuration files

Benchmark is defined in a configuration file. For example, here is a [config file](https://github.com/lambdal/deeplearning-benchmark/blob/master/scripts/config/config_pytorch_2xV100.sh) that creates benchmark jobs for a 2xV100 setup. It specifies the number of GPUs, the number of experiments for each task, and the input arguments for individual task (SSD, ResNet, TransformerXL etc.)
Benchmark is defined in a configuration file. For example, here is a [config file](https://github.com/lambdal/deeplearning-benchmark/blob/master/pytorch/scripts/config/config_pytorch_2xV100.sh) that creates benchmark jobs for a 2xV100 setup. It specifies the number of GPUs, the number of experiments for each task, and the input arguments for individual task (SSD, ResNet, TransformerXL etc.)

```
# Number of GPUs
Expand Down Expand Up @@ -114,7 +114,7 @@ PyTorch_SSD_AMP_PARAMS=(
...
```

See [this folder](https://github.com/lambdal/deeplearning-benchmark/blob/master/scripts/config) for reference of different GPU configurations.
See [this folder](https://github.com/lambdal/deeplearning-benchmark/tree/master/pytorch/scripts/config) for reference of different GPU configurations.

#### Run Benchmark

Expand Down Expand Up @@ -145,15 +145,15 @@ docker run --gpus '"device=list-of-gpus"' --rm --shm-size=64g \

#### Gather Results

We provide some simply script to gather the results (everything in the results folder) to [CSV](https://github.com/lambdal/deeplearning-benchmark/blob/master/pytorch-train-throughput-fp32.csv) files for both training throughput and batch size.
We provide some simply script to gather the results (everything in the results folder) to [CSV](https://github.com/lambdal/deeplearning-benchmark/blob/master/pytorch/pytorch-train-throughput-fp32.csv) files for both training throughput and batch size.

```
python scripts/compile_results_pytorch_throughput.py --precision fp32 --system all

python scripts/compile_results_pytorch_bs.py --precision fp32 --system all
```

To gather your own benchmarks, you need to add your system to the `list_system`. See the scripts ([1](https://github.com/lambdal/deeplearning-benchmark/blob/master/scripts/compile_results_pytorch_throughput.py),[2](https://github.com/lambdal/deeplearning-benchmark/blob/master/scripts/compile_results_pytorch_bs.py)) for details.
To gather your own benchmarks, you need to add your system to the `list_system`. See the scripts ([1](https://github.com/lambdal/deeplearning-benchmark/blob/master/pytorch/scripts/compile_results_pytorch_throughput.py),[2](https://github.com/lambdal/deeplearning-benchmark/blob/master/pytorch/scripts/compile_results_pytorch_bs.py)) for details.


### Notes
Expand Down Expand Up @@ -224,4 +224,4 @@ Different models have different way to set batch size -- some of them are set fo
- [x] Project created.
- [x] Add PyTorch SSD

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