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nv-kkudrynski merged 1 commit into from
Aug 20, 2020
Merged

[ConvNets/TF] Document synthetic dataset options #659

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6 changes: 3 additions & 3 deletions TensorFlow/Classification/ConvNets/resnet50v1.5/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -194,7 +194,7 @@ To train your model using mixed precision or TF32 with Tensor Cores or FP32, per
1. Clone the repository.
```
git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/TensorFlow/Classification/RN50v1.5
cd DeepLearningExamples/TensorFlow/Classification/ConvNets
```

2. Download and preprocess the dataset.
Expand Down Expand Up @@ -452,10 +452,9 @@ To benchmark the training performance on a specific batch size, run:
Each of these scripts runs 200 warm-up iterations and measures the first epoch.

To control warmup and benchmark length, use the `--warmup_steps`, `--num_iter` and `--iter_unit` flags. Features like XLA or DALI can be controlled
with `--use_xla` and `--use_dali` flags.
with `--use_xla` and `--use_dali` flags. If no `--data_dir=<path to imagenet>` flag is specified then the benchmarks will use a synthetic dataset.
Suggested batch sizes for training are 256 for mixed precision training and 128 for single precision training per single V100 16 GB.


#### Inference performance benchmark

To benchmark the inference performance on a specific batch size, run:
Expand All @@ -470,6 +469,7 @@ To benchmark the inference performance on a specific batch size, run:

By default, each of these scripts runs 20 warm-up iterations and measures the next 80 iterations.
To control warm-up and benchmark length, use the `--warmup_steps`, `--num_iter` and `--iter_unit` flags.
If no `--data_dir=<path to imagenet>` flag is specified then the benchmarks will use a synthetic dataset.

The benchmark can be automated with the `inference_benchmark.sh` script provided in `resnet50v1.5`, by simply running:
`bash ./resnet50v1.5/inference_benchmark.sh <data dir> <data idx dir>`
Expand Down
5 changes: 3 additions & 2 deletions TensorFlow/Classification/ConvNets/resnext101-32x4d/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -203,7 +203,7 @@ To train your model using mixed precision or TF32 with Tensor Cores or FP32, per
1. Clone the repository.
```
git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/TensorFlow/Classification/RN50v1.5
cd DeepLearningExamples/TensorFlow/Classification/ConvNets
```

2. Download and preprocess the dataset.
Expand Down Expand Up @@ -420,7 +420,7 @@ To benchmark the training performance on a specific batch size, run:
Each of these scripts runs 200 warm-up iterations and measures the first epoch.

To control warmup and benchmark length, use the `--warmup_steps`, `--num_iter` and `--iter_unit` flags. Features like XLA or DALI can be controlled
with `--use_xla` and `--use_dali` flags.
with `--use_xla` and `--use_dali` flags. If no `--data_dir=<path to imagenet>` flag is specified then the benchmarks will use a synthetic dataset.
Suggested batch sizes for training are 128 for mixed precision training and 64 for single precision training per single V100 16 GB.


Expand All @@ -438,6 +438,7 @@ To benchmark the inference performance on a specific batch size, run:

By default, each of these scripts runs 20 warm-up iterations and measures the next 80 iterations.
To control warm-up and benchmark length, use the `--warmup_steps`, `--num_iter` and `--iter_unit` flags.
If no `--data_dir=<path to imagenet>` flag is specified then the benchmarks will use a synthetic dataset.

The benchmark can be automated with the `inference_benchmark.sh` script provided in `resnext101-32x4d`, by simply running:
`bash ./resnext101-32x4d/inference_benchmark.sh <data dir> <data idx dir>`
Expand Down
5 changes: 3 additions & 2 deletions TensorFlow/Classification/ConvNets/se-resnext101-32x4d/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -198,7 +198,7 @@ To train your model using mixed precision or TF32 with Tensor Cores or FP32, per
1. Clone the repository.
```
git clone https://github.com/NVIDIA/DeepLearningExamples
cd DeepLearningExamples/TensorFlow/Classification/RN50v1.5
cd DeepLearningExamples/TensorFlow/Classification/ConvNets
```

2. Download and preprocess the dataset.
Expand Down Expand Up @@ -415,7 +415,7 @@ To benchmark the training performance on a specific batch size, run:
Each of these scripts runs 200 warm-up iterations and measures the first epoch.

To control warmup and benchmark length, use the `--warmup_steps`, `--num_iter` and `--iter_unit` flags. Features like XLA or DALI can be controlled
with `--use_xla` and `--use_dali` flags.
with `--use_xla` and `--use_dali` flags. If no `--data_dir=<path to imagenet>` flag is specified then the benchmarks will use a synthetic dataset.
Suggested batch sizes for training are 96 for mixed precision training and 64 for single precision training per single V100 16 GB.


Expand All @@ -433,6 +433,7 @@ To benchmark the inference performance on a specific batch size, run:

By default, each of these scripts runs 20 warm-up iterations and measures the next 80 iterations.
To control warm-up and benchmark length, use the `--warmup_steps`, `--num_iter` and `--iter_unit` flags.
If no `--data_dir=<path to imagenet>` flag is specified then the benchmarks will use a synthetic dataset.

The benchmark can be automated with the `inference_benchmark.sh` script provided in `se-resnext101-32x4d`, by simply running:
`bash ./se-resnext101-32x4d/inference_benchmark.sh <data dir> <data idx dir>`
Expand Down