```
For the Criteo Terabyte dataset, we recommend a frequency threshold of `T=3`(when using A100 40GB or V100 32 GB) or `T=2`(when using A100 80GB) if you intend to run the hybrid-parallel mode
on multiple GPUs. If you want to make the model fit into a single NVIDIA Tesla V100-32GB, you can set `T=15`.
The first argument means the frequency threshold to apply to the categorical variables. For a frequency threshold `T`, the categorical values that occur less
often than `T` will be replaced with one special value for each category. Thus, a larger value of `T` will require smaller embedding tables
and will substantially reduce the overall size of the model.
The second argument is the hardware to use (either GPU or CPU).
The third arguments is a framework to use (either NVTabular or Spark). In case of choosing a CPU preprocessing this argument is omitted as it only Apache Spark is supported on CPU.
The preprocessing scripts make use of the following environment variables to configure the data directory paths:
- `download_dir` – this directory should contain the original Criteo Terabyte CSV files
- `spark_output_path` – directory to which the parquet data will be written
- `conversion_intermediate_dir` – directory used for storing intermediate data used to convert from parquet to train-ready format
- `final_output_dir` – directory to store the final results of the preprocessing which can then be used to train DLRM
In the `final_output_dir` will be three subdirectories created: `train`, `test`, `validation`, and one json file – `model_size.json` – containing a maximal index of each category.
The `train` is the train dataset transformed from day_0 to day_22.
The `test` is the test dataset transformed from the prior half of day_23.
The `validation` is the dataset transformed from the latter half of day_23.
The model is tested on 3 datasets resulting from Criteo dataset preprocessing: small (Freqency threshold = 15), large (Freqency threshold = 3) and xlarge (Freqency threshold = 2). Each dataset occupies approx 370GB of disk space. Table below presents information on the supercomputer and GPU count that are needed to train model on particular dataset.
| Dataset | GPU VRAM consumption\* | Model checkpoint size\* | FL setting | DGX A100 40GB, 1GPU | DGX A100 40GB, 8GPU | DGX A100 80GB, 1GPU | DGX A100 80GB, 8GPU | DGX-1** or DGX-2, 1 GPU | DGX-1** or DGX-2, 8GPU | DGX-2, 16GPU |
| ------- | ---------------------- | ----------------------- | ---------- | -------------------- | -------------------- | -------------------- | -------------------- | ---------------------- | --------------------- | ------------ |
| small (FL=15) | 20.5 | 15.0 | 15 | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| large (FL=3) | 132.3 | 81.9 | 3 | NA | Yes | NA | Yes | NA | Yes | Yes |
| xlarge (FL=2) | 198.8 | 141.3 | 2 | NA | NA | NA | Yes | NA | NA | NA |
\*with default embedding dimension setting
\**DGX-1 V100 32GB
##### NVTabular
NVTabular preprocessing is calibrated to run on [DGX A100](https://www.nvidia.com/en-us/data-center/dgx-a100/) and [DGX-2](https://www.nvidia.com/en-us/data-center/dgx-2/) AI systems. However, it should be possible to change the values of `ALL_DS_MEM_FRAC`, `TRAIN_DS_MEM_FRAC`, `TEST_DS_MEM_FRAC`, `VALID_DS_MEM_FRAC` in `preproc/preproc_NVTabular.py`, so that they'll work on also on other hardware platforms such as DGX-1 or a custom one.
##### Spark
The script `spark_data_utils.py` is a PySpark application, which is used to preprocess the Criteo Terabyte Dataset. In the Docker image, we have installed Spark 3.0.1, which will start a standalone cluster of Spark. The scripts `run_spark_cpu.sh` and `run_spark_gpu.sh` start Spark, then run several PySpark jobs with `spark_data_utils.py`.
Note that the Spark job requires about 3TB disk space used for data shuffle.
Spark preprocessing is calibrated to run on [DGX A100](https://www.nvidia.com/en-us/data-center/dgx-a100/) and [DGX-2](https://www.nvidia.com/en-us/data-center/dgx-2/) AI systems. However, it should be possible to change the values in `preproc/DGX-2_config.sh` or `preproc/DGX-A100_config.sh`
so that they'll work on also on other hardware platforms such as DGX-1 or a custom one.
### Training process
The main training script resides in `dlrm/scripts/main.py`. Once the training is completed, it stores the checkpoint
in the path specified by `--save_checkpoint_path` and a training log in `--log_path`. The quality of the predictions
generated by the model is measured by the [ROC AUC metric](https://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics).
The speed of training and inference is measured by throughput i.e., the number
of samples processed per second. We use mixed precision training with static loss scaling for the bottom and top MLPs while embedding tables are stored in FP32 format.
### Inference process
This section describes inference with PyTorch in Python. If you're interested in inference using the Triton Inference Server, refer to [triton/README.md](triton/README.md) file.
Two modes for inference are currently supported by the `dlrm/scripts/main.py` script:
1. Inference benchmark – this mode will measure and print out throughput and latency numbers for multiple batch sizes. You can activate it by setting the batch sizes to be tested with the `inference_benchmark_batch_sizes` command-line argument.
2. Test-only – this mode can be used to run a full validation on a checkpoint to measure ROC AUC. You can enable it by passing the `--mode test` flag.
### Deploying DLRM Using NVIDIA Triton Inference Server
The NVIDIA Triton Inference Server provides a cloud inferencing solution optimized for NVIDIA GPUs. The server provides an inference service via an HTTP or gRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server. More information on how to perform inference using NVIDIA Triton Inference Server can be found in [triton/README.md](triton/README.md).
## Performance
The performance measurements in this document were conducted at the time of publication and may not reflect the performance achieved from NVIDIA’s latest software release. For the most up-to-date performance measurements, go to [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference).
### Benchmarking
The following section shows how to run benchmarks measuring the model performance in training and inference modes.
#### Training performance benchmark
To benchmark the training performance on a specific batch size, please follow the instructions
in the [Quick Start Guide](#quick-start-guide). You can also add the `--max_steps 1000 --benchmark_warmup_steps 500`
if you want to get a reliable throughput measurement without running the entire training.
You can also create a synthetic dataset by running `python -m dlrm.scripts.prepare_synthetic_dataset --synthetic_dataset_dir /tmp/dlrm_synthetic_data` if you haven't yet downloaded the dataset.
#### Inference performance benchmark
To benchmark the inference performance on a specific batch size, run:
```
python -m dlrm.scripts.main --mode inference_benchmark --dataset /data
```
You can also create a synthetic dataset by running `python -m dlrm.scripts.prepare_synthetic_dataset --synthetic_dataset_dir /tmp/dlrm_synthetic_data` if you haven't yet downloaded the dataset.
### Results
The following sections provide details on how we achieved our performance and accuracy in training and inference.
We used three model size variants to show memory scalability in a multi-GPU setup:
- small - refers to a model trained on Criteo dataset with frequency thresholding set to 15 resulting in smaller embedding tables - total model size: ~15 GB
- large - refers to a model trained on Criteo dataset with frequency thresholding set to 3 resulting in larger embedding tables - total model size: ~82 GB
- xlarge - refers to a model trained on Criteo dataset with frequency thresholding set to 2 resulting in larger embedding tables - total model size: ~142 GB
#### Training accuracy results
##### Training accuracy: NVIDIA DGX A100 (8x A100 80GB)
Our results were obtained by running training scripts as described in the Quick Start Guide in the DLRM Docker container in two configurations:
- on a single NVIDIA A100 80GB GPU (`dlrm/scripts/main.py`)
- in multi-GPU setup on DGX A100 with 8x Ampere A100 80GB (`dlrm/scripts/dist_main.py`)
| GPUs | Model size | Batch size / GPU | Accuracy (AUC) - TF32 | Accuracy (AUC) - mixed precision | Time to train - TF32 [minutes] | Time to train - mixed precision [minutes] | Time to train speedup (TF32 to mixed precision)
|----:|----|----|----:|----:|---:|---:|---:|
| 8 | xlarge | 64k | 0.8026 | 0.8026 | 6.75 | 4.73 | 1.43 |
| 8 | large | 64k | 0.8027 | 0.8027 | 6.98 | 4.72 | 1.48 |
| 1 | small | 32k | 0.8036 | 0.8036 | 25.88 | 17.17 | 1.51 |
##### Training accuracy: NVIDIA DGX-1 (8x V100 32GB)
Our results were obtained by running training scripts as described in the Quick Start Guide in the DLRM Docker container in two configurations:
- on a single Tesla V100 32GB GPU (`dlrm/scripts/main.py`)
- in multi-GPU setup on DGX-1 8 x Tesla V100 32 GPU (`dlrm/scripts/dist_main.py`)
| GPUs | Model size | Batch size / GPU | Accuracy (AUC) - FP32 | Accuracy (AUC) - mixed precision | Time to train - FP32 [minutes] | Time to train - mixed precision [minutes] | Time to train speedup (FP32 to mixed precision)
|----:|----|----|----:|----:|---:|---:|---:|
| 8 | large | 64k | 0.8026 | 0.8026 | 25.05 | 9.87 | 2.54 |
| 1 | small | 32k | 0.8036 | 0.8036 | 106.6 | 32.6 | 3.27 |
##### Training accuracy plots
Models trained with FP32, TF32, and Automatic Mixed Precision (AMP) achieve similar accuracy.
The plot represents ROC AUC metric as a function of steps (step is single batch) during training for default precision (FP32 for Volta architecture (DGX-1) and TF32 for Ampere GPU architecture (DGX-A100)), and AMP for all three datasets.
All other parameters of training are default.
Figure 1. Training stability for a FL2 dataset: distribution of ROC AUC across different configurations. 'All configurations' refer to the distribution of ROC AUC for cartesian product of architecture, training precision.
Figure 2. Training stability for a FL3 dataset: distribution of ROC AUC across different configurations. 'All configurations' refer to the distribution of ROC AUC for cartesian product of architecture, training precision.
Figure 3. Training stability for a FL15 dataset: distribution of ROC AUC across different configurations. 'All configurations' refer to the distribution of ROC AUC for cartesian product of architecture, training precision.
##### Training stability test
Training of the model is stable for multiple configurations achieving the standard deviation of 10e-4.
The model achieves similar ROC AUC scores for A100 and V100, training precisions.
The DLRM model was trained for one epoch (roughly 4 billion samples, 128028 batches for single-GPU and 64014 for multi-GPU training), starting from 20 different initial random seeds for each setup.
The training was performed in the pytorch:21.04-py3 NGC container with and without mixed precision enabled.
The provided charts and numbers consider single and 8 GPU training. After training, the models were evaluated on the test set.
The following plots compare distributions of ROC AUC on the test set.
In columns there is single vs 8 GPU training, in rows type of hardware: A100 and V100.
Figure 4. Training stability for a FL2 dataset: distribution of ROC AUC across different configurations. 'All configurations' refer to the distribution of ROC AUC for cartesian product of architecture, training precision. Single distribution is presented since only DGX A100 80GB is large enought to support dataset with FL=2. See [Preprocessing section](#preprocessing) for more details
Figure 5. Training stability for a FL3 dataset: distribution of ROC AUC across different configurations. 'All configurations' refer to the distribution of ROC AUC for cartesian product of architecture, training precision.
Figure 6. Training stability for a FL15 dataset: distribution of ROC AUC across different configurations. 'All configurations' refer to the distribution of ROC AUC for cartesian product of architecture, training precision.
Training stability was also compared in terms of point statistics for ROC AUC distribution for multiple configurations. Refer to the expandable table below.
##### Impact of mixed precision on training accuracy
The accuracy of training, measured with ROC AUC on the test set after the final epoch metric was not impacted by enabling mixed precision. The obtained results were statistically similar. The similarity was measured according to the following procedure:
The model was trained 20 times for default settings (FP32 or TF32 for Volta and Ampere architecture respectively) and 20 times for AMP. After the last epoch, the accuracy score ROC AUC was calculated on the test set.
Distributions for two hardware configurations (A100, V100) for 3 datasets are presented below.
Figure 7. Influence of AMP on ROC AUC distribution for A100 and V100 GPUs for single- and multi-gpu training on a dataset with a frequency threshold of 2.
Figure 8. Influence of AMP on ROC AUC distribution for A100 and V100 GPUs for single- and multi-gpu training on a dataset with a frequency threshold of 3.
Figure 9. Influence of AMP on ROC AUC distribution for A100 and V100 GPUs for single- and multi-gpu training on a dataset with a frequency threshold of 15.
Distribution of AUC ROC for single precision training (TF32 for A100, FP32 for Volta) and AMP training were compared in terms of mean, variance and [Kolmogorov–Smirnov test](https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test) to state statistical difference between single precision and AMP results. Refer to the expandable table below.
Full tabular data for AMP influence on AUC ROC
| Supercomputer | Dataset | GPUs | mean AUC ROC for TF32 (DGX A100)/ FP32 (DGX-1,DGX-2) | std AUC ROC for TF32 (DGX A100)/ FP32 (DGX-1,DGX-2) |mean AUC ROC for AMP | std AUC ROC for AMP | KS test value: statistics, p-value |
| ------------- | -----| ------- | ---------------------------------------------------- | ----------------------------------------------------|---------------------|-------------------- | -----------------------------------|
DGX A100|FL2|8|0.80262|0.00006|0.80262|0.00005|0.30000 (0.33559)|
DGX A100|FL3|8|0.80266|0.00008|0.80265|0.00006|0.20000 (0.83197)|
DGX A100|FL15|1|0.80360|0.00007|0.80359|0.00006|0.20000 (0.83197)|
DGX-2 / DGX-1|FL3|8|0.80259|0.00009|0.80257|0.00008|0.20000 (0.83197)|
DGX-2 / DGX-1|FL3|16|0.80262|0.00006|0.80261|0.00007|0.20000 (0.83197)|
DGX-2 / DGX-1|FL15|1|0.80362|0.00009|0.80361|0.00006|0.25000 (0.57134)|
Sample size was set to 20 experiments for each training setup.
#### Training performance results
We used throughput in items processed per second as the performance metric.
##### Training performance: NVIDIA DGX A100 (8x A100 80GB)
Our results were obtained by running the following commands:
- for single-GPU setup:
```
python -m dlrm.scripts.main --dataset /data [--amp]
```
- for multi-GPU setup:
```
python -m torch.distributed.launch --no_python --use_env --nproc_per_node 8 \
bash -c './bind.sh --cpu=dgxa100_ccx.sh --mem=dgxa100_ccx.sh python -m dlrm.scripts.dist_main \
--dataset /data [--amp]'
```
in the DLRM Docker container on NVIDIA DGX A100 (8x A100 80GB) GPUs. Performance numbers (in records of data per second) were averaged over an entire training epoch.
| GPUs | Model size | Batch size / GPU | Throughput - TF32 | Throughput - mixed precision | Throughput speedup (TF32 - mixed precision)
|----:|----|----|---:|---:|---:|
| 8 | xlarge | 64k | 10,700,000 | 16,400,000 | 1.53 |
| 8 | large | 64k | 10,600,000 | 16,200,000 | 1.53 |
| 1 | small | 32k | 2,740,000 | 4,180,000 | 1.53 |
To achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).
##### Training performance: NVIDIA DGX A100 (8x A100 40GB)
Our results were obtained by running the following commands:
- for single-GPU setup:
```
python -m dlrm.scripts.main --dataset /data [--amp]
```
- for multi-GPU setup:
```
python -m torch.distributed.launch --no_python --use_env --nproc_per_node 8 \
bash -c './bind.sh --cpu=dgxa100_ccx.sh --mem=dgxa100_ccx.sh python -m dlrm.scripts.dist_main \
--dataset /data/ [--amp]'
```
in the DLRM Docker container on NVIDIA DGX A100 (8x A100 40GB) GPUs. Performance numbers (in records of data per second) were averaged over an entire training epoch.
| GPUs | Model size | Batch size / GPU | Throughput - TF32 | Throughput - mixed precision | Throughput speedup (TF32 - mixed precision)
|----:|----|----|---:|---:|---:|
| 8 | large | 64k | 9,980,000 | 15,400,000 | 1.54 |
| 1 | small | 32k | 2,530,000 | 3,970,000 | 1.57 |
##### Training performance: NVIDIA DGX-1 (8x V100 32GB)
Our results were obtained by running the following commands:
- for single-GPU setup:
```
python -m dlrm.scripts.main --mode train --dataset /data [--amp]
```
- for multi-GPU setup:
```
python -m torch.distributed.launch --no_python --use_env --nproc_per_node 8 \
bash -c './bind.sh --cpu=exclusive -- python -m dlrm.scripts.dist_main \
--dataset /data [--amp]'
```
in the DLRM Docker container on NVIDIA DGX-1 with (8x V100 32GB) GPUs. Performance numbers (in records of data per second) were averaged over an entire training epoch.
| GPUs | Model size | Batch size / GPU | Throughput - FP32 | Throughput - mixed precision | Throughput speedup (FP32 - mixed precision) |
|----:|----|----|---:|---:|---:|
| 8 | large | 64k | 2,830,000 | 7,480,000 | 2.64 |
| 1 | small | 32k | 667,000 | 2,200,000 | 3.30 |
To achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).
##### Training performance: NVIDIA DGX-2 (16x V100 32GB)
Our results were obtained by running the following commands:
- for single-GPU setup:
```
python -m dlrm.scripts.main --dataset /data [--amp]
```
- for multi-GPU setup:
```
python -m torch.distributed.launch --no_python --use_env --nproc_per_node [8/16] \
bash -c './bind.sh --cpu=exclusive -- python -m dlrm.scripts.dist_main \
--dataset /data [--amp]'
```
in the DLRM Docker container on NVIDIA DGX-2 with (16x V100 32GB) GPUs. Performance numbers (in records of data per second) were averaged over an entire training epoch.
| GPUs | Model size | Batch size / GPU | Throughput - FP32 | Throughput - mixed precision | Throughput speedup (FP32 - mixed precision)
|----:|----|---|---:|---:|---:|
| 16 | large | 64k | 4,690,000 | 11,100,000 | 2.37 |
| 8 | large | 64k | 3,280,000 | 8,480,000 | 2.59 |
| 1 | small | 32k | 713,000 | 2,330,000 | 3.27 |
To achieve these same results, follow the steps in the [Quick Start Guide](#quick-start-guide).
## Release notes
### Changelog
April 2020
- Initial release
May 2020
- Performance optimizations
June 2020
- Updated performance tables to include A100 results and multi-GPU setup
- Multi-GPU optimizations
August 2020
- Preprocessing with Spark 3 on GPU
- Multiple performance optimizations
- Automatic placement and load balancing of embedding tables
- Improved README
March 2021
- Added NVTabular as a new preprocessing option
- Added a new dataset - xlarge, which uses a frequency threshold of 2
- Introduced a new GPU - A100 80GB, and its performance results
- Updated Spark preprocessing
- Added Adam as an optional optimizer for embedding and MLPs, for multi-GPU training
- Improved README
June 2021
- Updated container version
- Updated performance results
### Known issues
- Adam performance is not optimized.
- For some seeds, the model's loss can become NaN due to aggressive scheduling rate policy.
