Tesnorflow Environment Setup & User Guide

1 - Training

Training a model simply means learning (determining) good values for all the weights and the bias from labeled examples. Huawei Ascend 910 Chips mainly developed for high performance training but also supports inference.

1.1 - Tensorflow-v1.15

1.1.2 - Module Load

Environment Preperation::

bash module load GCC/9.5.0 OpenMPI TensorFlow-CANN/1.15.0

1.1.3 - Model Training

Training Script:

module cat <<EOF > batchscript.sh
#!/bin/bash
#SBATCH --partition=a800-9000
#SBATCH --time=00:10:00
#SBATCH --ntasks=1
#SBATCH --nodes=1
npu-smi info
<Training command>
EOF

sbatch batchscript.sh

1.1.4 - Example Usage

Environment Preperation:

module load GCC/9.5.0 OpenMPI TensorFlow-CANN/1.15.0
git clone https://gitee.com/ascend/ModelZoo-TensorFlow.git
cd ModelZoo-TensorFlow/TensorFlow/built-in/nlp/LeNet_ID0127_for_TensorFlow/

Training Script:

module cat <<EOF > batchscript.sh
#!/bin/bash
#SBATCH --partition=a800-9000
#SBATCH --time=00:10:00
#SBATCH --ntasks=1
#SBATCH --nodes=1
npu-smi info
python LeNet.py ---data_path <data_path>
EOF

Run the script:

sbatch batchscript.sh
>>> Submitted batch job 1079
cat slurm-1079.out
>>> [...]

1.2 - Tensorflow-v.2.4

1.2.1 - Module Load

Environment Preperation:

module load GCC/9.5.0 OpenMPI TensorFlow-CANN/2.4.1

1.2.2 Model Training

Training Script:

module cat <<EOF > batchscript.sh
#!/bin/bash
#SBATCH --partition=a800-9000
#SBATCH --time=00:10:00
#SBATCH --ntasks=1
#SBATCH --nodes=1
npu-smi info
<Training command>
EOF


sbatch batchscript.sh

1.2.3 - Example Usage

Environment Preperation:

module load GCC/9.5.0 OpenMPI TensorFlow-CANN/2.4.1
git clone https://gitee.com/tianyu__zhou/tf2_lenet_on_npu.git
cd tf2_lenet_on_npu

Training Script:

module cat <<EOF > batchscript.sh
#!/bin/bash
#SBATCH --partition=a800-9000
#SBATCH --time=00:10:00
#SBATCH --ntasks=1
#SBATCH --nodes=1
npu-smi info
python3 train_npu.py
EOF

Run the script:

sbatch batchscript.sh
>>> Submitted batch job 1079
cat slurm-1079.out
>>> [...]

For Further Examples: TensorFlow2 .

2 - Inference

Model inference is the process of using a trained model to infer a result from live data. Ascend 310 chips supports only inference.

Note::

  • A310 chips are developed much smaller than A910 chips to bring inference solutions in real life more easily with lesser power consumption with more affordable price. You can discover more from the link .

2.1 - Online Inference

Online inference means, running an operation without converting model. It supports to inference Tensorflow, Pytorch and Mindspore models in original form. While Huawei A310 chips supports Tensorflow and Mindspore models for online inference, A910 chips supports Tensorflow, Pytorch and Mindspore models.

2.2 - TensorFlow-v1.15

The modules, model and so on information used during the Tensorflow-v1.15 Online Inference performed on the specified cluster are given below.

2.2.1 - Module Load

Environmental Preperation:

module load GCC/9.5.0 OpenMPI TensorFlow-CANN/1.15.0

2.2.2 - Model Inference

Inference Script:

module cat <<EOF > batchscript.sh
#!/bin/bash
#SBATCH --partition=a800-9000
#SBATCH --time=00:10:00
#SBATCH --ntasks=1
#SBATCH --nodes=1
npu-smi info
<Inference command>
EOF

sbatch batchscript.sh

2.2.3 - Example Usage

Environmental Preperation:

module load GCC/9.5.0 OpenMPI Tensorlfow-CANN/1.15.0
git clone https://gitee.com/tianyu__zhou/pyacl_samples.git
cd ./pyacl_samples/facenet

  • Visit FaceNet Original Repository and click Download Model to download a pre-trained Facenet model.

Inference Script:

module cat <<EOF > batchscript.sh
#!/bin/bash
#SBATCH --partition=a800-9000
#SBATCH --time=00:10:00
#SBATCH --ntasks=1
#SBATCH --nodes=1
npu-smi info
python3 resnet50_infer_for_pytorch.py --data ./data/ --npu 0 --epochs 90 --resume ./ResNet50_for_Pytorch_1.4_model/resnet50_pytorch_1.4.pth.tar>EOF

Run the Script:

sbatch batchscript.sh
>>> Submitted batch job 1079
cat slurm-1079.out
>>> [...]