Application of Single-Node Tensor Cache

Linux Ascend GPU CPU Data Preparation Intermediate Expert

Overview

If you need to repeatedly access remote datasets or read datasets from disks, you can use the single-node cache operator to cache datasets in the local memory to accelerate dataset reading.

This tutorial demonstrates how to use the single-node cache service to cache data that has been processed with data augmentation.

Configuring the Environment

Before using the cache service, you need to install MindSpore and set related environment variables. The Conda environment is used as an example. The setting method is as follows:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:{path_to_conda}/envs/{your_env_name}/lib/python3.7/site-packages/mindspore:{path_to_conda}/envs/{your_env_name}/lib/python3.7/site-packages/mindspore/lib
export PATH=$PATH:{path_to_conda}/envs/{your_env_name}/bin

Starting the Cache Server

Before using the single-node cache service, you need to start the cache server.

$ cache_admin --start
Cache server startup completed successfully!
The cache server daemon has been created as process id 10394 and is listening on port 50052

Recommendation:
Since the server is detached into its own daemon process, monitor the server logs (under /tmp/mindspore/cache/log) for any issues that may happen after startup

If the system displays a message indicating that the libpython3.7m.so.1.0 file cannot be found, search for the file path in the virtual environment and set environment variables.

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:{path_to_conda}/envs/{your_env_name}/lib

Creating a Cache Session

If no cache session exists on the cache server, a cache session needs to be created to obtain the cache session ID.

$ cache_admin -g
Session created for server on port 50052: 1493732251

The cache session ID is randomly allocated by the server.

Creating a Cache Instance

Create the Python script my_training_script.py, use the DatasetCache API to define a cache instance named some_cache in the script, and specify the session_id parameter to a cache session ID created in the previous step.

import mindspore.dataset as ds

some_cache = ds.DatasetCache(session_id=1493732251, size=0, spilling=False)

Inserting a Cache Instance

The following uses the CIFAR-10 dataset as an example. Before running the sample, download and store the CIFAR-10 dataset by referring to Loading Dataset. The directory structure is as follows:

├─my_training_script.py
└─cifar-10-batches-bin
    ├── batches.meta.txt
    ├── data_batch_1.bin
    ├── data_batch_2.bin
    ├── data_batch_3.bin
    ├── data_batch_4.bin
    ├── data_batch_5.bin
    ├── readme.html
    └── test_batch.bin

To cache the enhanced data processed by data augmentation of the map operator, the created some_cache instance is used as the input parameter of the cache API in the map operator.

import mindspore.dataset.vision.c_transforms as c_vision

dataset_dir = "cifar-10-batches-bin/"
data = ds.Cifar10Dataset(dataset_dir=dataset_dir, num_samples=5, shuffle=False, num_parallel_workers=1)

# apply cache to map
rescale_op = c_vision.Rescale(1.0 / 255.0, -1.0)
data = data.map(input_columns=["image"], operations=rescale_op, cache=some_cache)

num_iter = 0
for item in data.create_dict_iterator(num_epochs=1):  # each data is a dictionary
    # in this example, each dictionary has a key "image"
    print("{} image shape: {}".format(num_iter, item["image"].shape))
    num_iter += 1

Run the Python script my_training_script.py. The following information is displayed:

image shape: (32, 32, 3)
image shape: (32, 32, 3)
image shape: (32, 32, 3)
image shape: (32, 32, 3)
image shape: (32, 32, 3)

You can run the cache_admin --list_sessions command to check whether there are five data records in the current session. If yes, the data is successfully cached.

$ cache_admin --list_sessions
Listing sessions for server on port 50052

     Session    Cache Id  Mem cached  Disk cached  Avg cache size  Numa hit
  1493732251  3618046178       5          n/a          12442         5

Destroying a Cache Session

After the training is complete, you can destroy the current cache and release the memory.

$ cache_admin --destroy_session 1493732251
Drop session successfully for server on port 50052

The preceding command is used to destroy the cache whose session ID is 1493732251.

Stopping the Cache Server

After using the cache server, you can stop the cache server. This operation will destroy all cache sessions on the current server and release the memory.

$ cache_admin --stop
Cache server on port 50052 has been stopped successfully.