matorage documentation¶

Efficiently store/load and manage models and training data needed for deep learning learning with matorage!

Matorage is Tensor(multidimensional matrix) Object Storage Manager with high availability distributed systems for Deep Learning framework(Pytorch, Tensorflow V2, Keras).

Features¶

Boilerplate data pipeline!
remove pre-processing time on training!
manager your trained model & optimizer

For researchers who need to focus on model training:

Help researchers manage training data and models easily.
By storing data in pre-processed Tensor(multidimensional matrix), they can focus only model training.
Reduce storage space through multiple compression methods.
Manage data and models that occur during learning

For AI Developer who need to focus on creating data pipeline:

Enables concurrency data save & load
High availability guaranteed through backend storage.
Easily create pipeline from user endpoints data.

Guides¶

Quick Start with Pytorch Example!¶

For an example of tensorflow, refer to the detailed document.

If you want to see the full code, see below

0. Install matorage with pip¶

$ pip install matorage

1. Set up Minio Server with docker¶

quick start with NAS(network access storage) using docker!

It can be managed through the web through the address http://127.0.0.1:9000/, and security is managed through MINIO_ACCESS_KEY and MINIO_SECRET_KEY.

$ mkdir ~/shared # create nas storage folder
$ docker run -it -p 9000:9000 \
    --restart always -e \
    "MINIO_ACCESS_KEY=minio" -e \
    "MINIO_SECRET_KEY=miniosecretkey" \
    -v ~/shared:/container/vol \
    minio/minio gateway nas /container/vol

2. Save pre-processed dataset¶

First, create a DataConfig by importing matorage.

This is an example of pre-processing mnist and storing it in distributed storage. additional is freely in the form of a dict, and records the shape and type of tensor to be stored in attributes.

from matorage import DataConfig

traindata_config = DataConfig(
    endpoint='127.0.0.1:9000',
    access_key='minio',
    secret_key='miniosecretkey',
    dataset_name='mnist',
    additional={
        "mode": "train",
        "framework" : "pytorch",
        ...
        "blah" : "blah"
    },
    attributes=[
        ('image', 'float32', (1, 28, 28)),
        ('target', 'int64', (1))
    ]
)

Now do a simple pre-processing and save the data.

from matorage import DataSaver

traindata_saver = DataSaver(config=traindata_config)
train_loader = DataLoader(dataset, batch_size=60, num_workers=8)
for (image, target) in tqdm(train_loader):
    traindata_saver({
        'image': image,
        'target': target
    })
traindata_saver.disconnect()

3. Load dataset from matorage¶

Now fetch data iteratively from storage with the same config as the saved dataset when training.

from matorage.torch import Dataset

train_dataset = Dataset(config=traindata_config, clear=True)
train_loader = DataLoader(
    train_dataset, batch_size=64, num_workers=8, shuffle=True
)

for batch_idx, (image, target) in enumerate(tqdm(train_loader)):
    image, target = image.to(device), target.to(device)

Only an index can be fetched through lazy load.

train_dataset = Dataset(config=traindata_config, clear=True)
print(train_dataset[0], len(train_dataset))

4. Save & Load Model when training¶

During training, you can save and load models of specific steps or epochs in distributed storage through inmemory. First, make the model config the same as the dataset.

from matorage import ModelConfig
from matorage.torch import ModelManager

model_config = ModelConfig(
    endpoint='127.0.0.1:9000',
    access_key='minio',
    secret_key='miniosecretkey',
    model_name='mnist_simple_training',
    additional={
        "version" : "1.0.1",
        ...
        "blah" : "blah"
    }
)

model_manager = ModelManager(config=model_config)
print(model_manager.get_metadata)
model_manager.save(model, epoch=1)
print(model_manager.get_metadata)

When an empty model is loaded with specific steps or epochs, the appropriate weight is filled into the model.

print(model.state_dict())
model_manager.load(model, epoch=1)
print(model.state_dict())

5. Save & Load Optimizer when training¶

Save and load of optimizer is similar to managing model.

from matorage import OptimizerConfig
from matorage.torch import OptimizerManager

optimizer_config = OptimizerConfig(
    endpoint='127.0.0.1:9000',
    access_key='minio',
    secret_key='miniosecretkey',
    optimizer_name='adam',
    additional={
        "model" : "1.0.1",
        ...
        "blah" : "blah"
    }
)

optimizer_manager = OptimizerManager(config=optimizer_config)
print(optimizer_manager.get_metadata)
# The optimizer contains information about the step.
optimizer_manager.save(optimizer)
print(optimizer_manager.get_metadata)

When an empty optimizer is loaded with specific steps, the appropriate weight is filled into the optimizer.

optimizer = optim.Adam(model.parameters(), lr=0.01)
optimizer_manager.load(optimizer, step=938)

Framework Requirement¶

torch(>=1.0.0), torchvision(>=0.2.2)
tensorflow(>=2.2), tensorflow_io(>=0.13)

Author¶

Tae Hwan Jung(@graykode)

We are looking for a contributor.