# TensorBay Python SDK
[](https://github.com/Graviti-AI/tensorbay-python-sdk/actions/workflows/pre-commit.yaml)
[](https://github.com/Graviti-AI/tensorbay-python-sdk/actions/workflows/unit_test.yaml)
[](https://tensorbay-python-sdk.graviti.com/en/latest/?badge=latest)
[](https://pepy.tech/project/tensorbay)
[](https://coveralls.io/github/Graviti-AI/tensorbay-python-sdk)
[](https://github.com/Graviti-AI/tensorbay-python-sdk/blob/main/LICENSE)
[](https://join.slack.com/t/graviticommunity/shared_invite/zt-qivjowva-8RxtilBsHIf218sOsLTHOQ)
[](https://pypi.org/project/tensorbay/)
[](https://pypi.org/project/tensorbay/)
---
**IMPORTANT**: TensorBay system underwent a huge refactoring, which broke the downward compatibility
of OpenAPI and SDK. As a result, the SDK under version v1.6.0 does not work anymore.
**Please update tensorbay SDK to v1.6.0 or a higher version.**
---
TensorBay Python SDK is a python library to access [TensorBay](https://gas.graviti.com/tensorbay/)
and manage your datasets.
It provides:
- A pythonic way to access your TensorBay resources by TensorBay OpenAPI.
- An easy-to-use CLI tool `gas` (Graviti AI service) to communicate with TensorBay.
- A consistent dataset format to read and write your datasets.
## Installation
```console
pip3 install tensorbay
```
## Documentation
More information can be found on the [documentation site](https://tensorbay-python-sdk.graviti.com/)
## Usage
An **AccessKey** is needed to communicate with TensorBay.
Please visit [this page](https://gas.graviti.com/tensorbay/developer) to get an **AccessKey** first.
### Authorize a client object
```python
from tensorbay import GAS
gas = GAS("<YOUR_ACCESSKEY>")
```
### Create a Dataset
```python
gas.create_dataset("<DATASET_NAME>")
```
### List Dataset names
```python
dataset_names = gas.list_dataset_names()
```
### Upload images to the Dataset
```python
from tensorbay.dataset import Data, Dataset
# Organize the local dataset by the "Dataset" class before uploading.
dataset = Dataset("<DATASET_NAME>")
# TensorBay uses "segment" to separate different parts in a dataset.
segment = dataset.create_segment("<SEGMENT_NAME>")
segment.append(Data("0000001.jpg"))
segment.append(Data("0000002.jpg"))
dataset_client = gas.upload_dataset(dataset, jobs=8)
# TensorBay provides dataset version control feature, commit the uploaded data before using it.
dataset_client.commit("Initial commit")
```
### Read images from the Dataset
```python
from PIL import Image
dataset = Dataset("<DATASET_NAME>", gas)
segment = dataset[0]
for data in segment:
with data.open() as fp:
image = Image.open(fp)
width, height = image.size
image.show()
```
### Delete the Dataset
```python
gas.delete_dataset("<DATASET_NAME>")
```
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