| Name | luxonis-train JSON |
| Version |
0.0.1
JSON |
| download |
| home_page | |
| Summary | Luxonis training framework for seamless training of various neural networks. |
| upload_time | 2024-01-17 17:17:20 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.10 |
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| keywords |
ml
training
luxonis
oak
|
| VCS |
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# Luxonis Training Framework
[](https://opensource.org/licenses/Apache-2.0)
[](https://github.com/astral-sh/ruff)
[](https://github.com/luxonis/luxonis-train/actions)
Luxonis training framework (`luxonis-train`) is intended for training deep learning models that can run fast on OAK products.
**The project is in an alpha state - please report any feedback.**
## Table Of Contents
- [Installation](#installation)
- [Training](#training)
- [Customizations](#customizations)
- [Tuning](#tuning)
- [Exporting](#exporting)
- [Credentials](#credentials)
- [Contributing](#contributing)
## Installation
`luxonis-train` is hosted on PyPi and can be installed with `pip` as:
```bash
pip install luxonis-train
```
This command will also create a `luxonis_train` executable in your `PATH`.
See `luxonis_train --help` for more information.
## Usage
The entire configuration is specified in a `yaml` file. This includes the model
structure, used losses, metrics, optimizers etc. For specific instructions and example
configuration files, see [Configuration](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md).
### Data Preparation
This library requires data to be in the Luxonis Dataset Format.
For instructions on how to create a dataset in the LDF, follow the
[examples](https://github.com/luxonis/luxonis-ml/tree/main/examples) in
the [luxonis-ml](https://github.com/luxonis/luxonis-ml) repository.
## Training
Once you've created your `config.yaml` file you can train the model using this command:
```bash
luxonis_train train --config config.yaml
```
If you wish to manually override some config parameters you can do this by providing the key-value pairs. Example of this is:
```bash
luxonis_train train --config config.yaml trainer.batch_size 8 trainer.epochs 10
```
where key and value are space separated and sub-keys are dot (`.`) separated. If the configuration field is a list, then key/sub-key should be a number (e.g. `trainer.preprocessing.augmentations.0.name RotateCustom`).
## Tuning
To improve training performance you can use `Tuner` for hyperparameter optimization.
To use tuning, you have to specify [tuner](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md#tuner) section in the config file.
To start the tuning, run
```bash
luxonis_train tune --config config.yaml
```
You can see an example tuning configuration [here](https://github.com/luxonis/luxonis-train/blob/main/configs/example_tuning.yaml).
## Exporting
We support export to `ONNX`, and `DepthAI .blob format` which is used for OAK cameras. By default, we export to `ONNX` format.
To use the exporter, you have to specify the [exporter](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md#exporter) section in the config file.
Once you have the config file ready you can export the model using
```bash
luxonis_train export --config config.yaml
```
You can see an example export configuration [here](https://github.com/luxonis/luxonis-train/blob/main/configs/example_export.yaml).
## Customizations
We provide a registry interface through which you can create new
[nodes](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/nodes/README.md),
[losses](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/losses/README.md),
[metrics](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/metrics/README.md),
[visualizers](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/visualizers/README.md),
[callbacks](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/callbacks/README.md),
[optimizers](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md#optimizer),
and [schedulers](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md#scheduler).
Registered components can be then referenced in the config file. Custom components need to inherit from their respective base classes:
- Node - [BaseNode](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/models/nodes/base_node.py)
- Loss - [BaseLoss](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/losses/base_loss.py)
- Metric - [BaseMetric](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/metrics/base_metric.py)
- Visualizer - [BaseVisualizer](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/visualizers/base_visualizer.py)
- Callback - [Callback from lightning.pytorch.callbacks](lightning.pytorch.callbacks)
- Optimizer - [Optimizer from torch.optim](https://pytorch.org/docs/stable/optim.html#torch.optim.Optimizer)
- Scheduler - [LRScheduler from torch.optim.lr_scheduler](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate)
Here is an example of how to create a custom components:
```python
from torch.optim import Optimizer
from luxonis_train.utils.registry import OPTIMIZERS
from luxonis_train.attached_modules.losses import BaseLoss
@OPTIMIZERS.register_module()
class CustomOptimizer(Optimizer):
...
# Subclasses of BaseNode, LuxonisLoss, LuxonisMetric
# and BaseVisualizer are registered automatically.
class CustomLoss(BaseLoss):
# This class is automatically registered under `CustomLoss` name.
def __init__(self, k_steps: int, **kwargs):
super().__init__(**kwargs)
...
```
And then in the config you reference this `CustomOptimizer` and `CustomLoss` by their names:
```yaml
losses:
- name: CustomLoss
params: # additional parameters
k_steps: 12
```
For more information on how to define custom components, consult the respective in-source documentation.
## Credentials
Local use is supported by default. In addition, we also integrate some cloud services which can be primarily used for logging and storing. When these are used, you need to load environment variables to set up the correct credentials.
You have these options how to set up the environment variables:
- Using standard environment variables
- Specifying the variables in a `.env` file. If a variable is both in the environment and present in `.env` file, the exported variable takes precedense.
- Specifying the variables in the [ENVIRON](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md#environ) section of the config file. Note that this is not a recommended way. Variables defined in config take precedense over environment and `.env` variables.
### S3
If you are working with LuxonisDataset that is hosted on S3, you need to specify these env variables:
```bash
AWS_ACCESS_KEY_ID=**********
AWS_SECRET_ACCESS_KEY=**********
AWS_S3_ENDPOINT_URL=**********
```
### MLFlow
If you want to use MLFlow for logging and storing artifacts you also need to specify MLFlow-related env variables like this:
```bash
MLFLOW_S3_BUCKET=**********
MLFLOW_S3_ENDPOINT_URL=**********
MLFLOW_TRACKING_URI=**********
```
### WanDB
If you are using WanDB for logging, you have to sign in first in your environment.
### POSTGRESS
There is an option for remote storage for [Tuning](#tuning). We use POSTGRES and to connect to the database you need to specify the folowing env variables:
```bash
POSTGRES_USER=**********
POSTGRES_PASSWORD=**********
POSTGRES_HOST=**********
POSTGRES_PORT=**********
POSTGRES_DB=**********
```
## Contributing
If you want to contribute to the development, install the dev version of the package:
```bash
pip install luxonis-train[dev]
```
Consult the [Contribution guide](https://github.com/luxonis/luxonis-train/blob/main/CONTRIBUTING.md) for further instructions.
Raw data
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"maintainer_email": "Luxonis <support@luxonis.com>",
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"author_email": "Luxonis <support@luxonis.com>",
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"description": "# Luxonis Training Framework\n\n\n\n\n\n[](https://opensource.org/licenses/Apache-2.0)\n\n[](https://github.com/astral-sh/ruff)\n\n\n[](https://github.com/luxonis/luxonis-train/actions)\n\nLuxonis training framework (`luxonis-train`) is intended for training deep learning models that can run fast on OAK products.\n\n**The project is in an alpha state - please report any feedback.**\n\n## Table Of Contents\n\n- [Installation](#installation)\n- [Training](#training)\n- [Customizations](#customizations)\n- [Tuning](#tuning)\n- [Exporting](#exporting)\n- [Credentials](#credentials)\n- [Contributing](#contributing)\n\n## Installation\n\n`luxonis-train` is hosted on PyPi and can be installed with `pip` as:\n\n```bash\npip install luxonis-train\n```\n\nThis command will also create a `luxonis_train` executable in your `PATH`.\nSee `luxonis_train --help` for more information.\n\n## Usage\n\nThe entire configuration is specified in a `yaml` file. This includes the model\nstructure, used losses, metrics, optimizers etc. For specific instructions and example\nconfiguration files, see [Configuration](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md).\n\n### Data Preparation\n\nThis library requires data to be in the Luxonis Dataset Format.\n\nFor instructions on how to create a dataset in the LDF, follow the\n[examples](https://github.com/luxonis/luxonis-ml/tree/main/examples) in\nthe [luxonis-ml](https://github.com/luxonis/luxonis-ml) repository.\n\n## Training\n\nOnce you've created your `config.yaml` file you can train the model using this command:\n\n```bash\nluxonis_train train --config config.yaml\n```\n\nIf you wish to manually override some config parameters you can do this by providing the key-value pairs. Example of this is:\n\n```bash\nluxonis_train train --config config.yaml trainer.batch_size 8 trainer.epochs 10\n```\n\nwhere key and value are space separated and sub-keys are dot (`.`) separated. If the configuration field is a list, then key/sub-key should be a number (e.g. `trainer.preprocessing.augmentations.0.name RotateCustom`).\n\n## Tuning\n\nTo improve training performance you can use `Tuner` for hyperparameter optimization.\nTo use tuning, you have to specify [tuner](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md#tuner) section in the config file.\n\nTo start the tuning, run\n\n```bash\nluxonis_train tune --config config.yaml\n```\n\nYou can see an example tuning configuration [here](https://github.com/luxonis/luxonis-train/blob/main/configs/example_tuning.yaml).\n\n## Exporting\n\nWe support export to `ONNX`, and `DepthAI .blob format` which is used for OAK cameras. By default, we export to `ONNX` format.\n\nTo use the exporter, you have to specify the [exporter](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md#exporter) section in the config file.\n\nOnce you have the config file ready you can export the model using\n\n```bash\nluxonis_train export --config config.yaml\n```\n\nYou can see an example export configuration [here](https://github.com/luxonis/luxonis-train/blob/main/configs/example_export.yaml).\n\n## Customizations\n\nWe provide a registry interface through which you can create new\n[nodes](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/nodes/README.md),\n[losses](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/losses/README.md),\n[metrics](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/metrics/README.md),\n[visualizers](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/visualizers/README.md),\n[callbacks](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/callbacks/README.md),\n[optimizers](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md#optimizer),\nand [schedulers](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md#scheduler).\n\nRegistered components can be then referenced in the config file. Custom components need to inherit from their respective base classes:\n\n- Node - [BaseNode](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/models/nodes/base_node.py)\n- Loss - [BaseLoss](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/losses/base_loss.py)\n- Metric - [BaseMetric](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/metrics/base_metric.py)\n- Visualizer - [BaseVisualizer](https://github.com/luxonis/luxonis-train/blob/main/luxonis_train/attached_modules/visualizers/base_visualizer.py)\n- Callback - [Callback from lightning.pytorch.callbacks](lightning.pytorch.callbacks)\n- Optimizer - [Optimizer from torch.optim](https://pytorch.org/docs/stable/optim.html#torch.optim.Optimizer)\n- Scheduler - [LRScheduler from torch.optim.lr_scheduler](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate)\n\nHere is an example of how to create a custom components:\n\n```python\nfrom torch.optim import Optimizer\nfrom luxonis_train.utils.registry import OPTIMIZERS\nfrom luxonis_train.attached_modules.losses import BaseLoss\n\n@OPTIMIZERS.register_module()\nclass CustomOptimizer(Optimizer):\n ...\n\n# Subclasses of BaseNode, LuxonisLoss, LuxonisMetric\n# and BaseVisualizer are registered automatically.\n\nclass CustomLoss(BaseLoss):\n # This class is automatically registered under `CustomLoss` name.\n def __init__(self, k_steps: int, **kwargs):\n super().__init__(**kwargs)\n ...\n```\n\nAnd then in the config you reference this `CustomOptimizer` and `CustomLoss` by their names:\n\n```yaml\nlosses:\n - name: CustomLoss\n params: # additional parameters\n k_steps: 12\n\n```\n\nFor more information on how to define custom components, consult the respective in-source documentation.\n\n## Credentials\n\nLocal use is supported by default. In addition, we also integrate some cloud services which can be primarily used for logging and storing. When these are used, you need to load environment variables to set up the correct credentials.\n\nYou have these options how to set up the environment variables:\n\n- Using standard environment variables\n- Specifying the variables in a `.env` file. If a variable is both in the environment and present in `.env` file, the exported variable takes precedense.\n- Specifying the variables in the [ENVIRON](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md#environ) section of the config file. Note that this is not a recommended way. Variables defined in config take precedense over environment and `.env` variables.\n\n### S3\n\nIf you are working with LuxonisDataset that is hosted on S3, you need to specify these env variables:\n\n```bash\nAWS_ACCESS_KEY_ID=**********\nAWS_SECRET_ACCESS_KEY=**********\nAWS_S3_ENDPOINT_URL=**********\n```\n\n### MLFlow\n\nIf you want to use MLFlow for logging and storing artifacts you also need to specify MLFlow-related env variables like this:\n\n```bash\nMLFLOW_S3_BUCKET=**********\nMLFLOW_S3_ENDPOINT_URL=**********\nMLFLOW_TRACKING_URI=**********\n```\n\n### WanDB\n\nIf you are using WanDB for logging, you have to sign in first in your environment.\n\n### POSTGRESS\n\nThere is an option for remote storage for [Tuning](#tuning). We use POSTGRES and to connect to the database you need to specify the folowing env variables:\n\n```bash\nPOSTGRES_USER=**********\nPOSTGRES_PASSWORD=**********\nPOSTGRES_HOST=**********\nPOSTGRES_PORT=**********\nPOSTGRES_DB=**********\n```\n\n## Contributing\n\nIf you want to contribute to the development, install the dev version of the package:\n\n```bash\npip install luxonis-train[dev]\n```\n\nConsult the [Contribution guide](https://github.com/luxonis/luxonis-train/blob/main/CONTRIBUTING.md) for further instructions.\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. 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