pax_global_header00006660000000000000000000000064146002643200014507gustar00rootroot0000000000000052 comment=21b9c595adf3258c23f9eb4c565fe0c30e36f134 ascend-playground/000077500000000000000000000000001460026432000144725ustar00rootroot00000000000000ascend-playground/.gitignore000066400000000000000000000060051460026432000164630ustar00rootroot00000000000000# Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # poetry # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control #poetry.lock # pdm # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. #pdm.lock # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it # in version control. # https://pdm.fming.dev/#use-with-ide .pdm.toml # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ # PyCharm # JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. .idea/ ascend-playground/LICENSE000066400000000000000000000021241460026432000154760ustar00rootroot00000000000000MIT License Copyright (c) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice (including the next paragraph) shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ascend-playground/README.md000066400000000000000000000010361460026432000157510ustar00rootroot00000000000000# 🎮 Ascend Playground This is the Playground of Ascend ecosystem. ## Project Structure ``` AscendPlayground ├── LICENSE ├── README.md ├── ms_with_npu ├── torch_with_gpu └── torch_with_npu ``` ## Instructions * First, install `conda` as we use it to manage different development environment. * If using `torch` and `cuda`, run the following command - `conda create -f environment-torch.yml` * If using `mindspore/torch` and `ascend`, run the following command - `conda create -f evironment-ascend.yml` ascend-playground/ms_with_npu/000077500000000000000000000000001460026432000170265ustar00rootroot00000000000000ascend-playground/ms_with_npu/main.py000066400000000000000000000021371460026432000203270ustar00rootroot00000000000000# # C2Net Boilerplate # import os from c2net.context import prepare, upload_output # Initialize c2net context object and load your development data c2net_context = prepare() # Path to your codebase, alias of 'git clone + cd' code_hub_name = "Ascend-Playground" code_path = os.path.join(c2net_context.code_path, code_hub_name.lower(), "ms_with_npu") # Path to your associated datasets dataset_name = "" dataset_path = os.path.join(c2net_context.dataset_path, dataset_name) # Path to your associated pretrained-models model_name = "" model_path = os.path.join(c2net_context.pretrain_model_path, model_name) # Output Path of data, model ckpt, and console log output_path = c2net_context.output_path # Check paths print(f"Paths: {c2net_context.__dict__}") # Check Codebase print(f"Codebase in '{code_path}': {os.listdir(code_path)}") # Check datasets print(f"Datasets in '{dataset_path}': {os.listdir(dataset_path)}") # Check pretrained print(f"Models in '{model_path}': {os.listdir(model_path)}") # # Main logic # if __name__ == "__main__": # # Final output stage # _ = upload_output() # Invoke to upload ascend-playground/ms_with_npu/mnist.py000066400000000000000000000024121460026432000205310ustar00rootroot00000000000000""" MNIST Dataset Preparations of Mindspore """ import mindspore as ms from mindspore.dataset import vision, transforms from mindspore.dataset import MnistDataset def get_datasets(path_to_data: str = "MNIST_Data"): """ Get MNIST Mindspore dataset """ # Define dataset train_dataset = MnistDataset(f"{path_to_data}/train") test_dataset = MnistDataset(f"{path_to_data}/test") return train_dataset, test_dataset def get_mnist_datapipe( path_to_data: str = "MNIST_Data", batch_size: int = 10, **kwargs ): """ Data Preprocessing Pipeline """ # Get dataset dataset = MnistDataset(dataset_dir=path_to_data, *kwargs) # Define Transformations image_transforms = [ vision.Rescale(1.0 / 255.0, 0), vision.Normalize(mean=(0.1307,), std=(0.3081,)), vision.HWC2CHW(), ] label_transform = transforms.TypeCast(ms.int32) datapipe = ( dataset.map(image_transforms, "image") .map(label_transform, "label") .batch(batch_size) ) return datapipe # for data in test_dataset.create_dict_iterator(): # print(f"Shape of image [N, C, H, W]: {data['image'].shape} {data['image'].dtype}") # print(f"Shape of label: {data['label'].shape} {data['label'].dtype}") # break ascend-playground/ms_with_npu/model.py000066400000000000000000000032601460026432000205010ustar00rootroot00000000000000import mindspore as ms import mindspore.nn as nn import mindspore.ops as P from ms_with_npu.mnist import get_mnist_datapipe # Define model class Network(nn.Cell): def __init__( self, image_size: int = 28, hidden_size: int = 512, num_classes: int = 10, ): super().__init__() self.flatten = nn.Flatten() self.mlp = nn.SequentialCell( nn.Dense(pow(image_size, 2), hidden_size), nn.ReLU(), nn.Dense(hidden_size, hidden_size // 2), nn.ReLU(), nn.Dense(hidden_size // 2, num_classes), ) def construct(self, x): x = self.flatten(x) logits = self.mlp(x) return logits model = Network() # Instantiate loss function and optimizer learning_rate = 1e-2 loss_fn = nn.CrossEntropyLoss() optimizer = nn.SGD(model.trainable_params(), learning_rate) # Define forward pass def forward(data, label): logits = P.argmax(model(data)) loss = loss_fn(logits, label) return loss, logits # Define Gradient function grad_fn = ms.value_and_grad( forward, optimizer.parameters, has_aux=True ) # has_aux=True for ignoring logits # Define backward pass @ms.jit() def training_step(data, label): (loss, logits), grads = grad_fn(data, label) # get values and gradients optimizer(grads) # update gradient return loss, logits if __name__ == "__main__": epochs = 10 datapipe = get_mnist_datapipe(path_to_data="MNIST_Data") for epoch in range(epochs): print(f"Epoch {epoch + 1}/{epochs}") for data, label in dataset: loss, logits = training_step(data, label) print(f"Loss: {loss.asnumpy()}") ascend-playground/requirements.txt000066400000000000000000000011731460026432000177600ustar00rootroot00000000000000asttokens==2.4.1 astunparse==1.6.3 c2net==0.1.9 certifi==2024.2.2 charset-normalizer==3.3.2 filelock==3.13.1 fsspec==2024.2.0 idna==3.6 Jinja2==3.1.3 MarkupSafe==2.1.5 mindspore @ https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.2.11/MindSpore/cpu/aarch64/mindspore-2.2.11-cp39-cp39-macosx_11_0_arm64.whl#sha256=84d5a921ad5f53c7f75bae510b2891a7d9624d8a69be2a4d620baa7db6f53581 mpmath==1.3.0 networkx==3.2.1 numpy==1.26.4 packaging==23.2 pillow==10.2.0 protobuf==4.25.3 psutil==5.9.8 requests==2.31.0 scipy==1.12.0 six==1.16.0 sympy==1.12 torch==2.1.0 torchaudio==2.1.0 torchvision==0.16.0 typing_extensions==4.10.0 urllib3==2.2.1 ascend-playground/template.ipynb000066400000000000000000000237171460026432000173620ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "246fc59c-0435-478c-be5c-fbaa55dabbfe", "metadata": {}, "source": [ "# Degbug Template of c2net" ] }, { "cell_type": "markdown", "id": "3f7250da-7ab7-40ac-be26-ed7514cb5165", "metadata": {}, "source": [ "## Logging Setup" ] }, { "cell_type": "code", "execution_count": 4, "id": "aab55c8c-e359-4fd1-990b-b7af87f3e794", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mDEPRECATION: pytorch-lightning 1.7.7 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n", "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0m" ] } ], "source": [ "!pip install -qU pip rich" ] }, { "cell_type": "code", "execution_count": 11, "id": "4029b99d-09df-4cef-a104-4096746e4737", "metadata": {}, "outputs": [], "source": [ "import logging\n", "from rich import print\n", "from rich.logging import RichHandler\n", "\n", "FORMAT = \"%(message)s\"\n", "LEVEL = \"DEBUG\"\n", "logging.basicConfig(\n", " level=LEVEL, format=FORMAT, \n", " datefmt=\"[%Y-%m-%d %H:%M:%S]\", # \"[%X]\"\n", " handlers=[RichHandler()]\n", ")\n", "\n", "log = logging.getLogger(\"rich\")" ] }, { "cell_type": "markdown", "id": "53ddb1d3-89b4-4d05-ae14-9fb2677f4ce9", "metadata": {}, "source": [ "## Project Setup" ] }, { "cell_type": "code", "execution_count": 2, "id": "029aa0d9-2541-46f3-9b53-f6016a1ef956", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " ___ _ _ _ \n", " |__ \\ | | | | | | \n", " ___ ) | _ __ ___ | |_ ______ | |__ ___ | |_ __ _ \n", " / __| / / | '_ \\ / _ \\| __||______|| '_ \\ / _ \\| __| / _` |\n", "| (__ / /_ | | | || __/| |_ | |_) || __/| |_ | (_| |\n", " \\___||____||_| |_| \\___| \\__| |_.__/ \\___| \\__| \\__,_|\n", " \n", " \n", "\n", "If you have any problems while preparing the data, you can submit an issue in this repository: https://openi.pcl.ac.cn/OpenIOSSG/c2net-pypi\n", " \n", "Detected .code_cache_file already exists, code has been prepared!\n", "Detected .dataset_cache_file already exists, dataset has been prepared!\n", "please set c2net_context.output_path as the output location\n" ] } ], "source": [ "# c2net Boilerplate\n", "import os\n", "from c2net import prepare\n", "\n", "c2net_context = prepare() # Initialize c2net context object and load your development data\n", "code_path = c2net_context.code_path # Path to your codebase, alias of 'git clone + cd'\n", "dataset_path = c2net_context.dataset_path # Path to your associated datasets\n", "model_path = c2net_context.pretrain_model_path # Path to your associated pretrained-models\n", "output_path = c2net_context.output_path # Output Path of data, model ckpt, and console log" ] }, { "cell_type": "code", "execution_count": 19, "id": "1c111c02-7520-4d37-975b-3c8090ee0dc5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n" ], "text/plain": [ "Models in \u001b[32m'/tmp/pretrainmodel'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Check paths\n", "print(f\"Paths: {c2net_context.__dict__}\")\n", "# Check Codebase\n", "print(f\"Codebase in '{code_path}': {os.listdir(code_path)}\")\n", "# Check datasets\n", "print(f\"Datasets in '{dataset_path}': {os.listdir(dataset_path)}\")\n", "# Check pretrained\n", "print(f\"Models in '{model_path}': {os.listdir(model_path)}\")" ] }, { "cell_type": "markdown", "id": "ce5f87c5-eb5c-45b7-b979-a3114be7480f", "metadata": {}, "source": [ "## Debug" ] }, { "cell_type": "code", "execution_count": null, "id": "1d8a1d7a-b5ae-493b-9641-5e1fcf238a7c", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "316555f4-7ff9-4c49-a79d-9de7f53d6203", "metadata": {}, "source": [ "## Project Ouput" ] }, { "cell_type": "code", "execution_count": null, "id": "764d6b82-488e-4a63-a105-e263c9e16ad5", "metadata": {}, "outputs": [], "source": [ "# Training Task only\n", "from c2net.context import upload_output\n", "\n", "_ = upload_output() # Invoke to upload" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 5 } ascend-playground/template.py000066400000000000000000000020771460026432000166650ustar00rootroot00000000000000# # C2Net Boilerplate # import os from c2net.context import prepare, upload_output # Initialize c2net context object and load your development data c2net_context = prepare() # Path to your codebase, alias of 'git clone + cd' code_hub_name = "" code_path = os.path.join(c2net_context.code_path, code_hub_name.lower()) # Path to your associated datasets dataset_name = "" dataset_path = os.path.join(c2net_context.dataset_path, dataset_name) # Path to your associated pretrained-models model_name = "" model_path = os.path.join(c2net_context.pretrain_model_path, model_name) # Output Path of data, model ckpt, and console log output_path = c2net_context.output_path # Check paths print(f"Paths: {c2net_context.__dict__}") # Check Codebase print(f"Codebase in '{code_path}': {os.listdir(code_path)}") # Check datasets print(f"Datasets in '{dataset_path}': {os.listdir(dataset_path)}") # Check pretrained print(f"Models in '{model_path}': {os.listdir(model_path)}") # # Main logic # if __name__ == "__main__": # # Final output stage # _ = upload_output() # Invoke to upload ascend-playground/torch_with_npu/000077500000000000000000000000001460026432000175265ustar00rootroot00000000000000ascend-playground/torch_with_npu/train.py000066400000000000000000000000001460026432000212030ustar00rootroot00000000000000