%global _empty_manifest_terminate_build 0 Name: python-deepsparse-nightly Version: 1.5.0.20230502 Release: 1 Summary: An inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application License: Neural Magic DeepSparse Community License, Apache URL: https://github.com/neuralmagic/deepsparse Source0: https://mirrors.nju.edu.cn/pypi/web/packages/94/c3/98d162c69cffc4512c7216922d60b8f6ff221f5a6ca59585119dd18c78d2/deepsparse-nightly-1.5.0.20230502.tar.gz BuildArch: noarch Requires: python3-sparsezoo-nightly Requires: python3-numpy Requires: python3-onnx Requires: python3-pydantic Requires: python3-requests Requires: python3-tqdm Requires: python3-protobuf Requires: python3-click Requires: python3-beautifulsoup4 Requires: python3-black Requires: python3-flake8 Requires: python3-isort Requires: python3-m2r2 Requires: python3-mistune Requires: python3-myst-parser Requires: python3-flaky Requires: python3-ndjson Requires: python3-rinohtype Requires: python3-sphinx Requires: python3-sphinx-copybutton Requires: python3-sphinx-markdown-tables Requires: python3-wheel Requires: python3-pytest Requires: python3-sphinx-multiversion Requires: python3-sphinx-rtd-theme Requires: python3-onnxruntime Requires: python3-flask Requires: python3-flask-cors Requires: python3-Pillow Requires: python3-importlib-metadata Requires: python3-torch Requires: python3-requests Requires: python3-pydantic Requires: python3-nltk Requires: python3-pandas Requires: python3-dill Requires: python3-tqdm Requires: python3-networkx Requires: python3-mmh3 Requires: python3-quantulum3 Requires: python3-posthog Requires: python3-azure-ai-formrecognizer Requires: python3-azure-core Requires: python3-more-itertools Requires: python3-docx Requires: python3-langdetect Requires: python3-tika Requires: python3-sentence-transformers Requires: python3-scipy Requires: python3-scikit-learn Requires: python3-seqeval Requires: python3-mlflow Requires: python3-elasticsearch Requires: python3-elastic-apm Requires: python3-rapidfuzz Requires: python3-jsonschema Requires: python3-sqlalchemy Requires: python3-sqlalchemy-utils Requires: python3-psycopg2-binary Requires: python3-faiss-cpu Requires: python3-pymilvus Requires: python3-weaviate-client Requires: python3-pinecone-client Requires: python3-SPARQLWrapper Requires: python3-selenium Requires: python3-webdriver-manager Requires: python3-beautifulsoup4 Requires: python3-markdown Requires: python3-magic Requires: python3-pytesseract Requires: python3-pillow Requires: python3-pdf2image Requires: python3-onnxruntime Requires: python3-onnxruntime-tools Requires: python3-ray Requires: python3-aiorwlock Requires: python3-grpcio Requires: python3-beir Requires: python3-mypy Requires: python3-typing-extensions Requires: python3-pytest Requires: python3-responses Requires: python3-tox Requires: python3-coverage Requires: python3-multipart Requires: python3-psutil Requires: python3-pylint Requires: python3-black[jupyter] Requires: python3-pydoc-markdown Requires: python3-mkdocs Requires: python3-jupytercontrib Requires: python3-watchdog Requires: python3-requests-cache Requires: python3-onnxruntime Requires: python3-openpifpaf Requires: python3-opencv-python Requires: python3-pycocotools Requires: python3-scipy Requires: python3-uvicorn Requires: python3-fastapi Requires: python3-pydantic Requires: python3-requests Requires: python3-multipart Requires: python3-prometheus-client Requires: python3-psutil Requires: python3-torchvision Requires: python3-opencv-python Requires: python3-torchvision Requires: python3-opencv-python Requires: python3-ultralytics %description

tool icon   DeepSparse

An inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application

Documentation Slack Support Main GitHub release Contributor Covenant YouTube Medium Twitter
[DeepSparse](https://github.com/neuralmagic/deepsparse) is a CPU inference runtime that takes advantage of sparsity within neural networks to execute inference quickly. Coupled with [SparseML](https://github.com/neuralmagic/sparseml), an open-source optimization library, DeepSparse enables you to achieve GPU-class performance on commodity hardware.

NM Flow

For details of training sparse models for deployment with DeepSparse, [check out SparseML](https://github.com/neuralmagic/sparseml). ### ✨NEW✨ DeepSparse ARM Alpha 💪 Neural Magic is bringing performant deep learning inference to ARM CPUs! In our recent product release, we launched alpha support for DeepSparse on AWS Graviton and Ampere. We are working towards a general release across ARM server, embedded, and mobile platforms in 2023. **If you would like to trial the alpha or want early access to the general release, [sign up for the waitlist](https://neuralmagic.com/deepsparse-arm-waitlist/).** ## Installation DeepSparse is available in two editions: 1. DeepSparse Community is free for evaluation, research, and non-production use with our [DeepSparse Community License](https://neuralmagic.com/legal/engine-license-agreement/). 2. DeepSparse Enterprise requires a [trial license](https://neuralmagic.com/deepsparse-free-trial/) or [can be fully licensed](https://neuralmagic.com/legal/master-software-license-and-service-agreement/) for production, commercial applications. #### Install via Docker (Recommended) DeepSparse Community is available as a container image hosted on [GitHub container registry](https://github.com/neuralmagic/deepsparse/pkgs/container/deepsparse). ```bash docker pull ghcr.io/neuralmagic/deepsparse:1.4.2 docker tag ghcr.io/neuralmagic/deepsparse:1.4.2 deepsparse-docker docker run -it deepsparse-docker ``` - [Check out the Docker page](https://github.com/neuralmagic/deepsparse/tree/main/docker/) for more details. #### Install via PyPI DeepSparse Community is also available via PyPI. We recommend using a virtual enviornment. ```bash pip install deepsparse ``` - [Check out the Installation page](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/installation.md) for optional dependencies. ## Hardware Support and System Requirements [Supported Hardware for DeepSparse](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/hardware-support.md) DeepSparse is tested on Python versions 3.7-3.10, ONNX versions 1.5.0-1.12.0, ONNX opset version 11 or higher, and manylinux compliant systems. Please note that DeepSparse is only supported natively on Linux. For those using Mac or Windows, running Linux in a Docker or virtual machine is necessary to use DeepSparse. ## Deployment APIs DeepSparse includes three deployment APIs: - **Engine** is the lowest-level API. With Engine, you pass tensors and receive the raw logits. - **Pipeline** wraps the Engine with pre- and post-processing. With Pipeline, you pass raw data and receive the prediction. - **Server** wraps Pipelines with a REST API using FastAPI. With Server, you send raw data over HTTP and receive the prediction. ### Engine The example below downloads a 90% pruned-quantized BERT model for sentiment analysis in ONNX format from SparseZoo, compiles the model, and runs inference on randomly generated input. ```python from deepsparse import Engine from deepsparse.utils import generate_random_inputs, model_to_path # download onnx, compile zoo_stub = "zoo:nlp/sentiment_analysis/obert-base/pytorch/huggingface/sst2/pruned90_quant-none" batch_size = 1 compiled_model = Engine(model=zoo_stub, batch_size=batch_size) # run inference (input is raw numpy tensors, output is raw scores) inputs = generate_random_inputs(model_to_path(zoo_stub), batch_size) output = compiled_model(inputs) print(output) # > [array([[-0.3380675 , 0.09602544]], dtype=float32)] << raw scores ``` ### DeepSparse Pipelines Pipeline is the default API for interacting with DeepSparse. Similar to Hugging Face Pipelines, DeepSparse Pipelines wrap Engine with pre- and post-processing (as well as other utilities), enabling you to send raw data to DeepSparse and receive the post-processed prediction. The example below downloads a 90% pruned-quantized BERT model for sentiment analysis in ONNX format from SparseZoo, sets up a pipeline, and runs inference on sample data. ```python from deepsparse import Pipeline # download onnx, set up pipeline zoo_stub = "zoo:nlp/sentiment_analysis/obert-base/pytorch/huggingface/sst2/pruned90_quant-none" sentiment_analysis_pipeline = Pipeline.create( task="sentiment-analysis", # name of the task model_path=zoo_stub, # zoo stub or path to local onnx file ) # run inference (input is a sentence, output is the prediction) prediction = sentiment_analysis_pipeline("I love using DeepSparse Pipelines") print(prediction) # > labels=['positive'] scores=[0.9954759478569031] ``` #### Additional Resources - Check out the [Use Cases Page](https://github.com/neuralmagic/deepsparse/tree/main/docs/use-cases) for more details on supported tasks. - Check out the [Pipelines User Guide](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/deepsparse-pipelines.md) for more usage details. ### DeepSparse Server Server wraps Pipelines with REST APIs, enabling you to stand up model serving endpoint running DeepSparse. This enables you to send raw data to DeepSparse over HTTP and receive the post-processed predictions. DeepSparse Server is launched from the command line, configured via arguments or a server configuration file. The following downloads a 90% pruned-quantized BERT model for sentiment analysis in ONNX format from SparseZoo and launches a sentiment analysis endpoint: ```bash deepsparse.server \ --task sentiment-analysis \ --model_path zoo:nlp/sentiment_analysis/obert-base/pytorch/huggingface/sst2/pruned90_quant-none ``` Sending a request: ```python import requests url = "http://localhost:5543/predict" # Server's port default to 5543 obj = {"sequences": "Snorlax loves my Tesla!"} response = requests.post(url, json=obj) print(response.text) # {"labels":["positive"],"scores":[0.9965094327926636]} ``` #### Additional Resources - Check out the [Use Cases Page](https://github.com/neuralmagic/deepsparse/tree/main/docs/use-cases) for more details on supported tasks. - Check out the [Server User Guide](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/deepsparse-server.md) for more usage details. ## ONNX DeepSparse accepts models in the ONNX format. ONNX models can be passed in one of two ways: - **SparseZoo Stub**: [SparseZoo](https://sparsezoo.neuralmagic.com/) is an open-source repository of sparse models. The examples on this page use SparseZoo stubs to identify models and download them for deployment in DeepSparse. - **Local ONNX File**: Users can provide their own ONNX models, whether dense or sparse. For example: ```bash wget https://github.com/onnx/models/raw/main/vision/classification/mobilenet/model/mobilenetv2-7.onnx ``` ```python from deepsparse import Engine from deepsparse.utils import generate_random_inputs onnx_filepath = "mobilenetv2-7.onnx" batch_size = 16 # Generate random sample input inputs = generate_random_inputs(onnx_filepath, batch_size) # Compile and run compiled_model = Engine(model=onnx_filepath, batch_size=batch_size) outputs = compiled_model(inputs) print(outputs[0].shape) # (16, 1000) << batch, num_classes ``` ## Inference Modes DeepSparse offers different inference scenarios based on your use case. **Single-stream** scheduling: the latency/synchronous scenario, requests execute serially. [`default`] single stream diagram It's highly optimized for minimum per-request latency, using all of the system's resources provided to it on every request it gets. **Multi-stream** scheduling: the throughput/asynchronous scenario, requests execute in parallel. multi stream diagram The most common use cases for the multi-stream scheduler are where parallelism is low with respect to core count, and where requests need to be made asynchronously without time to batch them. - [Check out the Scheduler User Guide](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/scheduler.md) for more details. ## Product Usage Analytics DeepSparse Community Edition gathers basic usage telemetry including, but not limited to, Invocations, Package, Version, and IP Address for Product Usage Analytics purposes. Review Neural Magic's [Products Privacy Policy](https://neuralmagic.com/legal/) for further details on how we process this data. To disable Product Usage Analytics, run the command: ```bash export NM_DISABLE_ANALYTICS=True ``` Confirm that telemetry is shut off through info logs streamed with engine invocation by looking for the phrase "Skipping Neural Magic's latest package version check." For additional assistance, reach out through the [DeepSparse GitHub Issue queue](https://github.com/neuralmagic/deepsparse/issues). ## Additional Resources - [Benchmarking Performance](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/deepsparse-benchmarking.md) - [User Guide](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide) - [Use Cases](https://github.com/neuralmagic/deepsparse/tree/main/docs/use-cases) - [Cloud Deployments and Demos](https://github.com/neuralmagic/deepsparse/tree/main/examples/) #### Versions - [DeepSparse](https://pypi.org/project/deepsparse) | stable - [DeepSparse-Nightly](https://pypi.org/project/deepsparse-nightly/) | nightly (dev) - [GitHub](https://github.com/neuralmagic/deepsparse/releases) | releases #### Info - [Blog](https://www.neuralmagic.com/blog/) - [Resources](https://www.neuralmagic.com/resources/) ## Community ### Be Part of the Future... And the Future is Sparse! Contribute with code, examples, integrations, and documentation as well as bug reports and feature requests! [Learn how here.](https://github.com/neuralmagic/deepsparse/blob/main/CONTRIBUTING.md) For user help or questions about DeepSparse, sign up or log in to our **[Deep Sparse Community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)**. We are growing the community member by member and happy to see you there. Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/deepsparse/issues) You can get the latest news, webinar and event invites, research papers, and other ML Performance tidbits by [subscribing](https://neuralmagic.com/subscribe/) to the Neural Magic community. For more general questions about Neural Magic, complete this [form.](http://neuralmagic.com/contact/) ### License [DeepSparse Community](https://docs.neuralmagic.com/products/deepsparse) is licensed under the [Neural Magic DeepSparse Community License.](https://github.com/neuralmagic/deepsparse/blob/main/LICENSE-NEURALMAGIC) Some source code, example files, and scripts included in the deepsparse GitHub repository or directory are licensed under the [Apache License Version 2.0](https://github.com/neuralmagic/deepsparse/blob/main/LICENSE) as noted. [DeepSparse Enterprise](https://docs.neuralmagic.com/products/deepsparse-ent) requires a Trial License or [can be fully licensed](https://neuralmagic.com/legal/master-software-license-and-service-agreement/) for production, commercial applications. ### Cite Find this project useful in your research or other communications? Please consider citing: ```bibtex @InProceedings{ pmlr-v119-kurtz20a, title = {Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks}, author = {Kurtz, Mark and Kopinsky, Justin and Gelashvili, Rati and Matveev, Alexander and Carr, John and Goin, Michael and Leiserson, William and Moore, Sage and Nell, Bill and Shavit, Nir and Alistarh, Dan}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {5533--5543}, year = {2020}, editor = {Hal Daumé III and Aarti Singh}, volume = {119}, series = {Proceedings of Machine Learning Research}, address = {Virtual}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/kurtz20a/kurtz20a.pdf}, url = {http://proceedings.mlr.press/v119/kurtz20a.html} } @article{DBLP:journals/corr/abs-2111-13445, author = {Eugenia Iofinova and Alexandra Peste and Mark Kurtz and Dan Alistarh}, title = {How Well Do Sparse Imagenet Models Transfer?}, journal = {CoRR}, volume = {abs/2111.13445}, year = {2021}, url = {https://arxiv.org/abs/2111.13445}, eprinttype = {arXiv}, eprint = {2111.13445}, timestamp = {Wed, 01 Dec 2021 15:16:43 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2111-13445.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` %package -n python3-deepsparse-nightly Summary: An inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application Provides: python-deepsparse-nightly BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-deepsparse-nightly

tool icon   DeepSparse

An inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application

Documentation Slack Support Main GitHub release Contributor Covenant YouTube Medium Twitter
[DeepSparse](https://github.com/neuralmagic/deepsparse) is a CPU inference runtime that takes advantage of sparsity within neural networks to execute inference quickly. Coupled with [SparseML](https://github.com/neuralmagic/sparseml), an open-source optimization library, DeepSparse enables you to achieve GPU-class performance on commodity hardware.

NM Flow

For details of training sparse models for deployment with DeepSparse, [check out SparseML](https://github.com/neuralmagic/sparseml). ### ✨NEW✨ DeepSparse ARM Alpha 💪 Neural Magic is bringing performant deep learning inference to ARM CPUs! In our recent product release, we launched alpha support for DeepSparse on AWS Graviton and Ampere. We are working towards a general release across ARM server, embedded, and mobile platforms in 2023. **If you would like to trial the alpha or want early access to the general release, [sign up for the waitlist](https://neuralmagic.com/deepsparse-arm-waitlist/).** ## Installation DeepSparse is available in two editions: 1. DeepSparse Community is free for evaluation, research, and non-production use with our [DeepSparse Community License](https://neuralmagic.com/legal/engine-license-agreement/). 2. DeepSparse Enterprise requires a [trial license](https://neuralmagic.com/deepsparse-free-trial/) or [can be fully licensed](https://neuralmagic.com/legal/master-software-license-and-service-agreement/) for production, commercial applications. #### Install via Docker (Recommended) DeepSparse Community is available as a container image hosted on [GitHub container registry](https://github.com/neuralmagic/deepsparse/pkgs/container/deepsparse). ```bash docker pull ghcr.io/neuralmagic/deepsparse:1.4.2 docker tag ghcr.io/neuralmagic/deepsparse:1.4.2 deepsparse-docker docker run -it deepsparse-docker ``` - [Check out the Docker page](https://github.com/neuralmagic/deepsparse/tree/main/docker/) for more details. #### Install via PyPI DeepSparse Community is also available via PyPI. We recommend using a virtual enviornment. ```bash pip install deepsparse ``` - [Check out the Installation page](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/installation.md) for optional dependencies. ## Hardware Support and System Requirements [Supported Hardware for DeepSparse](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/hardware-support.md) DeepSparse is tested on Python versions 3.7-3.10, ONNX versions 1.5.0-1.12.0, ONNX opset version 11 or higher, and manylinux compliant systems. Please note that DeepSparse is only supported natively on Linux. For those using Mac or Windows, running Linux in a Docker or virtual machine is necessary to use DeepSparse. ## Deployment APIs DeepSparse includes three deployment APIs: - **Engine** is the lowest-level API. With Engine, you pass tensors and receive the raw logits. - **Pipeline** wraps the Engine with pre- and post-processing. With Pipeline, you pass raw data and receive the prediction. - **Server** wraps Pipelines with a REST API using FastAPI. With Server, you send raw data over HTTP and receive the prediction. ### Engine The example below downloads a 90% pruned-quantized BERT model for sentiment analysis in ONNX format from SparseZoo, compiles the model, and runs inference on randomly generated input. ```python from deepsparse import Engine from deepsparse.utils import generate_random_inputs, model_to_path # download onnx, compile zoo_stub = "zoo:nlp/sentiment_analysis/obert-base/pytorch/huggingface/sst2/pruned90_quant-none" batch_size = 1 compiled_model = Engine(model=zoo_stub, batch_size=batch_size) # run inference (input is raw numpy tensors, output is raw scores) inputs = generate_random_inputs(model_to_path(zoo_stub), batch_size) output = compiled_model(inputs) print(output) # > [array([[-0.3380675 , 0.09602544]], dtype=float32)] << raw scores ``` ### DeepSparse Pipelines Pipeline is the default API for interacting with DeepSparse. Similar to Hugging Face Pipelines, DeepSparse Pipelines wrap Engine with pre- and post-processing (as well as other utilities), enabling you to send raw data to DeepSparse and receive the post-processed prediction. The example below downloads a 90% pruned-quantized BERT model for sentiment analysis in ONNX format from SparseZoo, sets up a pipeline, and runs inference on sample data. ```python from deepsparse import Pipeline # download onnx, set up pipeline zoo_stub = "zoo:nlp/sentiment_analysis/obert-base/pytorch/huggingface/sst2/pruned90_quant-none" sentiment_analysis_pipeline = Pipeline.create( task="sentiment-analysis", # name of the task model_path=zoo_stub, # zoo stub or path to local onnx file ) # run inference (input is a sentence, output is the prediction) prediction = sentiment_analysis_pipeline("I love using DeepSparse Pipelines") print(prediction) # > labels=['positive'] scores=[0.9954759478569031] ``` #### Additional Resources - Check out the [Use Cases Page](https://github.com/neuralmagic/deepsparse/tree/main/docs/use-cases) for more details on supported tasks. - Check out the [Pipelines User Guide](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/deepsparse-pipelines.md) for more usage details. ### DeepSparse Server Server wraps Pipelines with REST APIs, enabling you to stand up model serving endpoint running DeepSparse. This enables you to send raw data to DeepSparse over HTTP and receive the post-processed predictions. DeepSparse Server is launched from the command line, configured via arguments or a server configuration file. The following downloads a 90% pruned-quantized BERT model for sentiment analysis in ONNX format from SparseZoo and launches a sentiment analysis endpoint: ```bash deepsparse.server \ --task sentiment-analysis \ --model_path zoo:nlp/sentiment_analysis/obert-base/pytorch/huggingface/sst2/pruned90_quant-none ``` Sending a request: ```python import requests url = "http://localhost:5543/predict" # Server's port default to 5543 obj = {"sequences": "Snorlax loves my Tesla!"} response = requests.post(url, json=obj) print(response.text) # {"labels":["positive"],"scores":[0.9965094327926636]} ``` #### Additional Resources - Check out the [Use Cases Page](https://github.com/neuralmagic/deepsparse/tree/main/docs/use-cases) for more details on supported tasks. - Check out the [Server User Guide](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/deepsparse-server.md) for more usage details. ## ONNX DeepSparse accepts models in the ONNX format. ONNX models can be passed in one of two ways: - **SparseZoo Stub**: [SparseZoo](https://sparsezoo.neuralmagic.com/) is an open-source repository of sparse models. The examples on this page use SparseZoo stubs to identify models and download them for deployment in DeepSparse. - **Local ONNX File**: Users can provide their own ONNX models, whether dense or sparse. For example: ```bash wget https://github.com/onnx/models/raw/main/vision/classification/mobilenet/model/mobilenetv2-7.onnx ``` ```python from deepsparse import Engine from deepsparse.utils import generate_random_inputs onnx_filepath = "mobilenetv2-7.onnx" batch_size = 16 # Generate random sample input inputs = generate_random_inputs(onnx_filepath, batch_size) # Compile and run compiled_model = Engine(model=onnx_filepath, batch_size=batch_size) outputs = compiled_model(inputs) print(outputs[0].shape) # (16, 1000) << batch, num_classes ``` ## Inference Modes DeepSparse offers different inference scenarios based on your use case. **Single-stream** scheduling: the latency/synchronous scenario, requests execute serially. [`default`] single stream diagram It's highly optimized for minimum per-request latency, using all of the system's resources provided to it on every request it gets. **Multi-stream** scheduling: the throughput/asynchronous scenario, requests execute in parallel. multi stream diagram The most common use cases for the multi-stream scheduler are where parallelism is low with respect to core count, and where requests need to be made asynchronously without time to batch them. - [Check out the Scheduler User Guide](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/scheduler.md) for more details. ## Product Usage Analytics DeepSparse Community Edition gathers basic usage telemetry including, but not limited to, Invocations, Package, Version, and IP Address for Product Usage Analytics purposes. Review Neural Magic's [Products Privacy Policy](https://neuralmagic.com/legal/) for further details on how we process this data. To disable Product Usage Analytics, run the command: ```bash export NM_DISABLE_ANALYTICS=True ``` Confirm that telemetry is shut off through info logs streamed with engine invocation by looking for the phrase "Skipping Neural Magic's latest package version check." For additional assistance, reach out through the [DeepSparse GitHub Issue queue](https://github.com/neuralmagic/deepsparse/issues). ## Additional Resources - [Benchmarking Performance](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/deepsparse-benchmarking.md) - [User Guide](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide) - [Use Cases](https://github.com/neuralmagic/deepsparse/tree/main/docs/use-cases) - [Cloud Deployments and Demos](https://github.com/neuralmagic/deepsparse/tree/main/examples/) #### Versions - [DeepSparse](https://pypi.org/project/deepsparse) | stable - [DeepSparse-Nightly](https://pypi.org/project/deepsparse-nightly/) | nightly (dev) - [GitHub](https://github.com/neuralmagic/deepsparse/releases) | releases #### Info - [Blog](https://www.neuralmagic.com/blog/) - [Resources](https://www.neuralmagic.com/resources/) ## Community ### Be Part of the Future... And the Future is Sparse! Contribute with code, examples, integrations, and documentation as well as bug reports and feature requests! [Learn how here.](https://github.com/neuralmagic/deepsparse/blob/main/CONTRIBUTING.md) For user help or questions about DeepSparse, sign up or log in to our **[Deep Sparse Community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)**. We are growing the community member by member and happy to see you there. Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/deepsparse/issues) You can get the latest news, webinar and event invites, research papers, and other ML Performance tidbits by [subscribing](https://neuralmagic.com/subscribe/) to the Neural Magic community. For more general questions about Neural Magic, complete this [form.](http://neuralmagic.com/contact/) ### License [DeepSparse Community](https://docs.neuralmagic.com/products/deepsparse) is licensed under the [Neural Magic DeepSparse Community License.](https://github.com/neuralmagic/deepsparse/blob/main/LICENSE-NEURALMAGIC) Some source code, example files, and scripts included in the deepsparse GitHub repository or directory are licensed under the [Apache License Version 2.0](https://github.com/neuralmagic/deepsparse/blob/main/LICENSE) as noted. [DeepSparse Enterprise](https://docs.neuralmagic.com/products/deepsparse-ent) requires a Trial License or [can be fully licensed](https://neuralmagic.com/legal/master-software-license-and-service-agreement/) for production, commercial applications. ### Cite Find this project useful in your research or other communications? Please consider citing: ```bibtex @InProceedings{ pmlr-v119-kurtz20a, title = {Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks}, author = {Kurtz, Mark and Kopinsky, Justin and Gelashvili, Rati and Matveev, Alexander and Carr, John and Goin, Michael and Leiserson, William and Moore, Sage and Nell, Bill and Shavit, Nir and Alistarh, Dan}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {5533--5543}, year = {2020}, editor = {Hal Daumé III and Aarti Singh}, volume = {119}, series = {Proceedings of Machine Learning Research}, address = {Virtual}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/kurtz20a/kurtz20a.pdf}, url = {http://proceedings.mlr.press/v119/kurtz20a.html} } @article{DBLP:journals/corr/abs-2111-13445, author = {Eugenia Iofinova and Alexandra Peste and Mark Kurtz and Dan Alistarh}, title = {How Well Do Sparse Imagenet Models Transfer?}, journal = {CoRR}, volume = {abs/2111.13445}, year = {2021}, url = {https://arxiv.org/abs/2111.13445}, eprinttype = {arXiv}, eprint = {2111.13445}, timestamp = {Wed, 01 Dec 2021 15:16:43 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2111-13445.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` %package help Summary: Development documents and examples for deepsparse-nightly Provides: python3-deepsparse-nightly-doc %description help

tool icon   DeepSparse

An inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application

Documentation Slack Support Main GitHub release Contributor Covenant YouTube Medium Twitter
[DeepSparse](https://github.com/neuralmagic/deepsparse) is a CPU inference runtime that takes advantage of sparsity within neural networks to execute inference quickly. Coupled with [SparseML](https://github.com/neuralmagic/sparseml), an open-source optimization library, DeepSparse enables you to achieve GPU-class performance on commodity hardware.

NM Flow

For details of training sparse models for deployment with DeepSparse, [check out SparseML](https://github.com/neuralmagic/sparseml). ### ✨NEW✨ DeepSparse ARM Alpha 💪 Neural Magic is bringing performant deep learning inference to ARM CPUs! In our recent product release, we launched alpha support for DeepSparse on AWS Graviton and Ampere. We are working towards a general release across ARM server, embedded, and mobile platforms in 2023. **If you would like to trial the alpha or want early access to the general release, [sign up for the waitlist](https://neuralmagic.com/deepsparse-arm-waitlist/).** ## Installation DeepSparse is available in two editions: 1. DeepSparse Community is free for evaluation, research, and non-production use with our [DeepSparse Community License](https://neuralmagic.com/legal/engine-license-agreement/). 2. DeepSparse Enterprise requires a [trial license](https://neuralmagic.com/deepsparse-free-trial/) or [can be fully licensed](https://neuralmagic.com/legal/master-software-license-and-service-agreement/) for production, commercial applications. #### Install via Docker (Recommended) DeepSparse Community is available as a container image hosted on [GitHub container registry](https://github.com/neuralmagic/deepsparse/pkgs/container/deepsparse). ```bash docker pull ghcr.io/neuralmagic/deepsparse:1.4.2 docker tag ghcr.io/neuralmagic/deepsparse:1.4.2 deepsparse-docker docker run -it deepsparse-docker ``` - [Check out the Docker page](https://github.com/neuralmagic/deepsparse/tree/main/docker/) for more details. #### Install via PyPI DeepSparse Community is also available via PyPI. We recommend using a virtual enviornment. ```bash pip install deepsparse ``` - [Check out the Installation page](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/installation.md) for optional dependencies. ## Hardware Support and System Requirements [Supported Hardware for DeepSparse](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/hardware-support.md) DeepSparse is tested on Python versions 3.7-3.10, ONNX versions 1.5.0-1.12.0, ONNX opset version 11 or higher, and manylinux compliant systems. Please note that DeepSparse is only supported natively on Linux. For those using Mac or Windows, running Linux in a Docker or virtual machine is necessary to use DeepSparse. ## Deployment APIs DeepSparse includes three deployment APIs: - **Engine** is the lowest-level API. With Engine, you pass tensors and receive the raw logits. - **Pipeline** wraps the Engine with pre- and post-processing. With Pipeline, you pass raw data and receive the prediction. - **Server** wraps Pipelines with a REST API using FastAPI. With Server, you send raw data over HTTP and receive the prediction. ### Engine The example below downloads a 90% pruned-quantized BERT model for sentiment analysis in ONNX format from SparseZoo, compiles the model, and runs inference on randomly generated input. ```python from deepsparse import Engine from deepsparse.utils import generate_random_inputs, model_to_path # download onnx, compile zoo_stub = "zoo:nlp/sentiment_analysis/obert-base/pytorch/huggingface/sst2/pruned90_quant-none" batch_size = 1 compiled_model = Engine(model=zoo_stub, batch_size=batch_size) # run inference (input is raw numpy tensors, output is raw scores) inputs = generate_random_inputs(model_to_path(zoo_stub), batch_size) output = compiled_model(inputs) print(output) # > [array([[-0.3380675 , 0.09602544]], dtype=float32)] << raw scores ``` ### DeepSparse Pipelines Pipeline is the default API for interacting with DeepSparse. Similar to Hugging Face Pipelines, DeepSparse Pipelines wrap Engine with pre- and post-processing (as well as other utilities), enabling you to send raw data to DeepSparse and receive the post-processed prediction. The example below downloads a 90% pruned-quantized BERT model for sentiment analysis in ONNX format from SparseZoo, sets up a pipeline, and runs inference on sample data. ```python from deepsparse import Pipeline # download onnx, set up pipeline zoo_stub = "zoo:nlp/sentiment_analysis/obert-base/pytorch/huggingface/sst2/pruned90_quant-none" sentiment_analysis_pipeline = Pipeline.create( task="sentiment-analysis", # name of the task model_path=zoo_stub, # zoo stub or path to local onnx file ) # run inference (input is a sentence, output is the prediction) prediction = sentiment_analysis_pipeline("I love using DeepSparse Pipelines") print(prediction) # > labels=['positive'] scores=[0.9954759478569031] ``` #### Additional Resources - Check out the [Use Cases Page](https://github.com/neuralmagic/deepsparse/tree/main/docs/use-cases) for more details on supported tasks. - Check out the [Pipelines User Guide](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/deepsparse-pipelines.md) for more usage details. ### DeepSparse Server Server wraps Pipelines with REST APIs, enabling you to stand up model serving endpoint running DeepSparse. This enables you to send raw data to DeepSparse over HTTP and receive the post-processed predictions. DeepSparse Server is launched from the command line, configured via arguments or a server configuration file. The following downloads a 90% pruned-quantized BERT model for sentiment analysis in ONNX format from SparseZoo and launches a sentiment analysis endpoint: ```bash deepsparse.server \ --task sentiment-analysis \ --model_path zoo:nlp/sentiment_analysis/obert-base/pytorch/huggingface/sst2/pruned90_quant-none ``` Sending a request: ```python import requests url = "http://localhost:5543/predict" # Server's port default to 5543 obj = {"sequences": "Snorlax loves my Tesla!"} response = requests.post(url, json=obj) print(response.text) # {"labels":["positive"],"scores":[0.9965094327926636]} ``` #### Additional Resources - Check out the [Use Cases Page](https://github.com/neuralmagic/deepsparse/tree/main/docs/use-cases) for more details on supported tasks. - Check out the [Server User Guide](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/deepsparse-server.md) for more usage details. ## ONNX DeepSparse accepts models in the ONNX format. ONNX models can be passed in one of two ways: - **SparseZoo Stub**: [SparseZoo](https://sparsezoo.neuralmagic.com/) is an open-source repository of sparse models. The examples on this page use SparseZoo stubs to identify models and download them for deployment in DeepSparse. - **Local ONNX File**: Users can provide their own ONNX models, whether dense or sparse. For example: ```bash wget https://github.com/onnx/models/raw/main/vision/classification/mobilenet/model/mobilenetv2-7.onnx ``` ```python from deepsparse import Engine from deepsparse.utils import generate_random_inputs onnx_filepath = "mobilenetv2-7.onnx" batch_size = 16 # Generate random sample input inputs = generate_random_inputs(onnx_filepath, batch_size) # Compile and run compiled_model = Engine(model=onnx_filepath, batch_size=batch_size) outputs = compiled_model(inputs) print(outputs[0].shape) # (16, 1000) << batch, num_classes ``` ## Inference Modes DeepSparse offers different inference scenarios based on your use case. **Single-stream** scheduling: the latency/synchronous scenario, requests execute serially. [`default`] single stream diagram It's highly optimized for minimum per-request latency, using all of the system's resources provided to it on every request it gets. **Multi-stream** scheduling: the throughput/asynchronous scenario, requests execute in parallel. multi stream diagram The most common use cases for the multi-stream scheduler are where parallelism is low with respect to core count, and where requests need to be made asynchronously without time to batch them. - [Check out the Scheduler User Guide](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/scheduler.md) for more details. ## Product Usage Analytics DeepSparse Community Edition gathers basic usage telemetry including, but not limited to, Invocations, Package, Version, and IP Address for Product Usage Analytics purposes. Review Neural Magic's [Products Privacy Policy](https://neuralmagic.com/legal/) for further details on how we process this data. To disable Product Usage Analytics, run the command: ```bash export NM_DISABLE_ANALYTICS=True ``` Confirm that telemetry is shut off through info logs streamed with engine invocation by looking for the phrase "Skipping Neural Magic's latest package version check." For additional assistance, reach out through the [DeepSparse GitHub Issue queue](https://github.com/neuralmagic/deepsparse/issues). ## Additional Resources - [Benchmarking Performance](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide/deepsparse-benchmarking.md) - [User Guide](https://github.com/neuralmagic/deepsparse/tree/main/docs/user-guide) - [Use Cases](https://github.com/neuralmagic/deepsparse/tree/main/docs/use-cases) - [Cloud Deployments and Demos](https://github.com/neuralmagic/deepsparse/tree/main/examples/) #### Versions - [DeepSparse](https://pypi.org/project/deepsparse) | stable - [DeepSparse-Nightly](https://pypi.org/project/deepsparse-nightly/) | nightly (dev) - [GitHub](https://github.com/neuralmagic/deepsparse/releases) | releases #### Info - [Blog](https://www.neuralmagic.com/blog/) - [Resources](https://www.neuralmagic.com/resources/) ## Community ### Be Part of the Future... And the Future is Sparse! Contribute with code, examples, integrations, and documentation as well as bug reports and feature requests! [Learn how here.](https://github.com/neuralmagic/deepsparse/blob/main/CONTRIBUTING.md) For user help or questions about DeepSparse, sign up or log in to our **[Deep Sparse Community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)**. We are growing the community member by member and happy to see you there. Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/deepsparse/issues) You can get the latest news, webinar and event invites, research papers, and other ML Performance tidbits by [subscribing](https://neuralmagic.com/subscribe/) to the Neural Magic community. For more general questions about Neural Magic, complete this [form.](http://neuralmagic.com/contact/) ### License [DeepSparse Community](https://docs.neuralmagic.com/products/deepsparse) is licensed under the [Neural Magic DeepSparse Community License.](https://github.com/neuralmagic/deepsparse/blob/main/LICENSE-NEURALMAGIC) Some source code, example files, and scripts included in the deepsparse GitHub repository or directory are licensed under the [Apache License Version 2.0](https://github.com/neuralmagic/deepsparse/blob/main/LICENSE) as noted. [DeepSparse Enterprise](https://docs.neuralmagic.com/products/deepsparse-ent) requires a Trial License or [can be fully licensed](https://neuralmagic.com/legal/master-software-license-and-service-agreement/) for production, commercial applications. ### Cite Find this project useful in your research or other communications? Please consider citing: ```bibtex @InProceedings{ pmlr-v119-kurtz20a, title = {Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks}, author = {Kurtz, Mark and Kopinsky, Justin and Gelashvili, Rati and Matveev, Alexander and Carr, John and Goin, Michael and Leiserson, William and Moore, Sage and Nell, Bill and Shavit, Nir and Alistarh, Dan}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {5533--5543}, year = {2020}, editor = {Hal Daumé III and Aarti Singh}, volume = {119}, series = {Proceedings of Machine Learning Research}, address = {Virtual}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/kurtz20a/kurtz20a.pdf}, url = {http://proceedings.mlr.press/v119/kurtz20a.html} } @article{DBLP:journals/corr/abs-2111-13445, author = {Eugenia Iofinova and Alexandra Peste and Mark Kurtz and Dan Alistarh}, title = {How Well Do Sparse Imagenet Models Transfer?}, journal = {CoRR}, volume = {abs/2111.13445}, year = {2021}, url = {https://arxiv.org/abs/2111.13445}, eprinttype = {arXiv}, eprint = {2111.13445}, timestamp = {Wed, 01 Dec 2021 15:16:43 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2111-13445.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` %prep %autosetup -n deepsparse-nightly-1.5.0.20230502 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-deepsparse-nightly -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 1.5.0.20230502-1 - Package Spec generated