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docker/Dockerfile.ci_adreno:19","Warn: containerImage not pinned by hash: docker/Dockerfile.ci_arm:21: pin your Docker image by updating ubuntu:22.04 to ubuntu:22.04@sha256:1aa979d85661c488ce030ac292876cf6ed04535d3a237e49f61542d8e5de5ae0","Warn: containerImage not pinned by hash: docker/Dockerfile.ci_cpu:19: pin your Docker image by updating ubuntu:22.04 to ubuntu:22.04@sha256:1aa979d85661c488ce030ac292876cf6ed04535d3a237e49f61542d8e5de5ae0","Warn: containerImage not pinned by hash: docker/Dockerfile.ci_gpu:20: pin your Docker image by updating nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04 to nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04@sha256:622e78a1d02c0f90ed900e3985d6c975d8e2dc9ee5e61643aed587dcf9129f42","Warn: containerImage not pinned by hash: docker/Dockerfile.ci_hexagon:20: pin your Docker image by updating tvmcihexagon/ci-hexagon-base:v0.02_SDK4.5.0.3 to tvmcihexagon/ci-hexagon-base:v0.02_SDK4.5.0.3@sha256:8326ea84adb046190d6a569f56dfe5ba7cf39712003bd36ae4f6ff8afd929fed","Warn: 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ubuntu:22.04@sha256:1aa979d85661c488ce030ac292876cf6ed04535d3a237e49f61542d8e5de5ae0","Warn: containerImage not pinned by hash: docker/Dockerfile.conda_cuda100:18","Warn: containerImage not pinned by hash: docker/Dockerfile.conda_cuda90:18","Warn: containerImage not pinned by hash: docker/Dockerfile.demo_android:19: pin your Docker image by updating ubuntu:22.04 to ubuntu:22.04@sha256:1aa979d85661c488ce030ac292876cf6ed04535d3a237e49f61542d8e5de5ae0","Warn: containerImage not pinned by hash: docker/Dockerfile.demo_cpu:20","Warn: containerImage not pinned by hash: docker/Dockerfile.demo_gpu:21","Warn: containerImage not pinned by hash: docker/Dockerfile.demo_mrvl:19: pin your Docker image by updating tlcpack/ci-cpu:20230604-060130-0af9ff90e to tlcpack/ci-cpu:20230604-060130-0af9ff90e@sha256:71cf04d894d60896598fe188064d5bf8398c02aa65e24c064cf4c5c8fd8d3ae4","Warn: containerImage not pinned by hash: docker/Dockerfile.demo_opencl:22: pin your Docker image by updating ubuntu:22.04 to ubuntu:22.04@sha256:1aa979d85661c488ce030ac292876cf6ed04535d3a237e49f61542d8e5de5ae0","Warn: containerImage not pinned by hash: docker/Dockerfile.demo_rocm:19: pin your Docker image by updating ubuntu:22.04 to ubuntu:22.04@sha256:1aa979d85661c488ce030ac292876cf6ed04535d3a237e49f61542d8e5de5ae0","Warn: containerImage not pinned by hash: docker/Dockerfile.docs:18: pin your Docker image by updating ubuntu:22.04 to ubuntu:22.04@sha256:1aa979d85661c488ce030ac292876cf6ed04535d3a237e49f61542d8e5de5ae0","Warn: pipCommand not pinned by hash: docker/Dockerfile.ci_lint:41","Warn: pipCommand not pinned by hash: docker/Dockerfile.demo_cpu:25","Warn: pipCommand not pinned by hash: docker/Dockerfile.demo_cpu:28","Warn: pipCommand not pinned by hash: docker/Dockerfile.demo_gpu:26","Warn: pipCommand not pinned by hash: docker/Dockerfile.demo_opencl:43","Warn: pipCommand not pinned by hash: docker/Dockerfile.demo_opencl:46","Warn: pipCommand not pinned by hash: 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Project is vulnerable to: GHSA-3ww4-gg4f-jr7f","Warn: Project is vulnerable to: PYSEC-2024-225 / GHSA-6vqw-3v5j-54x4","Warn: Project is vulnerable to: GHSA-9v9h-cgj8-h64p","Warn: Project is vulnerable to: GHSA-h4gh-qq45-vh27","Warn: Project is vulnerable to: PYSEC-2024-60 / GHSA-jjg7-2v4v-x38h","Warn: Project is vulnerable to: PYSEC-2023-228 / GHSA-mq26-g339-26xf","Warn: Project is vulnerable to: GHSA-9hjg-9r4m-mvj7","Warn: Project is vulnerable to: GHSA-9wx4-h78v-vm56","Warn: Project is vulnerable to: PYSEC-2023-74 / GHSA-j8r2-6x86-q33q","Warn: Project is vulnerable to: PYSEC-2025-49 / GHSA-5rjg-fvgr-3xxf","Warn: Project is vulnerable to: GHSA-cx63-2mw6-8hw5","Warn: Project is vulnerable to: PYSEC-2022-43012 / GHSA-r9hx-vwmv-q579","Warn: Project is vulnerable to: GHSA-34jh-p97f-mpxf","Warn: Project is vulnerable to: GHSA-pq67-6m6q-mj2v","Warn: Project is vulnerable to: PYSEC-2024-187 / GHSA-rqc4-2hc7-8c8v","Warn: Project is vulnerable to: GHSA-jfmj-5v4g-7637","Warn: Project is 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See the NOTICE file --\u003e\n\u003c!--- distributed with this work for additional information --\u003e\n\u003c!--- regarding copyright ownership.  The ASF licenses this file --\u003e\n\u003c!--- to you under the Apache License, Version 2.0 (the --\u003e\n\u003c!--- \"License\"); you may not use this file except in compliance --\u003e\n\u003c!--- with the License.  You may obtain a copy of the License at --\u003e\n\n\u003c!---   http://www.apache.org/licenses/LICENSE-2.0 --\u003e\n\n\u003c!--- Unless required by applicable law or agreed to in writing, --\u003e\n\u003c!--- software distributed under the License is distributed on an --\u003e\n\u003c!--- \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --\u003e\n\u003c!--- KIND, either express or implied.  See the License for the --\u003e\n\u003c!--- specific language governing permissions and limitations --\u003e\n\u003c!--- under the License. --\u003e\n\n\u003cimg src=https://raw.githubusercontent.com/apache/tvm-site/main/images/logo/tvm-logo-small.png width=128/\u003e Open Deep Learning Compiler Stack\n==============================================\n[Documentation](https://tvm.apache.org/docs) |\n[Contributors](CONTRIBUTORS.md) |\n[Community](https://tvm.apache.org/community) |\n[Release Notes](NEWS.md)\n\nApache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the\nproductivity-focused deep learning frameworks and the performance- and efficiency-focused hardware backends.\nTVM works with deep learning frameworks to provide end-to-end compilation for different backends.\n\nLicense\n-------\nTVM is licensed under the [Apache-2.0](LICENSE) license.\n\nGetting Started\n---------------\nCheck out the [TVM Documentation](https://tvm.apache.org/docs/) site for installation instructions, tutorials, examples, and more.\nThe [Getting Started with TVM](https://tvm.apache.org/docs/get_started/overview.html) tutorial is a great\nplace to start.\n\nContribute to TVM\n-----------------\nTVM adopts the Apache committer model. We aim to create an open-source project maintained and owned by the community.\nCheck out the [Contributor Guide](https://tvm.apache.org/docs/contribute/).\n\nHistory and Acknowledgement\n---------------------------\nTVM started as a research project for deep learning compilation.\nThe first version of the project benefited a lot from the following projects:\n\n- [Halide](https://github.com/halide/Halide): Part of TVM's TIR and arithmetic simplification module\n originates from Halide. We also learned and adapted some parts of the lowering pipeline from Halide.\n- [Loopy](https://github.com/inducer/loopy): use of integer set analysis and its loop transformation primitives.\n- [Theano](https://github.com/Theano/Theano): the design inspiration of symbolic scan operator for recurrence.\n\nSince then, the project has gone through several rounds of redesigns.\nThe current design is also drastically different from the initial design, following the\ndevelopment trend of the ML compiler community.\n\nThe most recent version focuses on a cross-level design with TensorIR as the tensor-level representation\nand Relax as the graph-level representation and Python-first transformations.\nThe project's current design goal is to make the ML compiler accessible by enabling most\ntransformations to be customizable in Python and bringing a cross-level representation that can jointly\noptimize computational graphs, tensor programs, and libraries. The project is also a foundation\ninfra for building Python-first vertical compilers for domains, such as LLMs.\n","funding_links":[],"categories":["Python","💻 Hardware-aware ML Testing","🎯 Tool Categories","ML Compiler","Deep Learning Framework","HarmonyOS","compiler","Lighter and Deployment Frameworks","Performance","Official Version","machine-learning","⚙️ **Compilers \u0026 Low-Level Frameworks**","Software","4. 机器学习项目 | ML","Open Source Projects","Optimization \u0026 Performance","Tools"],"sub_categories":["🎯 Optimization Frameworks","Deployment \u0026 Distribution","Windows Manager","ML Compiler","🔧 **TVM** - Apache","AI Infrastructure","1. DL Framework on Mobile","Resources","Hard-ware Integration"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapache%2Ftvm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fapache%2Ftvm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapache%2Ftvm/lists"}