Skip to main content

MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.

Project description

MindSpore Logo

PyPI - Python Version PyPI Downloads DockerHub LICENSE Slack PRs Welcome

查看中文

What Is MindSpore

MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios. MindSpore is designed to provide development experience with friendly design and efficient execution for the data scientists and algorithmic engineers, native support for Ascend AI processor, and software hardware co-optimization. At the meantime MindSpore as a global AI open source community, aims to further advance the development and enrichment of the AI software/hardware application ecosystem.

MindSpore Architecture

For more details please check out our Architecture Guide.

Automatic Differentiation

Currently, there are two automatic differentiation techniques in mainstream deep learning frameworks:

  • Operator Overloading (OO): Overloading the basic operators of the programming language to encapsulate their gradient rules. Record the operation trajectory of the network during forward execution in an operator overloaded manner, then apply the chain rule to the dynamically generated data flow graph to implement automatic differentiation.
  • Source Transformation (ST): This technology is evolving from the functional programming framework and performs automatic differential transformation on the intermediate expression (the expression form of the program during the compilation process) in the form of just-in-time compilation (JIT), supporting complex control flow scenarios, higher-order functions and closures.

PyTorch used OO. Compared to ST, OO generates gradient graph in runtime, so it does not need to take function call and control flow into consideration, which makes it easier to develop. However, OO can not perform gradient graph optimization in compilation time and the control flow has to be unfolded in runtime, so it is difficult to achieve extreme optimization in performance.

MindSpore implemented automatic differentiation based on ST. On the one hand, it supports automatic differentiation of automatic control flow, so it is quite convenient to build models like PyTorch. On the other hand, MindSpore can perform static compilation optimization on neural networks to achieve great performance.

Automatic Differentiation

The implementation of MindSpore automatic differentiation can be understood as the symbolic differentiation of the program itself. Because MindSpore IR is a functional intermediate expression, it has an intuitive correspondence with the composite function in basic algebra. The derivation formula of the composite function composed of arbitrary basic functions can be derived. Each primitive operation in MindSpore IR can correspond to the basic functions in basic algebra, which can build more complex flow control.

Automatic Parallel

The goal of MindSpore automatic parallel is to build a training method that combines data parallelism, model parallelism, and hybrid parallelism. It can automatically select a least cost model splitting strategy to achieve automatic distributed parallel training.

Automatic Parallel

At present, MindSpore uses a fine-grained parallel strategy of splitting operators, that is, each operator in the figure is split into a cluster to complete parallel operations. The splitting strategy during this period may be very complicated, but as a developer advocating Pythonic, you don't need to care about the underlying implementation, as long as the top-level API compute is efficient.

Installation

Pip mode method installation

MindSpore offers build options across multiple backends:

Hardware Platform Operating System Status
Ascend910 Ubuntu-x86 ✔️
Ubuntu-aarch64 ✔️
EulerOS-aarch64 ✔️
CentOS-x86 ✔️
CentOS-aarch64 ✔️
GPU CUDA 10.1 Ubuntu-x86 ✔️
CPU Ubuntu-x86 ✔️
Ubuntu-aarch64 ✔️
Windows-x86 ✔️

For installation using pip, take CPU and Ubuntu-x86 build version as an example:

  1. Download whl from MindSpore download page, and install the package.

    pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.2.0-rc1/MindSpore/cpu/ubuntu_x86/mindspore-1.2.0rc1-cp37-cp37m-linux_x86_64.whl
    
  2. Run the following command to verify the install.

    import numpy as np
    import mindspore.context as context
    import mindspore.nn as nn
    from mindspore import Tensor
    from mindspore.ops import operations as P
    
    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
    
    class Mul(nn.Cell):
        def __init__(self):
            super(Mul, self).__init__()
            self.mul = P.Mul()
    
        def construct(self, x, y):
            return self.mul(x, y)
    
    x = Tensor(np.array([1.0, 2.0, 3.0]).astype(np.float32))
    y = Tensor(np.array([4.0, 5.0, 6.0]).astype(np.float32))
    
    mul = Mul()
    print(mul(x, y))
    
    [ 4. 10. 18.]
    

Use pip mode method to install MindSpore in different environments. Refer to the following documents.

Source code compilation installation

Use the source code compilation method to install MindSpore in different environments. Refer to the following documents.

Docker Image

MindSpore docker image is hosted on Docker Hub, currently the containerized build options are supported as follows:

Hardware Platform Docker Image Repository Tag Description
CPU mindspore/mindspore-cpu x.y.z Production environment with pre-installed MindSpore x.y.z CPU release.
devel Development environment provided to build MindSpore (with CPU backend) from the source, refer to https://www.mindspore.cn/install/en for installation details.
runtime Runtime environment provided to install MindSpore binary package with CPU backend.
GPU mindspore/mindspore-gpu x.y.z Production environment with pre-installed MindSpore x.y.z GPU release.
devel Development environment provided to build MindSpore (with GPU CUDA10.1 backend) from the source, refer to https://www.mindspore.cn/install/en for installation details.
runtime Runtime environment provided to install MindSpore binary package with GPU CUDA10.1 backend.

NOTICE: For GPU devel docker image, it's NOT suggested to directly install the whl package after building from the source, instead we strongly RECOMMEND you transfer and install the whl package inside GPU runtime docker image.

  • CPU

    For CPU backend, you can directly pull and run the latest stable image using the below command:

    docker pull mindspore/mindspore-cpu:1.1.0
    docker run -it mindspore/mindspore-cpu:1.1.0 /bin/bash
    
  • GPU

    For GPU backend, please make sure the nvidia-container-toolkit has been installed in advance, here are some install guidelines for Ubuntu users:

    DISTRIBUTION=$(. /etc/os-release; echo $ID$VERSION_ID)
    curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | apt-key add -
    curl -s -L https://nvidia.github.io/nvidia-docker/$DISTRIBUTION/nvidia-docker.list | tee /etc/apt/sources.list.d/nvidia-docker.list
    
    sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit nvidia-docker2
    sudo systemctl restart docker
    

    Then edit the file daemon.json:

    $ vim /etc/docker/daemon.json
    {
        "runtimes": {
            "nvidia": {
                "path": "nvidia-container-runtime",
                "runtimeArgs": []
            }
        }
    }
    

    Restart docker again:

    sudo systemctl daemon-reload
    sudo systemctl restart docker
    

    Then you can pull and run the latest stable image using the below command:

    docker pull mindspore/mindspore-gpu:1.1.0
    docker run -it -v /dev/shm:/dev/shm --runtime=nvidia --privileged=true mindspore/mindspore-gpu:1.1.0 /bin/bash
    

    To test if the docker image works, please execute the python code below and check the output:

    import numpy as np
    import mindspore.context as context
    from mindspore import Tensor
    from mindspore.ops import functional as F
    
    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
    
    x = Tensor(np.ones([1,3,3,4]).astype(np.float32))
    y = Tensor(np.ones([1,3,3,4]).astype(np.float32))
    print(F.tensor_add(x, y))
    
    [[[ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.]],
    
    [[ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.]],
    
    [[ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.]]]
    

If you want to learn more about the building process of MindSpore docker images, please check out docker repo for the details.

Quickstart

See the Quick Start to implement the image classification.

Docs

More details about installation guide, tutorials and APIs, please see the User Documentation.

Community

Governance

Check out how MindSpore Open Governance works.

Communication

Contributing

Welcome contributions. See our Contributor Wiki for more details.

Maintenance phases

Project stable branches will be in one of the following states:

State Time frame Summary
Planning 1 - 3 months Features are under planning.
Development 3 months Features are under development.
Maintained 6 - 12 months All bugfixes are appropriate. Releases produced.
Unmaintained 0 - 3 months All bugfixes are appropriate. No Maintainers and No Releases produced.
End Of Life (EOL) N/A Version no longer accepting changes.

Maintenance status

Version Status Initial Release Date Next Phase EOL Date
r2.4 Maintained 2024-10-30 Unmaintained
2025-10-30 estimated
2025-10-30
r2.3 Maintained 2024-07-15 Unmaintained
2025-07-15 estimated
2025-07-15
r2.2 End Of Life 2023-10-18 2024-10-18
r2.1 End Of Life 2023-07-29 2024-07-29
r2.0 End Of Life 2023-06-15 2024-06-15
r1.10 End Of Life 2023-02-02 2024-02-02
r1.9 End Of Life 2022-10-26 2023-10-26
r1.8 End Of Life 2022-07-29 2023-07-29
r1.7 End Of Life 2022-04-29 2023-04-29
r1.6 End Of Life 2022-01-29 2023-01-29
r1.5 End Of Life 2021-10-15 2022-10-15
r1.4 End Of Life 2021-08-15 2022-08-15
r1.3 End Of Life 2021-07-15 2022-07-15
r1.2 End Of Life 2021-04-15 2022-04-29
r1.1 End Of Life 2020-12-31 2021-09-30
r1.0 End Of Life 2020-09-24 2021-07-30
r0.7 End Of Life 2020-08-31 2021-02-28
r0.6 End Of Life 2020-07-31 2020-12-30
r0.5 End Of Life 2020-06-30 2021-06-30
r0.3 End Of Life 2020-05-31 2020-09-30
r0.2 End Of Life 2020-04-30 2020-08-31
r0.1 End Of Life 2020-03-28 2020-06-30

Release Notes

The release notes, see our RELEASE.

License

Apache License 2.0

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

mindspore_dev-2.4.0.dev20241117-cp311-none-any.whl (349.3 MB view details)

Uploaded CPython 3.11

mindspore_dev-2.4.0.dev20241117-cp311-cp311-win_amd64.whl (101.7 MB view details)

Uploaded CPython 3.11 Windows x86-64

mindspore_dev-2.4.0.dev20241117-cp311-cp311-macosx_11_0_arm64.whl (235.6 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

mindspore_dev-2.4.0.dev20241117-cp311-cp311-macosx_10_15_x86_64.whl (251.0 MB view details)

Uploaded CPython 3.11 macOS 10.15+ x86-64

mindspore_dev-2.4.0.dev20241117-cp310-none-any.whl (347.4 MB view details)

Uploaded CPython 3.10

mindspore_dev-2.4.0.dev20241117-cp310-cp310-win_amd64.whl (101.7 MB view details)

Uploaded CPython 3.10 Windows x86-64

mindspore_dev-2.4.0.dev20241117-cp310-cp310-macosx_11_0_arm64.whl (235.4 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

mindspore_dev-2.4.0.dev20241117-cp310-cp310-macosx_10_15_x86_64.whl (250.5 MB view details)

Uploaded CPython 3.10 macOS 10.15+ x86-64

mindspore_dev-2.4.0.dev20241117-cp39-none-any.whl (347.4 MB view details)

Uploaded CPython 3.9

mindspore_dev-2.4.0.dev20241117-cp39-cp39-win_amd64.whl (101.9 MB view details)

Uploaded CPython 3.9 Windows x86-64

mindspore_dev-2.4.0.dev20241117-cp39-cp39-macosx_11_0_arm64.whl (235.3 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

mindspore_dev-2.4.0.dev20241117-cp39-cp39-macosx_10_15_x86_64.whl (250.5 MB view details)

Uploaded CPython 3.9 macOS 10.15+ x86-64

File details

Details for the file mindspore_dev-2.4.0.dev20241117-cp311-none-any.whl.

File metadata

  • Download URL: mindspore_dev-2.4.0.dev20241117-cp311-none-any.whl
  • Upload date:
  • Size: 349.3 MB
  • Tags: CPython 3.11
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.1

File hashes

Hashes for mindspore_dev-2.4.0.dev20241117-cp311-none-any.whl
Algorithm Hash digest
SHA256 576927255e9865b3b384fcc7298b0f63dba93b426c44778b7ba88fc1913017d5
MD5 f38a5e370dddaa47fcf4c0d5d1ab5103
BLAKE2b-256 12f6b4165ca793a32c6371c5bdde8c5f0f6b226d8ebffea6863eccd170eb91de

See more details on using hashes here.

File details

Details for the file mindspore_dev-2.4.0.dev20241117-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: mindspore_dev-2.4.0.dev20241117-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 101.7 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.1

File hashes

Hashes for mindspore_dev-2.4.0.dev20241117-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 007d716d7c62e99aece32fa535ab1cd40586a5f2367d3af5d930dae96413a332
MD5 d061fe17411d2060bcf3f8c346561b0c
BLAKE2b-256 f2691a0e650da8c80abdc596cf33037e3d48067cd6c759a87f91ab7f79af4d78

See more details on using hashes here.

File details

Details for the file mindspore_dev-2.4.0.dev20241117-cp311-cp311-manylinux1_x86_64.whl.

File metadata

  • Download URL: mindspore_dev-2.4.0.dev20241117-cp311-cp311-manylinux1_x86_64.whl
  • Upload date:
  • Size: 995.6 MB
  • Tags: CPython 3.11
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.1

File hashes

Hashes for mindspore_dev-2.4.0.dev20241117-cp311-cp311-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 b059cc988b6375f24d0290b7c1a7d7e90303886b55d32f7e408ec4e43e5e4d1e
MD5 aebee68a5f6d00093887b7ce7ba0e1cb
BLAKE2b-256 2d970f787cf1a3f7d369edf637cab84cd14ba1f84d2d85e980818b708993c145

See more details on using hashes here.

File details

Details for the file mindspore_dev-2.4.0.dev20241117-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

  • Download URL: mindspore_dev-2.4.0.dev20241117-cp311-cp311-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 235.6 MB
  • Tags: CPython 3.11, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.1

File hashes

Hashes for mindspore_dev-2.4.0.dev20241117-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 5828c2ac8a9762c59d79d0a25d1182e25b447c9c223bfe9dd87f2bd0b796f7fb
MD5 d2820eb12057d5dd3075f7c8009f8a65
BLAKE2b-256 272ef10147d757a2b8c050fa1948836540edb196cb1f5ba497fe39ce6215e54a

See more details on using hashes here.

File details

Details for the file mindspore_dev-2.4.0.dev20241117-cp311-cp311-macosx_10_15_x86_64.whl.

File metadata

  • Download URL: mindspore_dev-2.4.0.dev20241117-cp311-cp311-macosx_10_15_x86_64.whl
  • Upload date:
  • Size: 251.0 MB
  • Tags: CPython 3.11, macOS 10.15+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.1

File hashes

Hashes for mindspore_dev-2.4.0.dev20241117-cp311-cp311-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 c3321fde6ee95ad0b75d86bed93ffbd816a6d27c743a6d59e6f647475aa84b6e
MD5 1aad698785c03f9965bd7ecb8615e88f
BLAKE2b-256 61810330eed78852034534d6cc5cb984042a103306198b8e15fa58841590bace

See more details on using hashes here.

File details

Details for the file mindspore_dev-2.4.0.dev20241117-cp310-none-any.whl.

File metadata

  • Download URL: mindspore_dev-2.4.0.dev20241117-cp310-none-any.whl
  • Upload date:
  • Size: 347.4 MB
  • Tags: CPython 3.10
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.1

File hashes

Hashes for mindspore_dev-2.4.0.dev20241117-cp310-none-any.whl
Algorithm Hash digest
SHA256 6f25b0313b8504e0feeaf2059b0d745680d2dd94bfdae73b216cb09fd5925632
MD5 1e5deb4ceabf7e3c9ade6ff5abf905a2
BLAKE2b-256 f74098a99b6448dca981488251680c42929d4099a8261e8a3e16e824f82e8a1b

See more details on using hashes here.

File details

Details for the file mindspore_dev-2.4.0.dev20241117-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: mindspore_dev-2.4.0.dev20241117-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 101.7 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.1

File hashes

Hashes for mindspore_dev-2.4.0.dev20241117-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 6cd5f13e982e1550fc0c122fd76be916cbabdd107603fcdf5d564dc3877ed37c
MD5 0aa157752e2549135dea3a0ece1fc807
BLAKE2b-256 ed23691d74030af976fe35b8d0dd188e5fc117faa210e12cc278526fac93a6d3

See more details on using hashes here.

File details

Details for the file mindspore_dev-2.4.0.dev20241117-cp310-cp310-manylinux1_x86_64.whl.

File metadata

  • Download URL: mindspore_dev-2.4.0.dev20241117-cp310-cp310-manylinux1_x86_64.whl
  • Upload date:
  • Size: 992.1 MB
  • Tags: CPython 3.10
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.1

File hashes

Hashes for mindspore_dev-2.4.0.dev20241117-cp310-cp310-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 c95b5cef76b95c8b4176b768eec60e220fabe1b92c9cf91ddcc7ef86d20332a3
MD5 f214f5686024d12e5696c60004c2edb3
BLAKE2b-256 495510c4e053afc3d99bd78c40fc6c1c91572d9b005b4209686cb300db35fd60

See more details on using hashes here.

File details

Details for the file mindspore_dev-2.4.0.dev20241117-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

  • Download URL: mindspore_dev-2.4.0.dev20241117-cp310-cp310-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 235.4 MB
  • Tags: CPython 3.10, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.1

File hashes

Hashes for mindspore_dev-2.4.0.dev20241117-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 b676e4def72728c8dbe82eadbc5fbdcef395ca12c5c3bfb1e4577657a4b84755
MD5 56dffeef51873efaa2f7bb84ed9e816d
BLAKE2b-256 f26ca3737cdbf4f6c23b4ccb40d508449a2eacfb35d880424992e8c5bfe65ffb

See more details on using hashes here.

File details

Details for the file mindspore_dev-2.4.0.dev20241117-cp310-cp310-macosx_10_15_x86_64.whl.

File metadata

  • Download URL: mindspore_dev-2.4.0.dev20241117-cp310-cp310-macosx_10_15_x86_64.whl
  • Upload date:
  • Size: 250.5 MB
  • Tags: CPython 3.10, macOS 10.15+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.1

File hashes

Hashes for mindspore_dev-2.4.0.dev20241117-cp310-cp310-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 b5f4c77daa2d0c2759bbd56eadba7b4bc3a4e29c934b89d5b1c76bf1348cf162
MD5 033376a09c2f8db5d69161ca9d32f1e0
BLAKE2b-256 d6529eb7c36bec674ffa974d50f6b9e7901ddb704faf6dda764ce57f56294386

See more details on using hashes here.

File details

Details for the file mindspore_dev-2.4.0.dev20241117-cp39-none-any.whl.

File metadata

  • Download URL: mindspore_dev-2.4.0.dev20241117-cp39-none-any.whl
  • Upload date:
  • Size: 347.4 MB
  • Tags: CPython 3.9
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.1

File hashes

Hashes for mindspore_dev-2.4.0.dev20241117-cp39-none-any.whl
Algorithm Hash digest
SHA256 5512231f8ec62601a8839c7f59e8239cc7c90443b606b3d96ab66092d86218c1
MD5 c63827f1aba92f17ced4275086c3373c
BLAKE2b-256 901f3d7ee53d319726809c4426cc7e0e16a45fd614025307c8a3658f8abb3890

See more details on using hashes here.

File details

Details for the file mindspore_dev-2.4.0.dev20241117-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: mindspore_dev-2.4.0.dev20241117-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 101.9 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.1

File hashes

Hashes for mindspore_dev-2.4.0.dev20241117-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 815dd66bb183aa4f83cfbd23a1f3dd7ec74901ca65e74878859ec1dbf1fdbd6c
MD5 a6e33e4069b7ffec877ad9271ee6b6c4
BLAKE2b-256 d5b398eaf5f417bb2154ce9736338d06a58659a44316c78015b02e97696e2400

See more details on using hashes here.

File details

Details for the file mindspore_dev-2.4.0.dev20241117-cp39-cp39-manylinux1_x86_64.whl.

File metadata

  • Download URL: mindspore_dev-2.4.0.dev20241117-cp39-cp39-manylinux1_x86_64.whl
  • Upload date:
  • Size: 992.1 MB
  • Tags: CPython 3.9
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.1

File hashes

Hashes for mindspore_dev-2.4.0.dev20241117-cp39-cp39-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 fad6e7b31556b74751495e142ccafd22efc85137effcd69ef3dddfeaa409be7c
MD5 13543ba9343c6bf6bf464aab091eed59
BLAKE2b-256 257d15e6b468d5110c630b795d80b87e03a8dc755d6ae27d2f627d74f116fe06

See more details on using hashes here.

File details

Details for the file mindspore_dev-2.4.0.dev20241117-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

  • Download URL: mindspore_dev-2.4.0.dev20241117-cp39-cp39-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 235.3 MB
  • Tags: CPython 3.9, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.1

File hashes

Hashes for mindspore_dev-2.4.0.dev20241117-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 3668ec27a358fe83d6707191fe8e533372101392732c49e7bbc6a16e4b11fc6b
MD5 7d31f6b6cf5846826e22c5c1bc715d52
BLAKE2b-256 be00bb5cb4511d67a10edea864e5a728f8bb5f44410d484e067fcb108a75355f

See more details on using hashes here.

File details

Details for the file mindspore_dev-2.4.0.dev20241117-cp39-cp39-macosx_10_15_x86_64.whl.

File metadata

  • Download URL: mindspore_dev-2.4.0.dev20241117-cp39-cp39-macosx_10_15_x86_64.whl
  • Upload date:
  • Size: 250.5 MB
  • Tags: CPython 3.9, macOS 10.15+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/33.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/4.11.2 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.1

File hashes

Hashes for mindspore_dev-2.4.0.dev20241117-cp39-cp39-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 635ad4eee755cc532dd9290c1f8536345649b0686e61a42d5f250fa65b04686a
MD5 c9d420756e82fe5090c2df6e2bdc70a1
BLAKE2b-256 b37f12c2f890790d4b4119f174993dc2d45f851eed21ea52e15f9254c8cda2ff

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page