experimental/cuda-ubi9/: ray-2.23.0 metadata and description
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Ray provides a simple, universal API for building distributed applications.
| author |
Ray Team |
| author_email |
ray-dev@googlegroups.com |
| classifiers |
- Programming Language :: Python :: 3.8
- Programming Language :: Python :: 3.9
- Programming Language :: Python :: 3.10
- Programming Language :: Python :: 3.11
|
| keywords |
ray distributed parallel machine-learning hyperparameter-tuningreinforcement-learning deep-learning serving python |
| license |
Apache 2.0 |
| requires_dist |
- click >=7.0
- filelock
- jsonschema
- msgpack <2.0.0,>=1.0.0
- packaging
- protobuf !=3.19.5,>=3.15.3
- pyyaml
- aiosignal
- frozenlist
- requests
- opencensus ; extra == 'air'
- starlette ; extra == 'air'
- requests ; extra == 'air'
- virtualenv !=20.21.1,>=20.0.24 ; extra == 'air'
- prometheus-client >=0.7.1 ; extra == 'air'
- fsspec ; extra == 'air'
- pandas ; extra == 'air'
- pyarrow >=6.0.1 ; extra == 'air'
- pydantic !=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,<3 ; extra == 'air'
- aiohttp >=3.7 ; extra == 'air'
- uvicorn[standard] ; extra == 'air'
- tensorboardX >=1.9 ; extra == 'air'
- numpy >=1.20 ; extra == 'air'
- pandas >=1.3 ; extra == 'air'
- py-spy >=0.2.0 ; extra == 'air'
- fastapi ; extra == 'air'
- aiohttp-cors ; extra == 'air'
- smart-open ; extra == 'air'
- watchfiles ; extra == 'air'
- colorful ; extra == 'air'
- grpcio >=1.32.0 ; (python_version < "3.10") and extra == 'air'
- grpcio >=1.42.0 ; (python_version >= "3.10") and extra == 'air'
- memray ; (sys_platform != "win32") and extra == 'air'
- opencensus ; extra == 'all'
- lz4 ; extra == 'all'
- starlette ; extra == 'all'
- dm-tree ; extra == 'all'
- requests ; extra == 'all'
- virtualenv !=20.21.1,>=20.0.24 ; extra == 'all'
- prometheus-client >=0.7.1 ; extra == 'all'
- fsspec ; extra == 'all'
- gymnasium ==0.28.1 ; extra == 'all'
- pandas ; extra == 'all'
- pyarrow >=6.0.1 ; extra == 'all'
- aiohttp >=3.7 ; extra == 'all'
- scikit-image ; extra == 'all'
- opentelemetry-sdk ; extra == 'all'
- uvicorn[standard] ; extra == 'all'
- opentelemetry-exporter-otlp ; extra == 'all'
- tensorboardX >=1.9 ; extra == 'all'
- scipy ; extra == 'all'
- typer ; extra == 'all'
- numpy >=1.20 ; extra == 'all'
- grpcio ; extra == 'all'
- watchfiles ; extra == 'all'
- pandas >=1.3 ; extra == 'all'
- py-spy >=0.2.0 ; extra == 'all'
- ray-cpp ==2.23.0 ; extra == 'all'
- rich ; extra == 'all'
- fastapi ; extra == 'all'
- aiohttp-cors ; extra == 'all'
- opentelemetry-api ; extra == 'all'
- smart-open ; extra == 'all'
- pyyaml ; extra == 'all'
- pydantic !=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,<3 ; extra == 'all'
- colorful ; extra == 'all'
- grpcio >=1.32.0 ; (python_version < "3.10") and extra == 'all'
- grpcio >=1.42.0 ; (python_version >= "3.10") and extra == 'all'
- memray ; (sys_platform != "win32") and extra == 'all'
- grpcio ; extra == 'client'
- ray-cpp ==2.23.0 ; extra == 'cpp'
- numpy >=1.20 ; extra == 'data'
- pandas >=1.3 ; extra == 'data'
- pyarrow >=6.0.1 ; extra == 'data'
- fsspec ; extra == 'data'
- aiohttp >=3.7 ; extra == 'default'
- aiohttp-cors ; extra == 'default'
- colorful ; extra == 'default'
- py-spy >=0.2.0 ; extra == 'default'
- requests ; extra == 'default'
- opencensus ; extra == 'default'
- pydantic !=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,<3 ; extra == 'default'
- prometheus-client >=0.7.1 ; extra == 'default'
- smart-open ; extra == 'default'
- virtualenv !=20.21.1,>=20.0.24 ; extra == 'default'
- grpcio >=1.32.0 ; (python_version < "3.10") and extra == 'default'
- grpcio >=1.42.0 ; (python_version >= "3.10") and extra == 'default'
- memray ; (sys_platform != "win32") and extra == 'default'
- opentelemetry-api ; extra == 'observability'
- opentelemetry-sdk ; extra == 'observability'
- opentelemetry-exporter-otlp ; extra == 'observability'
- pandas ; extra == 'rllib'
- tensorboardX >=1.9 ; extra == 'rllib'
- requests ; extra == 'rllib'
- pyarrow >=6.0.1 ; extra == 'rllib'
- fsspec ; extra == 'rllib'
- dm-tree ; extra == 'rllib'
- gymnasium ==0.28.1 ; extra == 'rllib'
- lz4 ; extra == 'rllib'
- scikit-image ; extra == 'rllib'
- pyyaml ; extra == 'rllib'
- scipy ; extra == 'rllib'
- typer ; extra == 'rllib'
- rich ; extra == 'rllib'
- py-spy >=0.2.0 ; extra == 'serve'
- aiohttp >=3.7 ; extra == 'serve'
- opencensus ; extra == 'serve'
- starlette ; extra == 'serve'
- requests ; extra == 'serve'
- virtualenv !=20.21.1,>=20.0.24 ; extra == 'serve'
- uvicorn[standard] ; extra == 'serve'
- prometheus-client >=0.7.1 ; extra == 'serve'
- fastapi ; extra == 'serve'
- aiohttp-cors ; extra == 'serve'
- smart-open ; extra == 'serve'
- watchfiles ; extra == 'serve'
- colorful ; extra == 'serve'
- pydantic !=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,<3 ; extra == 'serve'
- py-spy >=0.2.0 ; extra == 'serve-grpc'
- aiohttp >=3.7 ; extra == 'serve-grpc'
- opencensus ; extra == 'serve-grpc'
- starlette ; extra == 'serve-grpc'
- requests ; extra == 'serve-grpc'
- virtualenv !=20.21.1,>=20.0.24 ; extra == 'serve-grpc'
- uvicorn[standard] ; extra == 'serve-grpc'
- prometheus-client >=0.7.1 ; extra == 'serve-grpc'
- fastapi ; extra == 'serve-grpc'
- aiohttp-cors ; extra == 'serve-grpc'
- smart-open ; extra == 'serve-grpc'
- watchfiles ; extra == 'serve-grpc'
- colorful ; extra == 'serve-grpc'
- pydantic !=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,<3 ; extra == 'serve-grpc'
- grpcio >=1.32.0 ; (python_version < "3.10") and extra == 'serve-grpc'
- grpcio >=1.42.0 ; (python_version >= "3.10") and extra == 'serve-grpc'
- memray ; (sys_platform != "win32") and extra == 'serve-grpc'
- grpcio >=1.32.0 ; (python_version < "3.10") and extra == 'serve'
- grpcio >=1.42.0 ; (python_version >= "3.10") and extra == 'serve'
- memray ; (sys_platform != "win32") and extra == 'serve'
- pandas ; extra == 'train'
- tensorboardX >=1.9 ; extra == 'train'
- requests ; extra == 'train'
- pyarrow >=6.0.1 ; extra == 'train'
- fsspec ; extra == 'train'
- pandas ; extra == 'tune'
- tensorboardX >=1.9 ; extra == 'tune'
- requests ; extra == 'tune'
- pyarrow >=6.0.1 ; extra == 'tune'
- fsspec ; extra == 'tune'
|
| requires_python |
>=3.8 |
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute:
Learn more about Ray AI Libraries:
Data: Scalable Datasets for ML
Train: Distributed Training
Tune: Scalable Hyperparameter Tuning
RLlib: Scalable Reinforcement Learning
Serve: Scalable and Programmable Serving
Or more about Ray Core and its key abstractions:
Tasks: Stateless functions executed in the cluster.
Actors: Stateful worker processes created in the cluster.
Objects: Immutable values accessible across the cluster.
Monitor and debug Ray applications and clusters using the Ray dashboard.
Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing
ecosystem of community integrations.
Install Ray with: pip install ray. For nightly wheels, see the
Installation page.
Why Ray?
Today’s ML workloads are increasingly compute-intensive. As convenient as they are, single-node development environments such as your laptop cannot scale to meet these demands.
Ray is a unified way to scale Python and AI applications from a laptop to a cluster.
With Ray, you can seamlessly scale the same code from a laptop to a cluster. Ray is designed to be general-purpose, meaning that it can performantly run any kind of workload. If your application is written in Python, you can scale it with Ray, no other infrastructure required.
Getting Involved
Platform |
Purpose |
Estimated Response Time |
Support Level |
|---|
Discourse Forum |
For discussions about development and questions about usage. |
< 1 day |
Community |
GitHub Issues |
For reporting bugs and filing feature requests. |
< 2 days |
Ray OSS Team |
Slack |
For collaborating with other Ray users. |
< 2 days |
Community |
StackOverflow |
For asking questions about how to use Ray. |
3-5 days |
Community |
Meetup Group |
For learning about Ray projects and best practices. |
Monthly |
Ray DevRel |
Twitter |
For staying up-to-date on new features. |
Daily |
Ray DevRel |