![]() There are mysterious memory usage growth in Ray clusters that disappear when running with jemalloc. Before we are able to figure out the root cause, it seems using jemalloc by default can be a good walkaround. Because of its efficiency, using jemalloc by default can be beneficial, but we need to run more benchmarks to verify. |
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Dockerfile | ||
README.md |
About
This is an internal image, the rayproject/ray
or rayproject/ray-ml
should be used!
This image has the system-level dependencies for Ray
and the Ray Autoscaler
. The ray-deps
image is built on top of this. This image is built periodically or when dependencies are added. Find the Dockerfile here.
Tags
Images are tagged
with the format {Ray version}[-{Python version}][-{Platform}]
. Ray version
tag can be one of the following:
Ray version tag | Description |
---|---|
latest |
The most recent Ray release. |
x.y.z |
A specific Ray release, e.g. 1.12.1 |
nightly |
The most recent Ray development build (a recent commit from Github master ) |
6 character Git SHA prefix |
A specific development build (uses a SHA from the Github master , e.g. 8960af ). |
The optional Python version
tag specifies the Python version in the image. All Python versions supported by Ray are available, e.g. py37
, py38
, py39
and py310
. If unspecified, the tag points to an image using Python 3.7
.
The optional Platform
tag specifies the platform where the image is intended for:
Platform tag | Description |
---|---|
-cpu |
These are based off of an Ubuntu image. |
-cuXX |
These are based off of an NVIDIA CUDA image with the specified CUDA version xx . They require the Nvidia Docker Runtime. |
-gpu |
Aliases to a specific -cuXX tagged image. |
no tag | Aliases to -cpu tagged images for ray , and aliases to -gpu tagged images for ray-ml . |
Other Images
-
rayproject/ray
- Ray and all of its dependencies. -
rayproject/ray-ml
- This image with common ML libraries to make development & deployment more smooth! -
rayproject/ray-deps
- Internal image with python-level dependencies.