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# RAPIDS - Open GPU Data Science | ||
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## What is RAPIDS? | ||
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Visit [rapids.ai](https://rapids.ai) for more information. | ||
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The RAPIDS suite of software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. | ||
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**NOTE:** Review our [system requirements](https://docs.rapids.ai/install#system-req) to ensure you have a compatible system! | ||
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### Current Version - RAPIDS v23.08 | ||
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RAPIDS Libraries included in the images: | ||
- `cuDF` | ||
- `cuML` | ||
- `cuGraph` | ||
- `RMM` | ||
- `RAFT` | ||
- `cuSpatial` | ||
- `cuSignal` | ||
- `cuxfilter` | ||
- `cuCIM` | ||
- `xgboost` | ||
- `dask-sql` | ||
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### Image Types | ||
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The RAPIDS images are based on [`nvidia/cuda`](https://hub.docker.com/r/nvidia/cuda) and [`rapidsai/mambaforge-cuda`](https://hub.docker.com/r/rapidsai/mambaforge-cuda). The RAPIDS images provide `amd64` & `arm64` architectures [where supported](https://docs.rapids.ai/install#system-req). | ||
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There are two types: | ||
- `rapidsai/base` - contains a RAPIDS environment ready for use. | ||
- **TIP: Use this image if you want to use RAPIDS as a part of your pipeline.** | ||
- `rapidsai/notebooks` - extends the `rapidsai/base` image by adding a [`jupyterlab` server](https://jupyter.org/), example notebooks, and dependencies. | ||
- **TIP: Use this image if you want to explore RAPIDS through notebooks and examples.** | ||
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### Image Tag Naming Scheme | ||
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The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below: | ||
``` | ||
23.08-cuda11.8-py3.10 | ||
^ ^ ^ | ||
| | Python version | ||
| | | ||
| CUDA version | ||
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RAPIDS version | ||
``` | ||
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**Note: Nightly builds of the images have the RAPIDS version appended with an `a` (ie `23.08a-cuda11.8-py3.10`)** | ||
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## Usage | ||
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The `rapidsai/base` image starts with an [`ipython` shell](https://ipython.org/) by default. | ||
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The `rapidsai/notebooks` image starts with the [JupyterLab notebook server](https://jupyter.org/) by default. | ||
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### Container Ports | ||
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`rapidsai/notebooks` exposes port `8888` for the [JupyterLab notebook server](https://jupyter.org/). | ||
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### Environment Variables | ||
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The following environment variables can be passed to the `docker run` commands: | ||
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- `EXTRA_CONDA_PACKAGES` - used to install additional `conda` packages in the container. Use a space separated list of values | ||
- `CONDA_TIMEOUT` - how long (in seconds) the `conda` command should wait before exiting | ||
- `EXTRA_PIP_PACKAGES` - used to install additional `pip` packages in the container. Use a space separated list of values | ||
- `PIP_TIMEOUT` - how long (in seconds) the `pip` command should wait before exiting | ||
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Example: | ||
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```sh | ||
$ docker run \ | ||
--rm \ | ||
-it \ | ||
--pull always \ | ||
--gpus all \ | ||
-shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \ | ||
-e EXTRA_CONDA_PACKAGES="jq" \ | ||
-e EXTRA_PIP_PACKAGES="beautifulsoup4" \ | ||
-p 8888:8888 \ | ||
rapidsai/notebooks:23.08-cuda11.8-py3.10 | ||
``` | ||
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### Bind Mounts | ||
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Mounting files/folders to the locations specified below provide additional functionality for the images. | ||
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- `/home/rapids/environment.yml` - a YAML file that contains a list of dependencies that will be installed by `conda`. The file should look like: | ||
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```yml | ||
dependencies: | ||
- beautifulsoup4 | ||
- jq | ||
``` | ||
Example: | ||
```sh | ||
$ docker run \ | ||
--rm \ | ||
-it \ | ||
--pull always \ | ||
--gpus all \ | ||
-shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \ | ||
-v $(pwd)/environment.yml:/home/rapids/environment.yml \ | ||
rapidsai/base:23.08-cuda11.8-py3.10 | ||
``` | ||
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### Use JupyterLab to Explore the Notebooks | ||
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The `rapidsai/notebooks` container has notebooks for the RAPIDS libraries in `/home/rapids/notebooks`. | ||
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### Extending RAPIDS Images | ||
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All RAPIDS images use `conda` as their package manager, and all RAPIDS packages are available in the `base` conda environment. These image run as the `rapids` user. | ||
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### Access Documentation within Notebooks | ||
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You can check the documentation for RAPIDS APIs inside the JupyterLab notebook using a `?` command, like this: | ||
``` | ||
[1] ?cudf.read_csv | ||
``` | ||
This prints the function signature and its usage documentation. If this is not enough, you can see the full code for the function using `??`: | ||
``` | ||
[1] ??cudf.read_csv | ||
``` | ||
Check out the RAPIDS [documentation](https://docs.rapids.ai/) for more detailed information. | ||
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## More Information | ||
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Check out the [RAPIDS User Guides](https://docs.rapids.ai/user-guide) and [XGBoost](https://xgboost.readthedocs.io/en/latest/) API docs. | ||
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## Where can I get help or file bugs/requests? | ||
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Please submit issues with the container to this GitHub repository: [https://github.com/rapidsai/docker](https://github.com/rapidsai/docker/issues/new) | ||
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For issues with RAPIDS libraries like cuDF, cuML, RMM, or others file an issue in the related GitHub project. | ||
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Additional help can be found on [Stack Overflow](https://stackoverflow.com/tags/rapids). |