`tensorflow` pip package has a new, optional installation method for Linux that installs necessary Nvidia CUDA libraries through pip. As long as the Nvidia driver is already installed on the system, you may now run `pip install tensorflow[and-cuda]` to install TensorFlow's Nvidia CUDA library dependencies in the Python environment. Aside from the Nvidia driver, no other pre-existing Nvidia CUDA packages are necessary.

*   Enable JIT-compiled i64-indexed kernels on GPU for large tensors with more...

                        Artifact artifact = node.getArtifact();
                        Map<String, String> props = new HashMap<>(artifact.getProperties());
                        ArtifactType derived = derive(parentType, currentType);
                        props.putAll(derived.getProperties());
                        node.setArtifact(artifact.setProperties(props));
                    }
                }
            }

/// tip

This is the ultimate **vibe-driven development** experience. You don't need to think about what your API does, just let the LLM handle it. 🧘

///

## Try It { #try-it }

Go ahead, try it:

{* ../../docs_src/vibe/tutorial001_py310.py *}

 * to be the last step of the pipeline.
 *
 * <h4>Transforming</h4>
 *
 * To derive the next step by asynchronously applying a function to an input step's value, call
 * {@link #transform(ClosingFunction, Executor)} or {@link #transformAsync(AsyncClosingFunction,
 * Executor)} on the input step.
 *
 * <h4>Catching</h4>
 *
 * To derive the next step from a failed input step, call {@link #catching(Class, ClosingFunction,

First, you might want to see the basic ways to [help FastAPI and get help](help-fastapi.md).

## Developing

If you already cloned the [fastapi repository](https://github.com/fastapi/fastapi) and you want to deep dive in the code, here are some guidelines to set up your environment.

### Install requirements

Create a virtual environment and install the required packages with [`uv`](https://github.com/astral-sh/uv):

<div class="termy">

common:rbe_linux_cuda --config=cuda_clang_official
common:rbe_linux_cuda --config=rbe_linux_cpu
# For Remote build execution -- GPU configuration
common:rbe_linux_cuda --repo_env=REMOTE_GPU_TESTING=1
# Update UMD version when RBE CUDA driver is updated.
common:rbe_linux_cuda --repo_env=HERMETIC_CUDA_UMD_VERSION="13.0.2"

common:rbe_linux_cuda_nvcc --config=rbe_linux_cuda
common:rbe_linux_cuda_nvcc --config=cuda_nvcc

    }
  }

  /**
   * Creates an {@code ImmutableMap<String, String>} from a {@code Properties} instance. Properties
   * normally derive from {@code Map<Object, Object>}, but they typically contain strings, which is
   * awkward. This method lets you get a plain-old-{@code Map} out of a {@code Properties}.
   *

    }
  }

  /**
   * Creates an {@code ImmutableMap<String, String>} from a {@code Properties} instance. Properties
   * normally derive from {@code Map<Object, Object>}, but they typically contain strings, which is
   * awkward. This method lets you get a plain-old-{@code Map} out of a {@code Properties}.
   *

</pre>

<p>
yields a function value representing <code>Mp</code> with signature
</p>

<pre>
func(tp *T, f float32) float32
</pre>

<p>
For a method with a value receiver, one can derive a function
with an explicit pointer receiver, so
</p>

<pre>
(*T).Mv
</pre>

<p>
yields a function value representing <code>Mv</code> with signature
</p>

<pre>

* ✏ Rewording in `docs/en/docs/tutorial/debugging.md`. PR [#9581](https://github.com/tiangolo/fastapi/pull/9581) by [@ivan-abc](https://github.com/ivan-abc).
* 📝 Add german blog post (Domain-driven Design mit Python und FastAPI). PR [#9261](https://github.com/tiangolo/fastapi/pull/9261) by [@msander](https://github.com/msander).