Articles:
  English:
  - author: Kurtis Pykes - NVIDIA
    link: https://developer.nvidia.com/blog/building-a-machine-learning-microservice-with-fastapi/
    title: Building a Machine Learning Microservice with FastAPI
  - author: Ravgeet Dhillon - Twilio
    link: https://www.twilio.com/en-us/blog/booking-appointments-twilio-notion-fastapi
    title: Booking Appointments with Twilio, Notion, and FastAPI
  - author: Abhinav Tripathi - Microsoft Blogs

  Args:
    environ_cp: copy of the os.environ.
    var_name: string for name of environment variable, e.g. "TF_NEED_CUDA".
    query_item: string for feature related to the variable, e.g. "CUDA for
      Nvidia GPUs".
    enabled_by_default: boolean for default behavior.
    question: optional string for how to ask for user input.
    yes_reply: optional string for reply when feature is enabled.

* Alpha support for scheduling pods on machines with NVIDIA GPUs whose kubelets use the `--experimental-nvidia-gpus` flag, using the alpha.kubernetes.io/nvidia-gpu resource  ([#24836](https://github.com/kubernetes/kubernetes/pull/24836), [@therc](https://github.com/therc))

The `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...

build:cuda_clang --config=cuda
# Enable TensorRT optimizations https://developer.nvidia.com/tensorrt
build:cuda_clang --config=tensorrt
build:cuda_clang --action_env=TF_CUDA_CLANG="1"
build:cuda_clang --@local_config_cuda//:cuda_compiler=clang
# Select supported compute capabilities (supported graphics cards).
# This is the same as the official TensorFlow builds.
# See https://developer.nvidia.com/cuda-gpus#compute

*   GCE nodes with NVIDIA GPUs attached now expose nvidia.com/gpu as a resource instead of alpha.kubernetes.io/nvidia-gpu. ([#54826](https://github.com/kubernetes/kubernetes/pull/54826),[ @mindprince](https://github.com/mindprince))

* Support for the `alpha.kubernetes.io/nvidia-gpu` resource, which was deprecated in 1.10, has been removed. Please use the resource exposed by DevicePlugins instead (`nvidia.com/gpu`). ([#61498](https://github.com/kubernetes/kubernetes/pull/61498), [@mindprince](https://github.com/mindprince))

    [rescheduler](https://kubernetes.io/docs/admin/rescheduler/) to guarantee
    resource availability for critical system pods.
* **[alpha]** `--experimental-nvidia-gpus` flag is replaced by `Accelerators` alpha
  feature gate along with addition of support for multiple Nvidia GPUs.
  - To use GPUs, pass `Accelerators=true` as part of `--feature-gates` flag.
  - More information [here](https://vishh.github.io/docs/user-guide/gpus/).

- Mounts `/home/kubernetes/bin/nvidia/vulkan/icd.d` on the host to `/etc/vulkan/icd.d` inside containers requesting GPU. ([#78868](https://github.com/kubernetes/kubernetes/pull/78868), [@chardch](https://github.com/chardch))

Ele é um projeto da Linux Foundation que fornece certificados HTTPS gratuitamente. De forma automatizada. Esses certificados usam toda a segurança criptográfica padrão e têm vida curta (cerca de 3 meses), então a segurança é realmente melhor por causa de sua vida útil reduzida.

Os domínios são verificados com segurança e os certificados são gerados automaticamente. Isso também permite automatizar a renovação desses certificados.