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Fix broken links to guide/custom-container #836

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Aug 7, 2024
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2 changes: 1 addition & 1 deletion 06_gpu_and_ml/llm-serving/trtllm_llama.py
Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,7 @@
#
# To run TensorRT-LLM, we must first install it. Easier said than done!
#
# In Modal, we define [container images](https://modal.com/docs/guide/custom-containers) that run our serverless workloads.
# In Modal, we define [container images](https://modal.com/docs/guide/custom-container) that run our serverless workloads.
# All Modal containers have access to GPU drivers via the underlying host environment,
# but we still need to install the software stack on top of the drivers, from the CUDA runtime up.
#
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2 changes: 1 addition & 1 deletion 06_gpu_and_ml/tensorflow/tensorflow_tutorial.py
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Expand Up @@ -15,7 +15,7 @@
#
# Configuring a system to properly run GPU-accelerated TensorFlow can be challenging.
# Luckily, Modal makes it easy to stand on the shoulders of giants and
# [use a pre-built Docker container image](https://modal.com/docs/guide/custom-containers#use-an-existing-container-image-with-from_registry) from a registry like Docker Hub.
# [use a pre-built Docker container image](https://modal.com/docs/guide/custom-container#use-an-existing-container-image-with-from_registry) from a registry like Docker Hub.
# We recommend TensorFlow's [official base Docker container images](https://hub.docker.com/r/tensorflow/tensorflow), which come with `tensorflow` and its matching CUDA libraries already installed.
#
# If you want to install TensorFlow some other way, check out [their docs](https://www.tensorflow.org/install) for options and instructions.
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