Hugging Face's logo

Spaces:
amazon
/
README
Running

App Files Community
1
Thibault Goehringer commited on
Commit
41f047d
•
1 Parent(s): 38a946a

Update classes

Browse files
Files changed (1) hide show
  1. README.md +75 -73
README.md CHANGED
@@ -7,83 +7,85 @@ sdk: static
7
  pinned: false
8
  ---
9
 
10
- <p class="lg:col-span-3">
11
- Hugging Face is working with Amazon Web Services to make it easier than
12
- ever for startups and enterprises to <strong
13
- >train and deploy Hugging Face models in Amazon SageMaker</strong
14
- >.
15
- </p>
16
- <a
17
- href="https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face"
18
- class="block overflow-hidden group"
19
- >
20
- <div
21
- class="w-full h-40 object-cover mb-2 bg-indigo-100 rounded-lg flex items-center justify-center dark:bg-gray-900 dark:group-hover:bg-gray-850"
22
  >
 
 
 
 
 
 
 
 
 
 
 
 
23
  <img
24
  alt=""
25
- src="/front/assets/promo/amazon_sagemaker_x_huggingface.png"
26
- class="w-40"
27
  />
28
- </div>
29
- <div class="underline">Read announcement blog post</div>
30
- </a>
31
- <a href="https://youtu.be/ok3hetb42gU" class="block overflow-hidden">
32
- <img
33
- alt=""
34
- src="/front/assets/promo/amazon_walkthrough_thumbnail.png"
35
- class="w-full h-40 object-cover mb-2 bg-gray-300 rounded-lg"
36
- />
37
- <div class="underline">Video Walkthrough with Philipp Schmid</div>
38
- </a>
39
- <a
40
- href="https://huggingface.co/docs/sagemaker"
41
- class="block overflow-hidden group"
42
- >
43
- <div
44
- class="w-full h-40 object-cover mb-2 bg-gray-900 group-hover:bg-gray-850 rounded-lg flex items-start justify-start"
45
  >
46
- <img
47
- alt=""
48
- src="/front/assets/promo/amazon_documentation.png"
49
- class="w-44 p-4"
50
- />
51
- </div>
52
- <div class="underline">Documentation: Hugging Face in SageMaker</div>
53
- </a>
54
- <div class="lg:col-span-3">
55
- <p class="mb-2">
56
- To train Hugging Face models in Amazon SageMaker, you can use the
57
- Hugging Face Deep Learning Contrainers (DLCs) and the Hugging Face
58
- support in the SageMaker Python SDK.
59
- </p>
60
- <p class="mb-2">
61
- The DLCs are fully integrated with the SageMaker distributed training
62
- libraries to train models more quickly using the latest generation of
63
- accelerated computing instances available on Amazon EC2. With the
64
- SageMaker Python SDK, you can start training with just a single line of
65
- code, enabling your teams to move from idea to production more quickly.
66
- </p>
67
- <p class="mb-2">
68
- To deploy Hugging Face models in Amazon SageMaker, you can use the
69
- Hugging Face Deep Learning Containers with the new Hugging Face
70
- Inference Toolkit.
71
- </p>
72
- <p class="mb-2">
73
- With the new Hugging Face Inference DLCs, deploy your trained models for
74
- inference with just one more line of code, or select any of the 10,000+
75
- models publicly available on the 🤗 Hub, and deploy them with Amazon
76
- SageMaker, to easily create production-ready endpoints that scale
77
- seamlessly, with built-in monitoring and enterprise-level security.
78
- </p>
79
- <p>
80
- More information: <a
81
- href="https://aws.amazon.com/blogs/machine-learning/aws-and-hugging-face-collaborate-to-simplify-and-accelerate-adoption-of-natural-language-processing-models/"
82
- class="underline">AWS blog post</a
83
- >,
84
- <a
85
- href="https://discuss.huggingface.co/c/sagemaker/17"
86
- class="underline">Community Forum</a
87
  >
88
- </p>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
89
  </div>
 
7
  pinned: false
8
  ---
9
 
10
+ <div class="grid lg:grid-cols-3 gap-x-4 gap-y-7">
11
+ <p class="lg:col-span-3">
12
+ Hugging Face is working with Amazon Web Services to make it easier than
13
+ ever for startups and enterprises to <strong
14
+ >train and deploy Hugging Face models in Amazon SageMaker</strong
15
+ >.
16
+ </p>
17
+ <a
18
+ href="https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face"
19
+ class="block overflow-hidden group"
 
 
20
  >
21
+ <div
22
+ class="w-full h-40 object-cover mb-2 bg-indigo-100 rounded-lg flex items-center justify-center dark:bg-gray-900 dark:group-hover:bg-gray-850"
23
+ >
24
+ <img
25
+ alt=""
26
+ src="/front/assets/promo/amazon_sagemaker_x_huggingface.png"
27
+ class="w-40"
28
+ />
29
+ </div>
30
+ <div class="underline">Read announcement blog post</div>
31
+ </a>
32
+ <a href="https://youtu.be/ok3hetb42gU" class="block overflow-hidden">
33
  <img
34
  alt=""
35
+ src="/front/assets/promo/amazon_walkthrough_thumbnail.png"
36
+ class="w-full h-40 object-cover mb-2 bg-gray-300 rounded-lg"
37
  />
38
+ <div class="underline">Video Walkthrough with Philipp Schmid</div>
39
+ </a>
40
+ <a
41
+ href="https://huggingface.co/docs/sagemaker"
42
+ class="block overflow-hidden group"
 
 
 
 
 
 
 
 
 
 
 
 
43
  >
44
+ <div
45
+ class="w-full h-40 object-cover mb-2 bg-gray-900 group-hover:bg-gray-850 rounded-lg flex items-start justify-start"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
46
  >
47
+ <img
48
+ alt=""
49
+ src="/front/assets/promo/amazon_documentation.png"
50
+ class="w-44 p-4"
51
+ />
52
+ </div>
53
+ <div class="underline">Documentation: Hugging Face in SageMaker</div>
54
+ </a>
55
+ <div class="lg:col-span-3">
56
+ <p class="mb-2">
57
+ To train Hugging Face models in Amazon SageMaker, you can use the
58
+ Hugging Face Deep Learning Contrainers (DLCs) and the Hugging Face
59
+ support in the SageMaker Python SDK.
60
+ </p>
61
+ <p class="mb-2">
62
+ The DLCs are fully integrated with the SageMaker distributed training
63
+ libraries to train models more quickly using the latest generation of
64
+ accelerated computing instances available on Amazon EC2. With the
65
+ SageMaker Python SDK, you can start training with just a single line of
66
+ code, enabling your teams to move from idea to production more quickly.
67
+ </p>
68
+ <p class="mb-2">
69
+ To deploy Hugging Face models in Amazon SageMaker, you can use the
70
+ Hugging Face Deep Learning Containers with the new Hugging Face
71
+ Inference Toolkit.
72
+ </p>
73
+ <p class="mb-2">
74
+ With the new Hugging Face Inference DLCs, deploy your trained models for
75
+ inference with just one more line of code, or select any of the 10,000+
76
+ models publicly available on the 🤗 Hub, and deploy them with Amazon
77
+ SageMaker, to easily create production-ready endpoints that scale
78
+ seamlessly, with built-in monitoring and enterprise-level security.
79
+ </p>
80
+ <p>
81
+ More information: <a
82
+ href="https://aws.amazon.com/blogs/machine-learning/aws-and-hugging-face-collaborate-to-simplify-and-accelerate-adoption-of-natural-language-processing-models/"
83
+ class="underline">AWS blog post</a
84
+ >,
85
+ <a
86
+ href="https://discuss.huggingface.co/c/sagemaker/17"
87
+ class="underline">Community Forum</a
88
+ >
89
+ </p>
90
+ </div>
91
  </div>