|
25 | 25 | },
|
26 | 26 | {
|
27 | 27 | "cell_type": "code",
|
28 |
| - "execution_count": 1, |
| 28 | + "execution_count": null, |
29 | 29 | "metadata": {
|
30 | 30 | "isConfigCell": true
|
31 | 31 | },
|
32 | 32 | "outputs": [],
|
33 | 33 | "source": [
|
34 | 34 | "from sagemaker import get_execution_role\n",
|
| 35 | + "#IAM execution role that gives SageMaker access to resources in your AWS account.\n", |
| 36 | + "role = get_execution_role()\n", |
35 | 37 | "\n",
|
36 | 38 | "#Bucket location to save your custom code in tar.gz format.\n",
|
37 |
| - "custom_code_upload_location = 's3://<bucket-name>/customcode/tensorflow_pipemode'\n", |
| 39 | + "bucket = '<bucket-name>'\n", |
| 40 | + "custom_code_upload_location = 's3://{}/customcode/tensorflow_pipemode'.format(bucket)\n", |
38 | 41 | "\n",
|
39 | 42 | "#Bucket location where results of model training are saved.\n",
|
40 |
| - "model_artifacts_location = 's3://<bucket-name>/artifacts'\n", |
41 |
| - "\n", |
42 |
| - "#IAM execution role that gives SageMaker access to resources in your AWS account.\n", |
43 |
| - "role = get_execution_role()\n" |
| 43 | + "model_artifacts_location = 's3://{}/artifacts'.format(bucket)" |
44 | 44 | ]
|
45 | 45 | },
|
46 | 46 | {
|
|
61 | 61 | "!cat \"pipemode.py\""
|
62 | 62 | ]
|
63 | 63 | },
|
| 64 | + { |
| 65 | + "cell_type": "markdown", |
| 66 | + "metadata": {}, |
| 67 | + "source": [ |
| 68 | + "The above script implements all the functions required for a sagemaker tensorflow training script (See: [Preparing TensorFlow Training Script](https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/tensorflow/README.rst#preparing-the-tensorflow-training-script)). " |
| 69 | + ] |
| 70 | + }, |
64 | 71 | {
|
65 | 72 | "cell_type": "markdown",
|
66 | 73 | "metadata": {},
|
|
72 | 79 | "\n",
|
73 | 80 | "The training and evaluation data were produced using the benchmarking source code in the sagemaker-tensorflow-extensions benchmarking sub-package. If you want to investigate this further, please visit the GitHub repository for sagemaker-tensorflow-extensions at https://github.com/aws/sagemaker-tensorflow-extensions. \n",
|
74 | 81 | "\n",
|
75 |
| - "The following example code shows how to use a PipeModeDataset in an input_fn." |
76 |
| - ] |
77 |
| - }, |
78 |
| - { |
79 |
| - "cell_type": "code", |
80 |
| - "execution_count": null, |
81 |
| - "metadata": {}, |
82 |
| - "outputs": [], |
83 |
| - "source": [ |
| 82 | + "The following example code shows how to use a PipeModeDataset in an input_fn.\n", |
| 83 | + "\n", |
| 84 | + "```python\n", |
84 | 85 | "from sagemaker_tensorflow import PipeModeDataset\n",
|
85 | 86 | "\n",
|
86 | 87 | "def input_fn():\n",
|
|
107 | 108 | " ds = ds.map(parse, num_parallel_calls=10)\n",
|
108 | 109 | " ds = ds.batch(64)\n",
|
109 | 110 | " \n",
|
110 |
| - " return ds" |
| 111 | + " return ds\n", |
| 112 | + "```" |
111 | 113 | ]
|
112 | 114 | },
|
113 | 115 | {
|
|
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