|
122 | 122 | "\n", |
123 | 123 | "The `sagemaker.tensorflow.TensorFlow` estimator handles locating the training container based on the framework version and the job type (Inference or Training), uploading your script to a S3 location and creating a SageMaker training job. Let's call out a couple important parameters here:\n", |
124 | 124 | "\n", |
125 | | - "* `framework_version` is set to `'2.14.1'` to indicate the TensorFlow version we want to use for executing your model training code. This will indicate to SageMaker which DLC should be used. Here's the list of the [available Deep Learning Container Images](https://github.com/aws/deep-learning-containers/blob/master/available_images.md).\n", |
| 125 | + "* `framework_version` is set to `'2.13.0'` to indicate the TensorFlow version we want to use for executing your model training code. This will indicate to SageMaker which DLC should be used. Here's the list of the [available Deep Learning Container Images](https://github.com/aws/deep-learning-containers/blob/master/available_images.md).\n", |
126 | 126 | "\n", |
127 | 127 | "* `entry_point` is the absolute or relative path to the local Python source file that should be executed as the entry point to training. \n", |
128 | 128 | "\n" |
|
152 | 152 | " role=role,\n", |
153 | 153 | " instance_count=instance_count,\n", |
154 | 154 | " instance_type=instance_type,\n", |
155 | | - " framework_version=\"2.14.1\",\n", |
| 155 | + " framework_version=\"2.13.0\",\n", |
156 | 156 | " py_version=\"py310\",\n", |
157 | 157 | ")" |
158 | 158 | ] |
|
314 | 314 | " role=role,\n", |
315 | 315 | " instance_count=instance_count,\n", |
316 | 316 | " instance_type=instance_type,\n", |
317 | | - " framework_version=\"2.14.1\",\n", |
| 317 | + " framework_version=\"2.13.0\",\n", |
318 | 318 | " py_version=\"py310\",\n", |
319 | 319 | " distribution={\"multi_worker_mirrored_strategy\": {\"enabled\": True}},\n", |
320 | 320 | ")" |
|
0 commit comments