
import tensorflow_cloud as tfc

tfc.run(
    entry_point="train_model.py", requirements_txt="requirements.txt", stream_logs=True
)


import tensorflow_cloud as tfc

gcp_bucket = "your-gcp-bucket"

tfc.run(
    entry_point="train_model.py",
    requirements_txt="requirements.txt",
    docker_image_bucket_name=gcp_bucket,
    stream_logs=True,
)

Add storage bucket to run() call
Add storage bucket to run() call
  
  import tensorflow_cloud as tfc
  
+ gcp_bucket = "your-gcp-bucket"
+ 
  tfc.run(
-     entry_point="train_model.py", requirements_txt="requirements.txt", stream_logs=True
+     entry_point="train_model.py",
+     requirements_txt="requirements.txt",
+     docker_image_bucket_name=gcp_bucket,
+     stream_logs=True,
  )
  
Add storage bucket to run() call
## Code Before:

import tensorflow_cloud as tfc

tfc.run(
    entry_point="train_model.py", requirements_txt="requirements.txt", stream_logs=True
)

## Instruction:
Add storage bucket to run() call
## Code After:

import tensorflow_cloud as tfc

gcp_bucket = "your-gcp-bucket"

tfc.run(
    entry_point="train_model.py",
    requirements_txt="requirements.txt",
    docker_image_bucket_name=gcp_bucket,
    stream_logs=True,
)
