ldZddlmZddlmZddlmZddlZddlZddlmZddl m Z ddl m Z dd l m Z dd l mZdd l mZdd lmZdd lmZej,ej.j0ej.j2ej.j4Gddej6Zy)z.Command to run a training application locally.)absolute_import)division)unicode_literalsN)base)flags) local_util) validation)build)run)log)filescPeZdZdZej dddZedZdZ y)CreatezmRun a custom training locally. Packages your training code into a Docker image and executes it locally. aI {description} You should execute this command in the top folder which includes all the code and resources you want to pack and run, or specify the 'work-dir' flag to point to it. Any other path you specified via flags should be a relative path to the work-dir and under it; otherwise it will be unaccessible. Supposing your directories are like the following structures: /root - my_project - my_training - task.py - util.py - setup.py - other_modules - some_module.py - dataset - small.dat - large.dat - config - dep - foo.tar.gz - bar.whl - requirements.txt - another_project - something If you set 'my_project' as the package, then you should execute the task.py by specifying "--script=my_training/task.py" or "--python-module=my_training.task", the 'requirements.txt' will be processed. And you will also be able to install extra packages by, e.g. specifying "--extra-packages=dep/foo.tar.gz,bar.whl" or include extra directories, e.g. specifying "--extra-dirs=dataset,config". If you set 'my_training' as the package, then you should execute the task.py by specifying "--script=task.py" or "--python-module=task", the 'setup.py' will be processed. However, you won't be able to access any other files or directories that are not in 'my_training' folder. See more details in the HELP info of the corresponding flags. a To execute an python module with required dependencies, run: $ {command} --python-module=my_training.task --executor-image-uri=gcr.io/my/image --requirements=pandas,scipy>=1.3.0 To execute a python script using local GPU, run: $ {command} --script=my_training/task.py --executor-image-uri=gcr.io/my/image --gpu To execute an arbitrary script with custom arguments, run: $ {command} --script=my_run.sh --executor-image-uri=gcr.io/my/image -- --my-arg bar --enable_foo To run an existing container training without building new image, run: $ {command} --executor-image-uri=gcr.io/my/custom-training-image ) DESCRIPTIONEXAMPLESc.tj|y)N)rAddLocalRunCustomJobFlags)parsers 'lib/surface/ai/custom_jobs/local_run.pyArgsz Create.Argsls ##F+c tj|}tj|j5t j jdj|j|jxs |j}|jrtj||j|j|j|j|j |j"|j$}|j&}t j jdt j jdt)j*||j,|j.|j0t j2jdj|t4j6j9t4j6j;|j|jxsd\}}t=j>|r.t j jdj|dddy#1swYyxYw) NzPackage is set to {}.) base_image host_workdir main_script python_module requirementsextra_packages extra_dirsoutput_image_namezA training image is built.zStarting to run ...) image_name enable_gpuservice_account_key user_argszrXs5&' (;@@HE#*D%%(($*;*;*@*@%%++-qIT  qI-qIr