This page explains the state of a training cluster through the lifecycle of a training job, and how AI Platform Training handles training errors. You can use this information to adapt your training code accordingly.
Lifecycle of a training job
This section explains how AI Platform Training handles worker VMs through the lifecycle of a training job.
Start workers in parallel
When a training job starts, AI Platform Training schedules as many workers as
possible in a short amount of time. As a result, workers may start up in
parallel instead of sequentially. In order to reduce startup latency,
AI Platform Training starts running your code on each worker as soon as it
becomes available. When all the workers are available, AI Platform Training
sets the job state to
RUNNING.
In most cases, your machine learning framework automatically handles the workers starting in parallel. If you're using a distribution strategy in your training code, you may need to adjust it manually to handle workers starting in parallel. Learn more about distribution strategies in TensorFlow and in PyTorch.
Restart workers during the training job
During a training job, AI Platform Training can restart your master, workers or parameter servers with the same hostname. This can occur for the following reasons:
- VM maintenance: When the VM running a worker is subjected to VM maintenance, AI Platform Training restarts the worker on another VM. Learn more about live migration for VM maintenance.
Non-zero exits: If any worker exits with a non-zero exit code, AI Platform Training restarts that worker immediately in the same VM.
- If a worker fails due to a common error, it is treated as a permanent error, and AI Platform Training shuts down the entire job. If any containers restart before AI Platform Training shuts down the entire job, these containers may produce logs in Cloud Logging.
- If a worker fails due to a non-permanent error (any error not listed in the common errors), AI Platform Training allows the restarted worker to continue running, with up to five restarts per worker. After five restarts, if a worker fails again, AI Platform Training retries the entire job up to three times before failing the entire job.
To handle worker restarts in your training code, save checkpoints regularly during training so that you can restore from checkpoints when a worker restarts. If you expect training to take more than four hours, we recommend that you save a checkpoint at least once every four hours. Learn how to use training checkpoints in TensorFlow and in PyTorch.
Successfully completing a job
A training job completes successfully when its primary replica exits with exit code 0. At that point, AI Platform Training shuts down all the other running workers.
How AI Platform Training handles training job errors
This section explains how AI Platform Training handles common training job errors and internal errors.
About one minute after a job ends, AI Platform Training sets the error code on the training job object, based on the exit code.
Handle common errors
AI Platform Training shuts down all workers if it encounters any of the following issues:
| Error Type | Error Message/Log | Note |
| User code exception | The replica REPLICA_NAME exited with a non-zero status of EXIT_CODE. Termination reason: REASON. | If the job encountered exit codes that could be transient,
AI Platform Training tries to restart the job up to three times.
The potentially transient error codes that prompt AI Platform Training to
retry the job include the following:
|
| Out-of-memory | The replica REPLICA_NAME ran out of memory and exited with a non-zero status of EXIT_CODE. |
GKE reserves memory on AI Platform Training nodes. On
the smallest machine types (such as n1-standard-4),
AI Platform Training system agents can take up to 40% of total memory.
For larger VMs, the overhead is relatively small. Compare
allocatable memory for n1-standard machine types.
|
| Insufficient capacity in your region (Compute Engine stockout) | Resources are insufficient in region: REGION_NAME. Try a different region or use a different accelerator. | A stockout happens when Compute Engine is at capacity for your selected CPU or GPU in your region. It is unrelated to your project quota. When this happens, AI Platform Training attempts to restart the job up to three times. |
Handle internal errors
If AI Platform Training has an internal error, it attempts to restart a job
twice (three attempts in total). If the restart attempts also fail,
AI Platform Training returns an internal error with the message:
Internal error occurred for the current attempt.