One of Caffe2’s most significant features is easy, built-in distributed training. This means that you can very quickly scale up or down without refactoring your design.
Under the Hood
- Gloo: Caffe2 leverages, Gloo, a communications library for multi-machine training.
- NCCL: Caffe2 also utilize’s NVIDIA’s NCCL for multi-GPU communications.
- Redis To facilitate management of nodes in distributed training, Caffe2 can use a simple NFS share between nodes, or you can provide a Redis server to handle the nodes’ communications.
For an example of distributed training with Caffe2 you can run the resnet50_trainer script on a single GPU machine. The defaults assume that you’ve already loaded the training data into a lmdb database, but you have the additional option of using LevelDB. A guide for using the script is below.
Try Distributed Training
We’re assuming that you’ve successfully built Caffe2 and that you have a system with at least one GPU, but preferably more to test out the distributed features.
resnet50_trainer.py script default output:
1 2 3 4 5 6 7 8 9 10 11 12 13 usage: resnet50_trainer.py [-h] --train_data TRAIN_DATA [--test_data TEST_DATA] [--db_type DB_TYPE] [--gpus GPUS] [--num_gpus NUM_GPUS] [--num_channels NUM_CHANNELS] [--image_size IMAGE_SIZE] [--num_labels NUM_LABELS] [--batch_size BATCH_SIZE] [--epoch_size EPOCH_SIZE] [--num_epochs NUM_EPOCHS] [--base_learning_rate BASE_LEARNING_RATE] [--weight_decay WEIGHT_DECAY] [--num_shards NUM_SHARDS] [--shard_id SHARD_ID] [--run_id RUN_ID] [--redis_host REDIS_HOST] [--redis_port REDIS_PORT] [--file_store_path FILE_STORE_PATH]
||the path to the database of training data|
||the path to the database of test data|
||a list of GPU IDs, where 0 would be the first GPU device #, comma separated|
||an integer for the total number of GPUs; alternative to using a list with
||number of color channels, defaults to 3|
||the height or width in pixels of the input images, assumes they’re square, defaults to 227, might not handle small sizes|
||number of labels, defaults to 1000|
||batch size, total over all GPUs, defaults to 32, expand as you increase GPUs|
||number of images per epoch, defaults to 1.5MM (1500000), definitely change this|
||number of epochs|
||initial learning rate, defaults to 0.1 (based on 256 global batch size)|
||weight decay (L2 regularization)|
||number of machines in a distributed run, defaults to 1|
||shard/node id, defaults to 0, next node would be 1, and so forth|
||unique run identifier, e.g. uuid|
||host of Redis server (for rendezvous)|
||Redis port for rendezvous|
||alternative to Redis, (NFS) path to shared directory to use for rendezvous temp files to coordinate between each shard/node|
Arguments for preliminary testing:
--num_gpus<#> (use this instead of listing out each one with
--batch_size<multiples of 32> (default=32)
The only required parameter is the training database. You can try that out first with no other parameters if you have your training set already in lmdb.
1 python resnet50_trainer.py --train_data <location of lmdb training database>
1 python resnet50_trainer.py --train_data <location of leveldb training database> --db_type leveldb
The script uses a default batch size of 32. When using 2 GPUs you want to increase the batch size according to the number of GPUs, so that you’re using as much of the memory on the GPU as possible. In the case of using 2 GPUs as in the example below, we double the batch size to 64:
1 python resnet50_trainer.py --train_data <location of lmdb training database> --num_gpus 2 --batch_size 64
You will notice that when you add the second GPU and double the batch size the number of iterations per epoch is half.
nvidia-smi you can examine the GPUs’ current status and see if you’re properly maxing it out in each run. Try running
watch -n1 nvidia-smi to continuously report the status while you run different experiments.
If you add the
--test_data parameter you will get occasional test runs intermingled which can provide a nice metric on how well the neural network is doing at that time. It’ll give you accuracy numbers and help you assess convergence.
As you run the script and training progresses, you will notice log files are deposited in the same folder. The naming convention will give you an idea of what that particular run’s parameters were. For example:
You can infer from this filename that the parameters were:
--base_learning_rate 0.10, followed by a timestamp.
When opening the log file you will find the parameters used for that run, a header to let you know what the comma separated values represent, and finally the log data.
The list of values recorded in the log: time_spent,cumulative_time_spent,input_count,cumulative_input_count,cumulative_batch_count,inputs_per_sec,accuracy,epoch,learning_rate,loss,test_accuracy
test_accuracy will be set to -1 if you don’t use test data, otherwise it will populate with an accuracy number.
There are many other parameters you can tinker with using this script. We look forward to hearing from you about your experiments and to see your models appear on Caffe2’s Model Zoo!