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ImageNet classification

Trains a ResNet50 model (He et al., 2016) for the ImageNet classification task (Russakovsky et al., 2015).

This example uses linear learning rate warmup and cosine learning rate schedule.

You can run this code and even modify it directly in Google Colab, no installation required:

https://colab.research.google.com/github/google/flax/blob/main/examples/imagenet/imagenet.ipynb

The Colab also demonstrates how to load pretrained checkpoints from Cloud storage at gs://flax_public/examples/imagenet/

Table of contents:

Requirements

  • TensorFlow dataset imagenet2012:5.*.*
  • ≈180GB of RAM if you want to cache the dataset in memory for faster IO

Example runs

While the example should run on a variety of hardware, we have tested the following GPU and TPU configurations:

Name Steps Walltime Top-1 accuracy Metrics Workdir
TPU v3-32 125100 2.1h 76.54% tfhub.dev gs://flax_public/examples/imagenet/tpu_v3_32
TPU v2-32 125100 2.5h 76.67% tfhub.dev gs://flax_public/examples/imagenet/tpu_v2_32
TPU v3-8 125100 4.4h 76.37% tfhub.dev gs://flax_public/examples/imagenet/tpu
v100_x8 250200 13.2h 76.72% tfhub.dev gs://flax_public/examples/imagenet/v100_x8
v100_x8_mixed_precision 62500 4.3h 76.27% tfhub.dev gs://flax_public/examples/imagenet/v100_x8_mixed_precision

Running locally

python main.py --workdir=./imagenet --config=configs/default.py

Overriding parameters on the command line

Specify a hyperparameter configuration by the means of setting --config flag. Configuration flag is defined using config_flags. config_flags allows overriding configuration fields. This can be done as follows:

python main.py --workdir=./imagenet_default --config=configs/default.py \
--config.num_epochs=100

Running fake data benchmarks

Execute the following code with flax/examples/imagenet as your current directory:

bash ../../tests/download_dataset_metadata.sh
python imagenet_fake_data_benchmark.py

If you get an error like this:

Cloning into 'datasets'...
fatal: cannot change to 'https://github.com/tensorflow/datasets/': No such file or directory
error: failed to initialize sparse-checkout

This mean your git version is outdated. Just update it and re-run.

Running on Cloud

Preparing the dataset

For running the ResNet50 model on imagenet dataset, you first need to prepare the imagenet2012 dataset. Download the data from http://image-net.org/ as described in the tensorflow_datasets catalog. Then point the environment variable $IMAGENET_DOWNLOAD_PATH to the directory where the downloads are stored and prepare the dataset by running

python -c "
import tensorflow_datasets as tfds
tfds.builder('imagenet2012').download_and_prepare(
    download_config=tfds.download.DownloadConfig(
        manual_dir='$IMAGENET_DOWNLOAD_PATH'))
"

The contents of the directory ~/tensorflow_datasets should be copied to your gcs bucket. Point the environment variable GCS_TFDS_BUCKET to your bucket and run the following command:

gsutil cp -r ~/tensorflow_datasets gs://$GCS_TFDS_BUCKET/datasets

Google Cloud TPU

See below for commands to set up a single VM with 8 TPUs attached (--accelerator-type v3-8), or for an entire TPU slice spanning multiple VMs (e.g. --accelerator-type v3-32). For more details about how to set up and use TPUs, refer to Cloud docs for single VM setup and pod slice setup.

First create a single TPUv3-8 VM and connect to it:

ZONE=us-central1-a
TPU_TYPE=v3-8
VM_NAME=imagenet

gcloud alpha compute tpus tpu-vm create $VM_NAME \
    --zone $ZONE \
    --accelerator-type $TPU_TYPE \
    --version v2-alpha

gcloud alpha compute tpus tpu-vm ssh $VM_NAME --zone $ZONE -- \
    -L 6006:localhost:6006

When connected install JAX:

pip install "jax[tpu]>=0.2.21" \
    -f https://storage.googleapis.com/jax-releases/libtpu_releases.html

Then install Flax + the example dependencies:

git clone --depth=1 --branch=main https://github.com/google/flax
cd flax
pip install -e .
cd examples/imagenet
pip install -r requirements.txt

And finally start the training:

export TFDS_DATA_DIR=gs://$GCS_TFDS_BUCKET/datasets
python3 main.py --workdir=$HOME/logs/imagenet_tpu --config=configs/tpu.py \
    --jax_backend_target="grpc://192.168.0.2:8470"

Note that you might want to set TFDS_DATA_DIR as explained above. You probably also want to start the long-running command above in a tmux session and start some monitoring in a separate pane (note that we forwarded port 6006 locally above):

tensorboard --logdir=$HOME/logs

When running on pod slices, after creating the TPU VM, there are different ways of running the training in SPMD fashion on the hosts connected to the TPUs that make up the slice. We simply send the same installation/execution shell commands to all hosts in parallel with the command below. If anything fails it's usually a good idea to connect to a single host and execute the commands interactively.

For convenience, the TPU creation commands are inlined below. Please note that we define GCS_TFDS_BUCKET to where your data stands in your cloud bucket. Also YOUR_BUCKET is the work directory you are experimenting in. You should choose ZONE based on where your TPU and work directory is. Here has some useful information on which zones you can have different types of TPUs.

VM_NAME=imagenet
REPO=https://github.com/google/flax
BRANCH=main
WORKDIR=gs://$YOUR_BUCKET/flax/examples/imagenet/$(date +%Y%m%d_%H%M)

gcloud alpha compute tpus tpu-vm create $VM_NAME \
    --zone=$ZONE \
    --version v2-alpha --accelerator-type v3-32
FLAGS="--config.batch_size=$((32*256))"

gcloud alpha compute tpus tpu-vm ssh $VM_NAME --zone $ZONE \
--worker=all --command "
pip install 'jax[tpu]>=0.2.21' -f https://storage.googleapis.com/jax-releases/libtpu_releases.html &&
pip install --user git+$REPO.git &&
git clone --depth=1 -b $BRANCH $REPO &&
cd flax/examples/imagenet &&
pip install -r requirements.txt &&
export TFDS_DATA_DIR=gs://$GCS_TFDS_BUCKET/datasets &&
python3 main.py --workdir=$WORKDIR --config=configs/tpu.py $FLAGS
"

Please don't forget to disconnect and delete your vm after you are done:

gcloud alpha compute tpus tpu-vm delete $VM_NAME \
  --zone $ZONE

Google Cloud GPU

Can be launched with utility script described in ../cloud/README.md

There are two configurations available:

  • configs/v100_x8.py : Full precision GPU training
  • configs/v100_x8_mixed_precision.py : Mixed precision GPU training. Note that mixed precision handling is implemented manually with training.dynamic_scale