tensorflow_hub library currently supports two modes for downloading
models. By default, a model is downloaded as a compressed archive and cached on
disk. Secondly, models can directly be read from remote storage into TensorFlow.
Either way, the calls to
tensorflow_hub functions in the actual Python code
can and should continue to use the canonical tfhub.dev URLs of models, which are
portable across systems and navigable for documentation. In the rare case that
user code needs the actual filesystem location (after downloading and
decompressing, or after resolving a model handle into a filesystem path),
it can be obtained by the function
Caching of compressed downloads
tensorflow_hub library by default caches models on the filesystem when
they have been downloaded from tfhub.dev (or other hosting sites)
and decompressed. This mode is recommended for most environments, except if disk
space is scarce but network bandwidth and latency are superb.
The download location defaults to a local temporary directory but can be
customized by setting the environment variable
or by passing the command-line flag
--tfhub_cache_dir. The default cache
/tmp/tfhub_modules (or whatever
"tfhub_modules") is evaluated to) should work in most cases.
Users who prefer persistent caching across system reboots can instead set
TFHUB_CACHE_DIR to a location in their home directory. For example, a user of
the bash shell on a Linux system can add a line like the following to
...restart the shell, and then this location will be used. When using a persistent location, be aware that there is no automatic cleanup.
Reading from remote storage
Users can instruct the
tensorflow_hub library to directly read models from
remote storage (GCS) instead of downloading the models locally with
os.environ["TFHUB_MODEL_LOAD_FORMAT"] = "UNCOMPRESSED"
or by setting the command-line flag
UNCOMPRESSED. This way, no caching directory is needed, which is especially
helpful in environments that provide little disk space but a fast internet
Running on TPU in Colab notebooks
On colab.research.google.com, downloading compressed models will conflict with the TPU runtime since the computation workload is delegated to another machine that does not have access to the cache location by default. There are two workarounds for this situation:
1) Use a GCS bucket that the TPU worker can access
The easiest solution is to instruct the
tensorflow_hub library to read the
models from TF Hub's GCS bucket as explained above. Users with their own GCS
bucket can instead specify a directory in their bucket as the cache location
with code like
import os os.environ["TFHUB_CACHE_DIR"] = "gs://my-bucket/tfhub-modules-cache"
...before calling the
2) Redirect all reads through the Colab host
Another workaround is to redirect all reads (even of large variables) through the Colab host:
load_options = tf.saved_model.LoadOptions(experimental_io_device='/job:localhost') reloaded_model = hub.load("https://tfhub.dev/...", options=load_options)