# deepdish

Serving Up Chicago-Style Deep Learning.

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The building blocks of Deep Learning 21 Nov 2015

# Creating an LMDB database in Python

LMDB is the database of choice when using Caffe with large datasets. This is a tutorial of how to create an LMDB database from Python. First, let’s look at the pros and cons of using LMDB over HDF5.

Reasons to use HDF5:

Reasons to use LMDB:

• LMDB uses memory-mapped files, giving much better I/O performance.
• Works well with really large datasets. The HDF5 files are always read entirely into memory, so you can’t have any HDF5 file exceed your memory capacity. You can easily split your data into several HDF5 files though (just put several paths to h5 files in your text file). Then again, compared to LMDB’s page caching the I/O performance won’t be nearly as good.

## LMDB from Python

You will need the Python package lmdb as well as Caffe’s python package (make pycaffe in Caffe). LMDB provides key-value storage, where each <key, value> pair will be a sample in our dataset. The key will simply be a string version of an ID value, and the value will be a serialized version of the Datum class in Caffe (which are built using protobuf).

import numpy as np
import lmdb
import caffe

N = 1000

# Let's pretend this is interesting data
X = np.zeros((N, 3, 32, 32), dtype=np.uint8)
y = np.zeros(N, dtype=np.int64)

# We need to prepare the database for the size. We'll set it 10 times
# greater than what we theoretically need. There is little drawback to
# setting this too big. If you still run into problem after raising
# this, you might want to try saving fewer entries in a single
# transaction.
map_size = X.nbytes * 10

env = lmdb.open('mylmdb', map_size=map_size)

with env.begin(write=True) as txn:
# txn is a Transaction object
for i in range(N):
datum = caffe.proto.caffe_pb2.Datum()
datum.channels = X.shape[1]
datum.height = X.shape[2]
datum.width = X.shape[3]
datum.data = X[i].tobytes()  # or .tostring() if numpy < 1.9
datum.label = int(y[i])
str_id = '{:08}'.format(i)

# The encode is only essential in Python 3
txn.put(str_id.encode('ascii'), datum.SerializeToString())


You can also open up and inspect an existing LMDB database from Python:

import numpy as np
import lmdb
import caffe

with env.begin() as txn:
raw_datum = txn.get(b'00000000')

datum = caffe.proto.caffe_pb2.Datum()
datum.ParseFromString(raw_datum)

flat_x = np.fromstring(datum.data, dtype=np.uint8)
x = flat_x.reshape(datum.channels, datum.height, datum.width)
y = datum.label


Iterating <key, value> pairs is also easy:

with env.begin() as txn:
cursor = txn.cursor()
for key, value in cursor:
print(key, value)