Decrease bazel memory usage

Question

I'm using bazel on a computer with 4 GB RAM (to compile the tensorflow project). Bazel does however not take into account the amount of memory I have and spawns too many jobs causing my machine to swap and leading to a longer build time.

I already tried setting the ram_utilization_factor flag through the following lines in my ~/.bazelrc

build --ram_utilization_factor 30
test --ram_utilization_factor 30

but that did not help. How are these factors to be understood anyway? Should I just randomly try out some others?

Answer 1

Some other flags that might help:

• --host_jvm_args can be used to set how much memory the JVM should use by setting -Xms and/or -Xmx, e.g., bazel --host_jvm_args=-Xmx4g --host_jvm_args=-Xms512m build //foo:bar (docs).
• --local_resources in conjunction with the --ram_utilization_factor flag (docs).
• --jobs=10 (or some other low number, it defaults to 200), e.g. bazel build --jobs=2 //foo:bar (docs).

Note that --host_jvm_args is a startup option so it goes before the command (build) and --jobs is a "normal" build option so it goes after the command.

Answer 2

For me, the --jobs argument from @kristina's answer worked:

bazel build --jobs=1 tensorflow:libtensorflow_all.so

Note: --jobs=1 must follow, not precede build, otherwise bazel will not recognize it. If you were to type bazel --jobs=1 build tensorflow:libtensorflow_all.so, you would get this error message:

Unknown Bazel startup option: '--jobs=1'.

Answer 3

Just wanted to second @sashoalm's comment that the --jobs=1 flag was what made bazel build finally work.

For reference, I'm running bazel on Lubuntu 17.04, running as a VirtualBox guest with about 1.5 GB RAM and two cores of an Intel i3 (I'm running a Thinkpad T460). I was following the O'Reilly tutorial on TensorFlow (https://www.oreilly.com/learning/dive-into-tensorflow-with-linux), and ran into trouble at the following step:

$ bazel build tensorflow/examples/label_image:label_image

Changing this to bazel build --jobs=1 tensorflow/... did the trick.