XGBoost on Spark crashes with SIGSEV
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I use Scala in Azure Databricks with the following setup:
- 5x worker node (
28.0 GB Memory, 8 Cores, 1.5 DBU) - 1x driver (
14.0 GB Memory, 4 Cores, 0.75 DBU)
I have a Spark Dataframe with 760k rows with two columns:
- label (
Double) - features (
SparseVectorof length84224each)
I want to use XGBoost on my Dataframe, to train the regression model:
val params = Map(
"objective" -> "reg:linear",
"max_depth" -> 6,
"eval_metric" -> "rmse"
)
var model = new XGBoostRegressor(params)
.setFeaturesCol("features")
.setLabelCol("label")
.setTreeMethod("approx")
.setNumRound(20)
.setNumEarlyStoppingRounds(3)
.setUseExternalMemory(true)
.setMaxDepth(6)
.setNumWorkers(10)
val trainedModel = model.fit(trainSample)
After launching, I get the following error:
SIGSEGV (0xb) at pc=0x00007f62a9d33e0e, pid=3954,
tid=0x00007f62c88db700
What I've tried so far:
When I set numWorkers to 1, the training starts, but obviously runs really slow, which I believe is no the way it should be used.
The documentation here: https://xgboost.readthedocs.io/en/latest/tutorials/external_memory.html and here: https://docs.databricks.com/spark/latest/mllib/third-party-libraries.html#xgboost does not help at all with my case.
My questions are:
- Is it possible to run XGBoost on Dataset that is bigger than memory of each individual worker? (I assume that it's YES, but correct me if I'm wrong)
- How to use External Memory properly, so that when I take even bigger dataset XGBoost will do the training?
- Is partitioning of the input Dataframe impacting the training somehow?
scala apache-spark xgboost databricks azure-databricks
asked Nov 22 '18 at 11:01
Marcin ZablockiMarcin Zablocki
5,23612535
add a comment |
I use Scala in Azure Databricks with the following setup:
- 5x worker node (
28.0 GB Memory, 8 Cores, 1.5 DBU) - 1x driver (
14.0 GB Memory, 4 Cores, 0.75 DBU)
I have a Spark Dataframe with 760k rows with two columns:
- label (
Double) - features (
SparseVectorof length84224each)
I want to use XGBoost on my Dataframe, to train the regression model:
val params = Map(
"objective" -> "reg:linear",
"max_depth" -> 6,
"eval_metric" -> "rmse"
)
var model = new XGBoostRegressor(params)
.setFeaturesCol("features")
.setLabelCol("label")
.setTreeMethod("approx")
.setNumRound(20)
.setNumEarlyStoppingRounds(3)
.setUseExternalMemory(true)
.setMaxDepth(6)
.setNumWorkers(10)
val trainedModel = model.fit(trainSample)
After launching, I get the following error:
SIGSEGV (0xb) at pc=0x00007f62a9d33e0e, pid=3954,
tid=0x00007f62c88db700
What I've tried so far:
When I set numWorkers to 1, the training starts, but obviously runs really slow, which I believe is no the way it should be used.
The documentation here: https://xgboost.readthedocs.io/en/latest/tutorials/external_memory.html and here: https://docs.databricks.com/spark/latest/mllib/third-party-libraries.html#xgboost does not help at all with my case.
My questions are:
- Is it possible to run XGBoost on Dataset that is bigger than memory of each individual worker? (I assume that it's YES, but correct me if I'm wrong)
- How to use External Memory properly, so that when I take even bigger dataset XGBoost will do the training?
- Is partitioning of the input Dataframe impacting the training somehow?
scala apache-spark xgboost databricks azure-databricks
asked Nov 22 '18 at 11:01
Marcin ZablockiMarcin Zablocki
5,23612535
Do you have access to the console of the driver??
– EmiCareOfCell44
Nov 22 '18 at 11:09
I am able to access stdout, stderr and log4j output of the driver
– Marcin Zablocki
Nov 22 '18 at 13:36
add a comment |
I use Scala in Azure Databricks with the following setup:
- 5x worker node (
28.0 GB Memory, 8 Cores, 1.5 DBU) - 1x driver (
14.0 GB Memory, 4 Cores, 0.75 DBU)
I have a Spark Dataframe with 760k rows with two columns:
- label (
Double) - features (
SparseVectorof length84224each)
I want to use XGBoost on my Dataframe, to train the regression model:
val params = Map(
"objective" -> "reg:linear",
"max_depth" -> 6,
"eval_metric" -> "rmse"
)
var model = new XGBoostRegressor(params)
.setFeaturesCol("features")
.setLabelCol("label")
.setTreeMethod("approx")
.setNumRound(20)
.setNumEarlyStoppingRounds(3)
.setUseExternalMemory(true)
.setMaxDepth(6)
.setNumWorkers(10)
val trainedModel = model.fit(trainSample)
After launching, I get the following error:
SIGSEGV (0xb) at pc=0x00007f62a9d33e0e, pid=3954,
tid=0x00007f62c88db700
What I've tried so far:
When I set numWorkers to 1, the training starts, but obviously runs really slow, which I believe is no the way it should be used.
The documentation here: https://xgboost.readthedocs.io/en/latest/tutorials/external_memory.html and here: https://docs.databricks.com/spark/latest/mllib/third-party-libraries.html#xgboost does not help at all with my case.
My questions are:
- Is it possible to run XGBoost on Dataset that is bigger than memory of each individual worker? (I assume that it's YES, but correct me if I'm wrong)
- How to use External Memory properly, so that when I take even bigger dataset XGBoost will do the training?
- Is partitioning of the input Dataframe impacting the training somehow?
scala apache-spark xgboost databricks azure-databricks
asked Nov 22 '18 at 11:01
Marcin ZablockiMarcin Zablocki
5,23612535
I use Scala in Azure Databricks with the following setup:
- 5x worker node (
28.0 GB Memory, 8 Cores, 1.5 DBU) - 1x driver (
14.0 GB Memory, 4 Cores, 0.75 DBU)
I have a Spark Dataframe with 760k rows with two columns:
- label (
Double) - features (
SparseVectorof length84224each)
I want to use XGBoost on my Dataframe, to train the regression model:
val params = Map(
"objective" -> "reg:linear",
"max_depth" -> 6,
"eval_metric" -> "rmse"
)
var model = new XGBoostRegressor(params)
.setFeaturesCol("features")
.setLabelCol("label")
.setTreeMethod("approx")
.setNumRound(20)
.setNumEarlyStoppingRounds(3)
.setUseExternalMemory(true)
.setMaxDepth(6)
.setNumWorkers(10)
val trainedModel = model.fit(trainSample)
After launching, I get the following error:
SIGSEGV (0xb) at pc=0x00007f62a9d33e0e, pid=3954,
tid=0x00007f62c88db700
What I've tried so far:
When I set numWorkers to 1, the training starts, but obviously runs really slow, which I believe is no the way it should be used.
The documentation here: https://xgboost.readthedocs.io/en/latest/tutorials/external_memory.html and here: https://docs.databricks.com/spark/latest/mllib/third-party-libraries.html#xgboost does not help at all with my case.
My questions are:
- Is it possible to run XGBoost on Dataset that is bigger than memory of each individual worker? (I assume that it's YES, but correct me if I'm wrong)
- How to use External Memory properly, so that when I take even bigger dataset XGBoost will do the training?
- Is partitioning of the input Dataframe impacting the training somehow?
scala apache-spark xgboost databricks azure-databricks
scala apache-spark xgboost databricks azure-databricks
asked Nov 22 '18 at 11:01
Marcin ZablockiMarcin Zablocki
5,23612535
asked Nov 22 '18 at 11:01
Marcin ZablockiMarcin Zablocki
5,23612535
asked Nov 22 '18 at 11:01
Marcin ZablockiMarcin Zablocki
5,23612535
asked Nov 22 '18 at 11:01
Marcin ZablockiMarcin Zablocki
5,23612535
asked Nov 22 '18 at 11:01
Marcin ZablockiMarcin Zablocki
5,23612535
5,23612535
Do you have access to the console of the driver??
– EmiCareOfCell44
Nov 22 '18 at 11:09
I am able to access stdout, stderr and log4j output of the driver
– Marcin Zablocki
Nov 22 '18 at 13:36
add a comment |
Do you have access to the console of the driver??
– EmiCareOfCell44
Nov 22 '18 at 11:09
I am able to access stdout, stderr and log4j output of the driver
– Marcin Zablocki
Nov 22 '18 at 13:36
Do you have access to the console of the driver??
– EmiCareOfCell44
Nov 22 '18 at 11:09
Do you have access to the console of the driver??
– EmiCareOfCell44
Nov 22 '18 at 11:09
I am able to access stdout, stderr and log4j output of the driver
– Marcin Zablocki
Nov 22 '18 at 13:36
I am able to access stdout, stderr and log4j output of the driver
– Marcin Zablocki
Nov 22 '18 at 13:36
add a comment |
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Do you have access to the console of the driver??
– EmiCareOfCell44
Nov 22 '18 at 11:09
I am able to access stdout, stderr and log4j output of the driver
– Marcin Zablocki
Nov 22 '18 at 13:36