- CPU: Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz
- GPU: NVIDIA V100
- Memory: 251GiB
- OS: Ubuntu 16.04.6 LTS (Xenial Xerus)
Docker Images:
- tensorflow/tensorflow:latest-gpu
- tensorflow/serving:latest-gpu
from ray.job_submission import JobSubmissionClient | |
client = JobSubmissionClient("http://127.0.0.1:8265") | |
kick_off_pytorch_benchmark = ( | |
# Run the benchmark. | |
"python3.8 ./run_clm_deepspeed_train.py --model_name_or_path EleutherAI/gpt-neox-20b --block_size 2048 --output_dir /nvme/out2 --num_train_epochs 3 --learning_rate 5e-5 --weight_decay 0. --num_workers 16 --upload_dir '[S3]' --per_device_train_batch_size 1 --per_device_eval_batch_size 1 --gradient_accumulation_steps 1 --train_file /tmp/gpt/train.csv --validation_file /tmp/gpt/val.csv --seed 42" | |
) | |
#!/usr/bin/env python | |
# coding=utf-8 | |
# Copyright 2022 The HuggingFace Inc. team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# |
package main | |
import ( | |
"fmt" | |
"github.com/aws/aws-sdk-go/aws/session" | |
"github.com/aws/aws-sdk-go/service/s3/s3manager" | |
"github.com/aws/aws-sdk-go/service/s3" | |
"os" | |
"strconv" | |
"time" |
cd /Library/Application\ Support/VMware\ Tools/ | |
sudo ./vmware-resolutionSet 3440 1440 | |
./vmware-resolutionSet 2560 1080 |
Note: $
denotes the start of a command. Don't actually type this.
x86_65.sh
. If I had a 32-bit computer, I'd select the x86.sh
version. If you accidentally try to install the wrong one, you'll get a warning in the terminal. I chose Anaconda3-5.2.0-Linux-x86_64.sh
.wget https://repo.continuum.io/archive/[YOUR VERSION]
. Example: $ wget https://repo.continuum.io/archive/Anaconda3-5.2.0-Linux-x86_64.sh
$ bash Anaconda[YOUR VERSION].sh
($ bash Anaconda3-5.2.0-Linux-x86_64.sh
)Gauges are a constant data type. They are not subject to averaging, and they don’t change unless you change them. That is, once you set a gauge value, it will be a flat line on the graph until you change it again. | |
Gauges are useful for things that are already averaged, or don’t need to reset periodically. System load, for example, could be graphed with a gauge. You might use incr to count the number of logins to a system, but a gauge to track how many active WebSocket connections you have. |
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) |