- 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) |